WO2019171337A1

WO2019171337A1 - Vehicle monitoring system and method

Info

Publication number: WO2019171337A1
Application number: PCT/IB2019/051879
Authority: WO
Inventors: Jesse Kangethe MATHERI; Rivoningo Keith MHLARI
Original assignee: Rikatec Pty Ltd
Current assignee: Rikatec Pty Ltd
Priority date: 2018-03-09
Filing date: 2019-03-08
Publication date: 2019-09-12
Anticipated expiration: 2020-09-09
Also published as: ZA201901446B

Abstract

The invention relates to a vehicle monitoring system 10 which includes an information module and a prediction/estimation module. The information module is configured to receive information on at least one vehicle. The information includes vehicle operation information. The prediction/estimation module which is configured to predict/estimate (a) a remaining life/lifespan of one or more operational components/arrangements of the vehicle, by utilising the received information, and/or (b) a remaining time before service/maintenance is required, of one or more operational components/arrangements of the vehicle, by utilising the received information. The prediction/estimation module may be configured to utilise a wear prediction formula(s)/algorithm(s) in order to predict/estimate the remaining life/lifespan of the one or more operational components/arrangements. The prediction/estimation module may also be configured to utilise a wear prediction formula(s)/algorithm(s) in order to predict/estimate the remaining time before service/maintenance is required, of the one or more operational components/arrangements.

Description

VEHICLE MONITORING SYSTEM AND METHOD

BACKGROUND OF THE INVENTION

THIS invention relates to a vehicle monitoring system and method.

Vehicle manufacturers usually advise vehicle owners that their vehicles should be serviced every X amount of kilometres (e.g. every 15,000km) or X number of months. This advice is however not based on the actual monitoring of any of the individual operational components of the vehicle. In the absence of this monitoring, it is often difficult to predict when certain components will actually fail. The reason for this is that component failure may be due to several driving- related factors which need to be taken into account.

Currently, when a vehicle owner experiences a mechanical breakdown, they have to call a roadside assist call centre or insurance company, to notify them of the breakdown. In many cases the customer is not aware of what is actually wrong with the vehicle. They will typically need to explain where the breakdown is and wait sometimes several hours until help arrives. Due to the lack of information received prior to arriving on the scene, the wrong tools or mechanics are sometimes sent. This can lead to delays and increased costs since other additional mechanical experts, who can actually fix the vehicle, may need to become involved.

The Inventors wish to alleviate at least some of the issues mentioned above. SUMMARY OF THE INVENTION

In accordance with a first aspect of the invention there is provided a vehicle monitoring system which includes: an information module which is configured to receive information on at least one vehicle, wherein the information includes vehicle operation information; and a prediction/estimation module which is configured to predict/estimate a remaining life/lifespan of one or more operational components/arrangements of the vehicle, by utilising the received information, and/or a remaining time before service/maintenance is required, of one or more operational components/arrangements of the vehicle, by utilising the received information.

A“module”, in the context of the specification, includes an identifiable portion of code, computational or executable instructions, or a computational object to achieve a particular function, operation, processing, or procedure. A module may be implemented in software, hardware or a combination of software and hardware. Furthermore, modules need not necessarily be consolidated into one device.

The information module may receive the information in real-time. The prediction/estimation module may be configured to implement the prediction/estimation in real-time.

The prediction/estimation module may be configured to: utilise a wear prediction formula(s)/algorithm(s) in order to predict/estimate the remaining life/lifespan of the one or more operational components/arrangements; and/or utilise a wear prediction formula(s)/algorithm(s) in order to predict/estimate the remaining time before service/maintenance is required, of the one or more operational components/arrangements.

The prediction/estimation module may also be configured to predict/estimate vehicle breakdown and, optionally, detect which part(s) is/are at fault; a vehicle resale value; fuel consumption and efficiency; and/or route mapping and trip analysis.

The prediction/estimation module may be configured to utilise a formula(s)/algorithm(s) in order to predict/estimate: remaining life/lifespan of one or more operational components/arrangements (i.e. predict a possible breakdown); maintenance that may be required; component/part wear and tear; driving habits; vehicle breakdown and, optionally, detect which part(s) is/are at fault; vehicle resale value; fuel efficiency; and/or route and trip analysis.

The information module may be configured to receive the information via a mobile telecommunication network. More specifically, the information may be received from a monitoring device which is installed in the vehicle. The monitoring device may form part of the system. The monitoring device may be a plug-in device which is configured to connect to a computer or diagnostic system of the vehicle. The monitoring device may be a vehicle diagnostic device. The monitoring device may be configured in order to allow it to be plugged into a diagnostic port of the vehicle (e.g. an OBDII diagnostic port).

Vehicle operation information refers to information regarding a vehicle, when the vehicle is being used/operated and includes, amongst others, vehicle speed, vehicle acceleration, vehicle braking, fuel consumption, vehicle engine load, engine RPM (revolutions per minute), vehicle throttle position use and travel distance of vehicle. In addition, the vehicle operation information may include one or more of the following: timestamp (i.e. time of occurrence with date and time); vin (i.e. the VIN number); latitude and longitude of vehicle (i.e. its location);

GPS speed; battery voltage; milage; fuel system status; coolant temperature; air intake temperature; obd standard (shows standard PID’s which are not manufacture customised ones); distance_with_mil (i.e. how many kilometres/miles the vehicle has been driven with the MIL light on (how long the driver has been ignoring the light)); distance_since_mil_cleared (i.e. how many kilometres/miles the vehicle has been driven since the MIL light has been cleared); barometric pressure; abs load (i.e. the normalized value of air mass per intake stroke displayed as a percent); relative throttle position; time since boot (i.e. the time since the device reconnected); diagnostic trouble codes; data timestamp; emergency button (e.g. if the emergency button is pressed); fuel pressure; run time since start; fuel rail pressure; fuel level; ambient air temperature; accelerator position; relative pedal position; oil temperature; fuel rate; driver demand engine (Indicates a percentage of peak available torque (e.g. reaches 100% at wide open throttle at any altitude or RPM for both naturally aspirated and boosted engines); boost pressure control for boosted engines; turbo rpm; a first turbo temperature (turbo temperature_1 ); a second turbo temperature (turbo_temperature_2); and/or engine run time.

The prediction/estimation module may be configured to store at least some of the vehicle operation information on a database. Historic vehicle operation information may therefore be retrieved from the database, if needed.

More specifically, the prediction/estimation module may be configured to store information on the predicted/estimated remaining life/lifespan or time before service/maintenance is required, on the database.

The prediction/estimation module may be configured to calculate a wear (or wear & tear) prediction or remaining life (for example in distance (e.g. km’s or miles) or in operational time (e.g. hours)) for any one or more of the following, by utilising the received vehicle operation information:

Brake pads of the vehicle; brake discs of the vehicle; clutch of the vehicle; ball joints of the vehicle; wheel bearings of the vehicle; and propshaft(s)/driveshaft(s) of a vehicle.

The remaining life (for example in distance (e.g. km’s or miles) or operational time (e.g. hours)) of the operational component(s)/arrangement(s) may be calculated/estimated by utilising a wear prediction formula(s)/algorithm(s). The one or more operational components/arrangements may include: brake pads of the vehicle; brake discs of the vehicle; clutch of the vehicle; ball joints of the vehicle; wheel bearings of the vehicle; and/or propshaft(s)/driveshaft(s) of the vehicle.

The prediction/estimation module may be configured to predict/estimate the remaining life/lifespan (e.g. in travelling distance) of the brake pads by utilising information on the following factors related to the vehicle, within a brake pad wear prediction formula/algorithm: distance travelled by the vehicle; engine load;

throttle position; vehicle speed; and/or engine rpm (revolutions per minute), wherein the information on these factors are included in the vehicle operation information.

More specifically, the prediction/estimation module may be configured to predict/estimate the remaining life/lifespan of the brake pads (e.g. in travelling distance) by utilising information on the following factors within the brake pad wear prediction formula/algorithm: distance travelled by the vehicle; an amount of time an engine of the vehicle spent at a particular load or within a particular load band (e.g. between 50% and 75% load); an amount of time a throttle of the vehicle spent at a particular position or within a particular throttle position range/band; an amount of time a vehicle spent at a particular speed or within a particular speed band (e.g. between 100 and 132 km/h); and an amount of time an engine spent at a particular rpm or within a particular rpm band (e.g. above 5000 rpm or between 3000-5000 rpm), wherein the information on these factors are included in the vehicle operation information.

The brake pad wear prediction formula/algorithm may be:

Estimated remaining life (in distance (e.g. km)) = (40000-((ådistance/100)*(100+ (average[ å(time spent at 50 < engine load £75)/ å time spent+ å(time spent at 50 <throttle position £75 )/ åtime spent + å( time spent at 3000 < rpm £5000)/ å time spent + å(time spent at 100 < car speed <132)/ å time spent]))+(4.5*( average[ å(time spent at 75< engine load £ 100)/ å time spent+ å(time spent at 75 < throttle position ³ 100)/ å time spent + å(time spent at rpm > 5000)/ åtime spent + å(time spent at car speed >132)/ å time spent])))))

The prediction/estimation module may be configured to predict/estimate the remaining life/lifespan of the brake discs by utilising information on the following factors related to the vehicle, within a brake disc wear prediction formula/algorithm: distance travelled by the vehicle; engine load;

throttle position; vehicle speed; and/or engine rpm, wherein the information on these factors are included in the vehicle operation information.

More specifically, the prediction/estimation module may be configured to predict/estimate the remaining life/lifespan of the brake discs (e.g. in travelling distance) by utilising information on the following factors within the brake disk wear prediction formula/algorithm: distance travelled by the vehicle; an amount of time an engine of the vehicle spent at a particular load or within a particular load band (e.g. between 50% and 75% load); an amount of time a throttle of the vehicle spent at a particular position or within a particular throttle position range/band; an amount of time a vehicle spent at a particular speed or within a particular speed band (e.g. between 100 and 132 km/h); and an amount of time an engine spent at a particular rpm or within a particular rpm band (e.g. above 5000 rpm or between 3000-5000 rpm), wherein the information on these factors are included in the vehicle operation information.

The brake disk wear prediction formula may be:

Estimated remaining life (in distance (e.g. km)) = (90000-((total distance/100)*(99+ (0.99*( average[ å(time spent at 50 < engine load £75 )/ å time spent+ å(time spent at 50 <throttle position £75)/ åtime spent + å( time spent at 3000 < rpm £5000)/ å time spent + å(time spent at 100 < car speed <132)/ å time spent]))+(2.475*( average[ å(time spent at 75< enginejoad £ 100)/ å time spent+ å(time spent at 75 < throttle position ³100)/ å time spent + å(time spent at rpm > 5000)/ åtime spent + å(time spent at car speed >132)/ å time spent]))))) The prediction/estimation module may be configured to predict/estimate the remaining life/lifespan of the clutch by utilising information on the following factors related to the vehicle, within a clutch wear prediction formula/algorithm: distance travelled by the vehicle; engine load;

More specifically, the prediction/estimation module may be configured to predict/estimate the remaining life/lifespan of the clutch (e.g. in travelling distance) by utilising information on the following factors within the clutch wear prediction formula/algorithm: distance travelled by the vehicle; an amount of time an engine of the vehicle spent at a particular load or within a particular load band (e.g. between 50% and 75% load); an amount of time a throttle of the vehicle spent at a particular position or within a particular throttle position range/band; an amount of time a vehicle spent at a particular speed or within a particular speed band (e.g. between 100 and 132 km/h); and an amount of time an engine spent at a particular rpm or within a particular rpm band (e.g. above 5000 rpm or between 3000-5000 rpm), wherein the information on these factors are included in the vehicle operation information. The clutch prediction formula may be:

Estimated remaining life (in distance (e.g. km)) = (150000-

((totaldistance/100)*(105+ (1.05*( average[ å(time spent at 50 < enginejoad £75)/ å time spent+ å(time spent at 50 <throttle position £75)/ åtime spent + å( time spent at 3000 < rpm £5000)/ å time spent + å(time spent at 100 < car speed <132)/ å time spent]))+(3.15*( average[ å(time spent at 75< enginejoad £ 100)/ å time spent+ å(time spent at 75 < throttle position ³100)/ å time spent + å(time spent at rpm > 5000)/ åtime spent + å(time spent at car speed >132)/ å time spent])))))

The prediction/estimation module may be configured to predict/estimate the remaining life/lifespan of the ball joints by utilising information on the following factors related to the vehicle, within a ball joint wear prediction formula/algorithm: distance travelled by the vehicle; engine load;

More specifically, the prediction/estimation module may be configured to predict/estimate the remaining life/lifespan of the ball joints (e.g. in travelling distance) by utilising information on the following factors within the ball joint wear prediction formula/algorithm: distance travelled by the vehicle; an amount of time an engine of the vehicle spent at a particular load or within a particular load band (e.g. between 50% and 75% load); an amount of time a throttle of the vehicle spent at a particular position or within a particular throttle position range/band; an amount of time a vehicle spent at a particular speed or within a particular speed band (e.g. between 100 and 132 km/h); and an amount of time an engine spent at a particular rpm or within a particular rpm band (e.g. above 5000 rpm or between 3000-5000 rpm), wherein the information on these factors are included in the vehicle operation information.

The ball joint wear prediction formula may be:

Estimated remaining life (in distance (e.g. km)) = (100000-((total distance/100)*(100+ average[ å(time spent at 50 < engine load £75)/ å time spent+ å(time spent at 50 <throttle position £75)/ åtime spent + å( time spent at 3000 < rpm £5000)/ å time spent + å(time spent at 100 < car speed <132)/ å time spent]))+(3*( average[ å(time spent at 75< engine load £ 100)/ å time spent+ å(time spent at 75 < throttle position ³ 100)/ å time spent + å(time spent at rpm > 5000)/ åtime spent + å(time spent at car speed >132)/ å time spent])))))

The prediction/estimation module may be configured to predict/estimate the remaining life/lifespan of the wheel bearings by utilising information on the following factors related to the vehicle, within a wheel bearing wear prediction formula/algorithm: distance travelled by the vehicle; engine load;

More specifically, the prediction/estimation module may be configured to predict/estimate the remaining life/lifespan of the wheel bearings (e.g. in travelling distance) by utilising information on the following factors within the wheel bearing wear prediction formula/algorithm: distance travelled by the vehicle; an amount of time an engine of the vehicle spent at a particular load or within a particular load band (e.g. between 50% and 75% load); an amount of time a throttle of the vehicle spent at a particular position or within a particular throttle position range/band; an amount of time a vehicle spent at a particular speed or within a particular speed band (e.g. between 100 and 132 km/h); and an amount of time an engine spent at a particular rpm or within a particular rpm band (e.g. above 5000 rpm or between 3000-5000 rpm), wherein the information on these factors are included in the vehicle operation information.

The wheel bearing wear prediction formula may be:

Estimated remaining life (in distance (e.g. km)) = (150000-((total distance/100)^*(99+ (1.05^*( average[ å(time spent at 50 < engine load £75)/ å time spent+ å(time spent at 50 <throttle position £75)/ åtime spent + å( time spent at 3000 < rpm £5000)/ å time spent + å(time spent at 100 < car speed <132)/ å time spent]))+(4.2*( average[ å(time spent at 75< engine load £ 100)/ å time spent+ å(time spent at 75 < throttle position ³ 100)/ å time spent + å(time spent at rpm > 5000)/ åtime spent + å(time spent at car speed >132)/ å time spent]))))) The prediction/estimation module may be configured to predict/estimate the remaining life/lifespan of the propshaft(s)/driveshaft(s) by utilising information on the following factors related to the vehicle, within a propshaft/driveshaft wear prediction formula/algorithm: distance travelled by the vehicle; engine load;

More specifically, the prediction/estimation module may be configured to predict/estimate the remaining life/lifespan of the propshaft(s)/driveshaft(s) (e.g. in travelling distance) by utilising information on the following factors within the propshaft/driveshaft wear prediction formula/algorithm: distance travelled by the vehicle; an amount of time an engine of the vehicle spent at a particular load or within a particular load band (e.g. between 50% and 75% load); an amount of time a throttle of the vehicle spent at a particular position or within a particular throttle position range/band; an amount of time a vehicle spent at a particular speed or within a particular speed band (e.g. between 100 and 132 km/h); and an amount of time an engine spent at a particular rpm or within a particular rpm band (e.g. above 5000 rpm or between 3000-5000 rpm), wherein the information on these factors are included in the vehicle operation information. The propshaft/driveshaft wear prediction formula may be:

Estimated remaining life (in distance (e.g. km)) = (90000-((total distance/100)*(100+ (0.99*( average[ å(time spent at 50 < engine load £75 )/ å time spent+ å(time spent at 50 <throttle position £75)/ åtime spent + å( time spent at 3000 < rpm £5000)/ å time spent + å(time spent at 100 < car speed <132)/ å time spent]))+(2.97*( average[ å(time spent at 75< engine load £ 100)/ å time spent+ å(time spent at 75 < throttle position ³100)/ å time spent + å(time spent at rpm > 5000)/ åtime spent + å(time spent at car speed >132)/ å time spent])))))

The prediction module may be configured to calculate an engine load score by using the following formulas:

Driving Style

A first/safe/green score/range = [åtime spent at 0£ engine load £50 and car speed > 0)/ å time spent at car speed > 0] ^* 100;

A second/intermediate/orange score/range = [åtime spent at 50 < engine load £75 and car speed > 0)/ å time spent at car speed > 0 ] ^* 100; and

A third/warning/red score/range = [å(time spent at 75< engine load £ 100 and car speed > 0) )/ å time spent at car speed > 0 ] ^* 100.

Driving Behaviour

A first/safe/green score/range = [åtime spent at 0£ engine load £50 and car speed > 0)/ å time spent] * 100;

A second/intermediate/orange score/range = [åtime spent at 50 < engine load £75 and car speed > 0)/ å time spent] * 100; and

A third/warning/red score/range = [å(time spent at 75< engine load £ 100 å(time spent at 0 £ engine load , rpm >0 and car speed = 0) )/ å time spent] * 100. The prediction module may be configured to utilise an aggregated amount of time spent in each of the different scores/ranges in order to determine a total engine load score, driver behaviour and/or a wear and tear factor contributed to the vehicle by the amount of time spent in these score ranges

The prediction module may be configured to calculate a throttle position score by using the following formulas:

Driving Style: a) A first/safe/green score = [å(time spent at 0 £ throttle position £50 and car speed > 0)/ åtime spent at car speed > 0] ^* 100;

b) A second/intermediate/orange score = [å(time spent at 50 throttle position £75 and car speed > 0)/ åtime spent at car speed > 0] ^* 100; and

c) A third/warning/red score = [å(time spent at 75 < throttle position)/ å time spent at car speed > 0 ] ^* 100.

Formulas (a) to (c) above typically relate to driving style.

The prediction module may be configured to calculate a throttle position score by using the following formulas: i. A first/safe/green score = [å(time spent at 0 £ throttle position £50 and car speed > 0)/ åtime spent] * 100;

ii. A second/intermediate/orange score = [å(time spent at 50 cthrottle position £75 and car speed > 0)/ åtime spent ] * 100; and

iii. A third/warning/red score = [å(time spent at 75 < throttle position ³100+ å(time spent at 0 £ throttle position , rpm >0 and car speed = 0))/ å time spent at car speed > 0 ] * 100.

Formulas (i) to (iii) above typically relates to driving behaviour.

The prediction module may be configured to utilise an aggregated amount of time spent in each of the different scores/ranges in order to determine a total engine load score, driver behaviour and/or a wear and tear factor contributed to the vehicle by the amount of time spent in these score ranges.

The prediction module may be configured to calculate an rpm score by using the following formulas: a) A first/safe/green score = [å( time spent at 0 £ rpm£ 3000 and car speed > 0)/ å time spent at car speed > 0 ] ^* 100; b) A second/intermediate/orange score = [å( time spent at 3000 < rpm £5000 and car speed > 0)/ å time spent at car speed > 0 ] ^* 100; and

c) A third/warning/red score = [å(time spent at rpm > 5000 and car speed > 0)/ åtime spent at car speed > 0 ] ^* 100.

Formulas (a) to (c) above typically relates to driving style.

The prediction module may be configured to calculate an rpm score by using the following formulas: i. A first/safe/green score = [å( time spent at 0 £ rpm£ 3000 and car speed > 0)/ å time spent ] * 100;

ii. A second/intermediate/orange score = [å( time spent at 3000 < rpm £5000 and car speed > 0)/ å time spent ] * 100; and

iii. A third/warning/red score = [å(time spent at rpm > 5000) + å(time spent at 0£rpm and car speed = 0) )/ åtime spent ] * 100.

Formulas (i) to (iii) above typically relate to driving behaviour. The prediction module may be configured to utilise an aggregated amount of time spent in each of the different scores/ranges in order to determine total engine load score, driver behaviour and/or a wear and tear factor contributed to the vehicle by the amount of time spent in these score ranges.

The prediction module may be configured to calculate a car speed score by using the following formulas: a) A first/safe/green score = [å(time spent at 0 £ car speed <100)/ å time spent at car speed> 0] ^* 100;

b) A second/intermediate/orange score = [å(time spent at 100 < car speed <132)/ å time spent] * 100; and

c) A third/warning/red score = [å(time spent at car speed >132)/ å time spent ] * 100.

Formulas (a) to (c) above typically relate to driving style.

The prediction module may be configured to calculate a car speed score by using the following formulas:

A first/safe/green score = [å(time spent at 0 £ car speed <100)/ å time spent] ^* 100;

A second/intermediate/orange score = [å(time spent at 100 < car speed <132)/ å time spent] * 100; and

A third/warning/red score = [å(time spent at car speed >132) +[å(time spent at car speed = 0, rpm > 0)/ å time spent ] * 100.

Formulas (i) to (iii) above typically relates to driving behaviour.

The prediction module may be configured to calculate an amount of time spent in different vehicle statuses when engine is turned on by using the following formulas:

Vehicle Idling = å(time spent at car speed= 0, rpm > 0 );

Vehicle Moving = å(time spent at car speed>0, rpm > 0 ); and

Vehicle Stationary = å(time spent at car speed= 0, rpm =0 ). The prediction module may be configured to define critical events by using the following formulas:

Engine Heating status:

Overheating = (coolant_temperature > 0 )

Freezing = (coolant temperature<-35 )

Oil Level status:

Oil Level Low = when dtcs in (('P101 O', 'P101 1 ', 'P1012'))

Engine Light Status:

Engine Light On: when mil = 1

The system may include a calculation module.

The calculation module may be configured to calculate: vehicle distance travelled, preferably using location (e.g. GPS) information which is received from the monitoring device; and/or harsh/hard braking and/or harsh/hard acceleration by utilising information which is received from the monitoring device on the speed of the vehicle, as well as time; and/or fuel consumption and/or fuel efficiency.

The calculation module may be configured to calculate a driver score by using the following formula: Driver score = [ ( åcount of harshbraking + åharsh acceleration)/ ^distance travelled] + ([å(time spent at 0 £ car speed <132)/ å time spent] * 100) +([å( time spent at 0 £ rpm£ 3000)/ å time spent ] * 100) + ([å(time spent at 0 £ throttle position £50)/ åtime spent] * 100)+ ([åtime spent at 0£ engine load £50)/ å time spent ] ^* 100))/4]

The calculation module may be configured to calculate a travelling distance by using the following formula:

Distance Travelled (in km) = 3956 ^* 2 ^* ASIN(SQRT( POWER(SIN((A latitude)) ^* pi()/180 / 2), 2) + COS(previous latitude^* pi()/180) ^* COS(current latitude ) ^* pi()/180) ^* POWER(SIN((A longitude)) ^* pi()/180 / 2), 2) )) ^* 1.609344

The calculation module may be configured to calculate a travelling distance check by using the following formula:

Distance travelled check = ACOS(CASE WHEN ABS(ACoslnput) > 1 THEN SIGN(ACoslnput)^*1 ELSE ACoslnput END)(SIN(PI()^*previous latitude/180.0)^*SIN(PI()^*current latitude )/180.0)+COS(PI()^*current latitude/180.0)^*COS(PI()^*previous latitude )/180.0)^*COS(PI()^*previous longitude )/180.0-PI()^*current longitude/180.0))^*6371

This formula is used to check the precision of the distance travelled, calculated from the“Distance Travelled” formula.

The condition in the formula above“ACoslnput) > 1” is to cater for cases where the input of ACos is not in the range -1 and +1 .

The calculation module may be configured to calculate fuel consumption by using the following formula:

Fuel consumption = D fuel_level^*fuel_capacity (tank size)/100

The calculation module may be configured to calculate fuel efficiency by using the following formula: Fuel Efficiency (l/km) = (D fuel_level*fuel_capacity (tank size)/100)/( 3956 * 2 ^* ASIN(SQRT( POWER(SIN((A latitude)) ^* pi()/180 / 2), 2) + COS(previous latitude^* pi()/180) ^* COS(current latitude ) ^* pi()/180) ^* POWER(SIN((A longitude)) ^* pi()/180 / 2), 2) )) ^* 1 .609344)

The system may include a vehicle assist module which is configured to receive a failure, alert or breakdown message from the monitoring device via the mobile telecommunication network. The message may include an indication of a specific failure or breakdown, e.g. a wheel bearing failure, which occurred. The message may include the error code.

The vehicle assist module may be configured to identify a current location of the vehicle by using location information (e.g. GPS information) received from the monitoring device. The vehicle assist module may be configured to identify one or more support/repair services which operate, or are able to assist, with vehicle failures/breakdowns in at the location where the vehicle is currently located, for example by querying a/the database on which support/repair services information is stored. The vehicle assist module may be further configured to send a support/repair assist message to the one or more support/repair services, which includes the current location of the vehicle and, optionally, an indication of the type of failure/breakdown.

The prediction/estimation module may be configured to implement a machine learning algorithm(s) in order to predict/estimate the remaining life/lifespan of one or more operational components/arrangements of the vehicle; and/or a remaining time before service/maintenance is required, of one or more operational components/arrangements of the vehicle, wherein the machine learning algorithm(s) is trained through the importation/analysis of historical vehicle data and is configured to make the prediction/estimation in real-time by utilising the vehicle operation information. More specifically, machine learning algorithms may be implemented by the prediction/estimation module for predictive maintenance, which may be formulated in one or both of the following ways: using a classification approach whereby the module predicts whether there is a possibility of a breakdown or component failure in the next n-steps in a vehicle; and using a regression approach whereby the amount of time which is left before the next breakdown or failure in a particular component of the vehicle is predicted (also referred to as Remaining Useful Life (RUL)).

The following data/data classes may be collected from the device and from the historical records of the vehicle (e.g. stored on the database):

Failure history: The failure history of a component within the vehicle e.g. error codes (DTC’s);

Maintenance history: The repair history of a vehicle, e.g. previous maintenance activities or component replacements;

Machine conditions and usage: The operating conditions of a vehicle e.g. data collected from device;

Machine features: The features of the vehicle, e.g. engine size, make and model, location; and/or

Operator features: The features of the driver, e.g. gender, age, driving habits.

The following procedure will be followed by the prediction/estimation module to predict and fix breakdowns before they arise:

- Import and analyze historical device/vehicle data;

- T rain a model to predict when breakdowns and component failure will occur; - Deploy model to run on live device data / vehicle operation information; and

- Predict breakdowns and component failure in real time.

In accordance with a second aspect of the invention there is provided a vehicle monitoring method which includes: receiving information on at least one vehicle, wherein the information includes vehicle operation information; and predicting/estimating, by using a processor, a remaining life/lifespan of one or more operational components/arrangements of the vehicle, by utilising the received information, and/or a remaining time before service/maintenance is required, of one or more operational components/arrangements of the vehicle, by utilising the received information.

The method may, more specifically, include receiving the information via a mobile telecommunication network. Even more specifically, the information may be received from a vehicle monitoring/diagnostic device/system which is installed in a vehicle, via the mobile telecommunication network.

The method may include storing at least some of the vehicle operation information on a database. Historic vehicle operation information may therefore be retrieved from the database, if needed.

The method may include storing information on the estimated/predicted remaining life/lifespan or remaining time of the one or more operational components/arrangements, before service/maintenance is required, on a/the database.

The method may include calculating a wear prediction or remaining life (for example in distance (e.g. km’s or miles) or operational time (e.g. hours)) for any one or more of the following, by utilising the received vehicle operation information: brake pads of the vehicle; brake discs of the vehicle; clutch of the vehicle; ball joints of the vehicle; wheel bearings of the vehicle; and propshaft(s)/driveshaft(s) of a vehicle.

The remaining life (for example in distance (e.g. km’s or miles) or operational time (e.g. hours)) of the operational component(s)/arrangement(s) may be calculated/estimated by utilising a wear prediction formula(s).

A wear prediction formula/algorithm for the vehicle brake pads may be:

The wear prediction formula/algorithm for the vehicle brake pads may therefore estimate a remaining life of vehicle brake pads (e.g. in travelling distance) by taking into account the following factors: distance travelled; engine load; throttle position; an amount of time an engine spent at a particular load or within a particular load band (e.g. between 50% and 75% load); car speed; an amount of time a vehicle spent at a particular speed or within a particular speed band (e.g. between 100 and 132 km/h); engine rmp; and/or an amount of time an engine spent at a particular rpm or within a particular rpm band (e.g. above 5000 rpm or between 3000-5000 rpm).

A wear prediction formula for the vehicle brake discs may be:

Estimated remaining life (in distance (e.g. km)) = (90000-((total distance/100)*(99+ (0.99*( average[ å(time spent at 50 < engine load £75)/ å time spent+ å(time spent at 50 <throttle position £75)/ åtime spent + å( time spent at 3000 < rpm £5000)/ å time spent + å(time spent at 100 < car speed <132)/ å time spent]))+(2.475*( average[ å(time spent at 75< enginejoad £ 100)/ å time spent+ å(time spent at 75 < throttle position ³100)/ å time spent + å(time spent at rpm > 5000)/ åtime spent + å(time spent at car speed >132)/ å time spent])))))

The wear prediction formula/algorithm for the vehicle brake discs may therefore estimate a remaining life of vehicle brake pads (e.g. in travelling distance) by taking into account the following factors: distance travelled; engine load; throttle position; an amount of time an engine spent at a particular load or within a particular load band (e.g. between 50% and 75% load); car speed; an amount of time a vehicle spent at a particular speed or within a particular speed band (e.g. between 100 and 132 km/h); engine rmp; and/or an amount of time an engine spent at a particular rpm or within a particular rpm band (e.g. above 5000 rpm or between 3000-5000 rpm).

A wear prediction formula for the vehicle clutch may be:

Estimated remaining life (in distance (e.g. km)) = (150000-

The wear prediction formula/algorithm for the vehicle clutch may therefore estimate a remaining life of the vehicle clutch (e.g. in travelling distance) by taking into account the following factors: distance travelled; engine load; throttle position; an amount of time an engine spent at a particular load or within a particular load band (e.g. between 50% and 75% load); car speed; an amount of time a vehicle spent at a particular speed or within a particular speed band (e.g. between 100 and 132 km/h); engine rmp; and/or an amount of time an engine spent at a particular rpm or within a particular rpm band (e.g. above 5000 rpm or between 3000-5000 rpm).

The wear prediction formula for the ball joints of the vehicle may be:

The wear prediction formula/algorithm for the vehicle ball joints may therefore estimate a remaining life of ball joints (e.g. in travelling distance) by taking into account the following factors: distance travelled; engine load; throttle position; an amount of time an engine spent at a particular load or within a particular load band (e.g. between 50% and 75% load); car speed; an amount of time a vehicle spent at a particular speed or within a particular speed band (e.g. between 100 and 132 km/h); engine rmp; and/or an amount of time an engine spent at a particular rpm or within a particular rpm band (e.g. above 5000 rpm or between 3000-5000 rpm).

The wear prediction formula for the wheel bearings of the vehicle may be: Estimated remaining life (in distance (e.g. km)) = (150000-((total distance/100)*(99+ (1.05*( average[ å(time spent at 50 < engine load £75)/ å time spent+ å(time spent at 50 <throttle position £75)/ åtime spent + å( time spent at 3000 < rpm £5000)/ å time spent + å(time spent at 100 < car speed <132)/ å time spent]))+(4.2*( average[ å(time spent at 75< engine load £ 100)/ å time spent+ å(time spent at 75 < throttle position ³ 100)/ å time spent + å(time spent at rpm > 5000)/ åtime spent + å(time spent at car speed >132)/ å time spent])))))

The wear prediction formula/algorithm for the vehicle wheel bearings may therefore estimate a remaining life of the wheel bearings (e.g. in travelling distance) by taking into account the following factors: distance travelled; engine load; throttle position; an amount of time an engine spent at a particular load or within a particular load band (e.g. between 50% and 75% load); car speed; an amount of time a vehicle spent at a particular speed or within a particular speed band (e.g. between 100 and 132 km/h); engine rmp; and/or an amount of time an engine spent at a particular rpm or within a particular rpm band (e.g. above 5000 rpm or between 3000-5000 rpm).

The wear prediction formula for the propshaft(s)/driveshaft(s) of the vehicle may be:

Estimated remaining life (in distance (e.g. km)) = (90000-((total distance/100)*(100+ (0.99*( average[ å(time spent at 50 < engine load £75)/ å time spent+ å(time spent at 50 <throttle position £75)/ åtime spent + å( time spent at 3000 < rpm £5000)/ å time spent + å(time spent at 100 < car speed <132)/ å time spent]))+(2.97*( average[ å(time spent at 75< engine load £ 100)/ å time spent+ å(time spent at 75 < throttle position ³100)/ å time spent + å(time spent at rpm > 5000)/ åtime spent + å(time spent at car speed >132)/ å time spent])))))

The wear prediction formula/algorithm for the vehicle propshaft(s)/driveshaft(s) may therefore estimate a remaining life of the propshaft(s)/driveshaft(s) (e.g. in travelling distance) by taking into account the following factors: distance travelled; engine load; throttle position; an amount of time an engine spent at a particular load or within a particular load band (e.g. between 50% and 75% load); car speed; an amount of time a vehicle spent at a particular speed or within a particular speed band (e.g. between 100 and 132 km/h); engine rmp; and/or an amount of time an engine spent at a particular rpm or within a particular rpm band (e.g. above 5000 rpm or between 3000-5000 rpm).

The method may include calculating an engine load score by using the following formulas: a) A first/safe/green score/range = [åtime spent at 0£ engine load £50 and car speed > 0)/ å time spent at car speed > 0] ^* 100; b) A second/intermediate/orange score/range = [åtime spent at 50 < engine load £75 and car speed > 0)/ å time spent at car speed > 0 ] ^* 100; and c) A third/warning/red score/range = [å(time spent at 75< engine load £ 100 and car speed > 0) )/ å time spent at car speed > 0 ] ^* 100.

Steps (a) to (c) relate to driving style.

The method may include calculating an engine load score by using the following formulas: i. A first/safe/green score/range = [åtime spent at 0£ engine load £50 and car speed > 0)/ å time spent] * 100; ii. A second/intermediate/orange score/range = [åtime spent at 50 < engine load £75 and car speed > 0)/ å time spent] * 100; and iii. A third/warning/red score/range = [å(time spent at 75< engine load £ 100 å(time spent at 0 £ engine load , rpm >0 and car speed = 0) )/ å time spent] * 100.

Steps (i) to (iii) relate to driving behaviour.

The method may include aggregating an amount of time spent in each of the different scores/ranges in order to determine a total engine load score, driver behaviour and/or a wear and tear factor contributed to the vehicle by the amount of time spent in these score ranges

The method may include calculating a throttle position score by using the following formulas: a) A first/safe/green score = [å(time spent at 0 £ throttle position £50 and car speed > 0)/ åtime spent at car speed > 0] ^* 100; b) A second/intermediate/orange score = [å(time spent at 50 <throttle position £75 and car speed > 0)/ åtime spent at car speed > 0] ^* 100; and

c) A third/warning/red score = [å(time spent at 75 < throttle position)/ å time spent at car speed > 0 ] * 100.

Steps (a) to (c) relate to driving style.

The method may include calculating a throttle position score by using the following formulas: i. A first/safe/green score = [å(time spent at 0 £ throttle position £50 and car speed > 0)/ åtime spent] * 100;

ii. A second/intermediate/orange score = [å(time spent at 50 <throttle position £75 and car speed > 0)/ åtime spent ] * 100; and

Steps (i) to (iii) relate to driving style.

The method may include calculating an rpm score by using the following formulas: a) A first/safe/green score = [å( time spent at 0 £ rpm£ 3000 and car speed > 0)/ å time spent at car speed > 0 ] * 100; b) A second/intermediate/orange score = [å( time spent at 3000 < rpm £5000 and car speed > 0)/ å time spent at car speed > 0 ] ^* 100; and c) A third/warning/red score = [å(time spent at rpm > 5000 and car speed > 0)/ åtime spent at car speed > 0 ] * 100.

Steps (a) to (c) relate to driving style.

The method may include calculating an rpm score by using the following formulas: i. A first/safe/green score = [å( time spent at 0 £ rpm£ 3000 and car speed > 0)/ å time spent ] * 100;

ii. A second/intermediate/orange score = [å( time spent at 3000 < rpm £5000 and car speed > 0)/ å time spent ] ^* 100; and iii. A third/warning/red score = [å(time spent at rpm > 5000) + å(time spent at 0£rpm and car speed = 0) )/ åtime spent ] * 100.

Steps (i) to (iii) relate to driving behaviour.

The method may include utilising an aggregated amount of time spent in each of the different scores/ranges in order to determine a total engine load score, driver behaviour and/or a wear and tear factor contributed to the vehicle by the amount of time spent in these score ranges

The method may include calculating a car speed score by using the following formulas: a) A first/safe/green score = [å(time spent at 0 £ car speed <100)/ å time spent at car speed> 0] * 100

b) A second/intermediate/orange score = [å(time spent at 100 < car speed <132)/ å time spent] * 100 c) A third/warning/red score = [å(time spent at car speed >132)/ å time spent ] * 100

Steps (a) to (c) relate to driving style. The method may include calculating a car speed score by using the following formulas: i. A first/safe/green score = [å(time spent at 0 £ car speed <100)/ å time spent] * 100

ii. A second/intermediate/orange score = [å(time spent at 100 < car speed <132)/ å time spent] * 100 iii. A third/warning/red score = [å(time spent at car speed >132) +[å(time spent at car speed = 0, rpm > 0)/ å time spent ] ^* 100

Steps (i) to (iii) relate to driving behaviour.

The method may include aggregating an amount of time spent in each of the different scores/ranges in order to determine a total engine load score, driver behaviour and/or a wear and tear factor contributed to the vehicle by the amount of time spent in these score ranges.

The method may include calculating: vehicle distance travelled, preferably using location (e.g. GPS) information which is received from the monitoring device; and/or harsh/hard braking and/or acceleration by utilising information which is received from the monitoring device on the speed of the vehicle, as well as time; and/or fuel consumption.

The monitoring device may typically send a fueljevel parameter, which is the amount of remaining fuel in the tank, as a percentage of the total capacity of the tank (tank size). This may then be used to calculate fuel consumption and fuel efficiency. Actual fuel consumption versus expected fuel consumption comparisons may also be done.

The method may include receiving a failure, alert or breakdown message from the monitoring device via the mobile telecommunication network. The message may include an indication of a specific failure or breakdown, e.g. a wheel bearing failure, which occurred.

The method may include identifying a current location of the vehicle by using location information (e.g. GPS information) received from the monitoring device. The method may include identifying one or more support/repair services which operate, or are able to assist, with vehicle failures/breakdown in an area/the location where the vehicle is currently located, for example by querying a database on which support/repair services information is stored. The method may include sending a support/repair assist message to the one or more support/repair services, which includes the current location of the vehicle and, optionally, an indication of the type of failure/breakdown.

The method may include receiving an acceptance message from one of the support/repair services indicating that they will assist with the failure, alert or breakdown.

The method may include collecting the following data/data classes from the monitoring device and from historical records of the vehicle (e.g. stored on a/the database):

Operator features: The features of the driver, e.g. gender, age, driving habits. The method may include implementing the following procedure in order to predict (and fix) breakdowns before they arise:

- Import and analyze historical device/vehicle data;

- Train a model to predict when breakdowns and component failure will occur;

- Deploy model to run on live device data / vehicle operation information; and

- Predict breakdowns and component failure in real time.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention will now be described, by way of example, with reference to the accompanying diagrammatic drawings. In the drawings:

Figure 1 shows a schematic layout of a vehicle monitoring system in accordance with the invention;

Figure 2 shows a functional layout of part of the system of Figure 1 which is typically implemented at a central monitoring station;

Figure 3 shows a functional layout of all the components of a monitoring device which forms part of the system of Figure 1 ;

Figure 4 shows a flow diagram of how the system manages vehicle failures/breakdowns;

Figure 5 shows a block diagram of the monitoring device’s (of Figure 3) emergency battery system;

Figure 6 shows a functional block diagram of one example of how the system in Figure 1 may operate;

Figure 7 shows a flow diagram of how the monitoring device connected to an OBDII port of a vehicle works, from initially starting the vehicle, to waking the device from sleep mode to reading the ECU and finally sending the raw codes to a back end;

Figure 8a shows a flow diagram of another example of how the system of

Figure 1 functions during a vehicle breakdown;

Figures 8b-r each show an enlarged view of part of the flow diagram illustrated in Figure 8a;

Figure 9 shows an example of a user interface screen which can be presented to a driver, when logging into the system (e.g. via a mobile app).

DESCRIPTION OF PREFERRED EMBODIMENTS

The present invention relates to a vehicle monitoring system. More specifically, the system is configured to monitor a large number of vehicles (e.g. a fleet). The system typically includes a monitoring/diagnostic device which is plugged into a vehicle diagnostic/computer system of each vehicle. The device can therefore be used as an add-on product, which can be plugged into existing vehicles.

This device retrieves certain vehicle operational information from the computing system of the vehicle. This information is then sent via a mobile communication network to a central monitoring station/center which stores the information on a database and also processes it in order to help predict component failures within the vehicle in advance, to thereby help prevent a vehicle breakdown. In addition, the monitoring station also receives error codes from the device of any vehicle failure/malfunction. When a breakdown/failure occurs, these codes can be used to identify the specific technical issue and be sent to nearby repair/support services. The device typically also sends location (e.g. GPS) information in order to identify the real-time location of the vehicle. The monitoring station can then use the location information in order to identify one or more repair/support services which might be able to assist with a particular breakdown.

In the event of a breakdown, vehicle parts/components in the engine are associated with OBD2 (on-board diagnostics) codes that are sent to the Electronic Control Unit (ECU) of the vehicle as error messages when they are faulty. The device is connected to a data port of the vehicle and reads and captures any serious (attention seeking, in the event of a breakdown) error codes sent to the ECU externally. More specifically, the device captures the codes from the ECU and sends them to the monitoring station via a mobile communication network and the Internet The monitoring station stores the information on a database and analyses the codes by linking them to an engine/vehicle part the code represents. After analysis, an alert message is sent to a web-based interface/server.

The web-based interface is typically used by call consultants at a 24 hour call centre. The interface provides a written description of the fault. The consultant can then send the description to the nearest dealer(s) or mechanic(s) (hereinafter referred to as“support provider(s)”). The nearest support provider(s) can be determined by matching the current location of the vehicle with support provider(s) which operate in that particular area/location. The consultant can speak to the customer and the mechanic, informing them of the situation, when the alert message has been received.

Reference is now specifically made to Figure 1. In this drawing, reference numeral 10 refers generally to a vehicle monitoring system in accordance with the invention. The system 10 includes a central monitoring/processing station 12 which is configured to receive and process vehicle information from a fleet of vehicles 100 (only one is illustrated in Figure 1 ). More specifically, a monitoring device 14 is plugged into a data/diagnostic port of each vehicle 100 which, in turn, is connected to a CPU (central processing unit) of the vehicle 100. The monitoring device 14 typically receives/retrieves information regarding the operation of the vehicle 100 and sends this information via a mobile communication network 105 (e.g. GSM/GPRS) to the central monitoring station 12 for further processing/analysis.

The information received/retrieved by the monitoring device 14 typically includes one or more of the following: a first turbo temperature (turbo temperature_1 ); a second turbo temperature (turbo_temperature_2);

Timestamp; (i.e. time of occurrence with date and time);

Vin; latitude and longitude of vehicle (i.e. its location);

GPS speed; battery voltage; milage; fuel system status; coolant temperature; air intake temperature; obd standard (shows standard PID’s which are not manufacture customised ones); distance with mil (i.e. how many kilometres/miles the vehicle has been driven with the MIL light on (how long the driver has been ignoring the light)); distance since mil cleared (i.e. how many kilometres/miles the vehicle has been driven since the MIL light has been cleared); barometric pressure; abs load (i.e. the normalized value of air mass per intake stroke displayed as a percent); relative throttle position; time since boot (i.e. the time since the device reconnected); diagnostic trouble codes; data timestamp; e-button (e.g. if the emergency button is pressed); fuel pressure; run time since start; fuel rail pressure; fuel level; ambient air temperature; accelerator position; relative pedal position; oil temperature; fuel rate; driver demand engine (Indicates a percentage of peak available torque (e.g. reaches 100% at wide open throttle at any altitude or RPM for both naturally aspirated and boosted engines); boost pressure control; turbo rpm; turbo temperature_1 ; turbo temperature_2; and/or engine run time. In addition, the information received/retrieved by the monitoring device 14 may include certain vehicle usage information. The vehicle usage information may include one or more of the following: a throttle position of the vehicle; engine rpm; car speed; and/or engine load.

Engine load indicates a percentage of peak available torque. It reaches 100% at wide open throttle at any altitude or RPM for both naturally aspirated and boosted engines. Its not calculated, but rather read from the vehicle by the monitoring device 14.

The term“vehicle operation information” hereinafter refers to all vehicle related information received by the monitoring station 12 from the monitoring devices 14.

The monitoring device 14 (see Figure 3) includes a processor/microcontroller 20 and an antenna 16 which is operatively connected thereto. The processor 20 is typically configured to utilise the antenna 16 in order to send the received/retrieved information to the central monitoring station 12. The processor 16, together with appropriate firmware (e.g. STN1 1 10 chip) and antenna 16, form a communication module for the device 14. The monitoring device 14 also includes an SD card 18 on which information can be stored and buffered if there is a loss of signal (when signal is regained, the information is sent to the station 12). The stored information can, for example, include the received/retrieved information as well as the software for the communication module. The monitoring device 14 also includes a location module, e.g. a GPS module (see reference numeral 19), which is configured to detect the location of the vehicle 100 in real-time. The real time location information is also sent to the central monitoring station 12. The central monitoring station 12 (see Figures 1 and 2) includes a processor/server 22 and a database/warehouse 24 on which information received from the various monitoring devices 14 can be stored and processed. More specifically, the server 22 is configured, by way of software, to implement an information module 23 which is configured to receive the information sent from the monitoring device 14 (e.g. via the mobile telecommunication network 105).

The server 22 is also configured, by way of software, to implement a prediction/estimation module 26 which is configured to predict/estimate a remaining life/lifespan/predictive wear and tear; and/or a remaining time before service/maintenance is required, of one or more operational components/arrangements of a particular vehicle 100 by utilising the information received from the monitoring device 14 plugged into the vehicle 100.

In this regard, it should be noted that historical vehicle operation information may also be used for the prediction/estimation calculation (i.e. stored on the database 24).

The prediction/estimation module 26 effectively tries to predict when a particular component/part requires replacement or maintenance, in order to help prevent an unexpected breakdown from occurring. The module 26 therefore implements a pro-active approach by implementing a preventative process rather than a reactive process (i.e. only acting after a breakdown/failure has occurred).

The prediction/estimation module 26 is configured to calculate a wear prediction or remaining life (for example in distance (e.g. km’s or miles) or in operational time (e.g. hours)) for one or more of the following, by utilising the received vehicle operation information: brake pads of the vehicle; brake discs of the vehicle; clutch of the vehicle; ball joints of the vehicle; wheel bearings of the vehicle; and/or propshaft(s)/driveshaft(s) of a vehicle.

The remaining life (for example in distance (e.g. km’s or miles) or operational time (e.g. hours) of the operational component(s)/arrangement(s)) can be calculated/estimated by utilising a wear prediction formula(s).

A wear prediction formula/algorithm for the vehicle brake pads may be:

A wear prediction formula for the vehicle brake discs may be:

The wear prediction formula/algorithm for the vehicle brake discs may therefore estimate a remaining life of vehicle brake pads (e.g. in travelling distance) by taking into account the following factors: distance travelled; engine load; throttle position; an amount of time an engine spent at a particular load or within a particular load band (e.g. between 50% and 75% load); car speed; an amount of time a vehicle spent at a particular speed or within a particular speed band (e.g. between 100 and 132 km/h); engine rmp; and/or an amount of time an engine spent at a particular rpm or within a particular rpm band (e.g. above 5000 rpm or between 3000-5000 rpm). A wear prediction formula for the vehicle clutch may be:

Estimated remaining life (in distance (e.g. km)) = (150000-((total distance/100)*(105+ (1.05*( average[ å(time spent at 50 < enginejoad £75)/ å time spent+ å(time spent at 50 <throttle position £75)/ åtime spent + å( time spent at 3000 < rpm £5000)/ å time spent + å(time spent at 100 < car speed <132)/ å time spent]))+(3.15*( average[ å(time spent at 75< enginejoad £ 100)/ å time spent+ å(time spent at 75 < throttle position ³100)/ å time spent + å(time spent at rpm > 5000)/ åtime spent + å(time spent at car speed >132)/ å time spent])))))

The wear prediction formula for the ball joints of the vehicle may be: Estimated remaining life (in distance (e.g. km)) = (100000-((total distance/100)*(100+ average[ å(time spent at 50 < engine load £75)/ å time spent+ å(time spent at 50 <throttle position £75)/ åtime spent + å( time spent at 3000 < rpm £5000)/ å time spent + å(time spent at 100 < car speed <132)/ å time spent]))+(3*( average[ å(time spent at 75< engine load £ 100)/ å time spent+ å(time spent at 75 < throttle position ³ 100)/ å time spent + å(time spent at rpm > 5000)/ åtime spent + å(time spent at car speed >132)/ å time spent])))))

The wear prediction formula for the wheel bearings of the vehicle may be:

Estimated remaining life (in distance (e.g. km)) = (150000-((total distance/100)*(99+ (1.05*( average[ å(time spent at 50 < engine load £75)/ å time spent+ å(time spent at 50 <throttle position £75 )/ åtime spent + å( time spent at 3000 < rpm £5000)/ å time spent + å(time spent at 100 < car speed <132)/ å time spent]))+(4.2*( average[ å(time spent at 75< engine load £ 100)/ å time spent+ å(time spent at 75 < throttle position ³100)/ å time spent + å(time spent at rpm > 5000)/ åtime spent + å(time spent at car speed >132)/ å time spent])))))

The prediction module 26 is configured to calculate an engine load score by using the following formulas:

For driving style:

A first/safe/green score/range = [åtime spent at 0£ engine load £50 and car speed > 0)/ å time spent at car speed > 0] * 100; A second/intermediate/orange score/range = [åtime spent at 50 < engine load £75 and car speed > 0)/ å time spent at car speed > 0 ] * 100; and

A third/warning/red score/range = [å(time spent at 75< engine load £ 100 and car speed > 0) )/ å time spent at car speed > 0 ] * 100.

For driving behaviour:

A third/warning/red score/range = [å(time spent at 75< engine load £ 100 å(time spent at 0 £ engine load , rpm >0 and car speed = 0) )/ å time spent] * 100.

The prediction module 26 is configured to utilise an aggregated amount of time spent in each of the different scores/ranges in order to determine a total engine load score, driver behaviour and/or a wear and tear factor contributed to the vehicle by the amount of time spent in these score ranges.

The prediction module 26 is configured to calculate a throttle position score by using the following formulas:

For driving style:

A first/safe/green score = [å(time spent at 0 £ throttle position £50 and car speed > 0)/ åtime spent at car speed > 0] * 100;

A second/intermediate/orange score = [å(time spent at 50 <throttle position £75 and car speed > 0)/ åtime spent at car speed > 0] * 100; and A third/warning/red score = [å(time spent at 75 < throttle position)/ å time spent at car speed > 0 ] * 100.

For driving behaviour:

A first/safe/green score = [å(time spent at 0 £ throttle position £50 and car speed > 0)/ åtime spent] * 100;

A second/intermediate/orange score = [å(time spent at 50 <throttle position £75 and car speed > 0)/ åtime spent ] * 100; and

A third/warning/red score = [å(time spent at 75 < throttle position ³100+ å(time spent at 0 £ throttle position , rpm >0 and car speed = 0))/ å time spent at car speed > 0 ] * 100.

The prediction module 26 is configured to an rpm score by using the following formulas:

For driving style:

A first/safe/green score = [å( time spent at 0 £ rpm£ 3000 and car speed > 0)/ å time spent at car speed > 0 ] * 100;

A second/intermediate/orange score = [å( time spent at 3000 < rpm £5000 and car speed > 0)/ å time spent at car speed > 0 ] ^* 100; and

A third/warning/red score = [å(time spent at rpm > 5000 and car speed > 0)/ åtime spent at car speed > 0 ] * 100.

For driving behaviour: A first/safe/green score = [å( time spent at 0 £ rpm£ 3000 and car speed > 0)/ å time spent ] ^* 100;

A second/intermediate/orange score = [å( time spent at 3000 < rpm £5000 and car speed > 0)/ å time spent ] * 100; and

A third/warning/red score = [å(time spent at rpm > 5000) + å(time spent at 0£rpm and car speed = 0) )/ åtime spent ] * 100.

The prediction module 26 is configured to calculate a car speed score by using the following formulas:

For driving style:

A first/safe/green score = [å(time spent at 0 £ car speed <100)/ å time spent at car speed> 0] ^* 100

A second/intermediate/orange score = [å(time spent at 100 < car speed <132)/ å time spent] * 100

A third/warning/red score = [å(time spent at car speed >132)/ å time spent ] ^* 100

For driving behaviour:

A first/safe/green score = [å(time spent at 0 £ car speed <100)/ å time spent] ^* 100

A second/intermediate/orange score = [å(time spent at 100 < car speed <132)/ å time spent] * 100 A third/warning/red score = [å(time spent at car speed >132) +[å(time spent at car speed = 0, rpm > 0)/ å time spent ] * 100

When the prediction module 26 establishes that the particular component/part of the vehicle may fail or require maintenance soon, then a notification message can be sent to the driver of the vehicle. More specifically, this notification may be sent to a mobile communication device of the driver—The prediction module 26 may also be configured to identify nearby support or maintenance services 202.1 , 202.2 (hereinafter collectively referred to as“202”) which are able to help with the maintenance or imminent failure, by using the current location of the vehicle 100. In other words, the driver can be notified of the imminent failure or acquired maintenance, together with a suggestion of where to take a vehicle (e.g. a nearby vehicle workshop).

The system also includes a calculation module 28 which is configured to calculate: vehicle distance travelled, preferably using location (e.g. GPS) information which is received from the monitoring device 14; and/or harsh/hard braking and/or harsh/hard acceleration by utilising information which is received from the monitoring device 14 on the speed of the vehicle, as well as time; and/or fuel consumption.

Harsh acceleration may be defined as in the table below:

Harsh braking may be defined as in the table below:

Table 2: Harsh braking

The calculation module 28 is, more specifically, configured to calculate a driver score by using the following formula:

Driver score = [ ( åcount of harshbraking + åharsh acceleration)/ ^distance travelled] + ([å(time spent at 0 £ car speed <132)/ å time spent] * 100) +([å( time spent at 0 £ rpm£ 3000)/ å time spent ] * 100) + ([å(time spent at 0 £ throttle position £50)/ åtime spent] * 100)+ ([åtime spent at 0£ engine load £50)/ å time spent ] * 100))/4] The calculation module may be configured to calculate a travelling distance by using the following formula:

The calculation module 28 is also configured to calculate a travelling distance check by using the following formula:

The monitoring device 14 typically sends a fuel level parameter, which is the amount of remaining fuel in the tank, as a percentage of the total capacity of the tank (tank size). This may then be used to calculate fuel consumption and fuel efficiency. The following formula may be used to calculate fuel consumption:

Fuel Consumption = D [fuel level*fueltank capacity)/100]

The following formula may be used to calculate fuel consumption:

Fuel Efficiency = å(D [fuel_level*fueltank_capacity)/100]) / å [3956 * 2 * ASIN(SQRT( POWER(SIN((A latitude)) * pi()/180 / 2), 2) + COS(previous latitude* pi()/180) * COS(current latitude ) * pi()/180) * POWER(SIN((A longitude)) ^* pi()/180 / 2), 2) )) ^* 1 .609344] Actual fuel consumption versus expected fuel consumption comparisons may also be done.

Bank data may be requested by vehicle drivers/clients on their fuel cards and this may provided to the system 10. Fuelling events can then be consolidated by the system 10 and an amount of fuel bought versus an amount of fuel consumed can be compared. Losses and savings may be calculated on this basis.

All the calculated information is typically stored on the database 24.

The system also includes a code evaluation module 30 which is configured to determine the specific failure/malfunction when an OBD2 code is received. More specifically, all OBD2 codes, together with their associated failure/malfunction details, are stored on the database 24. When an OBD2 code is therefore received, then the code evaluation module 30 retrieves the associated failure/malfunction details from the database.

The system 10 includes a web interface 40 which can be used/operated by call/assistance centres in order to assist clients with vehicle breakdowns. The web interface 40 is typically hosted by the server 22 or another processing unit which is communicatively connected thereto. The web interface 40 is configured to notify a call centre operator 200 when a breakdown has occurred. This notification would typically indicate the current location of the vehicle and provide the option to call the driver 209. After talking to the driver 209, the call centre 200 can, if needed, notify nearby support services 202 (e.g. towing/repair services) of the specific failure including the driver’s location via a web/mobile interface 204.

An example of how the system operates when a breakdown is detected, will now be described with reference to Figure 4.

When a vehicle breakdown occurs, an error message/code (e.g. an OBD2 code) is typically retrieved by the monitoring device 14 and sent to the monitoring station 12 (at block 400). The code evaluation module 30 then queries the database in order to retrieve the specific failure/malfunction which is associated with the error code (at block 402). An operator of the call centre then typically receives an alert via the web interface 40, providing him/her with details of the vehicle breakdown (at block 404). The operator is provided with the contact number of the vehicle driver 209 in order to call him to offer assistance.

If the client indicates that he requires help (at block 406), then the operator can use the web interface in order to locate nearby support services 202 which can assist with the vehicle repair/towing. In this regard, the database 24 typically includes details of a large number of support services which are registered with the system 10. The details, amongst others, include the geographical area/region within which they operate. By using the GPS location received from the monitoring device 14 of the broken down vehicle 100, one or more support services 202, which operate in the area in which the vehicle 100 is located, can be identified (at block 408). The identified service providers 202 are then typically notified, via a web interface or mobile app 204/206, that a particular breakdown has occurred and are provided with details of the breakdown including the geographic location of the breakdown, specific details of the type of breakdown (e.g. based on the error code) and identification information of the driver 209 (at block 410).

The functions of the web interface 40 can typically be implemented by means of a vehicle assist module.

The service provider 202 can then typically decide whether to accept/decline the offer to provide assistance. The service provider 202 which is first to accept the offer (at block 412) is then the one identified by the system 10 as the service provider which will provide support for the driver 209. Details of the service provider 202 will then be sent to the driver 209 (e.g. to a smart device 208 of the driver 209), including the real-time location of the driver on a geographic map (at block 414). The driver 209 will therefore be able to track the service provider while he/she is under way. In this regard, the service provider 202 will typically have a smart device 206 which is connected to the system 10 (e.g. via a/the hosted web interface). The current geographic location of the smart device 206 will typically be sent to the system 10 on a continual basis. The system 10 will then, in turn, send this information to the smart device 208 of the driver 209 in order to update him/her of the support provider’s location. When the support provider 202 arrives at the driver’s location, the smart device 206 of the support provider 202 is used to indicate to the system 10 that they have arrived at the broken down vehicle 100 (at blocks 416 and 417).

The support provider 202 will then typically either repair the vehicle 100 then and there or, alternatively, tow the vehicle 100 back to a workshop for repairs. Once repaired, the service provider 202 can use the web server/smart device 206 to inform the system 10 that the vehicle 100 has been repaired (at block 418). In addition, the service provider 202 can also provide a rating for the driver 209 (at block 420), which is then stored on the system 10.

Similarly, the driver 209 can also, after the repair has been completed, provide a rating for the support provider 202, which is again stored on the system 10 (at block 422).

From the above it should be clear that the driver 209 and support provider 202 can typically correspond with the system 10 via a smart device (e.g. by using a mobile app which is connectable/hosted by the system 10) or a general web interface 40.

Figures 8a-q illustrates another example (including screenshots of different user interfaces (e.g. implemented on a mobile app platform)) of how the system 10 functions during a vehicle breakdown.

Figure 9 illustrates an example of a user interface which can be presented to a driver, when using a driver mobile app of the system 10. The user interface can display various details of their vehicle, including historical driving behaviour.

The system 10 can be configured to allow registered/approved repair workshops to register as a help option, for when a fault/breakdown is detected. Only the nearest help option is typically shown to the call consultant. The consultant is then able to send the diagnostics and codes to the help option.

If the vehicle can be fixed on the side of the road in under 30 minutes, a mechanic with the right tools will typically be sent. If it cannot be fixed in under 30 minutes, then only a tow truck will be sent to tow the car to the repair shop. The Inventors believe that the system 10 provides an effective way of predicting possible vehicle breakdowns before they occur. In addition the system 10 also provides a user-friendly, efficient platform for assisting drivers in the event of an actual breakdown. Since the type of failure can be detected at the time when the breakdown occurs, support providers (e.g. vehicle repair companies) are able to identify up front whether they are able to assist. This helps to reduce time wastage and the unnecessary additional costs which might be incurred, when the support provider responding to the breakdown only later realises that they are not actually in a position to assist with the particular repair, and refers the repair to another company.

Claims

1 . A vehicle monitoring system which includes: an information module which is configured to receive information on at least one vehicle, wherein the information includes vehicle operation information; and a prediction/estimation module which is configured to predict/estimate a remaining life/lifespan of one or more operational components/arrangements of the vehicle, by utilising the received information, and/or a remaining time before service/maintenance is required, of one or more operational components/arrangements of the vehicle, by utilising the received information.

2. The system of claim 1 , wherein the prediction/estimation module is configured to: utilise a wear prediction formula(s)/algorithm(s) in order to predict/estimate the remaining life/lifespan of the one or more operational components/arrangements; and/or utilise a wear prediction formula(s)/algorithm(s) in order to predict/estimate the remaining time before service/maintenance is required, of the one or more operational components/arrangements.

3. The system of claim 1 , wherein the information module is configured to receive the information, via a mobile telecommunication network, from a monitoring device which is installed in the vehicle.

4. The system of claim 3, wherein the monitoring device forms part of the system, and wherein the monitoring device is a plug-in device which is configured to connect to a computer or diagnostic system of the vehicle.

5. The system of claim 4, wherein the monitoring device is configured in order to allow it to be plugged into a diagnostic port of the vehicle.

6. The system of claim 2, wherein the prediction/estimation module is configured to store information on the predicted/estimated remaining life/lifespan or time before service/maintenance is required, on a database.

7. The system of claim 2, wherein the one or more operational components/arrangements include: brake pads of the vehicle; brake discs of the vehicle; clutch of the vehicle; ball joints of the vehicle; wheel bearings of the vehicle; and/or propshaft(s)/driveshaft(s) of the vehicle.

8. The system of claim 2, wherein the one or more operational components/arrangements include brake pads of the vehicle, and the prediction/estimation module is configured to predict/estimate the remaining life/lifespan of the brake pads by utilising information on the following factors related to the vehicle, within a brake pad wear prediction formula/algorithm: distance travelled by the vehicle; engine load;

9. The system of claim 2, wherein the one or more operational components/arrangements include brake pads of the vehicle, and the prediction/estimation module is configured to predict/estimate the remaining life/lifespan of the brake pads by utilising information on the following factors within a brake pad wear prediction formula/algorithm: distance travelled by the vehicle; an amount of time an engine of the vehicle spent at a particular load or within a particular load band; an amount of time a throttle of the vehicle spent at a particular position or within a particular throttle position range/band; an amount of time a vehicle spent at a particular speed or within a particular speed band; and an amount of time an engine spent at a particular rpm or within a particular rpm band, wherein the information on these factors are included in the vehicle operation information.

10. The system of claim 8, wherein the operational components/arrangements include brake discs of the vehicle, and the prediction/estimation module is configured to predict/estimate the remaining life/lifespan of the brake discs by utilising information on the following factors related to the vehicle, within a brake disc wear prediction formula/algorithm: distance travelled by the vehicle; engine load;

1 1. The system of claim 8, wherein the operational components/arrangements include a clutch of the vehicle, and the prediction/estimation module is configured to predict/estimate the remaining life/lifespan of the clutch by utilising information on the following factors within a clutch wear prediction formula/algorithm: distance travelled by the vehicle; engine load;

12. The system of claim 8, wherein the operational components/arrangements include ball joints of the vehicle, and the prediction/estimation module is configured to predict/estimate the remaining life/lifespan of the ball joints by utilising information on the following factors within a ball joint wear prediction formula/algorithm: distance travelled by the vehicle; engine load;

13. The system of claim 8, wherein the operational components/arrangements include wheel bearings of the vehicle, and the prediction/estimation module is configured to predict/estimate the remaining life/lifespan of the wheel bearings by utilising information on the following factors within a wheel bearing wear prediction formula/algorithm: distance travelled by the vehicle; engine load;

14. The system of claim 8, wherein the operational components/arrangements include a propshaft(s)/driveshaft(s) of the vehicle, and the prediction/estimation module is configured to predict/estimate the remaining life/lifespan of the propshaft(s)/driveshaft(s) by utilising information on the following factors within a propshaft/driveshaft wear prediction formula/algorithm: distance travelled by the vehicle; engine load;

15. The system of claim 3, which includes a vehicle assist module which is configured to receive a failure, alert or breakdown message from the monitoring device via the mobile telecommunication network, wherein the message includes an indication of a specific failure or breakdown which occurred.

16. The system of claim 15, wherein the vehicle assist module is configured to identify a current location of the vehicle by using location information received from the monitoring device; and identify one or more support/repair services which operate, or are able to assist, with vehicle failures/breakdowns at the location where the vehicle is currently located.

17. The system of claim 16, wherein the vehicle assist module is further configured to send a support/repair assist message to the one or more support/repair services, which includes the current location of the vehicle and an indication of the type of failure/breakdown.

18. The system of claim 1 , wherein the prediction/estimation module is configured to implement a machine learning algorithm(s) in order to predict/estimate the remaining life/lifespan of one or more operational components/arrangements of the vehicle; and/or a remaining time before service/maintenance is required, of one or more operational components/arrangements of the vehicle, wherein the machine learning algorithm(s) is trained through the importation/analysis of historical vehicle data and is configured to make the prediction/estimation in real-time by utilising the vehicle operation information.

19. A vehicle monitoring method which includes: receiving information on at least one vehicle, wherein the information includes vehicle operation information; and predicting/estimating, by using a processor, a remaining life/lifespan of one or more operational components/arrangements of the vehicle, by utilising the received information, and/or a remaining time before service/maintenance is required, of one or more operational components/arrangements of the vehicle, by utilising the received information.

20. The method of claim 19, wherein the information is received from a vehicle monitoring/diagnostic device/system which is installed in a vehicle, via a mobile telecommunication network.

21. The method of claim 20, which includes storing information on the estimated/predicted remaining life/lifespan or remaining time of the one or more operational components/arrangements, before service/maintenance is required, on a database.

22. The method of claim 20, wherein the operational components/arrangements include: brake pads of the vehicle; brake discs of the vehicle; clutch of the vehicle; ball joints of the vehicle; wheel bearings of the vehicle; and propshaft(s)/driveshaft(s) of the vehicle.

23. The method of claim 19, which includes collecting, using a processor, the following data/data classes from the monitoring device and from historical records of the vehicle stored on a database: failure history of a component within the vehicle.; repair/maintenance history of the vehicle; operating conditions of the vehicle; and features/specifications of the vehicle.

24. The method of claim 19, which includes implementing the following procedure, by using a processor, in order to predict a possible failure of a operational component/arrangement of the vehicle, before it arises: importing and analysing historical vehicle data; training a machine learning model to predict when breakdowns and component failure will occur; implementing the model to run on vehicle operation information received in live/real-time; and predicting a possible failure in real-time.