WO2019171337A1 - Système et procédé de surveillance de véhicule - Google Patents

Système et procédé de surveillance de véhicule Download PDF

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Publication number
WO2019171337A1
WO2019171337A1 PCT/IB2019/051879 IB2019051879W WO2019171337A1 WO 2019171337 A1 WO2019171337 A1 WO 2019171337A1 IB 2019051879 W IB2019051879 W IB 2019051879W WO 2019171337 A1 WO2019171337 A1 WO 2019171337A1
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WIPO (PCT)
Prior art keywords
vehicle
time spent
information
spent
time
Prior art date
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PCT/IB2019/051879
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English (en)
Inventor
Jesse Kangethe MATHERI
Rivoningo Keith MHLARI
Original Assignee
Rikatec (Pty) Ltd.
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Publication of WO2019171337A1 publication Critical patent/WO2019171337A1/fr

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Classifications

    • G—PHYSICS
    • G07—CHECKING-DEVICES
    • G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
    • G07C5/00—Registering or indicating the working of vehicles
    • G07C5/08—Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle or waiting time
    • G07C5/0808—Diagnosing performance data
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring
    • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0259—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterized by the response to fault detection
    • G05B23/0283—Predictive maintenance, e.g. involving the monitoring of a system and, based on the monitoring results, taking decisions on the maintenance schedule of the monitored system; Estimating remaining useful life [RUL]
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/20—Pc systems
    • G05B2219/26—Pc applications
    • G05B2219/2637—Vehicle, car, auto, wheelchair
    • G—PHYSICS
    • G07—CHECKING-DEVICES
    • G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
    • G07C2205/00—Indexing scheme relating to group G07C5/00
    • G07C2205/02—Indexing scheme relating to group G07C5/00 using a vehicle scan tool

Definitions

  • THIS invention relates to a vehicle monitoring system and method.
  • 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.
  • 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.
  • 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);
  • 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.
  • 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;
  • 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.
  • 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%
  • the brake pad wear prediction formula/algorithm may be:
  • Estimated remaining life (in distance (e.g. km)) (40000-(( ⁇ distance/100)*(100+ (average[ ⁇ (time spent at 50 ⁇ engine load £75)/ ⁇ time spent+ ⁇ (time spent at 50 ⁇ throttle position £75 )/ ⁇ time spent + ⁇ ( time spent at 3000 ⁇ rpm £5000)/ ⁇ time spent + ⁇ (time spent at 100 ⁇ car speed ⁇ 132)/ ⁇ time spent]))+(4.5*( average[ ⁇ (time spent at 75 ⁇ engine load £ 100)/ ⁇ time spent+ ⁇ (time spent at 75 ⁇ throttle position 3 100)/ ⁇ time spent + ⁇ (time spent at rpm > 5000)/ ⁇ time spent + ⁇ (time spent at car speed >132)/ ⁇ 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;
  • 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.
  • 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
  • the brake disk wear prediction formula may be:
  • 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
  • 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:
  • 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;
  • 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.
  • 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%
  • the ball joint wear prediction formula may be:
  • Estimated remaining life (in distance (e.g. km)) (100000-((total distance/100)*(100+ average[ ⁇ (time spent at 50 ⁇ engine load £75)/ ⁇ time spent+ ⁇ (time spent at 50 ⁇ throttle position £75)/ ⁇ time spent + ⁇ ( time spent at 3000 ⁇ rpm £5000)/ ⁇ time spent + ⁇ (time spent at 100 ⁇ car speed ⁇ 132)/ ⁇ time spent]))+(3*( average[ ⁇ (time spent at 75 ⁇ engine load £ 100)/ ⁇ time spent+ ⁇ (time spent at 75 ⁇ throttle position 3 100)/ ⁇ time spent + ⁇ (time spent at rpm > 5000)/ ⁇ time spent + ⁇ (time spent at car speed >132)/ ⁇ 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;
  • 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.
  • 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
  • the wheel bearing wear prediction formula may be:
  • Estimated remaining life (in distance (e.g. km)) (150000-((total distance/100) * (99+ (1.05 * ( average[ ⁇ (time spent at 50 ⁇ engine load £75)/ ⁇ time spent+ ⁇ (time spent at 50 ⁇ throttle position £75)/ ⁇ time spent + ⁇ ( time spent at 3000 ⁇ rpm £5000)/ ⁇ time spent + ⁇ (time spent at 100 ⁇ car speed ⁇ 132)/ ⁇ time spent]))+(4.2*( average[ ⁇ (time spent at 75 ⁇ engine load £ 100)/ ⁇ time spent+ ⁇ (time spent at 75 ⁇ throttle position 3 100)/ ⁇ time spent + ⁇ (time spent at rpm > 5000)/ ⁇ time spent + ⁇ (time spent at car speed >132)/ ⁇ time spent]))))))))))))))))))))))))))))))))))))))))))))))))
  • 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.
  • the propshaft/driveshaft wear prediction formula may be:
  • Estimated remaining life (in distance (e.g. km)) (90000-((total distance/100)*(100+ (0.99*( average[ ⁇ (time spent at 50 ⁇ engine load £75 )/ ⁇ time spent+ ⁇ (time spent at 50 ⁇ throttle position £75)/ ⁇ time spent + ⁇ ( time spent at 3000 ⁇ rpm £5000)/ ⁇ time spent + ⁇ (time spent at 100 ⁇ car speed ⁇ 132)/ ⁇ time spent]))+(2.97*( average[ ⁇ (time spent at 75 ⁇ engine load £ 100)/ ⁇ time spent+ ⁇ (time spent at 75 ⁇ throttle position 3100)/ ⁇ time spent + ⁇ (time spent at rpm > 5000)/ ⁇ time spent + ⁇ (time spent at car speed >132)/ ⁇ time spent])))))))))))))))))))))))))))))))))))))))))))))))
  • the prediction module may be configured to calculate an engine load score by using the following formulas:
  • a first/safe/green score/range [ ⁇ time spent at 0£ engine load £50 and car speed > 0)/ ⁇ time spent at car speed > 0] * 100;
  • a second/intermediate/orange score/range [ ⁇ time spent at 50 ⁇ engine load £75 and car speed > 0)/ ⁇ time spent at car speed > 0 ] * 100;
  • a third/warning/red score/range [ ⁇ (time spent at 75 ⁇ engine load £ 100 and car speed > 0) )/ ⁇ time spent at car speed > 0 ] * 100.
  • a first/safe/green score/range [ ⁇ time spent at 0£ engine load £50 and car speed > 0)/ ⁇ time spent] * 100;
  • a second/intermediate/orange score/range [ ⁇ time spent at 50 ⁇ engine load £75 and car speed > 0)/ ⁇ 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:
  • a first/safe/green score [ ⁇ (time spent at 0 £ throttle position £50 and car speed > 0)/ ⁇ time spent at car speed > 0] * 100;
  • a second/intermediate/orange score [ ⁇ (time spent at 50 throttle position £75 and car speed > 0)/ ⁇ time spent at car speed > 0] * 100;
  • a third/warning/red score [ ⁇ (time spent at 75 ⁇ throttle position)/ ⁇ 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 [ ⁇ (time spent at 0 £ throttle position £50 and car speed > 0)/ ⁇ time spent] * 100;
  • a second/intermediate/orange score [ ⁇ (time spent at 50 cthrottle position £75 and car speed > 0)/ ⁇ time spent ] * 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.
  • a third/warning/red score [ ⁇ (time spent at rpm > 5000 and car speed > 0)/ ⁇ time 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 [ ⁇ ( time spent at 0 £ rpm£ 3000 and car speed > 0)/ ⁇ time spent ] * 100;
  • a second/intermediate/orange score [ ⁇ ( time spent at 3000 ⁇ rpm £5000 and car speed > 0)/ ⁇ time 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.
  • a second/intermediate/orange score [ ⁇ (time spent at 100 ⁇ car speed ⁇ 132)/ ⁇ time spent] * 100;
  • a third/warning/red score [ ⁇ (time spent at car speed >132)/ ⁇ 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 [ ⁇ (time spent at 0 £ car speed ⁇ 100)/ ⁇ time spent] * 100;
  • a second/intermediate/orange score [ ⁇ (time spent at 100 ⁇ car speed ⁇ 132)/ ⁇ 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 Moving ⁇ (time spent at car speed>0, rpm > 0 );
  • the prediction module may be configured to define critical events by using the following formulas:
  • Oil Level Low when dtcs in (('P101 O', 'P101 1 ', 'P1012'))
  • 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 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.
  • location e.g. GPS
  • 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
  • fuel consumption and/or fuel efficiency e.g., fuel efficiency
  • the calculation module may be configured to calculate a travelling distance by using the following formula:
  • the calculation module may be configured to calculate a travelling distance check by using the following formula:
  • This formula is used to check the precision of the distance travelled, calculated from the“Distance Travelled” formula.
  • 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 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.
  • 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)).
  • RUL Remaining Useful Life
  • 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
  • the features of the driver e.g. gender, age, driving habits.
  • 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:
  • Estimated remaining life (in distance (e.g. km)) (40000-(( ⁇ distance/100)*(100+ (average[ ⁇ (time spent at 50 ⁇ engine load £75)/ ⁇ time spent+ ⁇ (time spent at 50 ⁇ throttle position £75 )/ ⁇ time spent + ⁇ ( time spent at 3000 ⁇ rpm £5000)/ ⁇ time spent + ⁇ (time spent at 100 ⁇ car speed ⁇ 132)/ ⁇ time spent]))+(4.5*( average[ ⁇ (time spent at 75 ⁇ engine load £ 100)/ ⁇ time spent+ ⁇ (time spent at 75 ⁇ throttle position 3 100)/ ⁇ time spent + ⁇ (time spent at rpm > 5000)/ ⁇ time spent + ⁇ (time spent at car speed >132)/ ⁇ time spent]))))))))))))))))))))))))))))))))))))))))))))))))))
  • 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[ ⁇ (time spent at 50 ⁇ engine load £75)/ ⁇ time spent+ ⁇ (time spent at 50 ⁇ throttle position £75)/ ⁇ time spent + ⁇ ( time spent at 3000 ⁇ rpm £5000)/ ⁇ time spent + ⁇ (time spent at 100 ⁇ car speed ⁇ 132)/ ⁇ time spent]))+(2.475*( average[ ⁇ (time spent at 75 ⁇ enginejoad £ 100)/ ⁇ time spent+ ⁇ (time spent at 75 ⁇ throttle position 3100)/ ⁇ time spent + ⁇ (time spent at rpm > 5000)/ ⁇ time spent + ⁇ (time spent at car speed >132)/ ⁇ 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:
  • 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:
  • Estimated remaining life (in distance (e.g. km)) (100000-((total distance/100)*(100+ average[ ⁇ (time spent at 50 ⁇ engine load £75)/ ⁇ time spent+ ⁇ (time spent at 50 ⁇ throttle position £75)/ ⁇ time spent + ⁇ ( time spent at 3000 ⁇ rpm £5000)/ ⁇ time spent + ⁇ (time spent at 100 ⁇ car speed ⁇ 132)/ ⁇ time spent]))+(3*( average[ ⁇ (time spent at 75 ⁇ engine load £ 100)/ ⁇ time spent+ ⁇ (time spent at 75 ⁇ throttle position 3 100)/ ⁇ time spent + ⁇ (time spent at rpm > 5000)/ ⁇ time spent + ⁇ (time spent at car speed >132)/ ⁇ time spent]))))))))))))))
  • 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/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[ ⁇ (time spent at 50 ⁇ engine load £75)/ ⁇ time spent+ ⁇ (time spent at 50 ⁇ throttle position £75)/ ⁇ time spent + ⁇ ( time spent at 3000 ⁇ rpm £5000)/ ⁇ time spent + ⁇ (time spent at 100 ⁇ car speed ⁇ 132)/ ⁇ time spent]))+(2.97*( average[ ⁇ (time spent at 75 ⁇ engine load £ 100)/ ⁇ time spent+ ⁇ (time spent at 75 ⁇ throttle position 3100)/ ⁇ time spent + ⁇ (time spent at rpm > 5000)/ ⁇ time spent + ⁇ (time spent at car speed >132)/ ⁇ 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).
  • Steps (a) to (c) relate to driving style.
  • 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
  • a third/warning/red score [ ⁇ (time spent at 75 ⁇ throttle position)/ ⁇ 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 [ ⁇ (time spent at 0 £ throttle position £50 and car speed > 0)/ ⁇ time spent] * 100;
  • a second/intermediate/orange score [ ⁇ (time spent at 50 ⁇ throttle position £75 and car speed > 0)/ ⁇ time spent ] * 100;
  • Steps (i) to (iii) relate to driving style.
  • 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
  • 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 [ ⁇ ( time spent at 0 £ rpm£ 3000 and car speed > 0)/ ⁇ time spent ] * 100;
  • a second/intermediate/orange score [ ⁇ ( time spent at 3000 ⁇ rpm £5000 and car speed > 0)/ ⁇ time spent ] * 100; and iii.
  • 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
  • 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 [ ⁇ (time spent at 0 £ car speed ⁇ 100)/ ⁇ time spent] * 100
  • a second/intermediate/orange score [ ⁇ (time spent at 100 ⁇ car speed ⁇ 132)/ ⁇ time spent] * 100 iii.
  • 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.
  • location e.g. GPS
  • 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.
  • 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).
  • 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):
  • 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
  • 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:
  • FIG. 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
  • FIG. 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).
  • 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.
  • 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.
  • OBD2 on-board diagnostics
  • 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.
  • OBD2 Electronic Control Unit
  • 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)”).
  • 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 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.
  • a mobile communication network 105 e.g. GSM/GPRS
  • 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);
  • 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.
  • 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 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.
  • the central monitoring station 12 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.
  • 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.
  • 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)
  • the remaining life can be calculated/estimated by utilising a wear prediction formula(s).
  • a wear prediction formula/algorithm for the vehicle brake pads may be:
  • Estimated remaining life (in distance (e.g. km)) (40000-(( ⁇ distance/100)*(100+ (average[ ⁇ (time spent at 50 ⁇ engine load £75)/ ⁇ time spent+ ⁇ (time spent at 50 ⁇ throttle position £75 )/ ⁇ time spent + ⁇ ( time spent at 3000 ⁇ rpm £5000)/ ⁇ time spent + ⁇ (time spent at 100 ⁇ car speed ⁇ 132)/ ⁇ time spent]))+(4.5*( average[ ⁇ (time spent at 75 ⁇ engine load £ 100)/ ⁇ time spent+ ⁇ (time spent at 75 ⁇ throttle position 3 100)/ ⁇ time spent + ⁇ (time spent at rpm > 5000)/ ⁇ time spent + ⁇ (time spent at car speed >132)/ ⁇ time spent]))))))))))))))))))))))))))))))))))))))))))))))))))
  • 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[ ⁇ (time spent at 50 ⁇ engine load £75)/ ⁇ time spent+ ⁇ (time spent at 50 ⁇ throttle position £75)/ ⁇ time spent + ⁇ ( time spent at 3000 ⁇ rpm £5000)/ ⁇ time spent + ⁇ (time spent at 100 ⁇ car speed ⁇ 132)/ ⁇ time spent]))+(2.475*( average[ ⁇ (time spent at 75 ⁇ enginejoad £ 100)/ ⁇ time spent+ ⁇ (time spent at 75 ⁇ throttle position 3100)/ ⁇ time spent + ⁇ (time spent at rpm > 5000)/ ⁇ time spent + ⁇ (time spent at car speed >132)/ ⁇ 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-((total distance/100)*(105+ (1.05*( average[ ⁇ (time spent at 50 ⁇ enginejoad £75)/ ⁇ time spent+ ⁇ (time spent at 50 ⁇ throttle position £75)/ ⁇ time spent + ⁇ ( time spent at 3000 ⁇ rpm £5000)/ ⁇ time spent + ⁇ (time spent at 100 ⁇ car speed ⁇ 132)/ ⁇ time spent]))+(3.15*( average[ ⁇ (time spent at 75 ⁇ enginejoad £ 100)/ ⁇ time spent+ ⁇ (time spent at 75 ⁇ throttle position 3100)/ ⁇ time spent + ⁇ (time spent at rpm > 5000)/ ⁇ time spent + ⁇ (time spent at car speed >132)/ ⁇ time spent])))))))))))))))))))))))))))))))))))))))))))
  • 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/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[ ⁇ (time spent at 50 ⁇ engine load £75)/ ⁇ time spent+ ⁇ (time spent at 50 ⁇ throttle position £75 )/ ⁇ time spent + ⁇ ( time spent at 3000 ⁇ rpm £5000)/ ⁇ time spent + ⁇ (time spent at 100 ⁇ car speed ⁇ 132)/ ⁇ time spent]))+(4.2*( average[ ⁇ (time spent at 75 ⁇ engine load £ 100)/ ⁇ time spent+ ⁇ (time spent at 75 ⁇ throttle position 3100)/ ⁇ time spent + ⁇ (time spent at rpm > 5000)/ ⁇ time spent + ⁇ (time spent at car speed >132)/ ⁇ 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[ ⁇ (time spent at 50 ⁇ engine load £75)/ ⁇ time spent+ ⁇ (time spent at 50 ⁇ throttle position £75)/ ⁇ time spent + ⁇ ( time spent at 3000 ⁇ rpm £5000)/ ⁇ time spent + ⁇ (time spent at 100 ⁇ car speed ⁇ 132)/ ⁇ time spent]))+(2.97*( average[ ⁇ (time spent at 75 ⁇ engine load £ 100)/ ⁇ time spent+ ⁇ (time spent at 75 ⁇ throttle position 3100)/ ⁇ time spent + ⁇ (time spent at rpm > 5000)/ ⁇ time spent + ⁇ (time spent at car speed >132)/ ⁇ 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 prediction module 26 is configured to calculate an engine load score by using the following formulas:
  • a first/safe/green score/range [ ⁇ time spent at 0£ engine load £50 and car speed > 0)/ ⁇ time spent at car speed > 0] * 100;
  • a second/intermediate/orange score/range [ ⁇ time spent at 50 ⁇ engine load £75 and car speed > 0)/ ⁇ time spent at car speed > 0 ] * 100;
  • a third/warning/red score/range [ ⁇ (time spent at 75 ⁇ engine load £ 100 and car speed > 0) )/ ⁇ time spent at car speed > 0 ] * 100.
  • a first/safe/green score/range [ ⁇ time spent at 0£ engine load £50 and car speed > 0)/ ⁇ time spent] * 100;
  • a second/intermediate/orange score/range [ ⁇ time spent at 50 ⁇ engine load £75 and car speed > 0)/ ⁇ 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:
  • a first/safe/green score [ ⁇ (time spent at 0 £ throttle position £50 and car speed > 0)/ ⁇ time spent at car speed > 0] * 100;
  • a second/intermediate/orange score [ ⁇ (time spent at 50 ⁇ throttle position £75 and car speed > 0)/ ⁇ time spent at car speed > 0] * 100; and
  • a third/warning/red score [ ⁇ (time spent at 75 ⁇ throttle position)/ ⁇ time spent at car speed > 0 ] * 100.
  • a first/safe/green score [ ⁇ (time spent at 0 £ throttle position £50 and car speed > 0)/ ⁇ time spent] * 100;
  • a second/intermediate/orange score [ ⁇ (time spent at 50 ⁇ throttle position £75 and car speed > 0)/ ⁇ 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 an rpm score by using the following formulas:
  • a first/safe/green score [ ⁇ ( time spent at 0 £ rpm£ 3000 and car speed > 0)/ ⁇ time spent at car speed > 0 ] * 100;
  • a second/intermediate/orange score [ ⁇ ( time spent at 3000 ⁇ rpm £5000 and car speed > 0)/ ⁇ time spent at car speed > 0 ] * 100;
  • a third/warning/red score [ ⁇ (time spent at rpm > 5000 and car speed > 0)/ ⁇ time spent at car speed > 0 ] * 100.
  • a first/safe/green score [ ⁇ ( time spent at 0 £ rpm£ 3000 and car speed > 0)/ ⁇ time spent ] * 100;
  • a second/intermediate/orange score [ ⁇ ( time spent at 3000 ⁇ rpm £5000 and car speed > 0)/ ⁇ 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 car speed score by using the following formulas:
  • a first/safe/green score [ ⁇ (time spent at 0 £ car speed ⁇ 100)/ ⁇ time spent at car speed> 0] * 100
  • a second/intermediate/orange score [ ⁇ (time spent at 100 ⁇ car speed ⁇ 132)/ ⁇ time spent] * 100
  • a third/warning/red score [ ⁇ (time spent at car speed >132)/ ⁇ time spent ] * 100
  • a first/safe/green score [ ⁇ (time spent at 0 £ car speed ⁇ 100)/ ⁇ time spent] * 100
  • a second/intermediate/orange score [ ⁇ (time spent at 100 ⁇ car speed ⁇ 132)/ ⁇ 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.
  • 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).
  • 202 nearby support or maintenance services 202.1 , 202.2
  • 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.
  • location e.g. GPS
  • 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:
  • the calculation module 28 is, more specifically, configured to calculate a driver score by using the following formula:
  • 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.
  • 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 Efficiency ⁇ (D [fuel_level*fueltank_capacity)/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]
  • 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.
  • nearby support services 202 e.g. towing/repair services
  • 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 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.
  • the operator can use the web interface in order to locate nearby support services 202 which can assist with the vehicle repair/towing.
  • 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.
  • 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.
  • 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.
  • 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).
  • 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.
  • the system 10 provides an effective way of predicting possible vehicle breakdowns before they occur.
  • 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.
  • support providers e.g. vehicle repair companies

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  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • Automation & Control Theory (AREA)
  • Control Of Driving Devices And Active Controlling Of Vehicle (AREA)

Abstract

La présente invention concerne un système de surveillance de véhicule (10) qui comprend un module d'informations et un module de prédiction/estimation. Le module d'informations est conçu pour recevoir des informations sur au moins un véhicule. Les informations comprennent des informations de fonctionnement de véhicule. Le module de prédiction/estimation est conçu pour prédire/estimer (a) une durée de vie restante/durée de vie d'un ou plusieurs composants/agencements fonctionnels du véhicule, en utilisant les informations reçues et/ou (b) un temps restant avant qu'une opération d'entretien/de maintenance soit nécessaire pour un ou plusieurs composants/agencements fonctionnels du véhicule, en utilisant les informations reçues. Le module de prédiction/estimation peut être conçu pour utiliser une ou plusieurs formules/un ou plusieurs algorithmes de prédiction d'usure afin de prédire/estimer la durée de vie restante/durée de vie du ou des composants/agencements fonctionnels. Le module de prédiction/estimation peut également être conçu pour utiliser une ou plusieurs formules/un ou plusieurs algorithmes de prédiction d'usure afin de prédire/estimer le temps restant avant qu'une opération d'entretien/de maintenance soit nécessaire pour le ou les composants/agencements fonctionnels.
PCT/IB2019/051879 2018-03-09 2019-03-08 Système et procédé de surveillance de véhicule WO2019171337A1 (fr)

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CN111158350A (zh) * 2020-01-16 2020-05-15 斯润天朗（北京）科技有限公司 基于诊断的质量监控平台及系统
CN111399474A (zh) * 2020-02-29 2020-07-10 中南大学 一种基于健康指标的均衡控制模块寿命预测方法及装置
CN113687642A (zh) * 2021-08-13 2021-11-23 合肥维天运通信息科技股份有限公司 一种物流车辆隐患智能预警方法及系统
US20220027963A1 (en) * 2020-07-23 2022-01-27 Allstate Insurance Company Vehicle Valuation Engine to Determine Valuation Based on Usage and Fault History
DE102022002631A1 (de) 2021-07-20 2023-02-09 Mercedes-Benz Group AG System zur Schätzung der Lebensdauer von Bremsbelägen eines Fahrzeugs und Verfahren dazu
WO2023168159A1 (fr) 2022-03-03 2023-09-07 Caterpillar Inc. Système et procédé pour estimer l'utilisation potentielle, la rentabilité et le coût de propriété d'une machine sur la base de la valeur et de l'état mécanique de la machine

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CN113687642A (zh) * 2021-08-13 2021-11-23 合肥维天运通信息科技股份有限公司 一种物流车辆隐患智能预警方法及系统
WO2023168159A1 (fr) 2022-03-03 2023-09-07 Caterpillar Inc. Système et procédé pour estimer l'utilisation potentielle, la rentabilité et le coût de propriété d'une machine sur la base de la valeur et de l'état mécanique de la machine
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