Vehicle range adaptive estimation method and device, vehicle and storage medium

Abstract

The application relates to the technical field of new energy vehicles, in particular to a vehicle endurance mileage adaptive estimation method and device, a vehicle and a storage medium, the method comprises the following steps: after the whole vehicle is powered on, voltage and current data fed back by a battery management system are acquired every first preset time, and power consumption data information is obtained based on the voltage and current data; mileage data values of vehicle driving are acquired every second preset time, and time consumption data values of the process are acquired when the mileage data values increase by a preset interval mileage value; a unit power consumption value is determined based on the power consumption data information and the time consumption data values; the foregoing steps are repeated to obtain a power consumption data set comprising a plurality of unit power consumption values; one-time power consumption correction is performed based on the power consumption data set to obtain a dynamic power consumption value; and secondary power consumption correction is performed based on the dynamic power consumption value and set propaganda power consumption to obtain endurance mileage data to be fed back, the method reduces the gap between displayed endurance mileage and actual endurance mileage, and the method is suitable for a wider range of scenes.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of new energy vehicles, in particular to a vehicle endurance mileage adaptive estimation method and device, a vehicle and a storage medium. BACKGROUND

[0002] In a pure electric or extended range vehicle, in order to provide the most intuitive endurance mileage display for customers, mainstream electric vehicles are equipped with endurance mileage prediction function. The endurance mileage estimation of an electric vehicle mainly includes two parts: remaining energy estimation of a power battery and driving energy consumption prediction. The basic definition is the remaining capacity of the current battery pack divided by the driving energy consumption of the electric vehicle.

[0003] However, in the traditional endurance mileage estimation method, the endurance mileage obtained by dividing the remaining capacity of the current battery pack by the driving energy consumption of the electric vehicle is actually in an ideal state. There is a large gap between the displayed endurance mileage and the actual endurance mileage. Although some automobile manufacturers have considered temperature changes and driving habits to further reduce the gap, the estimation accuracy still has a large difference. In addition, the corresponding hardware cost required for the implementation of the method is high, resulting in high manufacturing cost of a single vehicle and small application range. SUMMARY

[0004] The present application aims to at least solve one of the technical problems existing in the prior art. To this end, the present application provides a vehicle endurance mileage adaptive estimation method, device, vehicle and storage medium.

[0005] In a first aspect, the present application provides a vehicle endurance mileage adaptive estimation method, comprising:

[0006] Step S10: After the whole vehicle is powered on, the voltage and current data fed back by the battery management system are obtained every first preset time, and the power consumption data information is obtained based on the voltage and current data;

[0007] Step S20: The mileage data value of the vehicle driving is obtained every second preset time, and when the mileage data value increases by a preset interval mileage value, the time consumption data value of the process is obtained;

[0008] Step S30: Based on the power consumption data information and the time consumption data value, a unit power consumption value is determined;

[0009] Step S40: Steps S10-S30 are repeated to obtain a power consumption data set comprising a plurality of unit power consumption values;

[0010] Step S50: Based on the power consumption data set, a power consumption correction is performed to obtain a dynamic power consumption value;

[0011] Step S60: based on the dynamic power consumption value and the set promotional power consumption, performing secondary power consumption correction to obtain the to-be-fed back endurance mileage data.

[0012] According to some embodiments of the present application, in the step S10, the power consumption data value is obtained based on the voltage and current data, and the step S20 includes:

[0013] An expression of the power consumption data value Q is created, and continuous power consumption data is obtained by integrating the expression, wherein the expression of the power consumption data value Q is:

[0014] In the formula, is the voltage value fed back by the battery management system every first preset time, is the current value fed back by the battery management system every first preset time.

[0015] According to some embodiments of the present application, in the step S30, based on the power consumption data value and the time consumption data value, a unit power consumption value is determined, and the step S40 includes:

[0016] According to the time consumption data value and the corresponding time initial point, a time interval domain in which the time consumption data value is located is obtained;

[0017] Based on the time interval domain and the power consumption data information, a unit power consumption value in a corresponding time period is obtained.

[0018] According to some embodiments of the present application, in the step S50, based on the power consumption data set, a first power consumption correction is performed to obtain a dynamic power consumption value, and the step S60 includes:

[0019] It is judged whether the mileage data value of the vehicle is equal to a preset correction mileage value;

[0020] If yes, a plurality of unit power consumption values in the power consumption data set are accumulated, and a dynamic power consumption value after a first power consumption correction is obtained according to the number of corresponding unit power consumption values.

[0021] According to some embodiments of the present application, in the step S60, based on the dynamic power consumption value and the set promotional power consumption, a secondary power consumption correction is performed to obtain the to-be-fed back endurance mileage data, and the step S70 includes:

[0022] A preset dynamic proportion coefficient corresponding to the dynamic power consumption value and the promotional power consumption is obtained, and based on the dynamic proportion coefficient, the dynamic power consumption value and the promotional power consumption, an average power consumption value of the secondary power consumption correction is determined;

[0023] Based on the average power consumption value and the remaining power of the battery, calibrated endurance mileage data is obtained.

[0024] According to some embodiments of the present application, the dynamic power consumption value and the promotional power consumption correspond to a preset dynamic proportionality coefficient, and based on the dynamic proportionality coefficient, the dynamic power consumption value and the promotional power consumption, a secondary power consumption corrected average power consumption value is determined, including:

[0025] Based on historical power consumption experience data of the vehicle, a first proportionality coefficient calibration table of the promotional power consumption and a second proportionality coefficient calibration table of the dynamic power consumption value are preset;

[0026] The SOC of the current battery pack of the vehicle is obtained, and based on the SOC, the first proportionality coefficient calibration table and the second proportionality coefficient calibration table, the values of the first proportionality coefficient and the second proportionality coefficient are determined.

[0027] Based on the values of the first proportionality coefficient and the second proportionality coefficient, and the dynamic power consumption value and the promotional power consumption, a secondary power consumption corrected average power consumption value AEC is determined,

[0028] Wherein, the average power consumption value AEC expression is: AEC=A* AEC_P+B* AEC_D

[0029] In the formula, A is the first proportionality coefficient, B is the second proportionality coefficient, AEC_P is the promotional power consumption, and AEC_D is the dynamic power consumption value.

[0030] According to some embodiments of the present application, based on the average power consumption value and the remaining power of the battery, the calibrated range data is obtained, including:

[0031] The current remaining power Q1 of the battery is obtained every third preset time, wherein the current remaining power Q1 of the battery is expressed as: Q1=SOC×SOH×Ah;

[0032] In the formula, SOC is the remaining power of the battery, SOH is the health status of the battery, and Ah is the rated capacity of the battery;

[0033] Based on the average power consumption value and the remaining power Q1 of the battery, the calibrated range data is obtained.

[0034] In a second aspect, the embodiments of the present application provide a vehicle range adaptive calibration device, including:

[0035] The first acquisition module is configured to obtain voltage and current data fed back by the battery management system every first preset time after the whole vehicle is powered on, and based on the voltage and current data, the power consumption data information is obtained;

[0036] The second acquisition module is configured to obtain the mileage data value of the vehicle every second preset time, and when the mileage data value increases by a preset interval mileage value, the time data value of the process is obtained.

[0037] The first determining module is configured to determine a unit power consumption value based on the power consumption data information and the time consumption data value, and obtain a power consumption data set including a plurality of unit power consumption values.

[0038] The first correction module is configured to perform a first power consumption correction based on the power consumption data set to obtain a dynamic power consumption value.

[0039] The second determining module is configured to obtain a current SOC of a battery pack of the vehicle, and determine values of a first proportional coefficient and a second proportional coefficient according to the SOC, a first proportional coefficient calibration table and a second proportional coefficient calibration table.

[0040] The second correction module is configured to perform a second power consumption correction based on the dynamic power consumption value and a set promotional power consumption to obtain the to-be-fed-back range data, wherein the second correction module comprises: a dynamic proportional coefficient obtaining unit configured to obtain a preset dynamic proportional coefficient corresponding to the dynamic power consumption value and the promotional power consumption; and a second power consumption correction value determining unit configured to determine an average power consumption value of the second power consumption correction based on the dynamic proportional coefficient, the dynamic power consumption value and the promotional power consumption.

[0041] In a third aspect, an embodiment of the present application provides a vehicle, comprising:

[0042] a processor;

[0043] a memory for storing instructions executable by the processor;

[0044] The processor is configured to:

[0045] implement the steps of the vehicle range adaptive estimation method according to the first aspect.

[0046] In a fourth aspect, an embodiment of the present application provides a computer readable storage medium having stored thereon computer program instructions, wherein the program instructions are executed by a processor to implement the steps of the vehicle range adaptive estimation method according to the first aspect.

[0047] Compared with the prior art, the technical scheme provided by the embodiment of the present application has at least the following beneficial effects or advantages:

[0048] 1) In the vehicle range adaptive estimation method of the application, the mileage data value and the corresponding power consumption data information of the vehicle driving are collected in real time, and the power consumption data set including a plurality of unit power consumption values corresponding to each interval mileage value is calculated, based on which the dynamic power consumption value for the first power consumption correction is obtained, based on the vehicle battery pack characteristics, the proportional coefficient value change of the promotional power consumption and the dynamic power consumption value under different battery pack SOC conditions is set, and the average power consumption expression is created based on the proportional coefficient value change to complete the second power consumption correction, based on the average power consumption obtained after the second correction and the real-time obtained residual power, the corrected range is calculated, and the corrected range data is transmitted to the vehicle entertainment system for display. The method greatly reduces the power consumption oscillation amplitude through the power consumption after the first mean correction, and maintains the characteristics of the driving condition. When the power consumption is corrected for the second time, the vehicle basically meets the manufacturer's promotion when the power is sufficient for driving, and as the power decreases, it is more biased towards the actual vehicle working condition, so that the displayed range is more in line with the actual range. In the actual test results, the average error between the corrected display range and the actual range is 1.6%.

[0049] 2) The vehicle range adaptive estimation method of the application is very close to the actual driving process, accurate, improves the driving experience of customers, and the power consumption and range obtained by the algorithm do not require additional hardware devices and road information, which reduces the cost and complexity of the algorithm, and has a wide application scenario.

[0050] Additional aspects and advantages of the application will be in part apparent and in part pointed out hereinafter in the description. BRIEF DESCRIPTION OF DRAWINGS

[0051] In order to more clearly illustrate the technical solutions of the embodiments of the application, the following will briefly introduce the drawings needed to be used in the embodiments. Obviously, the drawings in the following description only some embodiments of the application, and for those skilled in the art, without creative labor, other drawings can also be obtained from these drawings.

[0052] Figure 1 is a flow chart of the vehicle range adaptive estimation method according to the embodiment of the application;

[0053] Figure 2 is a first proportional coefficient calibration chart of promotional power consumption according to the embodiment of the application;

[0054] Figure 3 is a second proportional coefficient calibration chart of dynamic power consumption according to the embodiment of the application;

[0055] Figure 4 is a figure of the power consumption value of each stage of the method verification result according to the embodiment of the application;

[0056] Figure 5 is a figure of the change of the range according to the method verification result of the embodiment of the application;

[0057] Figure 6 is a figure of the change of the driven distance and the actual distance according to the method verification result of the embodiment of the application;

[0058] Figure 7 is a block diagram of the vehicle range self-adaptive calibration device according to the embodiment of the application;

[0059] Figure 8 is a functional block diagram of a vehicle according to the embodiment of the application. DETAILED DESCRIPTION

[0060] Embodiments of the present application are described in detail below with reference to the accompanying drawings. The embodiments described with reference to the accompanying drawings are exemplary, and it should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application.

[0061] It should be noted that, unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs. The terms used in the specification of the present application herein are only for the purpose of describing specific embodiments and are not intended to limit the present application. The term "and / or" used herein includes any and all combinations of one or more related listed items.

[0062] In recent years, electric vehicle technology and supporting facilities have made significant breakthroughs, but electric vehicles still have some gaps with traditional oil vehicles in some aspects. First, there are no charging stations everywhere, and energy cannot be replenished at any time and any place. Second, the charging time is too long. Currently, the SOC from 20% to 80% needs at least half an hour. Third, the energy of the battery decays greatly with temperature, and it is very inconvenient to travel in the northern region or in winter.

[0063] In view of the range anxiety caused by the above problems, the mainstream electric vehicles on the market currently all have a range prediction function, but the estimation methods of different manufacturers are not the same, and the estimation accuracy also has some differences. Many car owners react that, for example, a certain brand exaggerates the range, which leads to the inability to reach the destination smoothly; or a certain brand estimates too conservatively, which creates unnecessary anxiety. Therefore, compared with the range function, an accurate and scientific estimation algorithm is more important.

[0064] The electric vehicle range estimation mainly includes two parts of the remaining power of the power battery estimation and the driving energy consumption prediction, and the basic definition is: the remaining power of the current battery pack divided by the driving energy consumption of the electric vehicle. At present, the method for improving the calculation accuracy of the range mainly starts from the current remaining power and the vehicle energy consumption, and comprehensively considers the temperature change, driving road condition, driving habit and the like, but the range obtained based on the historical data in the prior art has a simple algorithm, but the error is large, and the range number has a large jumping range, and the future working condition cannot be accurately predicted. The calculation method based on real-time or future road information needs to be equipped with a large number of sensors on the vehicle to obtain the external terrain, weather, road condition and the like, and a neural network, machine learning or the like is used to improve the estimation accuracy of the range, which depends on the external data, has a high cost and a small applicable range.

[0065] Based on this, the inventors propose a vehicle range adaptive estimation method and device, a vehicle and a storage medium, which can reduce the gap between the displayed range and the actual range, and has a low running cost and a wider applicable scenario. The technical solution will be described below through multiple embodiments.

[0066] Embodiment 1

[0067] Please refer to Figures 1 to 3 The embodiment provides a vehicle range adaptive estimation method, which comprises the following steps:

[0068] Step S10: After the whole vehicle is powered on, the voltage and current data fed back by the battery management system are obtained every first preset time, and the power consumption data information is obtained based on the voltage and current data;

[0069] In this step, it can be understood that the voltage and current data fed back by the battery management system BMS after the whole vehicle is powered on, the battery management system is a product of the prior art, and the specific results and functional principles are not described here, the first preset time can be 1ms, 10ms, 100ms or 1s, etc., which can be selected according to actual needs, an expression of the power consumption data value Q is created and stored in a temporary storage, and the continuous power consumption data is obtained by integrating the expression based on the obtained voltage and current data, wherein the power consumption data value Q expression is:

[0070] In the formula, is the voltage value fed back by the battery management system every first preset time, is the current value fed back by the battery management system every first preset time.

[0071] Step S20: The mileage data value of the vehicle driving is obtained every second preset time, and when the mileage data value increases by a preset interval mileage value, the time data value of the process is obtained;

[0072] In this step, the mileage data value of the vehicle driving can be obtained by continuously collecting the vehicle speed and time through the speed sensor, and of course can be obtained by other methods, for example, through the navigation system to obtain the mileage data value of the vehicle driving, and the specific selection can be made according to the actual demand, which is not limited here, and the same for the second preset time, which can be 1 ms, 10 ms, 100 ms or 1 s, etc., and the specific selection can also be made according to the actual demand.

[0073] In order to calculate the unit power consumption, an interval mileage value can be set in this embodiment, which can be 0.5 km, 1 km, or 2 km, etc., and the specific selection can be made according to the actual demand, and preferably 1 km, and when each interval mileage value is increased, the time consumption data value corresponding to the interval mileage value is calculated once, for example, from 0 to 1 km, 1 km to 2 km, 2 km to 3 km, and so on, so that the time consumption data value corresponding to each interval mileage value is calculated.

[0074] Step S30: determining the unit power consumption value based on the power consumption data information and the time consumption data value;

[0075] In this step, the time consumption data value corresponding to the interval mileage value is obtained in step S20, and the corresponding power consumption data value (i.e. unit power consumption value) is found based on the power consumption data information in the time period, and the time consumption data value and the corresponding time initial point are used to obtain the time interval domain where the time consumption data value is located, and the time interval domain and the power consumption data information are used to obtain the unit power consumption value in the corresponding time period, and specifically, the unit power consumption value expression can be:

[0076]

[0077] In the formula, T1 is the corresponding time initial point, T2 is the time point through the time consumption data value corresponding time period, is the average value of the voltage feedback by the battery management system in the time period, is the average value of the voltage feedback by the battery management system in the time period.

[0078] In some embodiments, taking the vehicle driving process as an example, the driving distance is 1 kilometer, the starting time is 9:00, the driving time is 9:01, and the time consumption data value is 1 minute. Based on the power consumption data information, the unit power consumption value of the time period from 9:00 to 9:01 is calculated. It can be understood that, in order to facilitate data operation in the vehicle processor, the unit power consumption value of this period can be converted to the AEC of 100 kilometers. Specifically, the vehicle driving distance is calculated once per kilometer (calibrated, default 1 kilometer) to calculate the power consumption. The AEC of 100 kilometers (kwh / 100km) = the battery discharge quantity AQ in the distance (default 1 kilometer) ÷ driving distance (default 1 kilometer) * 100.

[0079] Step S40: repeating steps S10-S30 to obtain a power consumption data set including a plurality of unit power consumption values;

[0080] In this step, it can be understood that a unit power consumption value is obtained in the foregoing steps S10-S30, and in order to further provide the error of the corrected vehicle endurance distance, the steps S10-S30 are repeated to continuously collect and calculate a plurality of unit power consumption values to constitute a power consumption data set. It can be understood that the number of unit power consumption values included in the power consumption data set can be set according to actual needs, which is not limited herein, for example, taking the calculation of power consumption once per kilometer as an example, 50 unit power consumption values of the vehicle within 50 kilometers (calibrated, default 50 kilometers) can be calculated.

[0081] Step S50: based on the power consumption data set, performing a power consumption correction to obtain a dynamic power consumption value;

[0082] In this step, by judging whether the driving distance data value of the vehicle is equal to the preset correction distance value, if yes, the plurality of unit power consumption values in the power consumption data set are accumulated, and the dynamic power consumption value after the power consumption correction is obtained according to the number of corresponding unit power consumption values. It can be understood that the correction distance value can be preset, for example, the correction distance value in the foregoing step S40 is 50 kilometers, of course, the correction distance value can also be 60 kilometers, 70 kilometers or 100 kilometers, etc., which can be set according to actual needs, which is not limited herein,

[0083] When the driving distance data value of the vehicle is equal to the preset correction distance value, a power consumption correction is performed. The data judgment comparison logic of the driving distance data value of the vehicle and the preset correction distance value is the prior art, which is not described in detail herein. Taking the calculation of the vehicle within 50 kilometers as an example, the dynamic power consumption value is obtained by averaging the 50 power consumption values;

[0084] The calculation formula of the dynamic power consumption value is AEC_D= (AEC_1+...AEC_50) / 50.

[0085] Step S60: Based on the dynamic power consumption value and the set advertised power consumption, perform secondary power consumption correction to obtain the range data to be fed back.

[0086] In this step, the power consumption is corrected a second time based on the dynamic power consumption value obtained in step S50 and the advertised power consumption set by the vehicle. For example, based on the vehicle's historical power consumption experience data, a first proportional coefficient calibration table for advertised power consumption and a second proportional coefficient calibration table for the dynamic power consumption value are preset. The advertised power consumption refers to the fixed value set when the vehicle leaves the factory. The specific value can be selectively set according to different vehicles, and there is no restriction here.

[0087] Optionally, the calibration tables for the first proportional coefficient A of the advertised energy consumption and the second proportional coefficient B of the dynamic energy consumption value can be set according to different vehicle models and battery pack parameters. Specific settings are not limited here. For example, in one vehicle model, such as... Figure 2 and Figure 3 As shown, for the proportional coefficient A, when the vehicle's current remaining battery charge (SOC) is less than or equal to 40%, the first proportional coefficient A of the advertised energy consumption is 0. When the vehicle's current remaining battery charge (SOC) is greater than 40%, the value of the first proportional coefficient A of the advertised energy consumption is linearly related to the vehicle's current remaining battery charge (positive correlation). For the proportional coefficient B, when the vehicle's current remaining battery charge (SOC) is less than or equal to 40%, the second proportional coefficient B of the dynamic energy consumption value is 1. When the vehicle's current remaining battery charge (SOC) is greater than 40%, the value of the second proportional coefficient B of the advertised energy consumption is linearly related to the vehicle's current remaining battery charge (negative correlation).

[0088] It should be noted that during vehicle operation, the values ​​of the first proportional coefficient A and the second proportional coefficient B can be determined by obtaining the current SOC of the battery pack of the corresponding vehicle, and by retrieving the pre-stored first proportional coefficient calibration table and second proportional coefficient calibration table based on the SOC of the battery pack.

[0089] Based on the values ​​of the first and second proportional coefficients, the dynamic power consumption value, and the advertised power consumption, the average power consumption value AEC for the secondary power consumption correction is determined.

[0090] The average power consumption value AEC is expressed as: AEC = A * AEC_P + B * AEC_D

[0091] In the formula, A is the first proportional coefficient, B is the second proportional coefficient, AEC_P is the advertising power consumption, and AEC_D is the dynamic power consumption value.

[0092] Further, in order to obtain the corrected range value, the current remaining power Q1 of the battery is obtained every third preset time, the third preset time can be 1ms, 10ms, 100ms or 1s, etc., which can be selected according to actual needs, and is not limited here, wherein the expression of the current remaining power Q1 of the battery is: Q1=SOC×SOH×Ah, wherein SOC is the remaining power of the battery, SOH is the health status of the battery, and Ah is the rated capacity of the battery;

[0093] Based on the average power consumption value and the remaining power Q1 of the battery, the calibrated range data is obtained, and it can be understood that the range is calculated as: Milg=remaining power÷average power consumption, and the range result is transmitted to the instrument for display, wherein the remaining power=SOC×SOH×battery rated capacity;

[0094] It should be noted that in the above method, taking a vehicle corrected once every 50 kilometers as an example, when a new vehicle is delivered, 50 promotional power consumption values are written into the Flash as the average power consumption values of the new vehicle for 50 kilometers; after the vehicle is powered on, the 50 average power consumption values stored in the Flash are first read as initial values; when the vehicle travels and a new average power consumption AEC is calculated, the old power consumption value is replaced and written into the Flash; the array storing the power consumption values is iteratively updated according to the first-in-first-out principle, so that the values stored in the Flash are always the average power consumption for 50 kilometers.

[0095] In the above method steps, the mileage data value and the corresponding power consumption data information of the vehicle are collected in real time, and the power consumption data set including a plurality of unit power consumption values corresponding to each interval mileage value is calculated, based on which the dynamic power consumption value for the first power consumption correction is obtained, based on the characteristics of the vehicle battery pack, the proportional coefficient value change of the promotional power consumption and the dynamic power consumption value under different battery pack SOC conditions is set, and based on the proportional coefficient value change, the average power consumption expression is created to complete the second power consumption correction, based on the average power consumption obtained after the second correction and the real-time obtained remaining power, the corrected range is calculated, and the corrected range data is transmitted to the vehicle entertainment system for display. The method greatly reduces the power consumption oscillation amplitude through the once mean value corrected power consumption, and maintains the characteristics of the driving cycle. When the power consumption is corrected twice, the vehicle basically meets the manufacturer's promotion when the power is sufficient, and as the power decreases, it is more biased towards the actual vehicle working condition, so that the displayed range is more consistent with the actual range.

[0096] And, since the corrected electric consumption is very close to the actual driving process, the estimation is accurate, the driving experience of the customer is improved, the electric consumption and the range obtained by the algorithm do not require additional hardware devices and road information, which reduces the cost while reducing the complexity of the algorithm, and the application scenarios are wide.

[0097] In an example, referring to Figures 4 to 6 , in order to further verify the error value of the corrected range of the above method steps, according to the algorithm principle corresponding to the above method, a model is established in MATLAB Simulink, a pure electric vehicle instance is selected in AVLCruise software as a simulation basic model, relevant parameters are modified, a dynamic link library DLL is established with the model in Simulink, the vehicle is set to WLTC working condition cycle driving, and the SOC runs from 100% to 5%, and the result is shown in Figures 4 to 6 , it can be seen from Figure 4 that the initial 100km electric consumption changes smoothly after two corrections, and the fluctuation period still meets the WLTC working condition. Figure 5 In the Figure 6 , the range curve is in a downward trend as a whole, and no mutation occurs in the process.

[0098] Embodiment 2

[0099] Please refer to Figure 7 , the embodiment provides a vehicle range adaptive calibration device, and the vehicle range adaptive calibration device 200 comprises:

[0100] The first acquisition module 210 is configured to acquire voltage and current data fed back by the battery management system every first preset time after the whole vehicle is powered on, and to obtain electric consumption data information based on the voltage and current data;

[0101] The second acquisition module 220 is configured to acquire mileage data values of vehicle driving every second preset time, and to acquire time consumption data values when the mileage data values increase by a preset interval mileage value;

[0102] The first determination module 230 is configured to determine a unit electric consumption value based on the electric consumption data information and the time consumption data values, and to obtain an electric consumption data set comprising a plurality of unit electric consumption values;

[0103] The first correction module 240 is configured to perform one-time electric consumption correction based on the electric consumption data set to obtain a dynamic electric consumption value;

[0104] The one-time electric consumption correction process comprises judging whether the mileage data value of the vehicle driving is equal to a preset correction mileage value.

[0105] If yes, a plurality of unit power consumption values in the power consumption data set are accumulated, and a dynamic power consumption value after first power consumption correction is obtained according to the number of corresponding unit power consumption values.

[0106] The second determination module 250 is configured to obtain the SOC of the current battery pack of the vehicle, and determine the values of the first proportionality coefficient and the second proportionality coefficient according to the SOC, the first proportionality coefficient calibration table and the second proportionality coefficient calibration table.

[0107] The second correction module 260 is configured to perform second power consumption correction based on the dynamic power consumption value and the set promotional power consumption, wherein the second power consumption correction includes obtaining a preset dynamic proportionality coefficient corresponding to the dynamic power consumption value and the promotional power consumption, and determining an average power consumption value after second power consumption correction based on the dynamic proportionality coefficient, the dynamic power consumption value and the promotional power consumption.

[0108] Of course, it can be understood that after the second correction module 260 obtains the average power consumption value, the current remaining power Q1 of the battery is obtained every third preset time, wherein the expression of the current remaining power Q1 of the battery is Q1=SOC×SOH×Ah.

[0109] In the formula, SOC is the remaining power of the battery, SOH is the health status of the battery, and Ah is the rated capacity of the battery.

[0110] Based on the average power consumption value and the remaining power Q1 of the battery, the calibrated range data is obtained.

[0111] Embodiment 3

[0112] Please refer to Figure 8 The vehicle 600 can include various subsystems, such as an infotainment system 610, a perception system 620, a decision control system 630, a drive system 640, and a computing platform 650. Alternatively, the vehicle 600 can include more or fewer subsystems, and each subsystem can include multiple components. In addition, each subsystem and component of the vehicle 600 can be interconnected by wired or wireless means.

[0113] In some embodiments, the infotainment system 610 can include a communication system 611, an entertainment system 612, and a navigation system 613.

[0114] The communication system 611 can include a wireless communication system that can communicate wirelessly with one or more devices, either directly or via a communication network. For example, the wireless communication system can use 3G cellular communication, such as CDMA, EVDO, GSM / GPRS, or 4G cellular communication, such as LTE. Or 5G cellular communication. The wireless communication system can communicate with a wireless local area network (WLAN) using WiFi. In some embodiments, the wireless communication system can communicate directly with a device using an infrared link, Bluetooth, or ZigBee. Other wireless protocols, such as various vehicle communication systems, for example, the wireless communication system can include one or more dedicated short range communications (DSRC) devices, which can include public and / or private data communication between vehicles and / or roadside stations.

[0115] The entertainment system 612 can include a display device, a microphone, and a sound system, based on which a user can listen to the radio or play music in the vehicle, or connect the phone with the vehicle and realize the phone screen projection on the display device. The display device can be touchable, and the user can operate it by touching the screen.

[0116] In some cases, the user's voice signal can be obtained through the microphone, and some control of the vehicle 600 by the user can be realized according to the analysis of the user's voice signal, such as adjusting the temperature in the vehicle, etc. In other cases, the user can be played music through the sound system.

[0117] The navigation system 613 can include a map service provided by a map provider, thereby providing the vehicle 600 with navigation of the driving route, and the navigation system 613 can be used in cooperation with the global positioning system 621 and the inertial measurement unit 622 of the vehicle. The map service provided by the map provider can be a two-dimensional map or a high-precision map.

[0118] The perception system 620 can include several types of sensors that sense information about the environment surrounding the vehicle 600. For example, the perception system 620 can include a global positioning system 621 (which can be a GPS system, a Beidou system, or other positioning system), an inertial measurement unit (IMU) 622, a lidar 623, a millimeter wave radar 624, an ultrasonic radar 625, and a camera 626. The perception system 620 can also include sensors that monitor internal systems of the vehicle 600 (e.g., an in-vehicle air quality monitor, a fuel gauge, an oil temperature gauge, etc.). Sensor data from one or more of these sensors can be used to detect objects and their respective characteristics (location, shape, orientation, velocity, etc.). Such detection and identification are key functions for the safe operation of the vehicle 600.

[0119] The global positioning system 621 is used to estimate the geographic position of the vehicle 600.

[0120] The inertial measurement unit 622 is used to sense changes in the pose of the vehicle 600 based on inertial acceleration. In some embodiments, the inertial measurement unit 622 can be a combination of an accelerometer and a gyroscope.

[0121] The lidar 623 uses laser light to sense objects in the environment in which the vehicle 600 is located. In some embodiments, the lidar 623 can include one or more laser sources, a laser scanner, and one or more detectors, as well as other system components.

[0122] The millimeter wave radar 624 uses radio signals to sense objects within the surrounding environment of the vehicle 600. In some embodiments, in addition to sensing objects, the millimeter wave radar 624 can also be used to sense the speed and / or heading of the objects.

[0123] The ultrasonic radar 625 can use ultrasonic signals to sense objects around the vehicle 600.

[0124] The camera 626 is used to capture image information of the surrounding environment of the vehicle 600. The camera 626 can include a monocular camera, a binocular camera, a structured light camera, and a panoramic camera, etc., and the image information obtained by the camera 626 can include still images or video stream information.

[0125] The decision control system 630 includes a computing system 631 that makes analytical decisions based on the information obtained by the perception system 620, and also includes a vehicle controller 632 that controls the power system of the vehicle 600, as well as a steering system 633, a throttle 634, and a braking system 635 for controlling the vehicle 600.

[0126] The computing system 631 can operate to process and analyze various information acquired by the perception system 620 in order to identify targets, objects, and / or features in the environment surrounding the vehicle 600. Targets can include pedestrians or animals, and objects and / or features can include traffic signals, road boundaries, and obstacles. The computing system 631 can use object recognition algorithms, Structure from Motion (SFM) algorithms, video tracking, and / or the like. In some embodiments, the computing system 631 can be used to map the environment, track objects, estimate the velocity of objects, and / or the like. The computing system 631 can analyze the acquired information and derive a control strategy for the vehicle.

[0127] The vehicle controller 632 can be used to coordinate the control of the power battery and the engine 641 of the vehicle in order to improve the power performance of the vehicle 600.

[0128] The steering system 633 can be used to adjust the heading direction of the vehicle 600. For example, the steering system 633 can be a steering wheel system in one embodiment.

[0129] The accelerator 634 can be used to control the operating speed of the engine 641 and, in turn, the speed of the vehicle 600.

[0130] The braking system 635 can be used to control the deceleration of the vehicle 600. The braking system 635 can use friction to slow the wheels 644. In some embodiments, the braking system 635 can convert the kinetic energy of the wheels 644 into electrical current. The braking system 635 can also take other forms to slow the wheels 644 and, in turn, control the speed of the vehicle 600.

[0131] The drive system 640 can include components that provide motive power for the vehicle 600. In one embodiment, the drive system 640 can include the engine 641, the energy source 642, the transmission system 643, and the wheels 644. The engine 641 can be an internal combustion engine, an electric motor, an air compression engine, or other types of engines in combination, such as a hybrid engine that includes a gasoline engine and an electric motor, a hybrid engine that includes an internal combustion engine and an air compression engine. The engine 641 converts the energy source 642 into mechanical energy.

[0132] Examples of the energy source 642 include gasoline, diesel, other petroleum-based fuels, propane, other compressed gas-based fuels, ethanol, solar panels, batteries, and other sources of electrical power. The energy source 642 can also provide energy for other systems of the vehicle 600.

[0133] The drivetrain 643 can transmit mechanical power from the engine 641 to the wheels 644. The drivetrain 643 can include a transmission, a differential, and drive shafts. In one embodiment, the drivetrain 643 can also include other devices, such as a clutch. The drive shafts can include one or more shafts that can be coupled to one or more wheels 644.

[0134] Some or all of the functionality of the vehicle 600 is controlled by a computing platform 650. The computing platform 650 can include at least one processor 651 that can execute instructions 653 stored in a non-transitory computer readable medium, such as a memory 652. In some embodiments, the computing platform 650 can also be a plurality of computing devices that control individual components or subsystems of the vehicle 600 in a distributed manner.

[0135] The processor 651 can be any conventional processor, such as a commercially available CPU. Alternatively, the processor 651 can include a Graphics Processing Unit (GPU), a Field Programmable Gate Array (FPGA), a System on Chip (SOC), an Application Specific Integrated Circuit (ASIC), or a combination thereof. Although Figure 8 Although functionally illustrated as a single processor, memory, and other elements of a computer in the same block, one of ordinary skill in the art will appreciate that the processor, computer, or memory can actually include multiple processors, computers, or memories that can or can not be stored in the same physical housing. For example, the memory can be a hard drive or other storage medium located in a different housing than the computer. Accordingly, references to a processor or computer will be understood to include references to a collection of processors or computers or memories that can or can not operate in parallel. Rather than using a single processor to perform the steps described herein, some components, such as the steering assembly and the deceleration assembly, can each have their own processor that only performs calculations related to the functionality specific to the component.

[0136] In embodiments of the present disclosure, the processor 651 can perform the steps of the vehicle range adaptive estimation method in the embodiments described above.

[0137] In various aspects described herein, the processor 651 can be located remote from the vehicle and in wireless communication with the vehicle. In other aspects, some of the processes described herein are performed on a processor disposed within the vehicle while others are performed by a remote processor, including taking the necessary steps to perform a single maneuver.

[0138] In some embodiments, the memory 652 can include instructions 653 (e.g., program logic) that can be executed by the processor 651 to perform various functions of the vehicle 600. The memory 652 can also include additional instructions, including instructions to send data to, receive data from, interact with, and / or control one or more of the infotainment system 610, the perception system 620, the decision control system 630, and the drive system 640.

[0139] In addition to the instructions 653, the memory 652 can store data, such as road maps, route information, the vehicle's location, direction, speed, and other such vehicle data, and other information. Such information can be used by the vehicle 600 and the computing platform 650 during operation of the vehicle 600 in autonomous, semi-autonomous, and / or manual modes.

[0140] The computing platform 650 can control the functions of the vehicle 600 based on inputs received from various subsystems (e.g., the drive system 640, the perception system 620, and the decision control system 630). For example, the computing platform 650 can utilize inputs from the decision control system 630 in order to control the steering system 633 to avoid obstacles detected by the perception system 620. In some embodiments, the computing platform 650 can be operable to provide control over many aspects of the vehicle 600 and its subsystems.

[0141] Optionally, one or more of the components described above can be installed separately from or associated with the vehicle 600. For example, the memory 652 can exist partially or entirely separately from the vehicle 600. The components described above can be communicatively coupled together in a wired and / or wireless manner.

[0142] Optionally, the above components are just an example, in actual applications, components in each module described above can be added or deleted according to actual needs, Figure 8 It should not be understood as a limitation to the embodiments of the present disclosure.

[0143] Optionally, the vehicle 600 or a perception and computing device (e.g., the computing system 631, the computing platform 650) associated with the vehicle 600 can predict the behavior of the identified object based on the characteristics of the identified object and the state of the surrounding environment (e.g., traffic, rain, ice on the road, etc.). Optionally, each identified object depends on the behavior of the other identified objects, so the behavior of a single identified object can also be predicted by considering all of the identified objects together. The vehicle 600 can adjust its speed based on the predicted behavior of the identified object. In other words, the autonomous vehicle can determine what steady state the vehicle will need to adjust to (e.g., accelerate, decelerate, or stop) based on the predicted behavior of the object. Other factors can also be considered in determining the speed of the vehicle 600 during this process, such as the lateral position of the vehicle 600 in the road, the curvature of the road, the proximity of static and dynamic objects, etc.

[0144] In addition to providing instructions to adjust the speed of the autonomous vehicle, the computing device can also provide instructions to modify the steering angle of the vehicle 600 to cause the autonomous vehicle to follow a given trajectory and / or maintain a safe lateral and longitudinal distance from objects (e.g., vehicles in adjacent lanes on the road) near the autonomous vehicle.

[0145] The vehicle 600 described above can be different models of pure electric vehicles, and the embodiments of the present disclosure are not particularly limited.

[0146] In another exemplary embodiment, a computer program product is also provided, which contains a computer program capable of being executed by a programmable device, and the computer program has code portions for executing the vehicle range adaptive estimation method described above when executed by the programmable device.

[0147] Embodiment 4

[0148] Based on the same inventive concept, the present application also provides a computer readable storage medium, which stores computer program instructions, and the program instructions are executed by a processor to implement the steps of the vehicle range adaptive estimation method provided by the above embodiments.

[0149] The terms "first", "second", "third", etc. in the specification and claims of the present application and the drawings are to distinguish different objects, and are not intended to describe a particular order. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a series of steps or units are included, or optionally, other steps or units not listed are also included, or optionally, other steps or units inherent to the processes, methods, products or equipment are also included.

[0150] Only those parts of the drawings that are absolutely necessary for an understanding of the application have been shown in the drawings. Before any exemplary embodiments are described in detail, it is to be described that some exemplary embodiments are described as processes or methods that are depicted as flowcharts. Although the processes are described in a particular sequential order, many of the processes can be performed in parallel, concurrently or in any order. In addition, the order of the processes can be re-arranged. The processes can be terminated when their operations are completed, but the processes can also have additional steps not included in the figure, which could be performed after the processes end. The processes can correspond in part to method steps for implementing the described implementations.

[0151] The terms "component," "module," "system," "unit," and the like are used in the present specification to refer to computer-related entities, hardware, firmware, a combination of hardware and software, software, or software in execution. For example, a unit can be, but is not limited to, a process running on a processor, a processor, an object, an executable, a thread of execution, a program, and / or a distributed application between two or more computers. In addition, the units can be executed from various computer-readable media having various data structures stored thereon. The units can communicate, for example, according to signals having one or more data packets (e.g., from a second unit data from another unit with which local system, a distributed system, and / or a network is interacting. For example, the Internet, through signal with other systems.

[0152] In the description of the present specification, the description of the terms "one embodiment," "some embodiments," "certain embodiments," "example," "specific example," or "some examples," etc. means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In the present specification, the illustrative description of the above terms does not necessarily refer to the same embodiment or example.

[0153] It is obvious that the described embodiments are only some of the embodiments of the present application, not all of the embodiments. In this document, the reference to "embodiments" means that the specific features, structures, or characteristics described in connection with the embodiments can be included in at least one embodiment of the present application. The phrase appears at various places in the specification does not necessarily all refer to the same embodiment, nor is it necessarily mutually exclusive of other embodiments or alternatively or alternatively. It is obvious to those skilled in the art that the embodiments described herein can be combined with other embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0154] While embodiments of the application have been shown and described, it is to be understood that the embodiments described are merely divergences of the principles and application of the present application and that numerous modifications, changes, substitutions, and alterations can be made thereto without departing from the spirit and scope of the present application, which is defined by the following claims and their equivalents.

Claims

1. A method for adaptively predicting vehicle driving range, characterized in that, include: Step S10: After the vehicle is powered on, the voltage and current data fed back by the battery management system are obtained every first preset time interval, and the power consumption data information is obtained based on the voltage and current data. Step S20: Obtain the mileage data value of the vehicle every second preset time interval, and obtain the time data value of the process when the mileage data value increases by a preset interval mileage value. Step S30: Determine the unit power consumption value based on the power consumption data information and the time consumption data value; Step S40: Repeat steps S10-S30 to obtain an energy consumption dataset including multiple unit energy consumption values; Step S50: Based on the power consumption dataset, perform a power consumption correction to obtain a dynamic power consumption value, including determining whether the mileage data value of the vehicle is equal to a preset correction mileage value; if so, accumulate multiple unit power consumption values ​​in the power consumption dataset, and obtain the dynamic power consumption value after the power consumption correction based on the number of corresponding unit power consumption values. Step S60: Based on the dynamic energy consumption value and the set advertised energy consumption, perform a second energy consumption correction to obtain the range data to be fed back. This includes obtaining the preset dynamic ratio coefficients corresponding to the dynamic energy consumption value and the advertised energy consumption; determining the average energy consumption value for the second energy consumption correction based on the dynamic ratio coefficients, the dynamic energy consumption value, and the advertised energy consumption; specifically, based on the vehicle's historical energy consumption experience data, a first ratio coefficient calibration table for advertised energy consumption and a second ratio coefficient calibration table for dynamic energy consumption value are preset; the current SOC of the vehicle's battery pack is obtained; the values ​​of the first ratio coefficient and the second ratio coefficient are determined according to the SOC, the first ratio coefficient calibration table, and the second ratio coefficient calibration table; the average energy consumption value AEC for the second energy consumption correction is determined according to the values ​​of the first ratio coefficient and the second ratio coefficient, the dynamic energy consumption value, and the advertised energy consumption. The expression for the average energy consumption value AEC is: In the formula, A is the first proportional coefficient, B is the second proportional coefficient, AEC_P is the advertised power consumption, and AEC_D is the dynamic power consumption value; based on the average power consumption value and the remaining battery power, the calibrated driving range data is obtained.

2. The adaptive prediction method for vehicle driving range according to claim 1, characterized in that, In step S10, obtaining the power consumption data value based on the voltage and current data includes: An expression for the power consumption data value Q is created, and continuous power consumption data is obtained by integrating the expression. The expression for the power consumption data value Q is: In the formula, The voltage value fed back by the battery management system at each first preset time. This refers to the current value fed back by the battery management system at each first preset time interval.

3. The adaptive prediction method for vehicle driving range according to claim 1, characterized in that, In step S30, determining the unit power consumption value based on the power consumption data information and the time consumption data value includes: Based on the time consumption data value and the corresponding time initial point, the time interval domain in which the time consumption data value is located is obtained; Based on the time interval domain and the power consumption data, the unit power consumption value within the corresponding time period is obtained.

4. The adaptive prediction method for vehicle driving range according to claim 1, characterized in that, The process of obtaining calibrated driving range data based on the average power consumption value and the remaining battery power includes: The current remaining battery power Q1 is obtained every third preset time interval, wherein the current remaining battery power Q1 is expressed as: Q1=SOC×SOH×Ah; In the formula, SOC is the remaining power of the battery, SOH is the health status of the battery, and Ah is the rated capacity of the battery. Based on the average power consumption value and the remaining battery power Q1, the calibrated driving range data is obtained.

5. A vehicle range adaptive calibration device, characterized in that, The device is applied to the vehicle range adaptive prediction method as described in any one of claims 1-4, comprising: The first acquisition module is configured to acquire voltage and current data fed back by the battery management system every first preset time after the vehicle is powered on, and obtain power consumption data information based on the voltage and current data. The second acquisition module is configured to acquire the mileage data value of the vehicle every second preset time interval, and acquire the time data value of the process when the mileage data value increases by a preset interval mileage value. The first determining module is configured to determine the unit power consumption value based on the power consumption data information and the time consumption data value, and obtain a power consumption dataset including multiple unit power consumption values. The first correction module is configured to perform a power consumption correction based on the power consumption dataset to obtain a dynamic power consumption value; The second determining module is configured to obtain the current SOC of the battery pack of the vehicle, and determine the values ​​of the first proportional coefficient and the second proportional coefficient based on the SOC, the first proportional coefficient calibration table and the second proportional coefficient calibration table. The second correction module is configured to perform a second power consumption correction based on the dynamic power consumption value and the set advertised power consumption, to obtain the range data to be fed back. This includes obtaining the dynamic power consumption value and the preset dynamic ratio coefficient corresponding to the advertised power consumption, and determining the average power consumption value of the second power consumption correction based on the dynamic ratio coefficient, the dynamic power consumption value and the advertised power consumption.

6. A vehicle, characterized in that, include: processor; Memory used to store the processor's executable instructions; The processor is configured as follows: The steps of implementing the adaptive prediction method for vehicle driving range according to any one of claims 1 to 4.

7. A computer-readable storage medium having computer program instructions stored thereon, characterized in that, When the program instructions are executed by the processor, they implement the steps of the adaptive prediction method for vehicle driving range according to any one of claims 1 to 4.

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