Method and device for predicting cruising range, electric vehicle and computer-readable storage medium

Abstract

The present invention discloses a method and device for predicting a cruising range, an electric vehicle, and a computer-readable storage medium. The method comprises: obtaining temperature data of each battery cell in the electric vehicle, and correcting the initial remaining power of the electric vehicle according to the temperature data to obtain the actual remaining power, calculating the remaining mileage according to the actual remaining power and the average energy consumption of the electric vehicle, obtaining a distance error based on the accumulated mileage data and historical speed data obtained in a first time period, and then correcting the remaining mileage according to the distance error to determine the actual cruising range of the electric vehicle. This embodiment corrects the actual remaining power based on the temperature data, and recalibrates the remaining mileage through the accumulated mileage data and historical speed data, which can reduce the prediction error of the cruising range and improve the accuracy of the cruising range prediction.

Description

Technical Field

[0001] The present invention relates to the field of electric vehicle intelligent control technology, and in particular to a range prediction method and device, an electric vehicle, and a computer-readable storage medium. Background Art

[0002] In recent years, new energy vehicles have caused users to have great range anxiety during use, and they are worried that the vehicles may break down.

[0003] At present, the calculation method of the cruising range of new energy vehicles often only considers the power value under ideal working conditions, which leads to large errors in the cruising range results. Therefore, the problem of inaccurate calculation of the cruising range of electric vehicles needs to be solved urgently. Summary of the invention

[0004] The main purpose of the present invention is to provide a range prediction method, device, electric vehicle and computer-readable storage medium, aiming to improve the accuracy of electric vehicle range prediction. The technical solution is as follows:

[0005] In a first aspect, an embodiment of the present application provides a method for predicting a cruising range, including:

[0006] Acquire the initial remaining power of the battery of the electric vehicle and the temperature data of each battery cell in the battery, and correct the initial remaining power based on the temperature data to obtain the actual remaining power;

[0007] Calculating the remaining mileage based on the average energy consumption of the electric vehicle and the actual remaining power;

[0008] Acquire cumulative mileage data and historical speed data of the electric vehicle within a first time period, and calculate a distance error based on the cumulative mileage data and the historical speed data;

[0009] Based on the distance error and the remaining mileage, an actual cruising range of the electric vehicle is determined.

[0010] In a second aspect, an embodiment of the present application provides a cruising range prediction device, comprising:

[0011] An acquisition module, used to acquire the initial remaining power of the battery of the electric vehicle and the temperature data of each battery cell in the battery, and to correct the initial remaining power based on the temperature data to obtain the actual remaining power;

[0012] A remaining mileage calculation module, used to calculate the remaining mileage based on the average energy consumption of the electric vehicle and the actual remaining power;

[0013] an error calculation module, configured to obtain cumulative mileage data and historical speed data of the electric vehicle within a first time period, and calculate a distance error based on the cumulative mileage data and the historical speed data;

[0014] The cruising range calculation module is used to determine the actual cruising range of the electric vehicle based on the distance error and the remaining range.

[0015] In a third aspect, an embodiment of the present application provides an electric vehicle, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program implements the steps of the above method when executed by the processor.

[0016] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored, and the cruising range prediction program implements the steps of the above method when executed by a processor.

[0017] In an embodiment of the present invention, the temperature data of each battery cell in the electric vehicle is obtained, and the initial remaining power of the electric vehicle is corrected according to the temperature data to obtain the actual remaining power, and the remaining mileage is calculated according to the actual remaining power and the average energy consumption of the electric vehicle, and the distance error is obtained based on the accumulated mileage data and historical speed data obtained in the first time period, and then the remaining mileage is corrected according to the distance error to determine the actual cruising range of the electric vehicle. This embodiment corrects the actual remaining power based on the temperature data, and recalibrates the remaining mileage through the accumulated mileage data and historical speed data, which can reduce the prediction error of the cruising range and improve the accuracy of the cruising range prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0019] Figure 1 is an example schematic diagram of a method for predicting a cruising range provided in an embodiment of the present application;

[0020] Figure 2 It is a flowchart of a method for predicting a cruising range provided in an embodiment of the present application;

[0021] Figure 3 It is a detailed flow chart of correcting the initial remaining power in a cruising range prediction method provided in an embodiment of the present application;

[0022] Figure 4 It is a detailed flow chart of obtaining the accumulated mileage data and the historical speed data in a cruising range prediction method provided in an embodiment of the present application;

[0023] Figure 5 It is a structural schematic diagram of a cruising range prediction device provided in an embodiment of the present application;

[0024] Figure 6 It is a structural schematic diagram of a cruising range prediction device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0025] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0026] The cruising range prediction device can be a terminal device such as a mobile phone, computer, tablet computer, smart watch or vehicle-mounted device, or it can be a module in the terminal device for implementing the cruising range prediction method. The cruising range prediction device can obtain the initial remaining power of the battery of the electric vehicle and the temperature data of each battery cell in the battery, correct the initial remaining power based on the temperature data to obtain the actual remaining power, calculate the remaining mileage based on the average energy consumption of the electric vehicle and the actual remaining power, obtain the cumulative mileage data and historical speed data of the electric vehicle in a first time period, calculate the distance error based on the cumulative mileage data and the historical speed data, and determine the actual cruising range of the electric vehicle based on the distance error and the remaining mileage.

[0027] Please also see Figure 1 , an example schematic diagram of a method for predicting a range is provided for an embodiment of the present application. A cloud server collects and stores the driving data of an electric vehicle in real time, such as once every 30 seconds. The driving data may include the remaining power, speed, and mileage of the electric vehicle. A range prediction device is disposed in the electric vehicle. By obtaining the initial remaining power of the battery of the electric vehicle and the temperature data of each cell in the battery, the initial remaining power is corrected based on the temperature data to obtain the actual remaining power, thereby calculating the remaining mileage based on the average energy consumption. Then, the range prediction device obtains the cumulative mileage data and historical speed data of the electric vehicle in the first time period from the cloud, calculates the distance error based on the cumulative mileage data and the historical speed data, and then determines the actual range of the electric vehicle based on the distance error and the remaining mileage.

[0028] The cruising range prediction method provided in this application is described in detail below in conjunction with specific embodiments.

[0029] See also Figure 2 , which is a flow chart of a method for predicting a range of mileage provided in an embodiment of the present application. Figure 2 As shown, the method of the embodiment of the present application may include the following steps S10-S30.

[0030] S10, obtaining an initial remaining power of a battery of the electric vehicle and temperature data of each battery cell in the battery, and correcting the initial remaining power based on the temperature data to obtain an actual remaining power;

[0031] S20, calculating the remaining mileage based on the average energy consumption of the electric vehicle and the actual remaining power;

[0032] S30, acquiring cumulative mileage data and historical speed data of the electric vehicle in a first time period, and calculating a distance error based on the cumulative mileage data and the historical speed data;

[0033] S40: Determine the actual cruising range of the electric vehicle based on the distance error and the remaining range.

[0034] The cruising range prediction method of the present embodiment is used for electric vehicles. A commonly used method is to predict the cruising range by using the cruising range value and the state of charge SOC calibrated under ideal working conditions. However, since the cruising range value is calibrated under ideal working conditions, there will be a certain gap with the actual situation. If this value is used to calculate the cruising range under any circumstances, it will lead to errors. In addition, since the SOC will change with the environment and the road conditions of the vehicle, if the SOC is not corrected, it will also lead to large errors. Therefore, the cruising range prediction method of the present invention is proposed to reduce the error value of the cruising range estimation and improve the user experience.

[0035] The following is a detailed description of each step:

[0036] S10, obtaining an initial remaining power of a battery of the electric vehicle and temperature data of each battery cell in the battery, and correcting the initial remaining power based on the temperature data to obtain an actual remaining power;

[0037] Understandably, in daily car use, the road conditions are changeable, and because air conditioning and various electrical appliances consume electricity, the actual range will eventually decrease. In addition, different temperatures have a great impact on the battery capacity retention rate. Under low temperature conditions, the battery pack activity decreases, resulting in a decrease in battery capacity and a temporary decrease in range. The lower the temperature of the electric vehicle, the shorter the range. The order of range estimation accuracy in different temperature environments is normal temperature > high temperature > low temperature.

[0038] Specifically, the remaining power of the battery can be estimated by parameters such as the battery terminal voltage, charge and discharge current, and internal resistance. However, due to the above-mentioned influencing factors, the estimated initial remaining power is not accurate, so the present invention focuses on the temperature factor, and obtains the temperature data of the battery cell to correct the initial remaining power and obtain the actual power. The battery of an electric vehicle is usually composed of multiple battery cells. During use, the temperature of each battery cell is different, so the temperature of each battery cell is obtained as temperature data through a battery cell temperature sensor.

[0039] S20, calculating the remaining mileage based on the average energy consumption of the electric vehicle and the actual remaining power;

[0040] Specifically, the average energy consumption of the electric vehicle is obtained, and the remaining mileage is calculated based on the average energy consumption and the actual remaining power. The average energy consumption is the ratio of the power consumption of the electric vehicle to the driving distance. The energy consumption of the vehicle is related to the current driving speed. The current speed of the vehicle is collected in real time through the anti-lock braking system ABS. The battery system BMS calculates the real-time energy consumption Q (kwh / km) of the vehicle based on the pure discharge energy of the battery (obtained through the real-time voltage and real-time current) and the real-time vehicle speed sent by the ABS. As a specific calculation method, the mileage is first calculated based on v-the driving speed of the vehicle (km / h); and then the energy consumption of the vehicle in the driving state is calculated. The average energy consumption is obtained by obtaining the real-time energy consumption corresponding to multiple time points and taking the average value. The remaining mileage of the electric vehicle can be calculated based on the actual remaining power and the average power consumption per 100 kilometers. Optionally, the power consumed by the electric vehicle from the beginning of driving to the preset distance can also be obtained. For example, 100km is divided into 10 intervals, and the power consumed by the car every 10km is counted, and then the power consumption required for every 100 kilometers when the electric vehicle is in the driving state is counted.

[0041] S30, acquiring cumulative mileage data and historical speed data of the electric vehicle in a first time period, and calculating a distance error based on the cumulative mileage data and the historical speed data;

[0042] It is understandable that the driving distance of an electric vehicle can be obtained in two ways, one is the odometer, and the other is the integral calculation based on speed and time. In practical applications, the distances calculated by these two methods are usually different. Therefore, it is necessary to select a method that can more accurately represent the mileage based on the two calculation methods, that is, to calculate a distance error and correct the remaining mileage calculated above. Among them, the principle of the odometer is to obtain the mileage based on the size of the tire and the number of turns of the tire. Specifically, the accumulated mileage data can be obtained by the wheel speed signal obtained by the vehicle speed sensor and transmitted to the electronic control unit ECU, and the ECU calculates the mileage based on the preset tire diameter parameters. Exemplarily, the signal acquisition method of the vehicle speed sensor can be obtained by obtaining it on the output shaft of the gearbox, or by using the signal of the ABS wheel speed sensor.

[0043] Exemplarily, the first time period is taken as one minute. Within one minute, the mileage in the cumulative mileage data increases by 500 meters. The distance calculated based on the speed integral within this minute is 501 meters. Obviously, there is a certain difference between the distances calculated by the two methods, that is, the average energy consumption is not accurate. The distance that can be traveled with the same amount of electricity may be shorter or longer. So which method is used to calculate the distance as the subsequent mileage calculation method can be adjusted according to the distance values ​​calculated by the two methods. For example, the actual mileage is corrected by taking the middle value of the distance calculated by the two methods. Optionally, the distance obtained by speed integral can be used as the state equation, and the distance difference measured by the odometer can be used as the observation equation, and the optimal distance can be calculated by the Kalman filter algorithm.

[0044] It should be noted that the first time period may be any time period from the start of the electric vehicle to the current time. For example, if the accumulated mileage data and speed of the electric vehicle in the previous ten minutes are tracked, the first time period is the time period from ten minutes ago to the current time.

[0045] S40: Determine the actual cruising range of the electric vehicle based on the distance error and the remaining range.

[0046] Specifically, the distance error is the error generated when calculating the mileage of the electric vehicle. For example, the real-time energy consumption is calculated based on the integration of voltage, current, and time interval, and then the driving distance corresponding to the time interval is obtained according to the odometer to calculate the average energy consumption. However, the mileage obtained by the odometer may not be accurate. For example, the mileage calculated by speed-time integration may be more than the mileage calculated by the odometer. In fact, the electric vehicle can travel a longer distance under the same energy consumption. Therefore, it is necessary to calculate the distance error and then obtain the actual cruising range of the electric vehicle. Continuing with the example in the above embodiment, for example, the median of the mileage calculated by the two methods within one minute is taken, that is, 500.5m, then the error per 100 meters is 0.1 meter. Assuming that the remaining mileage initially calculated is 5km, the actual drivable distance obtained based on the distance error adjustment is 5005m, that is, the actual cruising range of the electric vehicle is 5005m.

[0047] It should be noted that the cruising range prediction method of the present invention is applicable to the prediction of the cruising range of pure electric vehicles, and can also be used to predict the pure electric cruising range of plug-in hybrid vehicles.

[0048] In the embodiment of the present application, the temperature data of each battery cell in the electric vehicle is obtained, and the initial remaining power of the electric vehicle is corrected according to the temperature data to obtain the actual remaining power, and the remaining mileage is calculated according to the actual remaining power and the average energy consumption of the electric vehicle, and the distance error is obtained based on the accumulated mileage data and historical speed data obtained in the first time period, and then the remaining mileage is corrected according to the distance error to determine the actual cruising range of the electric vehicle. This embodiment corrects the actual remaining power based on the temperature data, and recalibrates the remaining mileage through the accumulated mileage data and historical speed data, which can reduce the prediction error of the cruising range and improve the accuracy of the cruising range prediction.

[0049] See also Figure 3 , provides a detailed flow chart of correcting the initial remaining power in the cruising range prediction method in the embodiment of the present application. Figure 3 As shown, the method of the embodiment of the present application may include the following steps S11-S12.

[0050] S11, inputting the lowest temperature in the temperature data into a preset correction function to obtain a correction coefficient;

[0051] S12, correcting the initial remaining power according to the correction coefficient to obtain an actual remaining power.

[0052] It is understandable that, considering the impact of the SOC limit on the battery life, and the lower the temperature, the less accurate the SOC, in this case, if the remaining power corrected by the lowest temperature can be more in line with the actual situation, the accuracy of the remaining power can also be guaranteed when the temperature is high. On the contrary, if the highest temperature is used, then there may be problems at low temperatures, and the SOC may be overestimated. Therefore, in this embodiment, the lowest temperature is used to calculate the correction coefficient, and the correction coefficient is used to characterize the impact of temperature on the initial remaining power.

[0053] The temperature data is the battery cell temperature collected by the sensor in real time. For example, if the temperature of 100 battery cells is obtained, there will be a highest temperature and a lowest temperature among the 100 battery cell temperatures obtained. The lowest temperature is input into the preset correction function to calculate the correction coefficient. The preset correction function can be a sigmoid function or other functions.

[0054] Further, in one embodiment, the method of the embodiment of the present application may include the following steps S13-S14.

[0055] S13, obtaining the real-time voltage and real-time current of the battery in the electric vehicle in the second time period, and calculating the real-time energy consumption of the electric vehicle by integration;

[0056] Specifically, the BMS performs uninterrupted real-time detection on the battery module to obtain the cumulative consumption in the second time period. For example, the second time period is 2 minutes, and the current time is 12:00. The power consumed by the electric vehicle in 2 minutes can be calculated by collecting the real-time voltage and real-time current from 11:58 to 12:00.

[0057] S14, calculating average energy consumption according to the real-time energy consumption and the driving distance corresponding to the second time period.

[0058] Specifically, the driving distance in the second time period is obtained according to the odometer, and the real-time energy consumption is divided by the driving distance to obtain the average energy consumption.

[0059] Optionally, the second time period can also be divided into multiple intervals, and the real-time voltage and real-time current corresponding to each interval are taken to calculate the real-time energy consumption, and the average energy consumption is calculated according to the driving distance corresponding to each interval. For example, if the second time period is 2 minutes, and the sensor sampling interval of the electric vehicle is 30 seconds, the real-time consumption is calculated every 30 seconds, and the real-time consumption at four time points in the second time period at 30 seconds, 1 minute, 90 seconds, and 2 minutes is obtained. The driving distance at the corresponding time point in the second time period is obtained, that is, the driving distance at 30 seconds, 1 minute, 90 seconds, and 2 minutes is obtained respectively, and the energy consumption consumed by the driving distance corresponding to the four time points is calculated, and the average value is taken to obtain the average energy consumption.

[0060] Furthermore, in one embodiment, before calculating the remaining mileage based on the average energy consumption of the electric vehicle and the actual remaining power, the method further includes:

[0061] S15, confirming the driving condition of the electric vehicle, where the driving condition is used to indicate the driving state of the electric vehicle;

[0062] S16: Based on the driving condition, obtain the corresponding average energy consumption of the electric vehicle.

[0063] It can be understood that the driving condition is the current driving state of the electric vehicle, such as driving in the city, driving on the highway, driving uphill, downhill, driving on flat roads, etc. Under different driving conditions, the energy consumption of the electric vehicle is different. For example, the power consumed uphill at the same speed is definitely more than that consumed downhill. Therefore, in one embodiment, according to the driving condition of the electric vehicle, the average energy consumption of the vehicle under the driving condition in history is obtained. Among them, the calculation method of the historical average energy consumption can refer to the above steps S13 to S14. In addition, the average energy consumption of the electric vehicle of this model can be collected as a reference for calculating the cruising range with the authorization of the user. Specifically, the current driving condition can be judged by obtaining the power information of the electric vehicle. In addition, the driving environment can be photographed by a camera, and the driving condition can be judged by combining big data recognition. Optionally, the congestion of the current driving road is collected through networking, and the congestion duration is predicted, so as to obtain the average energy consumption under the congestion. By confirming the driving conditions and then confirming the average energy consumption based on the driving conditions, the average energy consumption obtained in different driving scenarios can be closer to the actual average energy consumption of the remaining range of the electric vehicle, thereby improving the accuracy of the range prediction.

[0064] In this embodiment, based on the battery cell temperature, a preset correction function is used as a correction coefficient, and the input of the preset correction function is the lowest temperature. The correction coefficient is first used to correct the SOC, and then the corrected SOC is used to calculate the actual remaining power, which can improve the accuracy of calculating the remaining power. In addition, by obtaining the real-time energy consumption in the second time period, the average energy consumption is calculated in combination with the driving distance in the second time period. Since the real-time driving data is obtained, it is closer to the energy consumption level of the user's daily use. For example, the energy consumption obtained includes the user's air conditioning usage, thereby improving the accuracy of the average energy consumption. Further, the driving condition of the vehicle is confirmed, and the corresponding average energy consumption is obtained according to the driving condition matching, so that the cruising range prediction can automatically adapt to various driving conditions, increase the adaptability of the cruising range prediction method, and improve the accuracy of the electric vehicle's cruising range prediction function.

[0065] See also Figure 4, provides a detailed flow chart of obtaining the accumulated mileage data and historical speed data in the cruising range prediction method in the embodiment of the present application. Figure 4 As shown, the method of the embodiment of the present application may include the following steps S31-S32.

[0066] S31, obtaining a driving condition of the electric vehicle, where the driving condition is used to indicate a driving state of the electric vehicle;

[0067] S32: Based on the driving condition, the accumulated mileage data and historical speed data of the electric vehicle in a first time period are obtained.

[0068] It is understandable that the driving condition of an electric vehicle will not only affect its energy consumption, but also have a certain impact on the distance error it generates. For example, when a car is driving on different roads (smooth, rough), there may be differences. Therefore, in order to improve the accuracy of distance error calculation, the driving condition of the electric vehicle is obtained, and the cumulative mileage and historical speed data of the electric vehicle in the first time period are obtained according to the driving condition.

[0069] Further, in one embodiment, the accumulated mileage data includes a first accumulated mileage and a second accumulated mileage;

[0070] The calculating the distance error based on the accumulated mileage data and the historical speed data includes:

[0071] S41, calculating a first driving distance corresponding to the first time period based on the first accumulated driving mileage and the second accumulated driving mileage; the first accumulated driving mileage is the accumulated driving mileage corresponding to the starting point of the first time period, and the second accumulated driving mileage is the accumulated driving mileage corresponding to the ending point of the first time period;

[0072] Specifically, the accumulated mileage corresponding to the starting point of the first time period is taken as the first driving distance, the accumulated mileage corresponding to the ending point of the first time period is taken as the second driving distance, and the difference between the two is taken to obtain the first driving distance.

[0073] S42, calculating a second driving distance corresponding to the first time period based on the historical speed data of the electric vehicle in the first time period;

[0074] Specifically, the second driving distance in the first time period is calculated by integrating the historical speed data of the electric vehicle in the first time period.

[0075] S43: Calculate a distance error based on the first driving distance and the second driving distance.

[0076] In one embodiment, the distance error is calculated based on the first driving distance and the second driving distance respectively calculated by the accumulated driving mileage data and the historical speed data. The first time period may be the time interval for collecting data in the cloud, for example, the cloud collects data every 30 seconds, then the first driving distance is the difference in the accumulated driving mileage collected twice, and the second driving distance is the distance value obtained by integrating the speeds collected twice.

[0077] Further, in one embodiment, the calculating the distance error based on the first driving distance and the second driving distance includes:

[0078] S431: Using a Kalman filter model, and based on the first driving distance and the second driving distance, obtaining a distance error.

[0079] Kalman filtering is an algorithm that uses linear system state equations and system input and output observation data to optimally estimate the system state. Since the observation data includes the influence of noise and interference in the system, the optimal estimate can also be regarded as a filtering process. In this embodiment, the optimal values ​​of the first driving distance and the second driving distance are obtained according to the Kalman filter model. In a feasible embodiment, when driving begins, two distances are tracked by Kalman: the first driving distance (that is, the distance obtained by the difference between two consecutive cumulative mileages) and the second driving distance (that is, the distance calculated by the speed and time integral), and the distance error is iteratively calculated.

[0080] The specific formula is as follows:

[0081] X(k)=AX(k-1)………(1)

[0082] Z(k)=HX(k)………(2)

[0083] In the above two equations, X(k) is the system state at time k, A is the system parameter, this article is a one-dimensional Kalman, the state variable has only one speed, so A is actually the time interval. Z(k) is the measurement value at time k, that is, the difference in accumulated mileage, and H is the parameter of the measurement system.

[0084] X(k|k-1)=AX(k-1|k-1) (3)

[0085] In formula (3), X(k|k-1) is the result predicted using the previous state, and X(k-1|k-1) is the optimal result of the previous state;

[0086] System iteration depends on the covariance corresponding to the system noise, denoted by P:

[0087] P(k|k-1)=AP(k-1|k-1)A'+Q......(4)

[0088] In formula (4), P(k|k-1) is the covariance corresponding to X(k|k-1), P(k-1|k-1) is the covariance corresponding to X(k-1|k-1), A' represents the transposed matrix of A, and Q is the covariance of the system process.

[0089] Now that we have the predictions for the current state, we can collect the measurements for the current state. Combining the predictions and measurements, we can get the optimal estimate X(k|k) for the current state (k):

[0090] X(k|k)=X(k|k-1)+Kg(k)(Z(k)-HX(k|k-1))………(5)

[0091] Where Kg is the Kalman Gain:

[0092] Kg(k)=P(k|k-1)H' / (HP(k|k-1)H'+R)......(6)

[0093] In this embodiment, the driving condition of the electric vehicle is obtained, the cumulative mileage data and the historical speed data corresponding to the driving condition are obtained, and the distance error is calculated based on the cumulative mileage data and the historical speed data to ensure the accuracy of the cruising range prediction under different conditions, and the Kalman filter model is integrated into the distance error calculation. The first driving distance calculated based on the first cumulative mileage and the second cumulative mileage in the cumulative mileage data, and the corresponding second driving distance calculated based on the historical speed data in the first time period are estimated through the Kalman filter model to obtain the optimal distance error, so that the cruising range can be calculated based on the distance error.

[0094] The following will be combined with the attached Figure 5 , the cruising range prediction device provided in the embodiment of the present application is introduced in detail. It should be noted that the attached Figure 5 The mileage prediction device in the present application is used to execute Figure 2-Figure 5 For the convenience of explanation, only the part related to the embodiment of the present application is shown. For the specific technical details not disclosed, please refer to the present application. Figure 2-Figure 4 The embodiment shown.

[0095] See also Figure 5 , which shows a schematic diagram of the structure of a cruising range prediction device provided by an exemplary embodiment of the present application. The cruising range prediction device can be implemented as all or part of the device through software, hardware or a combination of both. The device includes an acquisition module 10, a remaining mileage calculation module 20, an error calculation module 30 and a cruising range calculation module 40.

[0096] The acquisition module 10 is used to acquire the initial remaining power of the battery of the electric vehicle and the temperature data of each battery cell in the battery, and to correct the initial remaining power based on the temperature data to obtain the actual remaining power;

[0097] A remaining mileage calculation module 20, configured to calculate the remaining mileage based on the average energy consumption of the electric vehicle and the actual remaining power;

[0098] The error calculation module 30 is used to obtain the accumulated mileage data and the historical speed data of the electric vehicle in the first time period, and calculate the distance error based on the accumulated mileage data and the historical speed data;

[0099] The cruising range calculation module 40 is used to determine the actual cruising range of the electric vehicle based on the distance error and the remaining range.

[0100] Optionally, the acquisition module 10 is specifically used to input the lowest temperature in the temperature data into a preset correction function to obtain a correction coefficient;

[0101] The initial remaining power is corrected according to the correction coefficient to obtain the actual remaining power.

[0102] Optionally, the error calculation module 30 is specifically used to obtain the driving condition of the electric vehicle, and the driving condition is used to represent the driving state of the electric vehicle;

[0103] Based on the driving condition, the accumulated mileage data and historical speed data of the electric vehicle in a first time period are obtained.

[0104] Optionally, the error calculation module 30 is specifically configured to calculate a first driving distance corresponding to the first time period based on the first cumulative driving mileage and the second cumulative driving mileage; the first cumulative driving mileage is the cumulative driving mileage corresponding to the starting point of the first time period, and the second cumulative driving mileage is the cumulative driving mileage corresponding to the ending point of the first time period;

[0105] Calculating a second driving distance corresponding to the first time period based on the historical speed data of the electric vehicle in the first time period;

[0106] A distance error is calculated based on the first driving distance and the second driving distance.

[0107] Optionally, the error calculation module 30 is specifically configured to adopt a Kalman filter model and obtain a distance error based on the first driving distance and the second driving distance.

[0108] Optionally, the remaining mileage calculation module 20 is specifically used to obtain the real-time voltage and real-time current of the battery in the electric vehicle in the second time period, and calculate the real-time energy consumption of the electric vehicle by integration;

[0109] The average energy consumption is calculated according to the real-time energy consumption and the driving distance corresponding to the second time period.

[0110] Optionally, the remaining mileage calculation module 20 is specifically used to confirm the driving condition of the electric vehicle, and the driving condition is used to indicate the driving state of the electric vehicle;

[0111] Based on the driving condition, the corresponding average energy consumption of the electric vehicle is obtained.

[0112] It should be noted that the range prediction device provided in the above embodiment only uses the division of the above functional modules as an example when executing the range prediction method. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the range prediction device provided in the above embodiment and the range prediction method embodiment belong to the same concept, and the implementation process thereof is detailed in the method embodiment, which will not be repeated here.

[0113] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0114] The embodiment of the present application further provides a computer-readable storage medium on which a cruising range prediction program is stored. When the cruising range prediction program is executed by a processor, the above-mentioned Figure 2-Figure 4 The mileage prediction method of the embodiment shown in the figure can be specifically implemented by referring to Figure 2-Figure 4 The specific description of the illustrated embodiment will not be repeated here.

[0115] Please refer to Figure 6 , which shows a schematic diagram of the structure of a cruising range prediction device provided by an exemplary embodiment of the present application. The cruising range prediction device in the present application may include one or more of the following components: a processor 110, a memory 120, an input device 130, an output device 140, and a bus 150. The processor 110, the memory 120, the input device 130, and the output device 140 may be connected via a bus 150.

[0116] The processor 110 may include one or more processing cores. The processor 110 uses various interfaces and lines to connect the various parts of the entire cruising range prediction device, and executes various functions and processes data of the terminal 100 by running or executing instructions, programs, code sets or instruction sets stored in the memory 120, and calling data stored in the memory 120. Optionally, the processor 110 can be implemented in at least one hardware form of digital signal processing (DSP), field-programmable gate array (FPGA), and programmable logic array (PLA). The processor 110 can integrate one or a combination of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. Among them, the CPU mainly processes the operating system, user pages, and applications; the GPU is responsible for rendering and drawing display content; and the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 110, but may be implemented separately through a communication chip.

[0117] The memory 120 may include a random access memory (RAM) or a read-only memory (ROM). Optionally, the memory 120 includes a non-transitory computer-readable medium (Non-Transitory Computer-Readable Storage Medium). The memory 120 may be used to store instructions, programs, codes, code sets or instruction sets. The memory 120 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for implementing at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc. The operating system may be an Android system, including a system deeply developed based on the Android system, an IOS system developed by Apple, including a system deeply developed based on the IOS system or other systems.

[0118] The memory 120 can be divided into an operating system space and a user space. The operating system runs in the operating system space, and native and third-party applications run in the user space. In order to ensure that different third-party applications can achieve good operating results, the operating system allocates corresponding system resources to different third-party applications. However, the requirements for system resources in different application scenarios in the same third-party application are also different. For example, in the local resource loading scenario, the third-party application has higher requirements for disk reading speed; in the animation rendering scenario, the third-party application has higher requirements for GPU performance. The operating system and third-party applications are independent of each other, and the operating system often cannot perceive the current application scenario of the third-party application in a timely manner, resulting in the operating system being unable to perform targeted system resource adaptation according to the specific application scenario of the third-party application.

[0119] In order for the operating system to distinguish the specific application scenarios of third-party applications, it is necessary to open up data communication between third-party applications and the operating system so that the operating system can obtain the current scenario information of third-party applications at any time, and then perform targeted system resource adaptation based on the current scenario.

[0120] The input device 130 is used to receive input commands or data, and includes but is not limited to a keyboard, a mouse, a camera, a microphone, or a touch device. The output device 140 is used to output commands or data, and includes but is not limited to a display device and a speaker. In one example, the input device 130 and the output device 140 can be combined, and the input device 130 and the output device 140 are touch screen displays.

[0121] The touch display screen can be designed as a full screen, a curved screen or a special-shaped screen. The touch display screen can also be designed as a combination of a full screen and a curved screen, or a combination of a special-shaped screen and a curved screen, which is not limited in the embodiments of the present application.

[0122] In addition, those skilled in the art can understand that the structure of the cruising range prediction device shown in the above drawings does not constitute a limitation on the cruising range prediction device, and the cruising range prediction device may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently. For example, the cruising range prediction device also includes a radio frequency circuit, an input unit, a sensor, an audio circuit, a wireless fidelity (WiFi) module, a power supply, a Bluetooth module and other components, which will not be described in detail here.

[0123] exist Figure 6 In the illustrated cruising range prediction device, the processor 110 may be used to call a cruising range prediction application stored in the memory 120, and specifically perform the following operations:

[0124] Acquire the initial remaining power of the battery of the electric vehicle and the temperature data of each battery cell in the battery, and correct the initial remaining power based on the temperature data to obtain the actual remaining power;

[0125] Calculating the remaining mileage based on the average energy consumption of the electric vehicle and the actual remaining power;

[0126] Acquire cumulative mileage data and historical speed data of the electric vehicle within a first time period, and calculate a distance error based on the cumulative mileage data and the historical speed data;

[0127] Based on the distance error and the remaining mileage, an actual cruising range of the electric vehicle is determined.

[0128] In one embodiment, when the processor 110 corrects the initial remaining power based on the temperature data to obtain the actual remaining power, the processor 110 specifically performs the following operations:

[0129] Inputting the lowest temperature in the temperature data into a preset correction function to obtain a correction coefficient;

[0130] The initial remaining power is corrected according to the correction coefficient to obtain the actual remaining power.

[0131] In one embodiment, when the processor 110 acquires the accumulated mileage data and historical speed data of the electric vehicle in the first time period, the processor 110 specifically performs the following operations:

[0132] Acquiring a driving condition of the electric vehicle, wherein the driving condition is used to indicate a driving state of the electric vehicle;

[0133] Based on the driving condition, the accumulated mileage data and historical speed data of the electric vehicle in a first time period are obtained.

[0134] In one embodiment, when the processor 110 calculates the distance error based on the accumulated mileage data and the historical speed data, the processor 110 specifically performs the following operations:

[0135] Calculating a first driving distance corresponding to the first time period based on the first accumulated driving mileage and the second accumulated driving mileage; the first accumulated driving mileage is the accumulated driving mileage corresponding to the starting point of the first time period, and the second accumulated driving mileage is the accumulated driving mileage corresponding to the ending point of the first time period;

[0136] Calculating a second driving distance corresponding to the first time period based on the historical speed data of the electric vehicle in the first time period;

[0137] A distance error is calculated based on the first driving distance and the second driving distance.

[0138] In one embodiment, when the processor 110 calculates the distance error based on the first driving distance and the second driving distance, the processor 110 specifically performs the following operations:

[0139] A Kalman filter model is adopted, and a distance error is obtained based on the first driving distance and the second driving distance.

[0140] In one embodiment, before calculating the remaining mileage based on the average energy consumption of the electric vehicle and the actual remaining power, the processor 110 further performs the following operations:

[0141] Acquire the real-time voltage and real-time current of the battery in the electric vehicle in the second time period, and calculate the real-time energy consumption of the electric vehicle by integration;

[0142] The average energy consumption is calculated according to the real-time energy consumption and the driving distance corresponding to the second time period.

[0143] In one embodiment, before calculating the remaining mileage based on the average energy consumption of the electric vehicle and the actual remaining power, the processor 110 further performs the following operations:

[0144] confirming a driving condition of the electric vehicle, wherein the driving condition is used to indicate a driving state of the electric vehicle;

[0145] Based on the driving condition, the corresponding average energy consumption of the electric vehicle is obtained.

[0146] In an embodiment of the present application, the temperature data of each battery cell in the electric vehicle is obtained, and the initial remaining power of the electric vehicle is corrected according to the temperature data to obtain the actual remaining power, and the remaining mileage is calculated according to the actual remaining power and the average energy consumption of the electric vehicle, and the distance error is obtained based on the accumulated mileage data and historical speed data obtained in the first time period, and then the remaining mileage is corrected according to the distance error to determine the actual cruising range of the electric vehicle. This embodiment corrects the actual remaining power based on the temperature data, and recalibrates the remaining mileage through the accumulated mileage data and historical speed data, which can reduce the prediction error of the cruising range and improve the accuracy of the cruising range prediction. Furthermore, based on the battery cell temperature, a preset correction function is used as the correction coefficient, and the input of the preset correction function is the lowest temperature. The correction coefficient is first used to correct the SOC, and then the corrected SOC is used to calculate the actual remaining power, which can improve the accuracy of the remaining power calculation. In addition, by obtaining the real-time energy consumption in the second time period, the average energy consumption is calculated in combination with the driving distance in the second time period. Since the real-time driving data is obtained, it is closer to the energy consumption level of the user's daily use. For example, the energy consumption obtained includes the user's air conditioning usage, thereby improving the accuracy of the average energy consumption. Further, the driving condition of the vehicle is confirmed, and the corresponding average energy consumption is obtained according to the driving condition matching, so that the cruising range prediction can automatically meet various driving conditions, increase the adaptability of the cruising range prediction method, and improve the accuracy of the electric vehicle's cruising range prediction function. In addition, by obtaining the driving condition of the electric vehicle, the cumulative mileage data and historical speed data corresponding to the driving condition are obtained, so as to calculate the distance error according to the cumulative mileage data and the historical speed data, ensure the accuracy of the cruising range prediction under different working conditions, and integrate the Kalman filter model into the distance error calculation. The first driving distance calculated according to the first cumulative mileage and the second cumulative mileage in the cumulative mileage data and the corresponding second driving distance calculated according to the historical speed data in the first time period are estimated by the Kalman filter model to obtain the optimal distance error, so that the cruising range can be calculated according to the distance error.

[0147] A person skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. The storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0148] The above disclosure is only the preferred embodiment of the present application, which certainly cannot be used to limit the scope of rights of the present application. Therefore, equivalent changes made according to the claims of the present application are still within the scope covered by the present application.

Claims

1. A method for predicting a cruising range, characterized in that: include: Acquire the initial remaining power of the battery of the electric vehicle and the temperature data of each battery cell in the battery, and correct the initial remaining power based on the temperature data to obtain the actual remaining power; Calculating the remaining mileage based on the average energy consumption of the electric vehicle and the actual remaining power; Acquire cumulative mileage data and historical speed data of the electric vehicle within a first time period, and calculate a distance error based on the cumulative mileage data and the historical speed data; Determining an actual cruising range of the electric vehicle based on the distance error and the remaining mileage; The accumulated mileage data includes a first accumulated mileage and a second accumulated mileage; The calculating the distance error based on the accumulated mileage data and the historical speed data includes: Calculating a first driving distance corresponding to the first time period based on the first accumulated driving mileage and the second accumulated driving mileage; the first accumulated driving mileage is the accumulated driving mileage corresponding to the starting point of the first time period, and the second accumulated driving mileage is the accumulated driving mileage corresponding to the ending point of the first time period; Calculating a second driving distance corresponding to the first time period based on the historical speed data of the electric vehicle in the first time period; A Kalman filter model is adopted, and a distance error is obtained based on the first driving distance and the second driving distance.

2. The method according to claim 1, characterized in that The correcting the initial remaining power based on the temperature data to obtain the actual remaining power includes: Inputting the lowest temperature in the temperature data into a preset correction function to obtain a correction coefficient; The initial remaining power is corrected according to the correction coefficient to obtain the actual remaining power.

3. The method according to claim 1, characterized in that The acquiring of the accumulated mileage data and historical speed data of the electric vehicle within the first time period includes: Acquiring a driving condition of the electric vehicle, wherein the driving condition is used to indicate a driving state of the electric vehicle; Based on the driving condition, the accumulated mileage data and historical speed data of the electric vehicle in a first time period are obtained.

4. The method according to claim 1, characterized in that Before calculating the remaining mileage based on the average energy consumption of the electric vehicle and the actual remaining power, the method further includes: Acquire the real-time voltage and real-time current of the battery in the electric vehicle in the second time period, and calculate the real-time energy consumption of the electric vehicle by integration; The average energy consumption is calculated according to the real-time energy consumption and the driving distance corresponding to the second time period.

5. The method according to claim 1, characterized in that Before calculating the remaining mileage based on the average energy consumption of the electric vehicle and the actual remaining power, the method further includes: confirming a driving condition of the electric vehicle, wherein the driving condition is used to indicate a driving state of the electric vehicle; Based on the driving condition, the corresponding average energy consumption of the electric vehicle is obtained.

6. A cruising range prediction device, characterized in that: The device comprises: An acquisition module, used to acquire the initial remaining power of the battery of the electric vehicle and the temperature data of each battery cell in the battery, and to correct the initial remaining power based on the temperature data to obtain the actual remaining power; A remaining mileage calculation module, used to calculate the remaining mileage based on the average energy consumption of the electric vehicle and the actual remaining power; The error calculation module is used to obtain the cumulative mileage data and historical speed data of the electric vehicle in a first time period, and calculate the distance error based on the cumulative mileage data and the historical speed data; the cumulative mileage data includes a first cumulative mileage and a second cumulative mileage; the distance error calculation based on the cumulative mileage data and the historical speed data includes: calculating a first driving distance corresponding to the first time period based on the first cumulative mileage and the second cumulative mileage; the first cumulative mileage is the cumulative mileage corresponding to the starting point of the first time period, and the second cumulative mileage is the cumulative mileage corresponding to the end point of the first time period; based on the historical speed data of the electric vehicle in the first time period, calculating a second driving distance corresponding to the first time period; adopting a Kalman filter model, and obtaining the distance error based on the first driving distance and the second driving distance; The cruising range calculation module is used to determine the actual cruising range of the electric vehicle based on the distance error and the remaining range.

7. An electric vehicle, characterized in that: The electric vehicle comprises: a memory having a computer program stored thereon; A processor, configured to execute the computer program in the memory to implement the steps of the method according to any one of claims 1 to 5.

8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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