Discharge control method, device and system for electric vehicle

Abstract

The invention relates to the technical field of electric vehicle driving power supply control, in particular to a discharge control method, device and system for an electric vehicle. The method comprises the following steps: firstly, acquiring battery electric quantity data in a preset historical time period and vehicle speed data and journey data in multiple driving processes; further acquiring an actual energy consumption ratio of each driving process, and acquiring a driving energy consumption error of each driving process in combination with a preset energy consumption ratio; further acquiring a vehicle speed change influence coefficient of each driving process according to fluctuation similar characteristics of the vehicle speed data and the battery electric quantity data; further obtaining a driving influence coefficient according to the correlation degree of the overall vehicle speed data of all the driving processes and the actual energy consumption ratio in combination with the vehicle speed change influence coefficient of all the driving processes; and finally, according to the vehicle speed data, the driving energy consumption error and the driving influence coefficient in all the driving processes, the discharging power of the electric vehicle is adjusted by combining the vehicle speed data in the current driving process.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicle drive power control, and particularly to a discharge control method, device and system for electric vehicles. Background Art

[0002] The Battery Management System (BMS) is the core of electric vehicle battery management, responsible for comprehensively monitoring and managing the charging and discharging processes of the battery pack. With the wide application of electric vehicles, intelligent discharge control methods for electric vehicles have gradually emerged. Based on driver behavior prediction and real-time data analysis, by dynamically adjusting the battery discharge strategy, optimizing energy distribution, and improving the energy efficiency and endurance performance of the whole vehicle.

[0003] Due to the differences in driving styles among different drivers and the uncertainty of driving behaviors, while the discharge strategies of traditional BMS are usually statically set with fixed power output, it is difficult to adapt to complex driving conditions and personalized driving behaviors, resulting in low energy utilization efficiency. Summary of the Invention

[0004] In order to solve the technical problem that the discharge control of traditional BMS is difficult to adapt to personalized driving behaviors and affects the endurance of electric vehicles, the purpose of the present invention is to provide a discharge control method, device and system for electric vehicles, and the specific technical solutions adopted are as follows: A discharge control method for electric vehicles, the method includes: Obtain the driving data and battery power data within a preset historical time period; the driving data includes the vehicle speed data and distance data of multiple driving processes; According to the change of the battery power data and the distance data of each driving process, obtain the actual energy consumption ratio of each driving process; according to the difference between the actual energy consumption ratio and the preset energy consumption ratio of each driving process, obtain the driving energy consumption error of each driving process; according to the similar fluctuation characteristics of the vehicle speed data and the battery power data during each driving process, obtain the vehicle speed change influence coefficient of each driving process; according to the correlation degree between the overall vehicle speed data of all driving processes and the actual energy consumption ratio, and combining the vehicle speed change influence coefficients of all driving processes, obtain the driving influence coefficient; According to the vehicle speed data, the driving energy consumption error and the driving influence coefficient in all driving processes, and combining the vehicle speed data in the current driving process, adjust the discharge power of the electric vehicle.

[0005] Further, the method for obtaining the vehicle speed change influence coefficient includes: Select any one of the said driving processes as the target driving process; based on the vehicle speed data in the target driving process, obtain the vehicle speed acceleration at each moment; based on the battery power data in each driving process, obtain the power consumption speed at each moment. According to the similarity characteristics of the vehicle speed acceleration and the power consumption speed at all the same moments in the target driving process, obtain the vehicle speed change influence coefficient of the target driving process; the similarity characteristics of the vehicle speed acceleration and the power consumption speed at all the same moments are positively correlated with the vehicle speed change influence coefficient.

[0006] Furthermore, the method for obtaining the driving influence coefficient includes: According to the overall concentration characteristics of the vehicle speed change influence coefficients of all driving processes, obtain the first influence coefficient. At least based on the average value of the vehicle speed data in each driving process, obtain the vehicle speed factor of each driving process; the average value of the vehicle speed data is positively correlated with the vehicle speed factor; according to the relative entropy between the set of vehicle speed factors composed of the vehicle speed factors of all driving processes and the set of actual energy consumption ratios composed of all the actual energy consumption ratios, combined with the first influence coefficient, obtain the driving influence coefficient; the relative entropy is negatively correlated with the driving influence coefficient; the first influence coefficient and the driving influence coefficient are positively correlated.

[0007] Furthermore, the method for adjusting the discharge power of the electric vehicle includes: In each driving process, take the product of each acceleration and the corresponding driving energy consumption error and the driving influence coefficient as the numerator, and take the sum of the absolute values of the accelerations at all sampling moments as the denominator, and the fraction as the energy loss parameter of each acceleration. Based on the energy loss parameters of all types of accelerations in all driving processes, fit an energy loss curve; according to the energy loss curve and the vehicle speed data in the current driving process, adjust the discharge power of the electric vehicle.

[0008] Furthermore, the method for obtaining the energy loss parameter includes: Take the acceleration value as the abscissa of the curve data point, and take the average value of the energy loss parameters of each acceleration in all driving processes as the ordinate of the curve data point, and fit the energy loss curve.

[0009] Furthermore, the method for adjusting the discharge power of the electric vehicle according to the energy loss curve and the vehicle speed data in the current driving process includes: Obtain the vehicle speed acceleration of the current electric vehicle and the preset discharge power of the battery management system. Based on the vehicle speed acceleration of the current electric vehicle and the energy loss curve, obtain the current energy loss parameter; according to the current energy loss parameter and the preset discharge power, obtain the corrected discharge power of the electric vehicle. Both the current energy loss parameter and the preset discharge power are positively correlated with the corrected discharge power.

[0010] Further, the method for obtaining the actual energy consumption ratio includes: Take the ratio of the change value of the battery power data to the mileage data in each driving process as the actual energy consumption ratio of each driving process.

[0011] Further, the method for obtaining the driving energy consumption error includes: Take the difference between the actual energy consumption ratio and the preset energy consumption ratio in each driving process as the driving energy consumption error of each driving process.

[0012] The present invention also proposes a discharge control device for an electric vehicle. The device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of any one of the discharge control methods for an electric vehicle.

[0013] The present invention also proposes a discharge control system for an electric vehicle. The system includes: Acquisition module: used to obtain the driving data and battery power data within a preset historical time period; the driving data includes the vehicle speed data and mileage data of multiple driving processes; Analysis module: used to obtain the actual energy consumption ratio of each driving process according to the change of the battery power data and the mileage data in each driving process; obtain the driving energy consumption error of each driving process according to the difference between the actual energy consumption ratio and the preset energy consumption ratio of each driving process; obtain the vehicle speed change influence coefficient of each driving process according to the similar fluctuation characteristics of the vehicle speed data and the battery power data in each driving process; obtain the driving influence coefficient according to the correlation degree between the overall vehicle speed data of all driving processes and the actual energy consumption ratio, combined with the vehicle speed change influence coefficients of all driving processes; Control module: used to adjust the discharge power of the electric vehicle according to the vehicle speed data in all driving processes, combined with the driving influence coefficient and the driving energy consumption error.

[0014] The present invention has the following beneficial effects: The present invention first obtains the battery power data, vehicle speed data, and mileage data during a preset historical time period, providing a data basis for subsequent analysis. Further, it obtains the actual energy consumption ratio for each driving process and combines it with a preset energy consumption ratio to obtain the driving energy consumption error for each driving process, which characterizes the energy consumption error caused by driving behavior during each driving process, facilitating the subsequent evaluation of the impact of driving behavior from the perspective of the energy consumption error caused by each driving process and enabling more accurate adjustment of the discharge power. Further, based on the similar fluctuation characteristics of the vehicle speed data and the battery power data, it obtains the vehicle speed change influence coefficient from the perspective of the similar fluctuations between the vehicle speed data and the battery power data, providing a basis for subsequent analysis of the impact of driving behavior on battery energy loss and facilitating the final adjustment of the discharge power. Further, according to the correlation degree between the overall vehicle speed data and the actual energy consumption ratio of all driving processes and combining the vehicle speed change influence coefficients of all driving processes, it obtains the driving influence coefficient, reducing the error caused by road conditions and characterizing the degree of influence of the driver's driving behavior on battery energy consumption. Finally, based on the vehicle speed data, driving energy consumption error, and driving influence coefficient during all driving processes, it analyzes the battery energy consumption caused by the driver's driving behavior, analyzes the current driving behavior based on the vehicle speed data during the current driving process, adjusts the discharge power of the electric vehicle, adjusts the discharge strategy, optimizes the energy use of the battery, and improves the energy efficiency and endurance of the vehicle. The present invention analyzes historical driving data, analyzes the driver's driving behavior, and the impact of driving behavior on battery power, analyzes the energy consumption error caused by driving behavior, thereby formulating a more suitable discharge control scheme, optimizing the energy use of the battery, and improving the energy efficiency and endurance of the vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following described drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0016] Figure 1 It is a flowchart of a discharge control method for an electric vehicle provided by an embodiment of the present invention; Figure 2 It is a system block diagram of a discharge control system for an electric vehicle provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] To further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following specifically describes, with reference to the accompanying drawings and preferred embodiments, a discharge control method, device, and system for electric vehicles according to the present invention, including their specific implementation manners, structures, features, and effects. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which this invention belongs.

[0019] The following specifically describes the specific solutions of a discharge control method, device, and system for electric vehicles provided by the present invention with reference to the accompanying drawings.

[0020] Please refer to Figure 1 , which shows a flowchart of a discharge control method for electric vehicles provided by an embodiment of the present invention, specifically including: Step S1: Obtain driving data and battery power data within a preset historical time period; the driving data includes vehicle speed data and mileage data for multiple driving processes.

[0021] In the embodiments of the present invention, considering that the driving behaviors of different drivers vary. For example, on urban roads, drivers usually face frequent stops and starts. Some drivers may always drive smoothly at a low speed, while others may frequently accelerate sharply and brake suddenly, resulting in different energy consumptions. Therefore, by analyzing historical driving data, analyzing the driving behaviors of drivers, and the impact of driving behaviors on battery power, and analyzing the energy consumption errors caused by driving behaviors, a more suitable discharge control scheme can be formulated. Here, first, obtain the driving data and battery power data within a preset historical time period; the driving data includes vehicle speed data and mileage data for multiple driving processes.

[0022] As an example, the discharge control scheme is continuously updated at a frequency of once every 7 days. The length of the preset historical time period is one month. Each time it is updated, the driving data and battery power data of the most recent month are obtained. Detecting the insertion and removal of the key of the electric vehicle is regarded as one driving process, and the wheels must rotate during one driving process. Exclude the driving processes in which the wheels do not rotate; install voltage sensors and current sensors on each battery cell to measure the total voltage of the battery pack and the current changes during charging and discharging in real time. Install wheel speed sensors on each wheel to determine the vehicle speed through the rotation speed of the wheels. The battery management system collects voltage, current data, and vehicle speed sensor data at a sampling frequency of 100HZ, obtains the remaining battery power by the open circuit voltage method, and the battery power data communicates with the central control system of the vehicle through the CAN (Controller Area Network) bus and sends the data to the control unit of the vehicle. The data of the vehicle speed sensor is sent to the control unit of the vehicle through the CAN bus, and the control unit of the vehicle calculates the real-time speed of the vehicle according to the signal of the wheel rotation speed collected by the sensor.

[0023] It should be noted that in an embodiment of the present invention, only the situation where one driver drives an electric vehicle is considered. The unit of electric quantity is kilowatt-hour, the unit of speed is kilometer per hour, and the unit of distance is kilometer or kilometer; both the vehicle speed data and the battery power data have undergone preprocessing of standard normalization; both data collection and the open circuit voltage method are prior arts.

[0024] In other embodiments of the present invention, considering that the same electric vehicle may be used by multiple drivers and the driving habits of different drivers are different, sensors can be set on the seat in the cab to obtain parameters such as the height of the seat, the distance between the seat and the steering wheel, analyze the posture of the seat, and utilize the characteristic that the seat posture is fixed when the same driver drives the electric vehicle to mark the data within the historical time period, analyze the data of each driver separately, and select the corresponding discharge adjustment scheme by identifying the posture of the seat.

[0025] Step S2: According to the change of the battery power data and the distance data of each driving process, obtain the actual energy consumption ratio of each driving process; according to the difference between the actual energy consumption ratio and the preset energy consumption ratio of each driving process, obtain the driving energy consumption error of each driving process; according to the similar fluctuation characteristics of the vehicle speed data and the battery power data during each driving process, obtain the vehicle speed change influence coefficient of each driving process; according to the correlation degree between the overall vehicle speed data and the actual energy consumption ratio of all driving processes, and combining the vehicle speed change influence coefficients of all driving processes, obtain the driving influence coefficient.

[0026] The discharge control logic provided by the BMS usually adjusts the output of the battery according to a fixed preset power curve. Within a certain speed or acceleration range, the discharge power is fixed, ignoring the actual operation and behavior changes of the driver, which may lead to energy waste. For example, when the driver is used to accelerating and braking suddenly, more electric energy will be consumed. At this time, the vehicle speed changes greatly. Due to the lag in the adjustment of the discharge power, the energy distribution is not accurate enough, resulting in power waste. Therefore, analyze the driver's operation behavior during driving and make personalized adjustments to the discharge strategy.

[0027] Considering that the driver's driving behavior will affect the actual energy consumption ratio, first, according to the change of the battery power data and the mileage data of each driving process, obtain the actual energy consumption ratio of each driving process; according to the difference between the actual energy consumption ratio and the preset energy consumption ratio of each driving process, obtain the driving energy consumption error of each driving process, which characterizes the energy consumption error caused by the driving behavior in each driving process, facilitating the subsequent evaluation of the impact caused by the driving behavior from the perspective of the energy consumption error caused by each driving process, and facilitating a more accurate adjustment of the discharge power.

[0028] Preferably, in an embodiment of the present invention, the unit of the energy consumption ratio is set to the power consumption per kilometer, and the ratio of the change value of the battery power data to the mileage data of each driving process is used as the actual energy consumption ratio of each driving process.

[0029] In other embodiments of the present invention, the implementer can also calculate the power consumption per 100 kilometers, and multiply the ratio of the change value of the battery power data to the mileage data of each driving process by 100 as the actual energy consumption ratio of each driving process.

[0030] It should be noted that when calculating the actual energy consumption ratio, the original data of the mileage data is used.

[0031] Preferably, in an embodiment of the present invention, the preset energy consumption ratio is obtained through the factory test data of the electric vehicle. Considering that the test data is relatively ideal, and in actual driving, due to factors such as traffic jams, uneven driving speeds, and the air conditioning system of the electric vehicle, power consumption occurs, resulting in the actual energy consumption ratio usually being larger than the preset energy consumption ratio. Therefore, the difference between the actual energy consumption ratio and the preset energy consumption ratio of each driving process is used as the driving energy consumption error of each driving process; among them, in the form of a difference, the difference between the actual energy consumption ratio and the preset energy consumption ratio of each driving process represents the difference between the actual energy consumption ratio and the preset energy consumption ratio.

[0032] In another embodiment of the present invention, the implementer can also use the difference between the actual energy consumption ratio and the preset energy consumption ratio of each driving process as the numerator, the preset energy consumption ratio as the denominator, and the fraction as the driving energy consumption error of each driving process; through the difference ratio of the actual energy consumption ratio and the preset energy consumption ratio relative to the preset energy consumption ratio, representing the difference between the actual energy consumption ratio and the preset energy consumption ratio, thereby showing the degree of energy consumption error caused by driving behavior.

[0033] Considering that when the fluctuations of the vehicle speed data and the battery power data are more similar, it indicates that the change in vehicle speed has a greater impact on the battery power, and the behavior of controlling the vehicle speed during driving has a greater impact on the endurance of the electric vehicle. Therefore, according to the similar characteristics of the fluctuations of the vehicle speed data and the battery power data during each driving process, the vehicle speed change influence coefficient of each driving process is obtained. From the perspective of the similarity of the fluctuations of the vehicle speed data and the battery power data, the vehicle speed change influence coefficient is obtained, providing a basis for subsequent analysis of the impact of driving behavior on battery energy loss and facilitating the final adjustment of the discharge power.

[0034] Preferably, in an embodiment of the present invention, first select any driving process as the target driving process and analyze each driving process one by one; to facilitate the analysis of the fluctuation of the vehicle speed data, based on the vehicle speed data in the target driving process, the vehicle speed acceleration at each moment is obtained; similarly, based on the battery power data in each driving process, the power consumption speed at each moment is obtained, representing the acceleration of the power. Considering that the more similar the vehicle speed acceleration and the power consumption speed at the same moment are, the more similar the fluctuations of the vehicle speed data and the battery power data are. Therefore, according to the similar characteristics of the vehicle speed acceleration and the power consumption speed at all the same moments in the target driving process, the similar characteristics of the fluctuations of the vehicle speed data and the battery power data are represented, and the vehicle speed change influence coefficient of the target driving process is obtained; the similar characteristics of the vehicle speed acceleration and the power consumption speed at all the same moments are positively correlated with the vehicle speed change influence coefficient.

[0035] As an example, according to the battery power data, a power change curve is fitted, and the negative value of the slope of the battery power data at each moment is used as the power consumption speed at each moment; according to the vehicle speed data, a vehicle speed change curve is fitted, and the slope of the vehicle speed data at each moment is used as the vehicle speed acceleration at each moment; the calculation formula of the vehicle speed change influence coefficient includes: ; Wherein, represents the serial number of the target driving process; represents the vehicle speed change influence coefficient of the th target driving process; represents the exponential function with the natural constant as the base; The number of sampling moments during the driving process of a target; Indicates the serial number of the sampling moment; Indicates taking the absolute value; Indicates the th power consumption speed of the target during the th sampling moment during the driving process; Indicates the th vehicle speed acceleration of the target during the th sampling moment during the driving process.

[0036] In the calculation formula of the vehicle speed change influence coefficient, the difference characteristics between the vehicle speed acceleration and the power consumption speed are represented by the absolute value of the difference. The larger the absolute value of the difference, the more obvious the difference characteristics. The overall difference characteristics of the vehicle speed acceleration and the power consumption speed at all moments during the target driving process are represented by the average value. Then, through the negative correlation mapping function perform negative correlation mapping, so as to represent the similarity characteristics of the vehicle speed acceleration and the power consumption speed at all the same moments, reflect the similar fluctuation characteristics of the vehicle speed data and the battery power data, and be used as the vehicle speed change influence coefficient of the target driving process to represent the influence degree of the vehicle speed change on the battery power consumption speed, which is convenient for adjusting the discharge strategy through the accurate influence coefficient later, optimizing the energy use of the battery, and improving the energy efficiency and endurance of the vehicle.

[0037] It should be noted that in other embodiments of the present invention, the implementer can also analyze the correlation between the power change curve and the vehicle speed change curve through the existing Dynamic Time Warping (DTW) algorithm. Specifically, the DTW distance between the two curves can be obtained, and the DTW distance is negatively correlated and normalized through the mapping function as the vehicle speed change influence coefficient of the target driving process; or the area between the two curves can be calculated. The smaller the area, the more similar the fluctuations, and the larger the vehicle speed change influence coefficient.

[0038] Considering that the driving behavior of a driver will change under different road surface conditions. For example, on urban roads, the driver usually faces frequent stops and starts; on highways, the driver maintains a high speed for a long time; similarly, driving behavior performances under other road surface conditions such as mountain roads and muddy and slippery roads may also vary. Therefore, it is necessary to analyze by integrating all data during the driving process. Considering that different road surface conditions will directly affect the overall vehicle speed data during driving and at the same time affect the actual energy consumption situation. When the correlation degree between the overall vehicle speed data and the actual energy consumption ratio is higher, it indicates that the driving behavior has a greater impact on energy consumption. At the same time, the vehicle speed change influence coefficient reflects the influence characteristics of vehicle speed change on power consumption during driving. Therefore, according to the correlation degree between the overall vehicle speed data and the actual energy consumption ratio of all driving processes, combined with the vehicle speed change influence coefficient of all driving processes, the driving influence coefficient is obtained to reduce the error caused by road surface conditions and characterize the influence degree of the driver's driving behavior on battery energy consumption.

[0039] Preferably, in an embodiment of the present invention, considering that by calculating the overall characteristics of the vehicle speed change influence coefficient of all driving processes, the driving behavior patterns of the driver in different situations can be captured. This overall perspective helps to identify general trends and patterns. Considering that relative entropy is used to measure the difference between two probability distributions, the correlation degree between the overall vehicle speed data and the actual energy consumption ratio of all driving processes is represented by relative entropy. The smaller the relative entropy between the set of vehicle speed factors composed of the vehicle speed factors of all driving processes and the set of actual energy consumption ratios composed of all actual energy consumption ratios, the more similar the distribution of vehicle speed factors and the distribution of actual energy consumption ratios, and the greater the correlation degree between the overall vehicle speed data and the actual energy consumption ratio of all driving processes, indicating that the impact caused by driving behavior is greater. Based on this, according to the overall concentration characteristics of the vehicle speed change influence coefficient of all driving processes, the first influence coefficient is obtained. At least according to the average value of the vehicle speed data in each driving process, the vehicle speed factor of each driving process is obtained; the average value of the vehicle speed data is positively correlated with the vehicle speed factor. According to the relative entropy between the set of vehicle speed factors composed of the vehicle speed factors of all driving processes and the set of actual energy consumption ratios composed of all actual energy consumption ratios, combined with the first influence coefficient, the driving influence coefficient is obtained; the relative entropy is negatively correlated with the driving influence coefficient; the first influence coefficient and the driving influence coefficient are positively correlated.

[0040] As an example: By means of the average value, the overall concentration characteristics of the vehicle speed change influence coefficients of all driving processes are represented. The average value of the vehicle speed change influence coefficients of all driving processes is used as the first influence coefficient; the average value of the vehicle speed data in each driving process is used as the vehicle speed factor of each driving process; the vehicle speed factors of all driving processes form a vehicle speed factor set, and all actual energy consumption ratios form an actual energy consumption ratio set. The distribution probability of the elements in each set is obtained, and then the relative entropy is obtained. The relative entropy is subjected to negative correlation mapping and normalization through the negative correlation function to perform negative correlation mapping and normalization, which is used as the second influence coefficient; the product of the first influence coefficient and the second influence coefficient is used as the driving influence coefficient.

[0041] As another example, the vehicle speed factor is obtained by weighted summing the average value, mode, and median of the vehicle speed data. The average value, mode, and median of the vehicle speed data in each driving process are weighted and summed with weighted weights of 0.4, 0.4, and 0.2, respectively, and used as the vehicle speed factor of each driving process.

[0042] It should be noted that the relative entropy is already an existing technology. In other embodiments of the present invention, the implementer can also obtain the Pearson correlation coefficient between the vehicle speed factor and the actual energy consumption ratio, and take the absolute value of the Pearson correlation coefficient as the second influence coefficient. The closer the Pearson correlation coefficient is to 0, the less correlated the vehicle speed factor and the actual energy consumption ratio are, the smaller the influence of the driving behavior on the energy consumption, and the smaller the driving influence coefficient. At the same time, the Pearson correlation coefficient is also an existing technology and will not be elaborated here.

[0043] Step S3: Adjust the discharge power of the electric vehicle according to the vehicle speed data, driving energy consumption error, and driving influence coefficient in all driving processes, in combination with the vehicle speed data in the current driving process.

[0044] The driving influence coefficient characterizes the influence degree of the driver's driving behavior on the battery energy consumption. The driving energy consumption error represents the energy consumption error caused by the comprehensive influence of various factors during the driving process. At the same time, based on the vehicle speed data, the change characteristics of the vehicle speed can be analyzed. By comprehensively considering the three factors, the battery energy consumption caused by the driver's driving behavior can be analyzed. Then, in combination with the vehicle speed data in the current driving process, the current driving behavior can be analyzed, and the influence of the current driving behavior can be analyzed with the help of historical data, so as to adjust the discharge power of the electric vehicle. Therefore, according to the vehicle speed data, driving energy consumption error, and driving influence coefficient in all driving processes, in combination with the vehicle speed data in the current driving process, the discharge power of the electric vehicle is adjusted, the discharge strategy is adjusted, the energy use of the battery is optimized, and the energy efficiency and endurance of the vehicle are improved.

[0045] Preferably, in an embodiment of the present invention, considering that during driving, in addition to the energy consumption error caused by driving behavior, there may also be energy consumption errors caused by in-vehicle air conditioners, electronic devices or other electrical devices, the driving energy consumption error is multiplied by the driving influence coefficient to represent the energy loss caused by driving behavior; considering that different speed changes caused by the driver's driving behavior result in different battery energy losses, the acceleration is used to represent the speed change. During each driving process, the ratio of each acceleration to the sum of the absolute values of the accelerations at all sampling moments is used as the energy loss ratio caused by each acceleration; at the same time, different driving processes may include the same type of acceleration, so all driving processes are analyzed comprehensively and an energy loss curve is fitted to facilitate the analysis of the current driving behavior, and the discharge power of the electric vehicle is adjusted according to the energy loss curve.

[0046] Based on this, during each driving process, the product of each acceleration and the corresponding driving energy consumption error and driving influence coefficient is used as the numerator, and the sum of the absolute values of the accelerations at all sampling moments is used as the denominator, and the fraction is used as the energy loss parameter of each acceleration; Based on the energy loss parameters of all types of accelerations during all driving processes, an energy loss curve is fitted; according to the energy loss curve and the vehicle speed data during the current driving process, the discharge power of the electric vehicle is adjusted.

[0047] As an example, the accelerations are classified based on the numerical values of the accelerations, and the accelerations with the same numerical value are one type of acceleration. The calculation formula for the energy loss parameter includes: ; Wherein, represents the serial number of the target driving process; represents the numerical value of the acceleration; represents the th energy loss parameter of the acceleration with the numerical value of in the th target driving process; represents the driving energy consumption error of the th target driving process; represents the driving influence coefficient; represents the number of sampling moments in the th target driving process; represents taking the absolute value; represents the th th vehicle speed acceleration at the sampling moment in the

[0048] In the calculation formula of the energy loss parameter, is used to represent the energy loss caused by driving behavior, To represent the proportion of energy loss caused by each acceleration, so as to represent the energy loss caused by each acceleration; where the driving process must include starting and decelerating, the item is not zero.

[0049] Preferably, in an embodiment of the present invention, the average value is used to represent the energy loss characteristics of each acceleration during all driving processes. The abscissa of the curve data point is the acceleration value, and the ordinate of the curve data point is the average value of the energy loss parameters of each acceleration during all driving processes, and the energy loss curve is fitted.

[0050] As an example, the implementer can perform curve fitting on the curve data points through algorithms such as polynomial regression to obtain the energy loss curve; the implementer can also use existing technologies such as neural network models and spline regression to fit the energy loss curve.

[0051] Preferably, in an embodiment of the present invention, the vehicle speed acceleration of the current electric vehicle and the preset discharge power of the battery management system are obtained. Based on the vehicle speed acceleration of the current electric vehicle and the energy loss curve, the current energy loss parameter is obtained; according to the current energy loss parameter and the preset discharge power, the current corrected discharge power of the electric vehicle is obtained, and both the current energy loss parameter and the preset discharge power are positively correlated with the corrected discharge power.

[0052] As an example, the calculation formula of the corrected discharge power includes: ; wherein, represents the vehicle speed acceleration of the current electric vehicle; represents the corrected discharge power of the electric vehicle when the current vehicle speed acceleration is ; represents the preset discharge power of the current electric vehicle; represents the hyperbolic tangent function; represents the value on the energy loss curve when the current vehicle speed acceleration of the electric vehicle is ;

[0053] In the calculation formula of the corrected discharge power, by obtaining the value of the current vehicle speed acceleration of the electric vehicle on the energy loss curve , which shows the characteristics of the current driving behavior on battery energy loss. Since when constructing the energy loss curve, the positive or negative sign of the acceleration determines the positive or negative sign of the energy loss parameter, and thus determines the positive or negative sign of the ordinate of the curve data points. When the current vehicle speed acceleration is positive and the greater it is, the more necessary it is to increase the discharge power of the electric vehicle on the basis of the original preset discharge power to meet the current driving conditions, optimize the energy use. At the same time, in order to limit the adjustment range, with the help of function for mapping, and does not affect the sign, that is, does not affect the adjustment direction of the discharge power.

[0054] It should be noted that is not lower than the minimum power for the normal operation of the currently enabled in-vehicle system such as the air conditioning system, navigation system, lighting system, etc. When is less than the minimum power for the normal operation of the current in-vehicle system, the corrected discharge power is set to the minimum power for the normal operation of the current in-vehicle system; in other embodiments of the present invention, the implementer can also set the amplitude coefficient and the sensitivity coefficient , to meet the requirements for adjusting the preset discharge power of different vehicles. For example, , where , to control the adjustment amplitude; is used to control the sensitivity of the hyperbolic tangent function. The larger it is, the larger it is, and the hyperbolic tangent function is more likely to approach 1 or -1. As an example, takes 0.5, takes 0.5.

[0055] When the central control system obtains vehicle data in real time, analyzes the change in vehicle speed, that is, the acceleration, according to the adjusted corrected discharge power, it controls the battery output in real time, reduces the output in advance during deceleration, or increases the output in advance during acceleration, so that each unit of electric energy of the battery can be used more reasonably, thereby improving the energy utilization efficiency, avoiding power waste caused by delayed response or excessive operation under ordinary discharge control, and ultimately extending the cruising range.

[0056] An embodiment of the present invention also provides a discharge control device for an electric vehicle. The device includes a memory, a processor, and a computer program. The memory is used to store the corresponding computer program, and the processor is used to run the corresponding computer program. When the computer program runs in the processor, it can implement a discharge control method for an electric vehicle described in steps S1 - S3.

[0057] An embodiment of the present invention also provides a discharge control system for an electric vehicle. Please refer toFigure 2 , which shows a system block diagram of a discharge control system for electric vehicles provided by an embodiment of the present invention. The system includes: a collection module 201, an analysis module 202, and a control module 203, specifically including: Collection module 201: used to obtain driving data and battery power data within a preset historical time period; the driving data includes vehicle speed data and distance data of multiple driving processes; Analysis module 202: used to obtain the actual energy consumption ratio of each driving process according to the change in battery power data and distance data of each driving process; obtain the driving energy consumption error of each driving process according to the difference between the actual energy consumption ratio and the preset energy consumption ratio of each driving process; obtain the vehicle speed change influence coefficient of each driving process according to the similar fluctuation characteristics between vehicle speed data and battery power data during each driving process; obtain the driving influence coefficient according to the correlation degree between the overall vehicle speed data and the actual energy consumption ratio of all driving processes, combined with the vehicle speed change influence coefficients of all driving processes; Control module 203: used to adjust the discharge power of the electric vehicle according to the vehicle speed data of all driving processes, combined with the driving influence coefficient and the driving energy consumption error.

[0058] In summary, aiming at the technical problem that the traditional BMS discharge control is difficult to adapt to personalized driving behaviors and affects the endurance of electric vehicles, the present invention proposes a discharge control method, device, and system for electric vehicles. The present invention first obtains the battery power data within a preset historical time period, as well as the vehicle speed data and distance data of multiple driving processes; further obtains the actual energy consumption ratio of each driving process and combines it with the preset energy consumption ratio to obtain the driving energy consumption error of each driving process; further obtains the vehicle speed change influence coefficient of each driving process according to the similar fluctuation characteristics between vehicle speed data and battery power data; further obtains the driving influence coefficient according to the correlation degree between the overall vehicle speed data and the actual energy consumption ratio of all driving processes, combined with the vehicle speed change influence coefficients of all driving processes; finally, adjusts the discharge power of the electric vehicle according to the vehicle speed data, driving energy consumption error, and driving influence coefficient of all driving processes, combined with the vehicle speed data in the current driving process.

[0059] It should be noted that: the above sequence of embodiments of the present invention is only for description and does not represent the superiority or inferiority of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0060] Each embodiment in this specification is described in a progressive manner. The same or similar parts between each embodiment can be referred to each other, and the key point of each embodiment is to illustrate the differences from other embodiments.

Claims

1. A discharge control method for electric vehicles, characterized in that: The method comprises: Acquire driving data and battery power data within a preset historical time period; the driving data includes vehicle speed data and distance data of multiple driving processes; According to the change of the battery power data and the distance data during each driving process, the actual energy consumption ratio of each driving process is obtained; according to the difference between the actual energy consumption ratio and the preset energy consumption ratio during each driving process, the driving energy consumption error of each driving process is obtained; according to the similarity of the fluctuation characteristics of the vehicle speed data and the battery power data during each driving process, the vehicle speed change influence coefficient of each driving process is obtained; according to the correlation degree between the overall vehicle speed data of all driving processes and the actual energy consumption ratio, the driving influence coefficient is obtained in combination with the vehicle speed change influence coefficient of all driving processes; The discharge power of the electric vehicle is adjusted according to the vehicle speed data during all driving processes, the driving energy consumption error and the driving influence coefficient, combined with the vehicle speed data during the current driving process.

2. A discharge control method for electric vehicles according to claim 1, characterized in that: The method for obtaining the vehicle speed change influence coefficient includes: Select any of the driving processes as a target driving process; obtain the vehicle speed acceleration at each moment based on the vehicle speed data during the target driving process; obtain the power consumption rate at each moment based on the battery power data during each driving process; According to the similar characteristics of the vehicle speed acceleration and the power consumption rate at all the same moments in the target driving process, the vehicle speed change influence coefficient of the target driving process is obtained; the similar characteristics of the vehicle speed acceleration and the power consumption rate at all the same moments in the target driving process are positively correlated with the vehicle speed change influence coefficient.

3. A discharge control method for electric vehicles according to claim 1, characterized in that: The method for obtaining the driving influence coefficient includes: Obtaining a first influence coefficient according to the overall concentrated characteristics of the vehicle speed change influence coefficients of all driving processes; The vehicle speed factor of each driving process is obtained at least based on the average value of the vehicle speed data in each driving process; the average value of the vehicle speed data is positively correlated with the vehicle speed factor; the driving influence coefficient is obtained based on the relative entropy of the vehicle speed factor set composed of the vehicle speed factors of all driving processes and the actual energy consumption ratio set composed of all the actual energy consumption ratios, combined with the first influence coefficient; the relative entropy is negatively correlated with the driving influence coefficient; the first influence coefficient and the driving influence coefficient are positively correlated.

4. A discharge control method for electric vehicles according to claim 1, characterized in that: The method for adjusting the discharge power of an electric vehicle comprises: In each driving process, the product of each acceleration and the corresponding driving energy consumption error and the driving influence coefficient is used as the numerator, the absolute value and value of the acceleration at all sampling moments is used as the denominator, and the fraction is used as the energy loss parameter of each acceleration; Based on the energy loss parameters of all types of acceleration in all driving processes, an energy loss curve is fitted; and according to the energy loss curve and the vehicle speed data in the current driving process, the discharge power of the electric vehicle is adjusted.

5. A discharge control method for electric vehicles according to claim 4, characterized in that: The method for obtaining the energy loss parameter includes: The energy loss curve is fitted by taking the acceleration value as the abscissa of the curve data point and taking the average value of the energy loss parameter of each acceleration in all driving processes as the ordinate of the curve data point.

6. A discharge control method for electric vehicles according to claim 5, characterized in that: The method for adjusting the discharge power of the electric vehicle according to the energy loss curve and the vehicle speed data during the current driving process includes: The current vehicle speed acceleration of the electric vehicle and the preset discharge power of the battery management system are obtained, and the current energy loss parameter is obtained based on the current vehicle speed acceleration of the electric vehicle and the energy loss curve; the current corrected discharge power of the electric vehicle is obtained according to the current energy loss parameter and the preset discharge power, and the current energy loss parameter and the preset discharge power are both positively correlated with the corrected discharge power.

7. A discharge control method for electric vehicles according to claim 1, characterized in that: The method for obtaining the actual energy consumption ratio includes: The ratio of the change value of the battery power data to the distance data during each driving process is used as the actual energy consumption ratio during each driving process.

8. A discharge control method for electric vehicles according to claim 1, characterized in that: The method for obtaining the driving energy consumption error includes: The difference between the actual energy consumption ratio and the preset energy consumption ratio in each driving process is used as the driving energy consumption error in each driving process.

9. A discharge control device for electric vehicles, characterized in that: The device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of a discharge control method for an electric vehicle as described in any one of claims 1 to 8 when executing the computer program.

10. A discharge control system for electric vehicles, characterized in that: The system comprises: Acquisition module: used to obtain driving data and battery power data within a preset historical time period; the driving data includes vehicle speed data and distance data of multiple driving processes; Analysis module: used to obtain the actual energy consumption ratio of each driving process according to the change of the battery power data and the distance data of each driving process; obtain the driving energy consumption error of each driving process according to the difference between the actual energy consumption ratio and the preset energy consumption ratio of each driving process; obtain the vehicle speed change influence coefficient of each driving process according to the similarity of fluctuations of the vehicle speed data and the battery power data in each driving process; obtain the driving influence coefficient according to the correlation between the overall vehicle speed data of all driving processes and the actual energy consumption ratio, combined with the vehicle speed change influence coefficient of all driving processes; Control module: used to adjust the discharge power of the electric vehicle according to the vehicle speed data during all driving processes, combined with the driving influence coefficient and the driving energy consumption error.

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