A discharge control method, device and system for an electric vehicle

Abstract

The present application relates to electric vehicle drive power supply control technical field, specifically relates to a kind of discharge control method, equipment and system for electric vehicle.The present application first obtains the battery power data in preset historical time period and the speed data and distance data of multiple driving processes;Further, the actual energy consumption ratio of each driving process is obtained, and the preset energy consumption ratio is combined to obtain the driving energy consumption error of each driving process;Further, according to the fluctuation similar characteristics of speed data and battery power data, the speed change influence coefficient of each driving process is obtained;Further, according to the correlation degree of the overall speed data of all driving processes and the actual energy consumption ratio, the driving influence coefficient is obtained in combination with the speed change influence coefficient of all driving processes;Finally, according to the speed data, driving energy consumption error and driving influence coefficient in all driving processes, the discharge power of electric vehicle is adjusted in combination with the speed data in current driving process.

Description

Technical Field

[0001] This invention relates to the field of electric vehicle drive power supply control technology, specifically to a discharge control method, device, and system for electric vehicles. Background Technology

[0002] The Battery Management System (BMS) is the core of electric vehicle battery management, responsible for comprehensive monitoring and management of the battery pack's charging and discharging process. With the widespread application of electric vehicles, intelligent discharge control methods for electric vehicles are gradually emerging. Based on driver behavior prediction and real-time data analysis, these methods dynamically adjust the battery's discharge strategy to optimize energy distribution and improve the vehicle's energy efficiency and range performance.

[0003] Because different drivers have different driving styles and their driving behavior is uncertain, and traditional BMS discharge strategies are usually statically set with fixed power output, they are difficult to adapt to complex driving conditions and personalized driving behaviors, resulting in low energy utilization efficiency. Summary of the Invention

[0004] To address the technical problem that traditional BMS discharge control struggles to adapt to individual driving behaviors, thus impacting the range of electric vehicles, the present invention aims to provide a discharge control method, device, and system for electric vehicles. The specific technical solution adopted is as follows:

[0005] A discharge control method for electric vehicles, the method comprising:

[0006] Acquire driving data and battery power data within a preset historical time period; the driving data includes vehicle speed data and distance data from multiple driving processes;

[0007] Based on the changes in battery charge data and distance data during each driving process, the actual energy consumption ratio for each driving process is obtained; based on the difference between the actual energy consumption ratio and the preset energy consumption ratio for each driving process, the driving energy consumption error for each driving process is obtained; based on the similarity of fluctuations in vehicle speed data and battery charge data during each driving process, the vehicle speed change influence coefficient for each driving process is obtained; based on the correlation between the overall vehicle speed data and the actual energy consumption ratio for all driving processes, combined with the vehicle speed change influence coefficient for all driving processes, the driving influence coefficient is obtained.

[0008] Based on all vehicle speed data during driving, the driving energy consumption error, and the driving influence coefficient, combined with the vehicle speed data during the current driving process, the discharge power of the electric vehicle is adjusted.

[0009] Furthermore, the method for obtaining the influence coefficient of vehicle speed change includes:

[0010] Select any of the aforementioned driving processes as the target driving process; based on the vehicle speed data during the target driving process, obtain the vehicle speed acceleration at each moment; based on the battery power data during each driving process, obtain the power consumption rate at each moment.

[0011] Based on the similar characteristics of the vehicle speed acceleration and the power consumption rate at all the same moments during the target's driving process, the influence coefficient of vehicle speed change during the target's driving process is obtained; the similar characteristics of the vehicle speed acceleration and the power consumption rate at all the same moments are positively correlated with the influence coefficient of vehicle speed change.

[0012] Furthermore, the method for obtaining the driving influence coefficient includes:

[0013] Based on the overall concentrated characteristics of the influence coefficients of vehicle speed changes throughout all driving processes, the first influence coefficient is obtained;

[0014] At least based on the average value of the vehicle speed data during each driving process, a vehicle speed factor is obtained for each driving process; the average value of the vehicle speed data is positively correlated with the vehicle speed factor; based on the relative entropy of the set of vehicle speed factors composed 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, a 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.

[0015] Furthermore, the method for adjusting the discharge power of the electric vehicle includes:

[0016] During each driving process, the product of each acceleration with the corresponding driving energy consumption error and driving influence coefficient is used as the numerator, and the absolute value and value of acceleration at all sampling times are used as the denominator. The fraction is used as the energy loss parameter for each acceleration.

[0017] Based on the energy loss parameters of all types of acceleration during all driving processes, an energy loss curve is fitted; based on the energy loss curve and the vehicle speed data during the current driving process, the discharge power of the electric vehicle is adjusted.

[0018] Furthermore, the method for obtaining the energy loss parameters includes:

[0019] An energy loss curve is fitted by using the acceleration value as the x-axis of the curve data points and the average value of the energy loss parameter for each acceleration in all driving processes as the y-axis of the curve data points.

[0020] Furthermore, the method for adjusting the discharge power of the electric vehicle based on the energy loss curve and the vehicle speed data during the current driving process includes:

[0021] The current vehicle speed and acceleration of the electric vehicle and the preset discharge power of the battery management system are obtained. Based on the current vehicle speed and acceleration of the electric vehicle and the energy loss curve, the current energy loss parameters are obtained. According to the current energy loss parameters and the preset discharge power, the current corrected discharge power of the electric vehicle is obtained. The current energy loss parameters and the preset discharge power are both positively correlated with the corrected discharge power.

[0022] Furthermore, the method for obtaining the actual energy consumption ratio includes:

[0023] The ratio of the change in battery charge data to the distance data during each trip is taken as the actual energy consumption ratio for each trip.

[0024] Furthermore, the method for obtaining the driving energy consumption error includes:

[0025] The difference between the actual energy consumption ratio and the preset energy consumption ratio for each driving process is taken as the driving energy consumption error for each driving process.

[0026] The present invention also proposes a discharge control device for electric vehicles, the device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement any of the steps of the discharge control method for electric vehicles described above.

[0027] The present invention also proposes a discharge control system for electric vehicles, the system comprising:

[0028] Data acquisition module: used to acquire driving data and battery power data within a preset historical time period; the driving data includes vehicle speed data and distance data from multiple driving processes;

[0029] Analysis module: used to obtain the actual energy consumption ratio for each driving process based on the changes in battery charge data and distance data during each driving process; to obtain the driving energy consumption error for each driving process based on the difference between the actual energy consumption ratio and the preset energy consumption ratio during each driving process; to obtain the vehicle speed change influence coefficient for each driving process based on the similarity of fluctuations between the vehicle speed data and the battery charge data during each driving process; and to obtain the driving influence coefficient based on the correlation between the overall vehicle speed data and the actual energy consumption ratio for all driving processes, combined with the vehicle speed change influence coefficient for all driving processes.

[0030] Control module: Used to adjust the discharge power of electric vehicle based on vehicle speed data during all driving processes, combined with the driving influence coefficient and the driving energy consumption error.

[0031] The present invention has the following beneficial effects:

[0032] This invention first acquires battery charge data and vehicle speed and distance data from multiple driving processes within a preset historical time period, providing a data foundation for subsequent analysis. It then obtains the actual energy consumption ratio for each driving process and combines it with a preset energy consumption ratio to acquire the driving energy consumption error for each driving process. This characterizes the energy consumption error caused by driving behavior during each driving process, facilitating subsequent assessment of the impact of driving behavior from the perspective of energy consumption error caused by each driving process, and enabling more accurate adjustment of discharge power. Furthermore, based on the similarity in fluctuation characteristics between vehicle speed data and battery charge data, it acquires the influence coefficient of vehicle speed change from the perspective of the similarity in fluctuation between vehicle speed data and battery charge data, providing a basis for subsequent analysis of the impact of driving behavior on… This invention provides a basis for understanding the impact of battery energy loss, facilitating the final adjustment of discharge power. Furthermore, by analyzing the correlation between overall vehicle speed data and actual energy consumption ratio across all driving processes, and combining this with the influence coefficient of vehicle speed changes throughout all driving processes, a driving influence coefficient is obtained. This reduces errors caused by road conditions and characterizes the degree to which driver behavior affects battery energy consumption. Finally, based on vehicle speed data, driving energy consumption errors, and the driving influence coefficient across all driving processes, the battery energy consumption caused by driver behavior is analyzed. Combined with the current vehicle speed data, the current driving behavior is analyzed, allowing for adjustments to the electric vehicle's discharge power and discharge strategy. This optimizes battery energy use and improves vehicle energy efficiency and range. This invention analyzes historical driving data, driver behavior, and the impact of driving behavior on battery capacity. It analyzes energy consumption errors caused by driving behavior to develop a more suitable discharge control scheme, optimize battery energy use, and improve vehicle energy efficiency and range. Attached Figure Description

[0033] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0034] Figure 1 A flowchart illustrating a discharge control method for electric vehicles provided in one embodiment of the present invention;

[0035] Figure 2 This is a system block diagram of a discharge control system for electric vehicles provided in one embodiment of the present invention. Detailed Implementation

[0036] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a discharge control method, device, and system for electric vehicles proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0037] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0038] The following description, in conjunction with the accompanying drawings, details a specific solution for a discharge control method, device, and system for electric vehicles provided by the present invention.

[0039] Please see Figure 1 The diagram illustrates a flowchart of a discharge control method for electric vehicles according to an embodiment of the present invention, specifically including:

[0040] Step S1: Obtain driving data and battery power data within a preset historical time period; driving data includes vehicle speed data and distance data from multiple driving processes.

[0041] In this embodiment of the invention, considering the differences in driving behavior among different drivers, such as frequent stops and starts on urban roads, some drivers may maintain a steady low speed while others may frequently accelerate and brake suddenly, resulting in different energy consumption. Therefore, by analyzing historical driving data, analyzing driver behavior, and the impact of driving behavior on battery charge, and analyzing the energy consumption error caused by driving behavior, a more suitable discharge control scheme can be formulated. Here, driving data and battery charge data within a preset historical time period are first obtained; driving data includes vehicle speed data and distance data from multiple driving processes.

[0042] As an example, the discharge control scheme updates continuously every 7 days, with a preset historical period of one month. Each update acquires the driving data and battery charge data from the most recent month. A single driving cycle is defined as the insertion and removal of the EV key, and all driving cycles involve wheel rotation; cycles where wheels do not rotate are excluded. Voltage and current sensors are installed on each battery cell to measure the total battery pack voltage and current changes during charging and discharging in real time. Wheel speed sensors are installed on each wheel to determine the vehicle's speed based on wheel rotation. The battery management system collects voltage, current, and speed sensor data at a sampling frequency of 100Hz. The remaining battery charge is determined using the open-circuit voltage method. Battery charge data is communicated with the vehicle's central control system via the CAN (Controller Area Network) bus and sent to the vehicle's control unit. Speed ​​sensor data is also sent to the vehicle's control unit via the CAN bus, and the control unit calculates the vehicle's real-time speed based on the wheel rotation signals collected by the sensors.

[0043] It should be noted that, in one embodiment of the present invention, only the case of one driver driving an electric vehicle is considered. The unit of electric power is kilowatt-hour, the unit of speed is kilometers per hour, and the unit of distance is kilometers or kilometers. The vehicle speed data and battery power data have been preprocessed with standard normalization. The data acquisition and open-circuit voltage method are both existing technologies.

[0044] In other embodiments of the present invention, considering that the same electric vehicle may be used by multiple drivers and that different drivers have different driving habits, sensors can be installed on the driver's seat to obtain parameters such as seat height and distance between the seat and steering wheel, analyze the seat posture, and take advantage of the fact that the seat posture is fixed when the same driver drives the electric vehicle to mark the data in the historical time period, analyze the data of each driver separately, and select the corresponding discharge adjustment scheme by identifying the seat posture.

[0045] Step S2: Based on the changes in battery charge data and distance data during each driving process, obtain the actual energy consumption ratio for each driving process; based on the difference between the actual energy consumption ratio and the preset energy consumption ratio for each driving process, obtain the driving energy consumption error for each driving process; based on the similarity of fluctuations in vehicle speed data and battery charge data during each driving process, obtain the vehicle speed change influence coefficient for each driving process; based on the correlation between the overall vehicle speed data and the actual energy consumption ratio for all driving processes, and combined with the vehicle speed change influence coefficient for all driving processes, obtain the driving influence coefficient.

[0046] The discharge control logic provided by the BMS typically adjusts the battery output based on a fixed preset power curve. Within a certain speed or acceleration range, the discharge power is constant, ignoring the driver's actual operation and behavioral changes. This can lead to energy waste. For example, drivers who habitually accelerate or brake suddenly consume more energy. In such cases, the vehicle speed changes significantly, and due to the lag in discharge power adjustment, energy distribution is not precise enough, resulting in power waste. Therefore, analyzing the driver's operational behavior during driving allows for personalized adjustments to the discharge strategy.

[0047] Considering that the driver's driving behavior will affect the actual energy consumption ratio, the actual energy consumption ratio for each driving process is first obtained based on the changes in battery charge and distance data during each driving process. Based on the difference between the actual energy consumption ratio and the preset energy consumption ratio for each driving process, the driving energy consumption error for each driving process is obtained, which characterizes the energy consumption error caused by driving behavior during each driving process. This facilitates subsequent evaluation of the impact of driving behavior from the perspective of energy consumption error caused by each driving process, and makes it easier to adjust the discharge power more accurately.

[0048] Preferably, in one embodiment of the present invention, the unit of energy consumption ratio is set to electricity consumption per kilometer, and the ratio of the change in battery charge data to the distance data during each driving process is used as the actual energy consumption ratio for each driving process.

[0049] In other embodiments of the present invention, the implementer may also calculate the power consumption per 100 kilometers by multiplying the ratio of the change in battery power data to the distance data for each driving process by 100, as the actual energy consumption ratio for each driving process.

[0050] It should be noted that the raw distance data is used when calculating the actual energy consumption ratio.

[0051] Preferably, in one embodiment of the present invention, a preset energy consumption ratio is obtained through the factory test data of the electric vehicle. Considering that the test data is relatively ideal, the actual energy consumption ratio is usually larger than the preset energy consumption ratio due to factors such as traffic jams, uneven driving speeds, and the power consumption of the electric vehicle's air conditioning system during actual driving. Therefore, the difference between the actual energy consumption ratio and the preset energy consumption ratio for each driving process is used as the driving energy consumption error for each driving process. The difference is used to represent the difference between the actual energy consumption ratio and the preset energy consumption ratio for each driving process.

[0052] In another embodiment of the present invention, the implementer may also use the difference between the actual energy consumption ratio and the preset energy consumption ratio for each driving process as the numerator, the preset energy consumption ratio as the denominator, and the fraction as the driving energy consumption error for each driving process; the difference between the actual energy consumption ratio and the preset energy consumption ratio relative to the preset energy consumption ratio is represented by the ratio of 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.

[0053] Considering that the more similar the fluctuations of vehicle speed data and battery charge data, the greater the impact of vehicle speed changes on battery charge, and the greater the impact of driving speed control behavior on the electric vehicle's range, we obtain the vehicle speed change influence coefficient for each driving process based on the similarity of fluctuations between vehicle speed data and battery charge data. From the perspective of the similarity of fluctuations between vehicle speed data and battery charge data, we obtain the vehicle speed change influence coefficient, which provides a basis for subsequent analysis of the impact of driving behavior on battery energy loss and facilitates the final adjustment of discharge power.

[0054] Preferably, in one embodiment of the present invention, any driving process is first selected as the target driving process, and the driving processes are analyzed one by one; in order to facilitate the analysis of fluctuations in vehicle speed data, the vehicle speed acceleration at each moment is obtained based on the vehicle speed data in the target driving process; similarly, based on the battery power data in each driving process, the power consumption rate at each moment is obtained, which represents the acceleration of the power.

[0055] Considering that the more similar the vehicle speed acceleration and battery consumption rate are at the same moment, the more similar the fluctuations of vehicle speed data and battery power data are, the similarity characteristics of vehicle speed acceleration and battery consumption rate at all the same moment during the target driving process are used to represent the fluctuation similarity characteristics of vehicle speed data and battery power data, and to obtain the influence coefficient of vehicle speed change during the target driving process; the similarity characteristics of vehicle speed acceleration and battery consumption rate at all the same moment are positively correlated with the influence coefficient of vehicle speed change.

[0056] As an example, a battery charge change curve is fitted based on battery charge data, and the negative value of the slope of the battery charge data at each moment is taken as the charge consumption rate at that moment; a vehicle speed change curve is fitted based on vehicle speed data, and the slope of the vehicle speed data at each moment is taken as the vehicle speed acceleration at that moment; the formula for calculating the vehicle speed change influence coefficient includes:

[0057] ;

[0058] in, Indicates the sequence number of the target's driving process; Indicates the first The influence coefficient of vehicle speed change during the driving process of each target; Represented by natural constant An exponential function with base 1; Indicates the first The number of sampling moments during the driving process of each target; Indicates the sequence number of the sampling time; Indicates taking the absolute value; Indicates the first During the journey of the first target The rate of power consumption at each sampling time; Indicates the first During the journey of the first target Vehicle speed acceleration at each sampling time.

[0059] The formula for calculating the influence coefficient of vehicle speed change uses the absolute value of the difference to represent the difference between vehicle speed acceleration and energy consumption rate. The larger the absolute value of the difference, the more obvious the difference. The average value represents the overall difference between vehicle speed acceleration and energy consumption rate at all moments during the target driving process. Finally, a negative correlation mapping function is used... By performing a negative correlation mapping, the similar characteristics of vehicle speed acceleration and battery consumption rate at all the same time are represented, reflecting the similar fluctuation characteristics of vehicle speed data and battery power data. As the influence coefficient of vehicle speed change in the target driving process, it represents the degree of influence of vehicle speed change on battery power consumption rate, which is convenient for subsequent adjustment of discharge strategy through accurate influence coefficient, optimizing battery energy use, and improving vehicle energy efficiency and range.

[0060] It should be noted that, in other embodiments of the present invention, the implementer can also analyze the correlation between the battery change curve and the vehicle speed change curve using existing Dynamic Time Warping (DTW) algorithms. Specifically, the DTW distance between the two curves can be obtained, and the DTW distance can be mapped using a negative correlation mapping function. Negative correlation mapping and normalization can be performed to obtain the influence coefficient of vehicle speed change during the target driving process; alternatively, the area between the two curves can be calculated. The smaller the area, the more similar the fluctuations, and the larger the influence coefficient of vehicle speed change.

[0061] Considering that drivers' driving behavior changes under different road conditions—for example, in urban areas, drivers often face frequent stops and starts; on highways, drivers maintain high speeds for extended periods; similarly, driving behavior may differ on mountain roads, muddy or slippery roads, and other road conditions—it is necessary to analyze data from all driving processes. Given that different road conditions directly affect overall vehicle speed data and actual energy consumption, a higher correlation between overall vehicle speed data and actual energy consumption indicates a greater impact of driving behavior on energy consumption. Furthermore, the speed change influence coefficient reflects the impact of speed changes on battery consumption during driving. Therefore, by combining the correlation between overall vehicle speed data and actual energy consumption across all driving processes with the speed change influence coefficient, a driving influence coefficient is obtained. This reduces errors caused by road conditions and characterizes the degree to which driver behavior affects battery energy consumption.

[0062] Preferably, in one embodiment of the present invention, considering that by calculating the overall characteristics of the influence coefficient of vehicle speed changes in all driving processes, the driving behavior patterns of drivers under different conditions 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 between the overall vehicle speed data of all driving processes and the actual energy consumption ratio is expressed by relative entropy. The smaller the relative entropy between the set of vehicle speed factors composed of all driving processes and the actual energy consumption ratio composed of all actual energy consumption ratios, the more similar the distribution of vehicle speed factors and the distribution of actual energy consumption ratios are, and the greater the correlation between the overall vehicle speed data of all driving processes and the actual energy consumption ratio, the greater the influence caused by driving behavior.

[0063] Based on this, the first influence coefficient is obtained according to the overall concentrated characteristics of the influence coefficients of vehicle speed changes in all driving processes;

[0064] At least based on the average speed data of each driving process, obtain the speed factor for each driving process; the average speed data is positively correlated with the speed factor; based on the relative entropy of the set of speed factors composed of all driving processes and the set of actual energy consumption ratios composed of all actual energy consumption ratios, and 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.

[0065] As an example: The overall clustering characteristics of the influence coefficients of vehicle speed changes throughout all driving processes are represented by average values. The average value of the influence coefficients of vehicle speed changes throughout all driving processes is taken as the first influence coefficient. The average value of the vehicle speed data in each driving process is taken as the vehicle speed factor for each driving process. All vehicle speed factors throughout the driving processes are combined into a vehicle speed factor set, and all actual energy consumption ratios are combined into an actual energy consumption ratio set. The probability distribution of elements in each set is obtained, and thus the relative entropy is obtained. The relative entropy is then analyzed using a negative correlation function. Perform negative correlation mapping and normalization to obtain the second influence coefficient; multiply the first and second influence coefficients to obtain the driving influence coefficient.

[0066] As another example, the vehicle speed factor is obtained by weighting and summing the mean, mode, and median of the vehicle speed data. The mean, mode, and median of the vehicle speed data in each driving process are weighted and summed with weights of 0.4, 0.4, and 0.2, and this sum is used as the vehicle speed factor for each driving process.

[0067] It should be noted that relative entropy is already 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 driving behavior on energy consumption, and the smaller the driving influence coefficient. At the same time, the Pearson correlation coefficient is already existing technology and will not be described in detail.

[0068] Step S3: Based on all vehicle speed data, driving energy consumption error, and driving influence coefficient during the driving process, and combined with the vehicle speed data during the current driving process, adjust the discharge power of the electric vehicle.

[0069] The driving influence coefficient characterizes the degree to which the driver's driving behavior affects battery energy consumption. The driving energy consumption error represents the energy consumption error caused by a combination of various factors during driving. At the same time, based on vehicle speed data, the characteristics of vehicle speed changes can be analyzed. Combining these three factors, the battery energy consumption caused by the driver's driving behavior can be analyzed. By combining the vehicle speed data during the current driving process, the current driving behavior can be analyzed. By using historical data to analyze the impact of the current driving behavior, the discharge power of the electric vehicle can be adjusted. Therefore, based on all vehicle speed data, driving energy consumption error, and driving influence coefficient during the driving process, combined with the vehicle speed data during the current driving process, the discharge power of the electric vehicle can be adjusted, the discharge strategy can be adjusted, the energy use of the battery can be optimized, and the energy efficiency and range of the vehicle can be improved.

[0070] Preferably, in one 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 conditioning, electronic devices or other electrical equipment, the driving energy consumption error is multiplied by the driving influence coefficient to represent the energy loss caused by driving behavior; considering that the vehicle speed change caused by the driver's driving behavior is different, and the energy loss of the battery is different, the vehicle speed change is represented by acceleration. In each driving process, the ratio of each acceleration to the sum of the absolute values ​​of acceleration at all sampling times is taken as the energy loss ratio caused by each acceleration; at the same time, different driving processes may contain the same type of acceleration, so all driving processes are analyzed in combination and an energy loss curve is fitted to facilitate the analysis of the current driving behavior and adjust the discharge power of the electric vehicle according to the energy loss curve.

[0071] Based on this, during each driving process, the product of each acceleration with the corresponding driving energy consumption error and driving influence coefficient is used as the numerator, and the absolute value and value of the acceleration at all sampling times are used as the denominator. The fraction is used as the energy loss parameter for each acceleration.

[0072] Based on the energy loss parameters of all types of acceleration during all driving processes, an energy loss curve is fitted; based on the energy loss curve and the vehicle speed data during the current driving process, the discharge power of the electric vehicle is adjusted.

[0073] As an example, acceleration is categorized based on its numerical value; accelerations with the same numerical value are grouped into one category. The formulas for calculating energy loss parameters include:

[0074] ;

[0075] in, Indicates the sequence number of the target's driving process; A numerical value representing acceleration; Indicates the first The value during the travel of each target is: The energy loss parameter of acceleration; Indicates the first The driving energy consumption error during the driving process of a target vehicle; Indicates the driving impact coefficient; Indicates the first The number of sampling moments during the driving process of each target; Indicates the sequence number of the sampling time; Indicates taking the absolute value; Indicates the first During the journey of the first target Vehicle speed acceleration at each sampling time.

[0076] In the formula for calculating energy loss parameters, Used to represent the energy loss caused by driving behavior. This is used to represent the proportion of energy loss caused by each type of acceleration, thus This represents the energy loss caused by each acceleration; the driving process must include starting and deceleration. The term is not zero.

[0077] Preferably, in one embodiment of the present invention, the energy loss curve is fitted by using the average value to represent the energy loss characteristics of each acceleration during all driving processes, with the acceleration value as the abscissa of the curve data point and the average value of the energy loss parameter of each acceleration during all driving processes as the ordinate of the curve data point.

[0078] As an example, implementers can use algorithms such as multinomial regression to fit curve data points to obtain energy loss curves; implementers can also use existing techniques such as neural network models and spline regression to fit energy loss curves.

[0079] Preferably, in one embodiment of the present invention, the current vehicle speed and acceleration of the electric vehicle and the preset discharge power of the battery management system are obtained; based on the current vehicle speed and acceleration and energy loss curve of the electric vehicle, the current energy loss parameters are obtained; according to the current energy loss parameters and the preset discharge power, the current corrected discharge power of the electric vehicle is obtained, and the current energy loss parameters and the preset discharge power are both positively correlated with the corrected discharge power.

[0080] As an example, the formula for calculating the corrected discharge power includes:

[0081] ;

[0082] in, This indicates the current speed and acceleration of the electric vehicle. This indicates that the electric vehicle's acceleration at the current speed is... Corrected discharge power at that time; This indicates the current preset discharge power of the electric vehicle; Represents the hyperbolic tangent function; This indicates that the electric vehicle's acceleration at the current speed is... The value of on the energy loss curve at that time.

[0083] The formula for calculating the corrected discharge power is obtained by taking the value of the electric vehicle's current speed acceleration on the energy loss curve. This indicates the impact of current driving behavior on battery energy loss. Since the sign of acceleration determines the sign of the energy loss parameter when constructing the energy loss curve, and thus the sign of the ordinate of the curve data points, a higher positive acceleration at the current vehicle speed necessitates increasing the electric vehicle's discharge power beyond the preset power to meet current driving conditions and optimize energy usage. Furthermore, to limit the adjustment range, [further adjustments are needed]. function pairs Perform mapping without affecting The symbol indicates that it does not affect the direction of discharge power adjustment.

[0084] It should be noted that, Not lower than the minimum power required for normal operation of currently used vehicle infotainment systems such as air conditioning, navigation, and lighting systems. When the power is less than the minimum power required for normal operation of the current vehicle infotainment system, the corrected discharge power is set to the minimum power required for normal operation of the current vehicle infotainment system; in other embodiments of the present invention, the implementer may also set an amplitude coefficient. and sensitivity coefficient This is to accommodate the need for adjusting the preset discharge power of different vehicles, such as ,in Used to control The adjustment range; Used to control the sensitivity of the hyperbolic tangent function. The larger, The larger the value, the easier it is for the hyperbolic tangent function to approach 1 or -1. As an example, Take 0.5, Take 0.5.

[0085] When the central control system acquires vehicle data in real time and analyzes the changes in vehicle speed, i.e. acceleration, it controls the battery output in real time based on the adjusted discharge power. It reduces the output in advance when decelerating or increases the output in advance when accelerating, so that every unit of battery energy can be used more rationally, thereby improving energy utilization efficiency and avoiding power waste caused by delayed response or excessive operation under ordinary discharge control, ultimately extending the driving range.

[0086] An embodiment of the present invention also provides a discharge control device for electric vehicles. 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 the discharge control method for electric vehicles described in steps S1-S3.

[0087] One embodiment of the present invention also provides a discharge control system for electric vehicles; please refer to [link to relevant documentation]. Figure 2 This diagram illustrates a system block diagram of a discharge control system for electric vehicles according to an embodiment of the present invention. The system includes: a data acquisition module 201, an analysis module 202, and a control module 203, specifically comprising:

[0088] Data Acquisition Module 201: Used to acquire driving data and battery power data within a preset historical time period; driving data includes vehicle speed data and distance data from multiple driving processes;

[0089] Analysis module 202: used to obtain the actual energy consumption ratio for each driving process based on the changes in battery charge data and distance data during each driving process; to obtain the driving energy consumption error for each driving process based on the difference between the actual energy consumption ratio and the preset energy consumption ratio during each driving process; to obtain the vehicle speed change influence coefficient for each driving process based on the similarity of fluctuations in vehicle speed data and battery charge data during each driving process; and to obtain the driving influence coefficient based on the correlation between the overall vehicle speed data and the actual energy consumption ratio for all driving processes, combined with the vehicle speed change influence coefficient for all driving processes.

[0090] Control module 203: Used to adjust the discharge power of electric vehicle based on vehicle speed data during all driving processes, combined with driving influence coefficient and driving energy consumption error.

[0091] In summary, addressing the technical problem that traditional BMS discharge control struggles to adapt to personalized driving behaviors, thus impacting the range of electric vehicles, this invention proposes a discharge control method, device, and system for electric vehicles. This invention first acquires battery charge data and vehicle speed and distance data from multiple driving processes within a preset historical time period. Next, it acquires the actual energy consumption ratio for each driving process and, combined with the preset energy consumption ratio, obtains the driving energy consumption error for each driving process. Further, based on the similarity in fluctuation characteristics between vehicle speed data and battery charge data, it obtains the vehicle speed change influence coefficient for each driving process. Then, based on the correlation between the overall vehicle speed data and the actual energy consumption ratio across all driving processes, and combined with the vehicle speed change influence coefficient for all driving processes, it obtains a driving influence coefficient. Finally, based on the vehicle speed data, driving energy consumption error, and driving influence coefficient for all driving processes, and combined with the vehicle speed data during the current driving process, it adjusts the discharge power of the electric vehicle.

[0092] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0093] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A discharge control method for electric vehicles, characterized in that, The method includes: Acquire driving data and battery power data within a preset historical time period; the driving data includes vehicle speed data and distance data from multiple driving processes; Based on the changes in battery charge data and distance data during each driving process, the actual energy consumption ratio for each driving process is obtained; based on the difference between the actual energy consumption ratio and the preset energy consumption ratio for each driving process, the driving energy consumption error for each driving process is obtained; based on the similar fluctuation characteristics of the vehicle speed data and battery charge data during each driving process, the vehicle speed change influence coefficient for each driving process is obtained; based on the correlation between the overall vehicle speed data and the actual energy consumption ratio for all driving processes, combined with the vehicle speed change influence coefficient for all driving processes, the driving influence coefficient is obtained. Based on all vehicle speed data during driving, the driving energy consumption error, and the driving influence coefficient, combined with the vehicle speed data during the current driving process, the discharge power of the electric vehicle is adjusted. The method for obtaining the influence coefficient of vehicle speed change includes: Select any of the aforementioned driving processes as the target driving process; based on the vehicle speed data during the target driving process, obtain the vehicle speed acceleration at each moment; based on the battery power data during each driving process, obtain the power consumption rate at each moment. During the target driving process, the average absolute value of the difference between the vehicle speed acceleration and the power consumption rate at all the same time points is negatively correlated to obtain the vehicle speed change influence coefficient during the target driving process.

2. The discharge control method for electric vehicles according to claim 1, characterized in that, The method for obtaining the driving influence coefficient includes: Based on the overall concentrated characteristics of the influence coefficients of vehicle speed changes throughout all driving processes, the first influence coefficient is obtained; At least based on the average value of the vehicle speed data during each driving process, a vehicle speed factor is obtained for each driving process; the average value of the vehicle speed data is positively correlated with the vehicle speed factor; based on the relative entropy of the set of vehicle speed factors composed 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, a 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.

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

4. The discharge control method for electric vehicles according to claim 3, characterized in that, The method for obtaining the energy loss parameters includes: An energy loss curve is fitted by using the acceleration value as the x-axis of the curve data points and the average value of the energy loss parameter for each acceleration in all driving processes as the y-axis of the curve data points.

5. A discharge control method for electric vehicles according to claim 4, characterized in that, The method for adjusting the discharge power of an electric vehicle based on the energy loss curve and the vehicle speed data during the current driving process includes: The current vehicle speed and acceleration of the electric vehicle and the preset discharge power of the battery management system are obtained. Based on the current vehicle speed and acceleration of the electric vehicle and the energy loss curve, the current energy loss parameters are obtained. According to the current energy loss parameters and the preset discharge power, the current corrected discharge power of the electric vehicle is obtained. The current energy loss parameters and the preset discharge power are both positively correlated with the corrected discharge power.

6. The 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 in battery charge data to the distance data during each trip is taken as the actual energy consumption ratio for each trip.

7. The 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 for each driving process is taken as the driving energy consumption error for each driving process.

8. A discharge control device for electric vehicles, characterized in that, The discharge control device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the steps of the discharge control method for electric vehicles as described in any one of claims 1 to 7.

9. A discharge control system for electric vehicles, characterized in that, The system includes: Data acquisition module: used to acquire driving data and battery power data within a preset historical time period; the driving data includes vehicle speed data and distance data from multiple driving processes; Analysis module: used to obtain the actual energy consumption ratio for each driving process based on the changes in battery charge data and distance data during each driving process; to obtain the driving energy consumption error for each driving process based on the difference between the actual energy consumption ratio and the preset energy consumption ratio during each driving process; to obtain the vehicle speed change influence coefficient for each driving process based on the similarity of fluctuations in vehicle speed data and battery charge data during each driving process; and to obtain the driving influence coefficient based on the correlation between the overall vehicle speed data and the actual energy consumption ratio for all driving processes, combined with the vehicle speed change influence coefficient for all driving processes. Control module: used to adjust the discharge power of electric vehicle based on vehicle speed data during all driving processes, combined with the driving influence coefficient and the driving energy consumption error; The method for obtaining the influence coefficient of vehicle speed change includes: Select any of the aforementioned driving processes as the target driving process; based on the vehicle speed data during the target driving process, obtain the vehicle speed acceleration at each moment; based on the battery power data during each driving process, obtain the power consumption rate at each moment. During the target driving process, the average absolute value of the difference between the vehicle speed acceleration and the power consumption rate at all the same time points is negatively correlated to obtain the vehicle speed change influence coefficient during the target driving process.

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