Sliding energy recovery control method and device, controller and vehicle

Abstract

The invention provides a sliding energy recovery control method and device, a controller and a vehicle, and relates to the technical field of vehicle driving. The method comprises the following steps: acquiring a battery charge state, charging power and power system parameters of a target vehicle; according to the battery charge state, the charging power and the power system parameters, the maximum wheel end negative torque of the target vehicle and the maximum sliding deceleration corresponding to the maximum wheel end negative torque are determined; target deceleration is determined according to a deceleration reference table, the maximum sliding deceleration and the vehicle weight and the vehicle speed of the target vehicle, the deceleration reference table is used for inquiring the sliding deceleration under different driving conditions, and the driving conditions comprise the time period, the vehicle weight, the gradient and the vehicle speed; the deceleration reference table is determined by correcting a target deceleration in a historical energy recovery process based on a self-learning mechanism; and controlling a motor of the target vehicle to apply a braking torque according to the target deceleration to realize energy recovery. The control precision and the energy recovery efficiency are improved.

Description

Technical Field

[0001] The present application relates to the field of vehicle driving technology, and in particular to a coasting energy recovery control method, device, controller and vehicle. Background Art

[0002] With the rapid advancement of vehicle driving technology, coasting energy recovery technology, as a key means of improving vehicle energy efficiency, is widely used in various vehicles. This technology intelligently controls the intensity of energy recovery during coasting (i.e., when not actively accelerating or braking), maximizing energy utilization, thereby extending vehicle range and reducing energy consumption.

[0003] Existing coasting energy recovery technology mainly relies on manual operation by the driver. For example, the driver needs to manually adjust the recovery intensity by operating the joystick or other buttons in the cab. This process requires the driver to make real-time judgments and frequent adjustments based on road conditions to ensure that the vehicle adapts to actual road conditions; and predictive adjustments based on cloud-based algorithms. Using navigation data and vehicle information, a training set algorithm (such as a random forest regression algorithm) is constructed in the cloud to predict coasting deceleration and applied to the vehicle side. In actual scenarios, the vehicle adjusts the acceleration according to the actual situation during the coasting cycle and feeds the data back to the cloud to optimize the training set.

[0004] However, whether it is manual adjustment or automatic control based on cloud algorithms, there is a problem of insufficient control accuracy during the coasting energy recovery control process, which in turn affects the energy recovery efficiency. Summary of the Invention

[0005] The coasting energy recovery control method, device, controller and vehicle provided in the embodiments of the present application are used to solve the problem that the existing methods have insufficient control accuracy during the coasting energy recovery control process, thereby affecting the energy recovery efficiency.

[0006] In a first aspect, an embodiment of the present application provides a coasting energy recovery control method, comprising:

[0007] Obtain the target vehicle's battery state of charge, charging power, and power system parameters;

[0008] Determine the maximum wheel-end negative torque of the target vehicle and the maximum coasting deceleration corresponding to the maximum wheel-end negative torque based on the battery state of charge, charging power, and powertrain parameters;

[0009] The target deceleration is determined based on a deceleration benchmark table, the maximum coasting deceleration, and the target vehicle's weight and speed. The deceleration benchmark table is used to query the coasting deceleration under different driving conditions, including time of day, vehicle weight, slope, and speed. The deceleration benchmark table is determined by modifying the target deceleration during historical energy recovery processes based on a self-learning mechanism.

[0010] The motor of the target vehicle is controlled to apply braking torque according to the target deceleration to achieve energy recovery.

[0011] In one possible embodiment, the method further includes: during the energy recovery process, obtaining an actual deceleration and a target deceleration of the target vehicle under current driving conditions; iteratively correcting the target deceleration under the current driving conditions based on a difference between the actual coasting deceleration and the target deceleration until a first corrected deceleration is obtained when the difference between the actual coasting deceleration and the target deceleration satisfies a preset convergence condition; controlling the motor of the target vehicle to apply a braking torque according to the first corrected deceleration to achieve energy recovery; and after obtaining the first corrected deceleration under multiple driving conditions, performing linear regression processing on the first corrected deceleration under multiple driving conditions to generate a coasting deceleration distribution curve; and updating the deceleration reference table according to the coasting deceleration distribution curve.

[0012] In one possible embodiment, during the energy recovery process, the actual deceleration and target deceleration of the target vehicle under the current driving conditions are obtained, including: during the energy recovery process, determining whether the target vehicle meets self-learning conditions, the self-learning conditions including that the coasting energy recovery function of the target vehicle is activated, the operating status and slip rate meet preset requirements, and the accelerator pedal opening is zero and lasts for a preset time; when the target vehicle meets the self-learning conditions, the actual deceleration and target deceleration of the target vehicle under the current driving conditions are obtained.

[0013] In one possible embodiment, before controlling the motor of the target vehicle to apply braking torque according to the first corrected deceleration to achieve energy recovery, the method also includes: determining the maximum deceleration and minimum deceleration of the target vehicle at different vehicle weights and different vehicle speeds based on the battery state of charge, charging power and power system parameters; determining a correction range of the target deceleration based on the maximum deceleration and minimum deceleration of the target vehicle at different vehicle weights and different vehicle speeds; when the first corrected deceleration does not meet the correction range, adjusting the first corrected deceleration to obtain an adjusted first corrected deceleration; accordingly, controlling the motor of the target vehicle to apply braking torque according to the first corrected deceleration to achieve energy recovery, including: controlling the motor of the target vehicle to apply braking torque according to the adjusted first corrected deceleration to achieve energy recovery.

[0014] In one possible implementation, the power system parameters include at least motor external characteristics, vehicle speed, transmission speed ratio, rear axle speed ratio, vehicle weight and tire radius; the maximum wheel-end negative torque of the target vehicle and the maximum coasting deceleration corresponding to the maximum wheel-end negative torque are determined based on the battery state of charge, charging power and power system parameters, including: determining the maximum wheel-end negative torque of the target vehicle based on the battery state of charge, charging power, and the motor external characteristics, transmission speed ratio and rear axle speed ratio in the power system parameters; determining the maximum coasting deceleration corresponding to the maximum wheel-end negative torque based on the maximum wheel-end negative torque and the vehicle speed, vehicle weight and tire radius in the power system parameters.

[0015] In one possible implementation, the target deceleration is determined based on a deceleration reference table, a maximum coasting deceleration, and the weight and speed of the target vehicle, including: determining an initial deceleration from the deceleration reference table based on the weight and speed of the target vehicle, and the time and slope at which the target vehicle is located; when the initial deceleration is less than or equal to the maximum coasting deceleration, determining the initial deceleration as the target deceleration; and when the initial deceleration is greater than the maximum coasting deceleration, determining the maximum coasting deceleration as the target deceleration.

[0016] In one possible embodiment, after determining the target deceleration based on the deceleration reference table, the maximum coasting deceleration, and the weight and speed of the target vehicle, the method further includes: determining the wheel speed difference and steering angle of the target vehicle; when the speed of the target vehicle is greater than or equal to a preset speed threshold, determining the curve radius based on the wheel speed difference; when the speed of the target vehicle is less than the preset speed threshold, determining the curve radius based on the steering angle; correcting the target deceleration based on the curve radius to obtain a second corrected deceleration; accordingly, controlling the motor of the target vehicle to apply braking torque according to the target deceleration to achieve energy recovery, including: controlling the motor of the target vehicle to apply braking torque according to the second corrected deceleration to achieve energy recovery.

[0017] In a second aspect, an embodiment of the present application provides a coasting energy recovery control device, comprising:

[0018] An acquisition module is used to obtain the battery state of charge, charging power and power system parameters of the target vehicle;

[0019] a determination module, configured to determine a maximum wheel-end negative torque of the target vehicle and a maximum coasting deceleration corresponding to the maximum wheel-end negative torque based on a battery state of charge, a charging power, and power system parameters;

[0020] a processing module for determining a target deceleration based on a deceleration reference table, a maximum coasting deceleration, and the weight and speed of the target vehicle. The deceleration reference table is used to query the coasting deceleration under different driving conditions, including time period, vehicle weight, slope, and speed. The deceleration reference table is determined by correcting the target deceleration during historical energy recovery processes based on a self-learning mechanism;

[0021] The control module is used to control the motor of the target vehicle to apply braking torque according to the target deceleration to achieve energy recovery.

[0022] In a third aspect, an embodiment of the present application provides a controller, comprising: a memory, a processor;

[0023] Memory stores computer-executable instructions;

[0024] The processor executes the computer-executable instructions stored in the memory, so that the processor executes the above first aspect and / or various possible implementations of the first aspect.

[0025] In a fourth aspect, an embodiment of the present application provides a vehicle, comprising a vehicle body and a controller, wherein the controller is configured to execute the first aspect and / or various possible implementations of the first aspect.

[0026] In a fifth aspect, an embodiment of the present application provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed, they are used to implement the first aspect above and / or various possible implementation methods of the first aspect.

[0027] In a sixth aspect, an embodiment of the present application provides a computer program product, including a computer program, which, when executed, implements the above first aspect and / or various possible implementation methods of the first aspect.

[0028] The coasting energy recovery control method, device, controller and vehicle provided in the embodiments of the present application obtain the battery state of charge, charging power and power system parameters of the target vehicle; determine the maximum wheel-end negative torque of the target vehicle and the maximum coasting deceleration corresponding to the maximum wheel-end negative torque according to the battery state of charge, charging power and power system parameters; determine the target deceleration according to the deceleration reference table, the maximum coasting deceleration, and the weight and speed of the target vehicle, the deceleration reference table is used to query the coasting deceleration under different driving conditions, the driving conditions including: time period, vehicle weight, slope and speed, the deceleration reference table is based on the self-learning mechanism to correct the target deceleration in the historical energy recovery process and determine; control the target vehicle according to the target deceleration The vehicle's motor applies braking torque to achieve energy recovery. Based on the target vehicle's battery state of charge, charging power and powertrain parameters, it can accurately calculate the maximum wheel-end negative torque that the vehicle can apply in its current state, as well as the corresponding maximum coasting deceleration. The target deceleration is determined in combination with a deceleration reference table. The table provides refined coasting deceleration queries based on various driving conditions such as time period, vehicle weight, slope and speed to achieve optimal control in different driving environments. At the same time, the table uses a self-learning mechanism to collect and analyze deceleration data during the energy recovery process to correct and optimize it. This allows the deceleration reference table to adapt to different driving habits and road conditions, thereby continuously improving control accuracy and energy recovery efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0030] Figure 1 A flow chart of the coasting energy recovery control method provided in this application;

[0031] Figure 2 A schematic diagram of a coasting deceleration distribution curve provided in this application;

[0032] Figure 3 A schematic diagram of the structure of the coasting energy recovery control device provided in this application;

[0033] Figure 4 This is a schematic diagram of the structure of the electronic device provided in this application.

[0034] The above drawings illustrate specific embodiments of the present application, which will be described in more detail below. These drawings and the textual description are not intended to limit the scope of the present application in any way, but rather to illustrate the concepts of the present application to those skilled in the art by reference to specific embodiments. DETAILED DESCRIPTION

[0035] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.

[0036] In the existing technology, the manual adjustment method requires the driver to judge and adjust the coasting energy recovery intensity in real time according to road conditions and vehicle speed. This process is subject to subjectivity and lag in human judgment. The driver's reaction speed and judgment accuracy are affected by many factors, such as driving experience and distraction, resulting in untimely intervention in vehicle speed, making the adjustment of recovery intensity less precise, and the energy during coasting may not be optimally recovered. At the same time, the driver spends part of his energy frequently intervening in the coasting recovery intensity, which is not conducive to safe driving.

[0037] While cloud-based automatic control can predict coasting deceleration and adjust regeneration intensity through algorithms, establishing a training algorithm in the cloud, applying it to the vehicle, making judgments and adjustments there, and feeding it back to the cloud for iterative training presents challenges such as excessive input information, complex algorithms, high computational effort, slow iteration speeds, and significant computational resource consumption. This can lead to inflexible and inefficient adjustments to regeneration intensity, thus impacting energy recovery efficiency. Furthermore, communication delays between the cloud and vehicle can affect the real-time and accuracy of control.

[0038] Therefore, whether it is manual adjustment or automatic control based on cloud algorithms, there is a problem of insufficient control accuracy during the gliding energy recovery control process, which in turn affects the energy recovery efficiency.

[0039] To address the above-mentioned problems, embodiments of the present application provide a coasting energy recovery control method, device, controller, and vehicle. Based on the battery state of charge, charging power, and power system parameters of the target vehicle, the method can accurately calculate the maximum wheel-end negative torque that the vehicle can apply in the current state, as well as the corresponding maximum coasting deceleration. The method also determines the target deceleration in combination with a deceleration reference table. The table provides refined coasting deceleration queries based on various driving conditions such as time period, vehicle weight, slope, and speed, so as to achieve optimal control under different driving environments. At the same time, the table utilizes a self-learning mechanism to collect and analyze deceleration data during the energy recovery process for correction and optimization, so that the deceleration reference table can adapt to different driving habits and road conditions, thereby continuously improving control accuracy and energy recovery efficiency. Finally, the motor is accurately controlled to apply braking torque through the target deceleration to achieve efficient energy recovery during coasting, which not only ensures driving comfort and safety, but also improves the energy efficiency of the vehicle.

[0040] The following specific embodiments describe in detail the technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0041] The execution subject of the coasting energy recovery control method provided in the embodiment of the present application can be a computing device such as a server or a server cluster. Among them, the server can be a mobile phone, a computer, a tablet and other devices. The embodiment of the present application does not impose any special restrictions on the implementation method of the execution subject, as long as the execution subject can obtain the battery state of charge, charging power and power system parameters of the target vehicle; determine the maximum wheel-end negative torque of the target vehicle and the maximum coasting deceleration corresponding to the maximum wheel-end negative torque based on the battery state of charge, charging power and power system parameters; determine the target deceleration based on the deceleration reference table, the maximum coasting deceleration, and the weight and speed of the target vehicle. The deceleration reference table is used to query the coasting deceleration under different driving conditions. The driving conditions include: time period, vehicle weight, slope and speed. The deceleration reference table is based on the self-learning mechanism to correct the target deceleration in the historical energy recovery process and determine; according to the target deceleration, the motor of the target vehicle is controlled to apply braking torque to achieve energy recovery.

[0042] Figure 1 A schematic diagram of a coasting energy recovery control method provided in this application is shown in FIG. Figure 1 As shown, the method may include:

[0043] S101. Obtain the battery state of charge, charging power, and power system parameters of the target vehicle.

[0044] In this step, the battery's State of Charge (SOC) refers to the ratio of the battery's current remaining charge to its total charge, typically expressed as a percentage. Charging power refers to the maximum amount of power a battery can receive during charging. Powertrain parameters describe the performance and characteristics of the vehicle's powertrain, impacting acceleration, braking, energy efficiency, and the overall driving experience. These parameters may include motor speed, vehicle speed, shift lines, motor external characteristics, transmission ratio, rear axle ratio, vehicle weight, and tire radius.

[0045] These parameters can be obtained in real time through the vehicle's built-in sensors and control systems. For example, the battery management system can provide battery state of charge and charging power, and the powertrain controller can provide parameters such as motor power and torque.

[0046] Furthermore, upon receiving a coasting energy regeneration activation signal, the target vehicle's battery state of charge, charging power, and powertrain parameters are acquired. For example, if the vehicle is equipped with a dedicated coasting energy regeneration button, the activation signal may be generated when the driver manually activates the button. In actual operation, the driver should decide whether to activate this function based on the actual vehicle conditions and their own needs.

[0047] S102: Determine a maximum wheel-end negative torque of the target vehicle and a maximum coasting deceleration corresponding to the maximum wheel-end negative torque according to a battery state of charge, a charging power, and power system parameters.

[0048] Among them, when the regenerative braking power in the motor's external characteristics exceeds the battery's charging power, the motor's maximum regenerative braking torque may need to be adjusted to ensure that the actual recovered energy does not exceed the battery's acceptance capacity. When the SOC is close to full charge, in order to avoid damage to the battery caused by overcharging, the battery may not be able to receive more energy, which may limit the use of regenerative braking torque and thus the maximum wheel-end negative torque. Therefore, the battery state of charge and charging power can be used to adjust the motor's regenerative braking torque when determining the target vehicle's maximum wheel-end negative torque, so that the target vehicle's maximum wheel-end negative torque meets both actual driving needs and ensures battery safety.

[0049] Maximum negative wheel torque refers to the maximum braking torque the motor can provide during coasting, used for deceleration and energy recovery. Furthermore, by comprehensively considering battery status, charging power, and powertrain parameters, the maximum braking torque and resulting maximum coasting deceleration of the target vehicle under current conditions can be accurately calculated, providing a reference for subsequently determining the target deceleration.

[0050] S103. Determine a target deceleration based on a deceleration reference table, the maximum coasting deceleration, and the weight and speed of the target vehicle. The deceleration reference table is used to query the coasting deceleration under different driving conditions, including time period, vehicle weight, slope, and speed. The deceleration reference table is determined by correcting the target deceleration during historical energy recovery processes based on a self-learning mechanism.

[0051] In this step, the deceleration reference table can be a database that queries corresponding coasting decelerations based on various driving conditions (such as time period, vehicle weight, slope, and speed). The self-learning mechanism can refer to a method that continuously optimizes and corrects the decelerations in the table based on self-learning conditions and historical data.

[0052] To reduce computing resources and improve response speed, various driving conditions can be divided into time zones. Time zones can be based on factors such as traffic flow and driving habits, with a focus on peak hours. Vehicle weight affects inertia, braking performance, and energy recovery potential. Vehicle weight can be categorized into light, medium, and heavy vehicle types, or more specific weight ranges can be used. Slope is a key factor affecting vehicle coasting and energy recovery. Slope can be categorized into flat roads, slight uphill / downhill slopes, moderate uphill / downhill slopes, and steep uphill / downhill slopes, or more specific slope ranges can be used. Vehicle speed is a key factor influencing deceleration selection and energy recovery effectiveness. Braking performance and energy recovery potential vary significantly at different speeds. Multiple speed zones (speed ranges) can be defined, including low speeds (e.g., 0-30 km / h), medium speeds (e.g., 30-60 km / h), and high speeds (e.g., above 60 km / h). Within each speed range, smaller speed ranges can be further subdivided to more accurately determine the target deceleration. This creates multiple driving conditions based on the combination of time periods, vehicle weight, slope, and speed, each corresponding to a coasting deceleration value.

[0053] Based on driving conditions such as the current time period, vehicle weight, slope, and speed, the system searches a deceleration benchmark table to find the corresponding coasting deceleration as the target deceleration. This deceleration benchmark table is continuously optimized using historical data and a self-learning mechanism. Furthermore, the target deceleration is numerically constrained based on the maximum coasting deceleration. This ensures a target deceleration that meets energy recovery requirements while ensuring driving comfort and safety, maximizing energy recovery and smoothing vehicle deceleration during coasting.

[0054] S104 : Controlling the motor of the target vehicle to apply braking torque according to the target deceleration to achieve energy recovery.

[0055] Furthermore, the target deceleration can be converted into the braking torque value that the motor needs to apply. The motor controller controls the motor to apply the corresponding braking torque to achieve vehicle deceleration and energy recovery, which not only improves the vehicle's energy efficiency but also reduces energy waste during braking.

[0056] Furthermore, the method may also include continuously collecting deceleration data from actual coasting while the vehicle is in motion, and continuously revising and optimizing the deceleration reference table to enable the system to more accurately predict and adapt to coasting deceleration under different driving conditions. This optimization can be performed periodically or in real time, and can be determined based on the designed self-learning activation conditions or implementation methods.

[0057] The coasting energy recovery control method provided in the embodiment of the present application can accurately calculate the maximum wheel-end negative torque that can be applied by the vehicle in the current state and the corresponding maximum coasting deceleration based on the battery state of charge, charging power and power system parameters of the target vehicle, thereby providing a data basis for subsequent precise control; and in combination with a deceleration reference table, a target deceleration is determined. The table provides refined coasting deceleration queries based on various driving conditions such as time period, vehicle weight, slope and speed, so as to quickly determine the most suitable coasting deceleration under different driving environments, thereby achieving refined control; at the same time, the table uses a self-learning mechanism to correct and optimize the deceleration data during the energy recovery process, so that the deceleration reference table can adapt to different driving habits and road conditions, thereby continuously improving the control accuracy and energy recovery efficiency; finally, the motor is accurately controlled to apply braking torque through the target deceleration, which can maximize the recovery of energy during the coasting process while ensuring driving comfort and safety.

[0058] Based on the above embodiment, the method may further include: during the energy recovery process, obtaining the actual deceleration and target deceleration of the target vehicle under the current driving conditions; iteratively correcting the target deceleration under the current driving conditions based on the difference between the actual coasting deceleration and the target deceleration until the difference between the actual coasting deceleration and the target deceleration meets a preset convergence condition, thereby obtaining a first corrected deceleration; controlling the motor of the target vehicle to apply braking torque according to the first corrected deceleration to achieve energy recovery; and after obtaining the first corrected deceleration under multiple driving conditions, performing linear regression processing on the first corrected deceleration under multiple driving conditions to generate a coasting deceleration distribution curve; and updating the deceleration reference table according to the coasting deceleration distribution curve.

[0059] In this embodiment, actual deceleration may refer to the deceleration value actually achieved by the vehicle during coasting. Iterative correction may refer to the process of repeatedly obtaining the actual value, comparing the difference between the actual value and the target value, and adjusting the target value accordingly, so that the two gradually approach each other under current driving conditions. Furthermore, the correction condition can satisfy that a single correction is less than 0.1, and small iterations can be used to prevent fluctuations. For example, a correction value of 25% of the difference between the actual coasting deceleration and the target deceleration can be used. By limiting the correction value for each iteration to a small range, fluctuations or instability in the self-learning process caused by excessive correction values ​​can be prevented.

[0060] The preset convergence condition may refer to a judgment criterion set during the iterative correction process. Through multiple iterative corrections, an optimal or stable deceleration value is gradually approached, so that the difference between the actual coasting deceleration and the target deceleration becomes smaller and smaller until the preset accuracy requirement is met.

[0061] Linear regression is a statistical analysis method used to fit an optimal line or curve based on the distribution of data points, revealing the linear relationship between variables. The coasting deceleration distribution curve is a curve derived through linear regression based on the first corrected deceleration under multiple driving conditions. This curve describes how coasting deceleration varies with driving conditions and provides a reference for subsequent energy recovery control. Updating the deceleration reference table can involve adding new data points to the table or revising existing data points to make the table more accurate to actual driving conditions.

[0062] Furthermore, the actual deceleration may include multiple collected deceleration values. For example, when different driving conditions are determined by different speed ranges (speed intervals), the actual deceleration may include multiple deceleration values ​​within the current speed interval. In this case, the difference between the deceleration values ​​is the difference between the average of the multiple deceleration values ​​within the current speed interval and the target deceleration. Figure 2 A schematic diagram of a coasting deceleration distribution curve provided in this application is shown as follows: Figure 2 As shown in the figure, when different driving conditions are determined by different vehicle speed ranges, the coasting deceleration distribution curve is a curve showing the change of coasting deceleration with vehicle speed. The horizontal direction in the figure represents vehicle speed, and the vertical direction represents coasting deceleration. Different horizontal dotted line segments correspond to different vehicle speed ranges, and the dot on each horizontal dotted line segment corresponds to the first corrected deceleration in the vehicle speed range. The deceleration is determined by the vehicle speed in different driving conditions to achieve energy recovery.

[0063] Optionally, the number of corrections performed during each correction can be recorded and used to calculate the vehicle's coasting deceleration within the same road section, speed range, and time period during coasting energy recovery, thereby determining whether preset convergence conditions are met. When a vehicle frequently drives under the same conditions (e.g., traveling back and forth on the same road section daily within the same speed range and time period), its coasting deceleration will gradually stabilize and converge within a relatively small range of values. This is because vehicle performance, road conditions, and environmental factors are relatively stable under certain conditions, so coasting deceleration will also exhibit a certain regularity.

[0064] In some examples, multiple iterations of corrections are performed until the difference between the actual deceleration learned and the target deceleration recorded and corrected from the previous learning record is less than 25% of the target deceleration recorded and corrected from the previous learning record. This indicates that the deceleration has stabilized and no new learning results are recorded, terminating the learning process. If the difference is greater than or equal to 25%, it indicates a significant change in deceleration, possibly due to changes in road conditions, vehicle status, or environmental factors. At this point, the system records a new deceleration value and makes corrections based on this new value to adapt to the new driving conditions.

[0065] By learning the vehicle's coasting deceleration under the same driving conditions and setting reasonable convergence conditions and re-trigger conditions, the benchmark table can automatically adapt to different driving environments and vehicle states, improving the accuracy and stability of coasting energy recovery control. This self-learning mechanism helps reduce unnecessary learning times and data storage, while ensuring that the vehicle system can respond to changes in driving conditions in a timely manner, achieving more efficient and reliable energy recovery.

[0066] Therefore, by iteratively correcting the target deceleration, it is possible to more effectively cope with various changes in the actual driving process, improve control accuracy and energy recovery efficiency; at the same time, generating a coasting deceleration distribution curve and updating the deceleration reference table make the coasting energy recovery control method more intelligent and adaptive, and can continuously learn and adapt to new driving environments and driving habits.

[0067] Based on the above embodiment, during the energy recovery process, the method for obtaining the actual deceleration and target deceleration of the target vehicle under the current driving conditions may include: during the energy recovery process, determining whether the target vehicle meets the self-learning conditions, the self-learning conditions including that the coasting energy recovery function of the target vehicle is activated, the operating status and slip rate meet preset requirements, and the accelerator pedal opening is zero and lasts for a preset time; when the target vehicle meets the self-learning conditions, obtaining the actual deceleration and target deceleration of the target vehicle under the current driving conditions.

[0068] The operating status can refer to whether the vehicle has any faults during operation. The slip rate, which describes the degree of wheel slippage relative to the road surface, is a parameter that describes the contact between the vehicle's tires and the road surface. Preset requirements are used to determine whether the vehicle is in a safe driving state. For example, the operating status indicates that the vehicle has no faults and the slip rate is less than 15%.

[0069] In one example, during the energy recovery process, the system first checks whether the coasting energy recovery function is activated. It then monitors the vehicle's operating status and slip ratio to ensure they meet pre-set self-learning requirements. It also checks whether the accelerator pedal opening is zero and continuously monitors for a predetermined period of time to ensure the vehicle is in a stable coasting state. Furthermore, the self-learning conditions must be met: the coasting energy recovery function is activated, the vehicle is fault-free, the slip ratio is less than 15% (if exceeded, the self-learning state is exited), and the accelerator pedal is at zero for a calibrated minimum time (0.5s). Within a certain speed range, learning is triggered as soon as the accelerator is released, regardless of whether acceleration or braking is performed. Upon releasing the accelerator, the frequency and depth of accelerator and brake application, as well as their order relative to the coasting condition, are ignored. The system focuses on the presence of a coasting condition within that speed range, meaning there is an intention to coast within that range. The combination of accelerator and brake application is intended to achieve the desired coasting acceleration. Therefore, only the actual deceleration is recorded.

[0070] By setting the self-learning conditions, it is possible to ensure that self-learning is carried out at the appropriate time, thereby avoiding self-learning under inappropriate circumstances (such as unstable vehicle status, inactive coasting energy recovery function, etc.), which may lead to inaccurate learning results and affect the accuracy of coasting energy recovery control and energy recovery efficiency.

[0071] On the basis of the above embodiment, before controlling the motor of the target vehicle to apply braking torque according to the first corrected deceleration to achieve energy recovery, the method may further include: determining the maximum deceleration and minimum deceleration of the target vehicle at different vehicle weights and different vehicle speeds according to the battery state of charge, charging power and power system parameters; determining a correction range of the target deceleration based on the maximum deceleration and minimum deceleration of the target vehicle at different vehicle weights and different vehicle speeds; when the first corrected deceleration does not meet the correction range, adjusting the first corrected deceleration to obtain an adjusted first corrected deceleration; accordingly, controlling the motor of the target vehicle to apply braking torque according to the first corrected deceleration to achieve energy recovery, including: controlling the motor of the target vehicle to apply braking torque according to the adjusted first corrected deceleration to achieve energy recovery.

[0072] In this embodiment, maximum deceleration may refer to the maximum coasting deceleration achievable by the vehicle under given battery state of charge, charging power, and powertrain parameters. Minimum deceleration may refer to the minimum coasting deceleration achievable by the vehicle under given conditions, typically approaching the deceleration of the vehicle during natural coasting (without additional braking). For example, maximum deceleration may be determined based on maximum braking force and vehicle weight, where the maximum braking force is determined by the motor's external characteristics, mechanical brake torque, and tire radius; minimum deceleration may be determined based on minimum braking force and vehicle weight, where the minimum braking force is determined by the mechanical brake torque and tire radius. Optionally, when calculating maximum and minimum decelerations, the effects of air resistance and rolling resistance need to be comprehensively considered. Air resistance and rolling resistance are determined by vehicle speed, meaning that the calculated maximum and minimum decelerations may differ for different vehicle weights and speeds.

[0073] The correction range can be determined based on the maximum deceleration and the minimum deceleration, and is used to verify and adjust the first corrected deceleration. This range can be adjusted as needed to ensure that the first corrected deceleration is reasonable and feasible within this range. The adjustment method can be linear adjustment, proportional adjustment, or other suitable adjustment method, which is not particularly limited here.

[0074] By determining the maximum and minimum decelerations and setting the correction range accordingly, the first corrected deceleration can be ensured to be within a reasonable range, thereby avoiding problems such as vehicle instability or low energy recovery efficiency caused by improper deceleration settings. At the same time, the verification of the first corrected deceleration can more intelligently adjust the deceleration value according to the actual state and driving conditions of the vehicle, further improving the accuracy and safety of control.

[0075] Based on the above embodiment, the power system parameters include at least the external characteristics of the motor, the vehicle speed, the transmission speed ratio, the rear axle speed ratio, the vehicle weight and the tire radius; the method for determining the maximum wheel-end negative torque of the target vehicle and the maximum coasting deceleration corresponding to the maximum wheel-end negative torque according to the battery state of charge, the charging power and the power system parameters may include: determining the maximum wheel-end negative torque of the target vehicle according to the battery state of charge, the charging power, and the external characteristics of the motor, the transmission speed ratio and the rear axle speed ratio in the power system parameters; determining the maximum coasting deceleration corresponding to the maximum wheel-end negative torque according to the maximum wheel-end negative torque and the vehicle speed, the vehicle weight and the tire radius in the power system parameters.

[0076] Motor external characteristics describe the motor's performance under different operating conditions, including torque-speed characteristics, efficiency characteristics, and maximum regenerative braking torque. Transmission ratio refers to the ratio between the transmission's input speed and output speed, and is used to change the vehicle's gear ratio and driving speed.

[0077] Rear axle ratio refers to the ratio of the number of gears on the rear axle and is used to change the vehicle's transmission ratio and driving force. Vehicle weight refers to the vehicle's curb weight, which includes the vehicle's own weight and the weight of its load. Tire radius refers to the effective radius of the tire at standard pressure and load, which affects the vehicle's speed and deceleration.

[0078] In some examples, determining the maximum wheel-end negative torque of the target vehicle includes: determining the maximum regenerative braking torque of the motor using external motor characteristics; checking the current state of charge (SOC) of the battery. If the SOC is close to full charge, the battery may not be able to receive more energy, which may limit the use of regenerative braking torque; determining the maximum charging power of the battery. If the regenerative braking power of the motor exceeds the charging power of the battery, the actual regenerative braking torque may need to be adjusted; total transmission ratio = transmission ratio × rear axle speed ratio; maximum wheel-end negative torque = maximum regenerative braking torque of the motor × total transmission ratio × transmission efficiency, where transmission efficiency represents energy loss in the transmission system. Determining the maximum coasting deceleration corresponding to the maximum wheel-end negative torque includes: maximum braking force = maximum wheel-end negative torque / tire radius; maximum coasting deceleration = maximum braking force / vehicle weight.

[0079] By taking into account more detailed factors in the powertrain parameters, the maximum wheel-end negative torque and maximum coasting deceleration of the vehicle under different driving conditions can be calculated more accurately, thereby providing a more precise control basis for the energy recovery process to adapt to different driving conditions and vehicle states.

[0080] Based on the above embodiment, the method for determining the target deceleration according to the deceleration reference table, the maximum coasting deceleration, and the weight and speed of the target vehicle may include: determining the initial deceleration from the deceleration reference table according to the weight and speed of the target vehicle, and the time and slope at which the target vehicle is located; when the initial deceleration is less than or equal to the maximum coasting deceleration, determining the initial deceleration as the target deceleration; when the initial deceleration is greater than the maximum coasting deceleration, determining the maximum coasting deceleration as the target deceleration.

[0081] The time at which the target vehicle is located refers to a specific time point or period of the day, which is used to consider the impact of different time periods (such as daytime, nighttime, peak hours, etc.) on vehicle driving and energy recovery. The slope can be determined based on the road slope of the vehicle's current driving road.

[0082] For example, based on the target vehicle's weight, speed, current time (e.g., to account for traffic congestion or changes in driving habits), and slope, the corresponding initial deceleration value is retrieved from a deceleration benchmark table. The deceleration benchmark table contains deceleration data for different combinations of vehicle weight, speed, time, and slope. Then, by comparing the initial deceleration with the maximum coasting deceleration, the target deceleration is ensured to meet the vehicle's actual driving conditions while not exceeding the vehicle's maximum achievable coasting deceleration.

[0083] By taking actual driving conditions, such as time and grade, into account, the vehicle's initial deceleration can be determined more accurately and calibrated and adjusted based on the maximum coasting deceleration, ensuring that the target deceleration can adapt to different driving environments and requirements while achieving efficient energy recovery.

[0084] On the basis of the above embodiment, after determining the target deceleration according to the deceleration reference table, the maximum coasting deceleration, and the weight and speed of the target vehicle, the method may further include: determining the wheel speed difference and steering angle of the target vehicle; when the speed of the target vehicle is greater than or equal to the preset speed threshold, determining the curve radius according to the wheel speed difference; when the speed of the target vehicle is less than the preset speed threshold, determining the curve radius according to the steering angle; correcting the target deceleration according to the curve radius to obtain a second corrected deceleration; accordingly, controlling the motor of the target vehicle to apply braking torque according to the target deceleration to achieve energy recovery, including: controlling the motor of the target vehicle to apply braking torque according to the second corrected deceleration to achieve energy recovery.

[0085] In this embodiment, the wheel speed differential refers to the difference in rotational speed between the left and right wheels of a vehicle and is typically used to reflect the dynamic characteristics of a vehicle during a turn. The steering angle refers to the angle of deflection of the vehicle's steering wheel or front wheels relative to the straight-line direction of travel and is used to control the vehicle's direction of travel. The curve radius refers to the radius of curvature of the vehicle's trajectory when turning, which determines the severity of the vehicle's turn. The preset speed threshold is a set speed value used to determine whether the vehicle is in a high-speed or low-speed state, allowing different methods to be selected to determine the curve radius, such as setting the speed threshold to 30 km / h.

[0086] For example, vehicle sensors can acquire real-time wheel speed information from the vehicle's left and right wheels and calculate the wheel speed difference. Simultaneously, steering angle information can be acquired through steering wheel sensors or front wheel sensors. When the vehicle speed is greater than or equal to a preset speed threshold, the turning radius is calculated based on the wheel speed difference and the vehicle dynamics model, accounting for the dynamic characteristics of high-speed vehicle turning. When the vehicle speed is less than the preset speed threshold, the turning radius is calculated based on the steering angle and vehicle geometric parameters (such as front track and wheelbase) using geometric relationships to account for low-speed turning conditions.

[0087] The process of adjusting the target deceleration based on the curve radius is tailored to the specific vehicle and driving conditions. For example, for autonomous vehicles or advanced driver assistance systems, simulation analysis based on vehicle dynamics models or empirical formulas can be used; for traditional vehicles or manual driving, manual adjustments based on driver experience can be used. Furthermore, the empirical formula can be established based on extensive experimental data or actual driving data, linking curve radius to deceleration. This formula describes the deceleration value that should be adjusted to maintain vehicle stability and safety at different curve radii.

[0088] By acquiring wheel speed difference and steering angle information in real time and determining the curve radius accordingly, the dynamic characteristics of the vehicle during cornering can be estimated more accurately, thereby correcting the target deceleration and making the coasting energy recovery more adaptable to different cornering situations and driving needs. This can solve the problems of inaccurate control and inefficiency caused by not fully considering the dynamic characteristics of the vehicle during cornering.

[0089] Based on the above embodiment, the coasting energy recovery control method is described below by way of example. The method may include:

[0090] 1. Activate the coasting energy recovery button.

[0091] 2. Calculate the maximum wheel-end negative torque provided by the vehicle system based on the vehicle's battery SOC and charging power, the motor's external characteristics, and the transmission ratio. Calculate the maximum deceleration under coasting conditions. Determine the wheel-end torque and vehicle weight based on the motor speed, vehicle speed, shift line, motor's external characteristics, transmission gear ratio (the ratio of the engine output shaft speed to the drive shaft speed in a specific gear), rear axle ratio, and tire radius. Calculate the maximum and minimum deceleration values ​​of the original power system at different vehicle weights and speeds.

[0092] 3. Data acquisition and correction.

[0093] 3.1 Preset Values. Based on experience, coasting deceleration values ​​are preset for different time periods, vehicle weights, slopes, and speed ranges. This is particularly true for the morning, afternoon, and evening hours, when traffic conditions have a greater impact on vehicle speed. This results in a deceleration benchmark table, as shown in Table 1. The time periods are divided into: 7:00-9:00, 11:00-1:00, 17:00-19:00, and other time periods; vehicle weights are divided into unladen and heavily laden conditions; slopes are divided into: 10-30, 5-10, 2-5, -2 - 2, -5 - -2, -10 - -5, and -30 - -10; and speed ranges are divided into: 0-10, 10-20, 20-30, 30-40, 40-50, 50-70, and 70-90. Multiple driving conditions are formed based on the combination of time periods, vehicle weights, slopes, and speed ranges, each corresponding to a coasting deceleration value. It should be noted that the initial table is set based on human experience, and the table used each time thereafter is the table updated in the last self-learning process.

[0094] Table 1 Deceleration reference table

[0095]

[0096] 3.2 Correction based on cornering: When the vehicle speed is higher than 30 km / h (adjustable), the cornering radius is calculated based on the wheel speed difference. When the vehicle speed is lower than 30 km / h, the cornering radius is determined based on the steering angle, and the deceleration is corrected according to the turning radius.

[0097] 4. Self-Learning: Drivers' expectations of coasting deceleration vary significantly across different driving scenarios. For example, road conditions can vary significantly across different time periods at the same speed and on the same road section. This can lead to significant differences in the driver's expectations of coasting deceleration at the same speed over time. Because drivers' expectations of deceleration vary, the system adjusts the coasting deceleration based on driver behavior analysis in specific driving scenarios.

[0098] 4.1. Self-learning conditions are met:

[0099] 1. The vehicle has no faults; 2. The coasting energy recovery switch is activated; 3. The slip rate is less than or equal to 15%. When it is greater than 15%, the state learning is exited; 4. The accelerator pedal is at zero and maintained for the calibrated minimum time (0.5s). Within the speed range, regardless of whether the accelerator and brake are pressed during acceleration, the learning is triggered as long as the accelerator is released. If the accelerator is released, there is an intention to coast in this range, and the actual deceleration can be paid attention to.

[0100] 4.2. Record the actual coasting deceleration (including multiple values) of the vehicle in the speed range, calculate the average value, take the average value of the actual deceleration recorded this time, and correct the last recorded coasting deceleration by 25% of the difference between the average value of the actual deceleration recorded this time and the baseline value (the last recorded coasting deceleration in the speed range or driving conditions) (single correction should be less than 0.1, and small step iterations should be used to prevent fluctuations), and record the number of times.

[0101] Among them, when making corrections, the correction range can be limited based on the maximum and minimum deceleration values ​​of the vehicle at different vehicle weights and speeds to avoid excessive correction and improve correction accuracy.

[0102] 4.3. Iteration: Perform linear regression on the deceleration corrected by learning multiple speed segments and update it to the deceleration reference table. This value can be called the next time the coasting deceleration self-learning is triggered (e.g. Figure 2 (see the coasting deceleration distribution curve for details) and repeat 4.2.

[0103] 4.4 Learning Termination and Retriggering: The deceleration is calculated cumulatively. If you travel the same route, speed range, and time period daily, the coasting deceleration will converge to a small range. This involves multiple iterations until the difference between the current learned deceleration and the previous deceleration is less than 25% of the last recorded value. In this case, no learning is recorded. If the difference is greater than 25% of the last recorded value, the deceleration is recorded again and corrected.

[0104] The coasting energy recovery control method provided in the embodiments of the present application establishes a deceleration benchmark table based on the influence of different vehicle speed ranges, vehicle weight, slope, and traffic conditions at different time periods. This table is then continuously updated based on the curve radius and a self-learning mechanism, ensuring that the coasting deceleration rate more closely matches actual driving conditions and the driver's expectations. Thus, by utilizing the vehicle's existing signals and resources and taking into account the significant differences in traffic conditions during different time periods during actual driving, which in turn leads to different changes in vehicle speed and different expectations for coasting deceleration, the vehicle's coasting deceleration rate is self-learned to achieve automatic coasting deceleration control, reduce braking use, maximize energy recovery, and ultimately achieve further energy savings.

[0105] Figure 3 This is a schematic diagram of the structure of the coasting energy recovery control device provided in this application, as shown in Figure 3 As shown, the coasting energy recovery control device 30 provided in this embodiment includes:

[0106] An acquisition module 301 is used to obtain the battery state of charge, charging power and power system parameters of the target vehicle;

[0107] A determination module 302 is configured to determine a maximum wheel-end negative torque of the target vehicle and a maximum coasting deceleration corresponding to the maximum wheel-end negative torque based on the battery state of charge, the charging power, and the power system parameters;

[0108] Processing module 303 is configured to determine a target deceleration based on a deceleration reference table, a maximum coasting deceleration, and the weight and speed of the target vehicle. The deceleration reference table is used to query coasting deceleration under different driving conditions, including time period, vehicle weight, slope, and speed. The deceleration reference table is determined by modifying the target deceleration during historical energy recovery processes based on a self-learning mechanism.

[0109] The control module 304 is configured to control the motor of the target vehicle to apply braking torque according to the target deceleration to achieve energy recovery.

[0110] In one possible implementation, the processing module 303 may also be used to: obtain the actual deceleration and target deceleration of the target vehicle under the current driving conditions during the energy recovery process; iteratively correct the target deceleration under the current driving conditions based on the difference between the actual coasting deceleration and the target deceleration until the difference between the actual coasting deceleration and the target deceleration meets a preset convergence condition, thereby obtaining a first corrected deceleration; control the motor of the target vehicle to apply braking torque according to the first corrected deceleration to achieve energy recovery; and after obtaining the first corrected deceleration under multiple driving conditions, perform linear regression processing on the first corrected deceleration under multiple driving conditions to generate a coasting deceleration distribution curve; and update the deceleration reference table according to the coasting deceleration distribution curve.

[0111] In one possible implementation, the processing module 303 can also be used to: during the energy recovery process, determine whether the target vehicle meets the self-learning conditions, the self-learning conditions include that the coasting energy recovery function of the target vehicle is activated, the operating status and slip rate meet the preset requirements, and the accelerator pedal opening is zero and lasts for a preset time; when the target vehicle meets the self-learning conditions, obtain the actual deceleration and target deceleration of the target vehicle under the current driving conditions.

[0112] In one possible implementation, the processing module 303 can also be used to: determine the maximum deceleration and minimum deceleration of the target vehicle at different vehicle weights and different vehicle speeds based on the battery state of charge, charging power and power system parameters; determine the correction range of the target deceleration based on the maximum deceleration and minimum deceleration of the target vehicle at different vehicle weights and different vehicle speeds; when the first corrected deceleration does not meet the correction range, adjust the first corrected deceleration to obtain an adjusted first corrected deceleration; accordingly, control the motor of the target vehicle to apply braking torque according to the first corrected deceleration to achieve energy recovery, including: controlling the motor of the target vehicle to apply braking torque according to the adjusted first corrected deceleration to achieve energy recovery.

[0113] In one possible implementation, the determination module 302 can also be used to: determine the maximum wheel-end negative torque of the target vehicle based on the battery state of charge, charging power, and the motor external characteristics, transmission speed ratio, and rear axle speed ratio in the power system parameters; determine the maximum coasting deceleration corresponding to the maximum wheel-end negative torque based on the maximum wheel-end negative torque and the vehicle speed, vehicle weight, and tire radius in the power system parameters.

[0114] In one possible implementation, the processing module 303 can also be used to: determine the initial deceleration from the deceleration reference table based on the weight and speed of the target vehicle, as well as the time and slope at which the target vehicle is located; when the initial deceleration is less than or equal to the maximum coasting deceleration, determine the initial deceleration as the target deceleration; when the initial deceleration is greater than the maximum coasting deceleration, determine the maximum coasting deceleration as the target deceleration.

[0115] In one possible implementation, the processing module 303 can also be used to: determine the wheel speed difference and steering angle of the target vehicle; when the speed of the target vehicle is greater than or equal to a preset speed threshold, determine the curve radius according to the wheel speed difference; when the speed of the target vehicle is less than the preset speed threshold, determine the curve radius according to the steering angle; according to the curve radius, correct the target deceleration to obtain a second corrected deceleration; accordingly, control the motor of the target vehicle to apply a braking torque according to the target deceleration to achieve energy recovery, including: controlling the motor of the target vehicle to apply a braking torque according to the second corrected deceleration to achieve energy recovery.

[0116] The coasting energy recovery control device provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effects are similar, and will not be described in detail in this embodiment.

[0117] Figure 4 This is a schematic diagram of the structure of the electronic device provided in this application, which may be a controller. Figure 4As shown, the electronic device 40 provided in this embodiment includes: at least one processor 401 and a memory 402. Optionally, the device 40 further includes a communication component 403. The processor 401, the memory 402 and the communication component 403 are connected via a bus 404.

[0118] In a specific implementation process, at least one processor 401 executes the computer-executable instructions stored in the memory 402, so that the at least one processor 401 performs the above method.

[0119] The specific implementation process of the processor 401 can be found in the above method embodiment. Its implementation principle and technical effects are similar and will not be repeated here in this embodiment.

[0120] In the above embodiments, it should be understood that the processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASICs), etc. A general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the present invention may be directly executed by a hardware processor or by a combination of hardware and software modules within the processor.

[0121] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage.

[0122] A bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus. Buses can be categorized as address buses, data buses, and control buses. For ease of illustration, the buses in the drawings of this application are not limited to just one bus or just one type of bus.

[0123] The present application also provides a vehicle, comprising a vehicle body and a controller, the controller being configured to execute the method in the above embodiment. The specific implementation process of the controller can be found in the above method embodiment, and its implementation principles and technical effects are similar, so this embodiment will not be repeated here.

[0124] The present application also provides a computer program product, including a computer program, which implements the above method when executed by a processor.

[0125] The present application also provides a computer-readable storage medium, in which computer-executable instructions are stored. When a processor executes the computer-executable instructions, the above method is implemented.

[0126] The readable storage medium may be implemented by any type of volatile or non-volatile memory device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium may be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0127] An exemplary readable storage medium is coupled to a processor so that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be an integral part of the processor. The processor and the readable storage medium can be located in an application specific integrated circuit (ASIC). Of course, the processor and the readable storage medium can also exist in the device as discrete components.

[0128] The division of units is merely a logical functional division; actual implementations may employ alternative divisions, such as combining or integrating multiple units or components into another system, or omitting or disabling certain features. Furthermore, any direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between devices or units, either through an interface, electrical, mechanical, or other means.

[0129] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0130] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0131] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the method of the present invention. The aforementioned storage medium includes various media that can store program code, such as USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0132] Those skilled in the art will appreciate that all or part of the steps in the above-described method embodiments can be implemented using hardware associated with program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0133] Finally, it should be noted that those skilled in the art will readily identify other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. The present invention is intended to cover any variations, uses, or adaptations of the present invention that follow the general principles of the present invention and include common knowledge or customary techniques in the art not disclosed herein. The present invention is not limited to the precise structure described above and illustrated in the accompanying drawings, and various modifications and variations may be made without departing from the scope thereof. The scope of the present invention is limited solely by the appended claims.

Claims

1. A coasting energy recovery control method, characterized in that: include: Obtain the target vehicle's battery state of charge, charging power, and power system parameters; determining, based on the battery state of charge, the charging power, and the power system parameters, a maximum wheel-end negative torque of the target vehicle and a maximum coasting deceleration corresponding to the maximum wheel-end negative torque; determining a target deceleration based on a deceleration reference table, the maximum coasting deceleration, and the weight and speed of the target vehicle. The deceleration reference table is used to query the coasting deceleration under different driving conditions, including time period, vehicle weight, slope, and speed. The deceleration reference table is determined by correcting the target deceleration during historical energy recovery processes based on a self-learning mechanism; The motor of the target vehicle is controlled to apply braking torque according to the target deceleration to achieve energy recovery.

2. The method according to claim 1, characterized in that The method further comprises: During the energy recovery process, obtaining an actual deceleration and a target deceleration of the target vehicle under current driving conditions; iteratively correcting the target deceleration under the current driving condition based on the difference between the actual coasting deceleration and the target deceleration until the difference between the actual coasting deceleration and the target deceleration satisfies a preset convergence condition, thereby obtaining a first corrected deceleration; controlling a motor of the target vehicle to apply a braking torque according to the first modified deceleration to achieve energy recovery; After obtaining the first corrected decelerations under the plurality of driving conditions, performing linear regression processing on the first corrected decelerations under the plurality of driving conditions to generate a coasting deceleration distribution curve; The deceleration reference table is updated according to the coasting deceleration distribution curve.

3. The method according to claim 2, characterized in that During the energy recovery process, obtaining the actual deceleration and target deceleration of the target vehicle under the current driving conditions includes: During the energy recovery process, determining whether the target vehicle meets self-learning conditions, wherein the self-learning conditions include activation of a coasting energy recovery function of the target vehicle, a running state and a slip ratio meeting preset requirements, and an accelerator pedal opening being zero for a preset time; When the target vehicle meets the self-learning condition, the actual deceleration and the target deceleration of the target vehicle under the current driving condition are obtained.

4. The method according to claim 2, characterized in that Before controlling the motor of the target vehicle to apply braking torque according to the first modified deceleration to achieve energy recovery, the method further includes: determining, based on the battery state of charge, the charging power, and the power system parameters, a maximum deceleration and a minimum deceleration of the target vehicle at different vehicle weights and different vehicle speeds; determining a correction range of the target deceleration based on the maximum deceleration and the minimum deceleration of the target vehicle at different vehicle weights and different vehicle speeds; When the first corrected deceleration does not satisfy the correction range, adjusting the first corrected deceleration to obtain an adjusted first corrected deceleration; Accordingly, The controlling the motor of the target vehicle to apply braking torque according to the first modified deceleration to achieve energy recovery includes: The motor of the target vehicle is controlled to apply braking torque according to the adjusted first modified deceleration to achieve energy recovery.

5. The method according to any one of claims 1 to 4, characterized in that The power system parameters include at least motor external characteristics, vehicle speed, transmission speed ratio, rear axle speed ratio, vehicle weight and tire radius; The determining, based on the battery state of charge, the charging power, and the power system parameters, of the maximum wheel-end negative torque of the target vehicle and the maximum coasting deceleration corresponding to the maximum wheel-end negative torque includes: determining a maximum wheel-end negative torque of the target vehicle according to the battery state of charge, the charging power, and motor external characteristics, a transmission speed ratio, and a rear axle speed ratio among the power system parameters; A maximum coasting deceleration corresponding to the maximum wheel-end negative torque is determined according to the maximum wheel-end negative torque and the vehicle speed, vehicle weight, and tire radius among the power system parameters.

6. The method according to any one of claims 1 to 4, characterized in that Determining the target deceleration based on the deceleration reference table, the maximum coasting deceleration, and the weight and speed of the target vehicle includes: determining an initial deceleration from the deceleration reference table according to the weight and speed of the target vehicle, and the time and slope at which the target vehicle is located; When the initial deceleration is less than or equal to the maximum coasting deceleration, determining the initial deceleration as the target deceleration; When the initial deceleration is greater than the maximum coasting deceleration, the maximum coasting deceleration is determined as the target deceleration.

7. The method according to any one of claims 1 to 4, characterized in that After determining the target deceleration based on the deceleration reference table, the maximum coasting deceleration, and the weight and speed of the target vehicle, the method further includes: determining a wheel speed difference and a steering angle of the target vehicle; When the speed of the target vehicle is greater than or equal to a preset speed threshold, determining a curve radius according to the wheel speed difference; When the speed of the target vehicle is less than a preset speed threshold, determining a curve radius according to the steering angle; Correcting the target deceleration according to the curve radius to obtain a second corrected deceleration; Accordingly, The controlling the motor of the target vehicle to apply braking torque according to the target deceleration to achieve energy recovery includes: The motor of the target vehicle is controlled to apply a braking torque according to the second modified deceleration to achieve energy recovery.

8. A coasting energy recovery control device, characterized in that: include: An acquisition module is used to obtain the battery state of charge, charging power and power system parameters of the target vehicle; a determination module, configured to determine a maximum wheel-end negative torque of the target vehicle and a maximum coasting deceleration corresponding to the maximum wheel-end negative torque based on the battery state of charge, the charging power, and the power system parameters; a processing module, configured to determine a target deceleration based on a deceleration reference table, the maximum coasting deceleration, and the weight and speed of the target vehicle, wherein the deceleration reference table is used to query the coasting deceleration under different driving conditions, the driving conditions including time period, vehicle weight, slope, and speed, and the deceleration reference table is determined by correcting the target deceleration during historical energy recovery processes based on a self-learning mechanism; A control module is used to control the motor of the target vehicle to apply braking torque according to the target deceleration to achieve energy recovery.

9. A controller, characterized in that: include: Memory, processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory, so that the processor performs the method according to any one of claims 1 to 7.

10. A vehicle, characterized in that: The vehicle comprises a vehicle body and a controller, wherein the controller is used to execute the method according to any one of claims 1 to 7.

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