Sliding energy recovery control method and system and vehicle

Abstract

The invention provides a sliding energy recovery control method and system and a vehicle, and relates to the technical field of electric vehicles. The method comprises the steps that the vehicle speed, the actual deceleration, the multi-source quality observation parameters and the dynamic state parameters of the electric vehicle are obtained; the feedforward torque is determined on the basis of the vehicle speed, the preset deceleration, the target vehicle weight and the predicted gradient data, the target vehicle weight is obtained by conducting fusion noise reduction on multi-source quality observation parameters on the basis of Kalman filtering, and the predicted gradient data is obtained by conducting gradient trend prediction on the basis of the dynamic state parameters and the target vehicle weight; according to the difference value between the preset deceleration and the actual deceleration, feedback compensation torque is determined; linear superposition is conducted on the feed-forward torque and the feedback compensation torque, and the required braking torque is obtained; and determining a sliding energy recovery control strategy of the electric vehicle according to the required braking torque and the maximum recovery capacity of the motor. The accuracy of sliding energy recovery control is improved, and then the energy recovery efficiency is improved.

Description

Technical Field

[0001] The present application relates to the technical field of electric vehicles, and in particular to a coasting energy recovery control method, system and vehicle. Background Art

[0002] With the energy crisis and environmental pollution becoming increasingly severe, improving the energy efficiency and range of electric vehicles has become a key research focus in the industry. Coasting energy recovery, a key component of electric vehicle energy-saving technology, effectively extends vehicle range while reducing energy waste by recovering kinetic energy during coasting or braking and converting it into electricity. However, the complex operating conditions of electric vehicles place higher demands on coasting energy recovery control technology.

[0003] At present, the coasting energy recovery control of electric vehicles is mainly based on single-parameter control or fixed recovery mode. Single-parameter control usually adjusts the recovery torque according to the vehicle speed. Its design basis is to set a direct linear relationship between vehicle speed and recoverable energy. However, when faced with complex and changeable working conditions, vehicle speed as a single parameter is difficult to fully and accurately reflect the actual kinetic energy and recovery potential of electric vehicles during operation. The fixed recovery mode adjusts the recovery intensity according to the user-preset intensity (such as high, medium, and low). Although this mode provides users with a certain degree of flexibility, in actual driving, the complexity and variability of the working conditions make it difficult for users to predict and set the appropriate recovery intensity in advance.

[0004] Therefore, when faced with complex and changeable working conditions, existing methods still have the problem of inaccurate control, which in turn leads to low energy recovery efficiency. Summary of the Invention

[0005] The coasting energy recovery control method, system and vehicle provided in the embodiments of the present application are used to solve the problem that the existing control methods still have inaccurate control when facing complex and changeable working conditions, which leads to low 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 speed, actual deceleration, multi-source quality observation parameters and dynamic state parameters of the electric vehicle;

[0008] Determine the feedforward torque based on vehicle speed, preset deceleration, target vehicle weight, and predicted slope data. The target vehicle weight is obtained by fusing and denoising multi-source mass observation parameters using a Kalman filter, and the predicted slope data is obtained by predicting the slope trend based on dynamic state parameters and the target vehicle weight.

[0009] Determining a feedback compensation torque according to a difference between a preset deceleration and an actual deceleration;

[0010] The feedforward torque and the feedback compensation torque are linearly superimposed to obtain the required braking torque;

[0011] The coasting energy recovery control strategy of the electric vehicle is determined based on the required braking torque and the maximum recovery capacity of the motor.

[0012] In one possible implementation, determining the feedforward torque based on vehicle speed, preset deceleration, target vehicle weight, and predicted slope data includes:

[0013] Determine a target inertia term based on a target vehicle weight and a preset deceleration;

[0014] Determine the slope compensation item based on the target vehicle weight and the predicted slope data;

[0015] determining a wind resistance component and a tire torque corresponding to the wind resistance component based on the vehicle speed and the wind resistance characteristic parameter;

[0016] Determining a rolling resistance component and a friction torque corresponding to the rolling resistance component based on the target vehicle weight, the predicted slope data, and the rolling resistance characteristic parameters;

[0017] The feedforward torque is determined based on the target inertia term, the slope compensation term, the tire torque, and the friction resistance torque.

[0018] In one possible implementation, the multi-source mass observation parameters include a first mass estimate under a driving condition, a second mass estimate under a coasting condition, and measurement data from a suspension pressure sensor, and the dynamic state parameters include the current slope, vehicle acceleration, and current motor torque.

[0019] In one possible implementation, the method further includes:

[0020] Obtaining the motor output torque, rolling resistance torque, wind resistance characteristic parameters, vehicle acceleration and tire radius of the electric vehicle;

[0021] Determine the wind resistance moment according to wind resistance characteristic parameters and vehicle speed;

[0022] The motor output torque is updated according to the rolling resistance torque and the wind resistance torque to obtain the target motor output torque;

[0023] A first mass estimate under a driving condition is determined based on a target motor output torque, a vehicle acceleration, and a tire radius.

[0024] In one possible implementation, the method further includes:

[0025] Obtaining braking force and gravity components of an electric vehicle;

[0026] A second mass estimate for the coasting condition is determined based on the braking force, the gravity component, and the actual deceleration.

[0027] In one possible implementation, determining a coasting energy recovery control strategy for an electric vehicle based on required braking torque and a maximum recovery capability of the motor includes:

[0028] When the required braking torque is less than or equal to the maximum recovery torque corresponding to the maximum recovery capacity of the motor, determining that the coasting energy recovery control strategy of the electric vehicle is a motor energy recovery mode;

[0029] When the required braking torque is greater than the maximum recovery torque corresponding to the maximum recovery capacity of the motor, the coasting energy recovery control strategy of the electric vehicle is determined to be the electromechanical cooperative energy recovery mode.

[0030] In one possible implementation, the method further includes:

[0031] According to the preset safety protection requirements, the required braking torque is adjusted to obtain the target required braking torque. The safety protection requirements include the adjustment requirements of the required braking torque based on the battery temperature, battery state of charge, and motor temperature.

[0032] Accordingly, the coasting energy recovery control strategy of the electric vehicle is determined based on the required braking torque and the maximum recovery capacity of the motor, including:

[0033] The coasting energy recovery control strategy of the electric vehicle is determined based on the target required braking torque and the maximum recovery capacity of the motor.

[0034] In one possible implementation, before obtaining the vehicle speed, actual deceleration, multi-source quality observation parameters, and dynamic state parameters of the electric vehicle, the method further includes:

[0035] Obtain the accelerator pedal status, brake pedal status, vehicle speed, battery temperature, and motor temperature of the electric vehicle;

[0036] When the accelerator pedal state, brake pedal state, vehicle speed, battery temperature and motor temperature meet the preset energy recovery control conditions, the energy recovery mode is triggered and the steps of obtaining the vehicle speed, actual deceleration, multi-source quality observation parameters and dynamic state parameters of the electric vehicle are executed.

[0037] In one possible implementation, after determining the coasting energy recovery control strategy of the electric vehicle based on the required braking torque and the maximum recovery capacity of the motor, the method further includes:

[0038] Determining the accelerator pedal opening and wheel speed difference of the electric vehicle;

[0039] When the wheel speed difference exceeds a preset difference threshold, the regenerative torque in the coasting energy regeneration control strategy is adjusted to a preset proportion of the required braking torque;

[0040] When the accelerator pedal opening meets the preset opening requirement, the electric vehicle is switched from the energy recovery mode to the driving mode based on a preset transition strategy.

[0041] In a second aspect, an embodiment of the present application provides a coasting energy recovery control system, comprising:

[0042] An acquisition module is used to obtain the speed, actual deceleration, multi-source quality observation parameters and dynamic state parameters of the electric vehicle;

[0043] a processing module for determining a feedforward torque based on vehicle speed, a preset deceleration, a target vehicle weight, and predicted slope data, wherein the target vehicle weight is obtained by fusing and denoising multi-source mass observation parameters using a Kalman filter, and the predicted slope data is obtained by predicting a slope trend based on dynamic state parameters and the target vehicle weight;

[0044] a first determining module, configured to determine a feedback compensation torque according to a difference between a preset deceleration and an actual deceleration;

[0045] An obtaining module is used to linearly superimpose the feedforward torque and the feedback compensation torque to obtain the required braking torque;

[0046] The second determination module is used to determine the coasting energy recovery control strategy of the electric vehicle according to the required braking torque and the maximum recovery capacity of the motor.

[0047] In a third aspect, an embodiment of the present application provides a vehicle, comprising a vehicle body and a vehicle controller, wherein the vehicle controller is used to execute the first aspect and / or various possible implementations of the first aspect as described above.

[0048] In a fourth aspect, an embodiment of the present application provides an electronic device, including: a memory, a processor;

[0049] Memory stores computer-executable instructions;

[0050] 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.

[0051] 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.

[0052] 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.

[0053] The coasting energy recovery control method, system and vehicle provided in the embodiments of the present application obtain the vehicle speed, actual deceleration, multi-source mass observation parameters and dynamic state parameters of the electric vehicle; determine the feedforward torque based on the vehicle speed, preset deceleration, target vehicle weight and predicted slope data, wherein the target vehicle weight is obtained by fusing and denoising the multi-source mass observation parameters based on a Kalman filter, and the predicted slope data is obtained by predicting the slope trend based on the dynamic state parameters and the target vehicle weight; determine the feedback compensation torque based on the difference between the preset deceleration and the actual deceleration; linearly superimpose the feedforward torque and the feedback compensation torque to obtain the required braking torque; and determine the coasting energy recovery control strategy of the electric vehicle based on the required braking torque and the maximum recovery capacity of the motor, by obtaining the multi-source mass observation parameters of the electric vehicle and using the Kalman filter algorithm to perform fusion and noise reduction to obtain a more accurate target vehicle weight. At the same time, based on the dynamic state parameters and the slope trend prediction of the target vehicle weight, the control strategy can adapt to changes in road conditions in advance, which helps to optimize the distribution of braking torque and improve energy recovery efficiency. In addition, the torque is adjusted in real time according to the difference between the actual deceleration and the preset deceleration, making the required braking torque more accurate, ensuring the stability and accuracy of the braking process, and avoiding the problem of low recovery efficiency due to inaccurate control. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] 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.

[0055] Figure 1 A schematic flow chart of a coasting energy recovery control method provided in this application;

[0056] Figure 2 A schematic diagram of the execution process of a coasting energy recovery control method provided in this application;

[0057] Figure 3 A schematic diagram of a specific process of a coasting energy recovery control method provided in this application;

[0058] Figure 4 A schematic diagram of the structure of a coasting energy recovery control system provided in this application;

[0059] Figure 5 This is a schematic diagram of the structure of the electronic device provided in this application.

[0060] 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

[0061] 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 systems and methods consistent with certain aspects of the present application, as detailed in the appended claims.

[0062] In the prior art, coasting energy recovery control for electric vehicles is primarily based on single-parameter control or fixed recovery modes. Single-parameter control typically adjusts the recovery torque based on vehicle speed, and its design is based on the assumption that there is a direct linear relationship between vehicle speed and recoverable energy. However, when faced with complex and changing operating conditions, vehicle speed, as a single parameter, cannot fully and accurately reflect the actual kinetic energy and recovery potential of the electric vehicle during operation. Fixed recovery modes adjust the recovery intensity based on a user-preset intensity (e.g., high, medium, or low). While this mode provides a certain degree of flexibility, the complexity and variability of operating conditions during actual driving make it difficult for users to predict and set the appropriate recovery intensity in advance. It can be seen that these control methods can achieve energy recovery to a certain extent, but their adjustment effect is limited when faced with complex and changing operating conditions. Based on this, researchers have proposed a random slope-based energy recovery control method. This method dynamically adjusts the energy recovery intensity by providing real-time feedback on the random slope information of the actual road to achieve optimal recovery results. This method sets a variety of energy recovery modes and determines which energy recovery state to enter based on the vehicle speed, the opening of the accelerator pedal, and the brake pedal. At the same time, the corresponding energy recovery power is determined according to the current slope to achieve more refined control. Although this method improves the efficiency and adaptability of energy recovery to a certain extent, due to the significant difference in the actual vehicle weight when empty and fully loaded, the actual deceleration of the vehicle will fluctuate violently at the same slope and speed, resulting in the determined recovery power being unable to maintain the consistency of the driving experience when the vehicle weight changes, which will still affect the control accuracy. In addition, this method relies on a preset slope-power mapping table and cannot dynamically correct the control quantity, which limits the control accuracy and robustness. At the same time, a single mapping table is difficult to adapt to multiple scenarios, and the user experience lacks customization. Therefore, when faced with complex and changing working conditions, the existing methods still have the problem of inaccurate control, which leads to low energy recovery efficiency.

[0063] To address the aforementioned issues, embodiments of the present application provide a coasting energy recovery control method, system, and vehicle. These methods obtain multi-source mass observation parameters for an electric vehicle and use a Kalman filter algorithm for fusion and noise reduction to obtain a more accurate target vehicle weight. Furthermore, based on dynamic state parameters and slope trend predictions of the target vehicle weight, the control strategy can proactively adapt to changing road conditions, helping to optimize braking torque distribution and improve energy recovery efficiency. Furthermore, torque is adjusted in real time based on the difference between the actual deceleration and the preset deceleration, making the required braking torque more precise. This ensures stability and accuracy during braking and avoids the problem of low recovery efficiency caused by inaccurate control. Thus, a feedforward torque is derived based on vehicle speed, preset deceleration, target vehicle weight, and predicted slope data. This is then combined with feedback compensation torque to obtain the final required braking torque through linear superposition, providing precise control instructions for energy recovery. This allows the energy recovery strategy generated based on the required braking torque to maximize energy recovery during braking, improving the accuracy and efficiency of coasting energy recovery control.

[0064] 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.

[0065] 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 vehicle speed, actual deceleration, multi-source quality observation parameters and dynamic state parameters of the electric vehicle; based on the vehicle speed, preset deceleration, target vehicle weight and predicted slope data, the feedforward torque is determined, wherein the target vehicle weight is obtained by fusion and noise reduction of multi-source quality observation parameters based on Kalman filtering, and the predicted slope data is obtained by slope trend prediction based on dynamic state parameters and target vehicle weight; based on the difference between the preset deceleration and the actual deceleration, the feedback compensation torque is determined; the feedforward torque and the feedback compensation torque are linearly superimposed to obtain the required braking torque; based on the required braking torque and the maximum recovery capacity of the motor, the coasting energy recovery control strategy of the electric vehicle can be determined.

[0066] Figure 1 This is a flow chart of a coasting energy recovery control method provided by this application. The execution subject of this method can be a server or other server storing the coasting energy recovery control method. This embodiment is not particularly limited here. Figure 1 As shown, the method may include:

[0067] S101. Obtain the speed, actual deceleration, multi-source quality observation parameters, and dynamic state parameters of the electric vehicle.

[0068] The vehicle speed may refer to the current speed of the electric vehicle, which is usually obtained in real time through the vehicle's built-in speed sensor. The actual deceleration may refer to the deceleration rate of the electric vehicle during actual driving, which is measured by an acceleration sensor or the like.

[0069] Multi-source mass observation parameters are used to determine vehicle weight through multi-source fusion technology. They may include, but are not limited to, a first mass estimate under driving conditions, a second mass estimate under coasting conditions, and measurement data from suspension pressure sensors, among other factors influencing vehicle weight. These parameters can be obtained through a combination of multiple sensors and data fusion technology.

[0070] Dynamic state parameters are used to predict slope trends over a period of time (e.g., the next 500 milliseconds). They can include real-time dynamic data such as the vehicle's current slope, vehicle acceleration, and current motor torque. These parameters can be obtained through the vehicle's dynamic model or direct measurements, such as the current road inclination measured by a slope sensor, vehicle acceleration obtained by an inertial measurement unit, and real-time output motor torque feedback from the motor controller.

[0071] S102. Determine the feedforward torque based on the vehicle speed, preset deceleration, target vehicle weight, and predicted slope data, wherein the target vehicle weight is obtained by fusing and denoising multi-source quality observation parameters based on Kalman filtering, and the predicted slope data is obtained by predicting the slope trend based on the dynamic state parameters and the target vehicle weight.

[0072] In this step, the preset deceleration may refer to an expected deceleration value pre-set by the user or the execution device (such as a system storing a coasting energy recovery control method) in order to control the deceleration process of the vehicle. The preset deceleration can be set based on a variety of factors, including but not limited to: driving model (such as economy mode, sports mode, etc.), road conditions (such as flat roads, uphill, downhill, etc.), vehicle status (such as battery power, load, etc.) or user preferences. When the user sets it, a preset deceleration setting interface can be provided to the user, so that the user can set it through multiple gear selections or custom values, thereby achieving linearization and personalization of the coasting experience. By reasonably setting the preset deceleration, the optimal balance between energy recovery efficiency, driving comfort and safety can be found.

[0073] The feedforward torque can be a preliminarily determined braking torque, calculated in real time using a vehicle dynamics model based on vehicle speed, preset deceleration, target vehicle weight, and predicted slope data. Furthermore, the calculation process typically includes multiple mutually coupled components. Core components (such as the target inertia term, slope compensation term, windage component, and rolling resistance component) can be selected for calculation based on their impact on the braking torque. The calculated results of each component are then combined to produce the feedforward torque.

[0074] The Kalman filter can refer to a Kalman filter. When using the Kalman filter to fuse and reduce noise from multi-source quality observation parameters, an output error can be pre-set to ensure the accuracy of the target vehicle weight. For example, an output error of less than 3 percent can be set as the target vehicle weight. Optionally, a vehicle weight estimation optimization mechanism can be configured to dynamically adjust the Kalman filter noise parameters based on vehicle speed to improve the accuracy of the target station.

[0075] Slope trend prediction can refer to the use of weighted fusion methods and pre-built sliding window prediction models to predict the slope change trend over a period of time in the future. This can identify steep slopes ahead in advance, allowing pre-compensation in torque calculations. For example, before going uphill, torque output can be increased in advance to ensure that the vehicle can climb the slope smoothly and powerfully. By predicting slope data, the torque required for the vehicle on different slopes can be more accurately calculated, thereby improving the accuracy of torque calculations and enhancing the vehicle's climbing performance and driving experience. Optionally, a physical sliding window prediction model can be constructed using vehicle dynamics equations to reverse slope changes in real time, or a data-driven sliding window prediction model can be constructed using time series networks such as LSTM / Transformer to predict slope change trends.

[0076] S103 : Determine a feedback compensation torque according to a difference between the preset deceleration and the actual deceleration.

[0077] Furthermore, the feedback compensation torque is a torque that is adjusted in real time according to the difference between the actual deceleration and the preset deceleration to adjust the braking torque to the desired value, thereby ensuring the stability and accuracy of the braking process.

[0078] S104 : Linearly superimpose the feedforward torque and the feedback compensation torque to obtain the required braking torque.

[0079] S105 : Determine a coasting energy recovery control strategy for the electric vehicle based on the required braking torque and the maximum recovery capability of the motor.

[0080] Among them, the maximum recovery capacity of the motor may refer to the maximum energy that the motor of the electric vehicle can recover during braking. It is subject to motor performance and design limitations. The maximum recovery torque corresponding to the maximum recovery capacity of the motor can be determined by the external characteristics of the motor.

[0081] The coasting energy recovery control strategy may include the operating mode and control parameters (such as braking torque size, duration, etc.) of the motor and other auxiliary equipment to optimize the energy recovery efficiency.

[0082] The coasting energy recovery control method provided in the embodiments of the present application derives feedforward torque from vehicle speed, preset deceleration, target vehicle weight, and predicted slope data. This is then combined with feedback compensation torque to derive the final required braking torque through linear superposition. This provides precise control instructions for energy recovery, enabling the energy recovery strategy generated based on the required braking torque to maximize energy recovery during braking, improving the accuracy and efficiency of coasting energy recovery control. Furthermore, by predicting slope trends based on dynamic state parameters and target vehicle weight, the control strategy can adapt to varying road conditions and vehicle states, enhancing the driving experience and safety, especially in complex and changing road conditions.

[0083] On the basis of the above embodiments, the method for determining the feedforward torque based on the vehicle speed, preset deceleration, target vehicle weight and predicted slope data may include: determining the target inertia item based on the target vehicle weight and preset deceleration; determining the slope compensation item based on the target vehicle weight and predicted slope data; determining the wind resistance component and the tire torque corresponding to the wind resistance component based on the vehicle speed and wind resistance characteristic parameters; determining the rolling resistance component and the friction resistance torque corresponding to the rolling resistance component based on the target vehicle weight, predicted slope data and rolling resistance characteristic parameters; and determining the feedforward torque based on the target inertia item, slope compensation item, tire torque and friction resistance torque.

[0084] In this embodiment, it should be noted that the above-mentioned parameters are key parameters for determining the target inertia item, slope compensation item, wind resistance component, tire torque, rolling resistance component and friction resistance torque, respectively. It does not mean that they can be determined solely by relying on the above-mentioned parameters. In some examples, in order to ensure the accuracy and comprehensiveness of the torque calculation, other influencing factors need to be considered in combination.

[0085] Furthermore, the target inertia term can be used to describe the impact of the inertia force generated by vehicle deceleration on the braking torque requirement. To accurately convert the inertia force into the torque requirement, the tire radius is also included when determining the target inertia term. For example, the target inertia term is calculated by multiplying the target vehicle weight by the preset deceleration and then by the tire radius.

[0086] Slope compensation compensates for the additional braking torque required due to changes in road grade. In addition to the target vehicle weight and predicted slope data, the slope compensation term also includes gravity and tire radius. The slope sine is determined based on the predicted slope data. The slope compensation term is then calculated by multiplying the target vehicle weight by gravity, then by the slope sine, and by the tire radius.

[0087] Drag characteristic parameters may refer to parameters that describe the drag characteristics of a vehicle while traveling through the air, such as air density, drag coefficient, and the vehicle's frontal area. The drag component may represent the effect of air resistance on the braking torque during vehicle travel. Tire torque corresponds to the drag component and is the additional torque required from the motor due to wind resistance. For example, the drag component is calculated based on air density, drag coefficient, frontal area, and the square of vehicle speed and converted into tire torque.

[0088] Rolling resistance characteristic parameters can refer to parameters that describe the resistance characteristics experienced by a vehicle's tires during rolling, such as the rolling resistance coefficient. The rolling resistance component represents the effect of rolling resistance between the tire and the road surface on the braking torque during vehicle operation. Frictional resistance torque corresponds to the rolling resistance component and is the additional torque required by the motor due to rolling resistance. For example, the slope cosine value is determined based on predicted slope data, and the rolling resistance component and frictional resistance torque are calculated based on the vehicle weight, the slope cosine value, and the rolling resistance coefficient.

[0089] By considering the effects of wind resistance and rolling resistance on braking torque, the control strategy can better adapt to different driving environments and vehicle conditions, and improve the accuracy of torque calculation in different driving environments; at the same time, separately calculating the target inertia term, slope compensation term, tire torque and friction resistance torque can more accurately reflect the vehicle's braking torque requirements under different driving conditions, provide more comprehensive data support for the formulation of energy recovery strategies, and help improve the accuracy and efficiency of energy recovery.

[0090] Based on the above embodiment, the multi-source mass observation parameters include the first mass estimation under the driving condition, the second mass estimation under the gliding condition and the measurement data of the suspension pressure sensor, and the dynamic state parameters include the current slope, vehicle acceleration and current motor torque.

[0091] The first mass estimate may refer to a vehicle mass value estimated by a dynamic model or related algorithm when the electric vehicle is in a driving state, and may be obtained based on a comprehensive calculation of multiple parameters such as motor output torque, vehicle acceleration, and vehicle speed.

[0092] The second mass estimate may refer to the vehicle mass value estimated by a dynamic model or related algorithm when the electric vehicle is in a coasting state (i.e., the vehicle is coasting freely without pressing the accelerator or brake). It can be obtained based on a comprehensive calculation of parameters such as vehicle deceleration and braking force.

[0093] The suspension pressure sensor's measurement data refers to vehicle mass data directly measured by the pressure sensor installed in the vehicle's suspension system. This data can reflect the vehicle's actual current load.

[0094] The current slope can be the actual slope of the road the vehicle is currently traveling on, which can be obtained through GPS, an inertial navigation system, or a dedicated slope sensor. The vehicle acceleration can be the current acceleration or deceleration rate of the vehicle, which can be measured by an accelerometer. The current motor torque can be the current torque output by the motor, which can be directly obtained from the motor controller.

[0095] Multi-source mass observation parameters can more accurately estimate the actual mass of the vehicle and dynamically determine the target vehicle weight, which helps improve the accuracy of subsequent torque calculations and energy recovery strategies. Dynamic state parameters can enable control strategies to better adapt to different driving environments and vehicle states, thereby improving control accuracy and energy recovery efficiency.

[0096] Based on the above embodiments, the method may further include: obtaining the motor output torque, rolling torque, wind resistance characteristic parameters, vehicle acceleration and tire radius of the electric vehicle; determining the wind resistance torque based on the wind resistance characteristic parameters and vehicle speed; updating the motor output torque based on the rolling torque and wind resistance torque to obtain the target motor output torque; and determining the first mass estimate under the driving condition based on the target motor output torque, vehicle acceleration and tire radius.

[0097] Motor output torque refers to the actual torque output by the electric vehicle's motor, which is the source of power that propels the vehicle forward. Rolling resistance torque refers to the torque corresponding to the rolling resistance generated by the tires' contact with the ground during vehicle movement. Windage torque refers to the torque corresponding to the air resistance experienced by the vehicle during movement.

[0098] In one example, the windage torque and rolling torque are deducted from the motor output torque (the windage torque is dynamically calculated using air density, drag coefficient, frontal area, and vehicle speed squared), and then divided by the product of the tire radius and the real-time vehicle acceleration to obtain a first mass estimate.

[0099] By obtaining key parameters of the electric vehicle's motor output torque, rolling resistance torque, and other key parameters in real time, the first mass estimation under driving conditions can be determined. This not only improves the real-time and accuracy of the target vehicle weight estimation, but also ensures that the motor output meets the vehicle's driving needs while avoiding unnecessary energy waste.

[0100] Based on the above embodiment, the method may further include: obtaining a braking force and a gravity component of the electric vehicle; and determining a second mass estimation under a coasting condition based on the braking force, the gravity component and the actual deceleration.

[0101] The braking force refers to the force generated by the braking system to hinder the vehicle's movement during braking, and the actual braking force can be converted from the brake fluid pressure. The gravity component refers to the component of the vehicle's gravity in the direction of the slope.

[0102] In some examples, the second mass estimate is obtained by converting the actual braking force based on the brake fluid pressure, adding the component of the vehicle's gravity in the slope direction, and dividing it by the deceleration value measured by the inertial measurement unit.

[0103] Through precise data sources, the accuracy and reliability of mass estimation under coasting conditions are improved, and more accurate data support can be provided for energy recovery control, braking strategy formulation, etc. of electric vehicles.

[0104] Based on the above embodiments, a method for determining the coasting energy recovery control strategy of an electric vehicle according to the required braking torque and the maximum recovery capacity of the motor may include: when the required braking torque is less than or equal to the maximum recovery torque corresponding to the maximum recovery capacity of the motor, determining that the coasting energy recovery control strategy of the electric vehicle is a motor energy recovery mode; when the required braking torque is greater than the maximum recovery torque corresponding to the maximum recovery capacity of the motor, determining that the coasting energy recovery control strategy of the electric vehicle is an electromechanical cooperative energy recovery mode.

[0105] The motor energy recovery mode refers to a braking mode that relies solely on the motor for energy recovery when the required braking torque is less than or equal to the motor's maximum recovery capacity. The electromechanical collaborative energy recovery mode refers to a braking mode that uses the motor and mechanical braking system to jointly provide braking torque when the required braking torque exceeds the motor's maximum recovery capacity.

[0106] Furthermore, when the required braking torque does not exceed the maximum recovery capacity of the motor, all of it is distributed to the energy recovery system (such as the drive motor or a dedicated regenerative braking motor);

[0107] When the required braking torque exceeds the motor's capacity, the motor outputs recovery torque at its maximum capacity; the difference is compensated by the mechanical braking system, with a response time of less than 20 milliseconds. For example, if the required braking torque is 400Nm (the motor's maximum recovery capacity is 300Nm), the motor is placed in a maximum energy recovery state, outputting the maximum recovery torque within its capacity range, and calculating the difference in real time (400Nm-300Nm=100Nm). This difference is accurately compensated by the mechanical braking system within 20ms, forming a seamless hybrid braking process. The compensation process can use pressure-torque closed-loop control to ensure that the total output torque strictly matches the required value.

[0108] By judging the relationship between the required braking torque and the maximum recovery capacity of the motor, the motor energy recovery mode or the electromechanical collaborative energy recovery mode is intelligently selected, thereby achieving refined management of the electric vehicle's coasting energy recovery control strategy and improving the accuracy of coasting energy recovery control and energy recovery efficiency.

[0109] Based on the above embodiments, the method may further include: adjusting the required braking torque according to preset safety protection requirements to obtain a target required braking torque, the safety protection requirements including the adjustment requirements of the battery temperature, battery state of charge and motor temperature for the required braking torque; correspondingly, determining the coasting energy recovery control strategy of the electric vehicle according to the required braking torque and the maximum recovery capacity of the motor, including: determining the coasting energy recovery control strategy of the electric vehicle according to the target required braking torque and the maximum recovery capacity of the motor.

[0110] In this embodiment, the safety protection requirements may refer to a series of restrictive conditions that ensure the safe operation of the electric vehicle during braking and energy recovery. The target required braking torque is the braking torque value obtained by adjusting the original required braking torque according to the safety protection requirements, which serves as the basis for actual braking and energy recovery control. For example, when the motor temperature is between 100°C and 150°C, the required braking torque is reduced by 1% for every 1°C increase in temperature. For another example, when the battery state of charge is between 90% and 95%, 80% of the required braking torque is allowed to be output.

[0111] In one example, the required braking torque is 1000Nm, but the maximum recovery torque corresponding to the maximum discharge current allowed by the battery is only 900Nm. At this time, the required braking torque needs to be adjusted to 900Nm, so as to determine the final coasting energy recovery control strategy based on the braking torque of 900Nm.

[0112] It should be noted that the target required braking torque, adjusted based on safety protection requirements, can be determined before determining the electric vehicle's coasting energy recovery control strategy based on the required braking torque and the motor's maximum recovery capacity. Alternatively, after determining the electric vehicle's coasting energy recovery control strategy, the control parameters within the strategy can be further evaluated to determine whether the control parameters (such as the braking torque) meet the safety protection requirements. If not, further adjustments are required to ensure the safety and stability of the electric vehicle during braking.

[0113] Optionally, the safety protection requirements may also include a fault degradation strategy, that is, monitoring sensor data. If the data fails, the failed data is automatically supplemented based on the fault degradation strategy. For example, when the slope signal is abnormal, if the vehicle continues to decelerate and the deceleration is greater than 0.15g, it is judged as uphill, otherwise it is downhill, where g is the acceleration of gravity; when the vehicle weight signal is lost, the effective average value of the last ten minutes is activated.

[0114] By introducing safety protection requirements to adjust the required braking torque, a more reasonable and safe target required braking torque can be obtained. By correcting the required braking torque through the influence of the motor and battery on the braking and energy recovery process, the safety of electric vehicles during the energy recovery process can be ensured.

[0115] Based on the above embodiments, before obtaining the vehicle speed, actual deceleration, multi-source quality observation parameters and dynamic state parameters of the electric vehicle, the method may further include: obtaining the accelerator pedal status, brake pedal status, vehicle speed, battery temperature and motor temperature of the electric vehicle; when the accelerator pedal status, brake pedal status, vehicle speed, battery temperature and motor temperature meet the preset energy recovery control conditions, triggering the energy recovery mode, and executing the steps of obtaining the vehicle speed, actual deceleration, multi-source quality observation parameters and dynamic state parameters of the electric vehicle.

[0116] The accelerator pedal state may refer to the current position or state of the electric vehicle's accelerator pedal, and may be used to reflect the driver's intention or need for vehicle acceleration. The brake pedal state may refer to the current position or state of the electric vehicle's brake pedal, and may be used to reflect the driver's intention or need for vehicle braking.

[0117] Energy recovery control conditions may refer to a series of preset conditions used to determine whether the energy recovery mode can be activated, including restrictions on parameter ranges such as accelerator pedal status, brake pedal status, vehicle speed, battery temperature, and motor temperature.

[0118] In one example, the energy recovery control condition is that the energy recovery mode is activated when the following five conditions are met simultaneously:

[0119] 1. Accelerator pedal status: The basic condition is triggered when the accelerator pedal is fully released (opening degree 0%);

[0120] 2. Brake pedal status: When the opening is less than 10%, the vehicle maintains coasting recovery;

[0121] 3. Vehicle speed requirement: Start control when the vehicle speed is higher than 5 km / h;

[0122] 4. Battery temperature protection: the working range is -10℃ to 55℃;

[0123] 5. Motor temperature protection: Winding temperature is lower than the safety threshold of 150℃.

[0124] By increasing the acquisition and judgment of five key parameters such as the accelerator pedal status, the energy recovery mode is intelligently triggered, which not only ensures the safety and effectiveness of the energy recovery process, but also avoids the dangers and hidden dangers that may arise from energy recovery under inappropriate conditions.

[0125] On the basis of the above embodiment, after determining the coasting energy recovery control strategy of the electric vehicle according to the required braking torque and the maximum recovery capacity of the motor, the method may further include: determining the accelerator pedal opening and wheel speed difference of the electric vehicle; when the wheel speed difference exceeds a preset difference threshold, adjusting the recovery torque in the coasting energy recovery control strategy to a preset proportion of the required braking torque; when the accelerator pedal opening meets the preset opening requirement, switching the electric vehicle from the energy recovery mode to the driving mode based on a preset transition strategy.

[0126] In this embodiment, the accelerator pedal opening degree may refer to the degree to which the accelerator pedal is depressed, typically expressed as a percentage. The wheel speed difference may refer to the speed difference between the wheels of the electric vehicle, or the difference between the actual vehicle speed and the expected vehicle speed, and is used to determine the stability of the vehicle's driving state.

[0127] The preset difference threshold is a predefined limit for wheel speed differences. When the actual wheel speed difference exceeds this threshold, the vehicle's driving state is considered unstable and the energy recovery strategy needs to be adjusted. For example, if the wheel speed difference exceeds 0.2 m / s, the braking torque is limited to 20% (a preset ratio) of the actual calculated (required braking torque).

[0128] A preset opening requirement may refer to a specific accelerator pedal opening requirement. When this requirement is met, the driver is deemed to have a clear intention to accelerate and a switch to drive mode is required. For example, the accelerator pedal opening exceeds 2%. A preset transition strategy may refer to a preset control strategy for smoothly switching an electric vehicle from energy recovery mode to drive mode, ensuring a smooth and safe transition.

[0129] In some examples, the preset transition strategy satisfies:

[0130] 1. Achieve linear decay of energy recovery torque from 100% to zero through an S-shaped curve;

[0131] 2. The driving torque increases linearly from zero to 100%;

[0132] 3. The torque change rate is forced to limit not more than 1000Nm per second.

[0133] By increasing the monitoring of the accelerator pedal opening and the wheel speed difference, as well as the corresponding recovery torque adjustment and mode switching strategy, when the vehicle's driving state is unstable, the recovery torque is adjusted to ensure vehicle stability; when the driver intends to accelerate, the vehicle is switched to driving mode through a smooth transition strategy, further improving the flexibility and safety of the electric vehicle's coasting energy recovery control.

[0134] The following describes the execution process of the coasting energy recovery control method in detail through some embodiments, taking the coasting energy recovery control system as an example. Figure 2 A schematic diagram of the execution process of a coasting energy recovery control method provided in this application is shown as follows: Figure 2 As shown, the execution process includes a data acquisition phase, a control calculation phase, and an execution phase. The data acquisition phase primarily involves the system acquiring various data related to the electric vehicle and its operation to directly or indirectly determine the preset deceleration, provide vehicle speed feedback (i.e., the electric vehicle's speed), calculate vehicle weight (i.e., target vehicle weight), and slope feedback (i.e., predicted slope data). Based on these four parameters—preset deceleration, vehicle speed, target vehicle weight, and predicted slope data—the system then proceeds to the control calculation phase for further processing. The control calculation phase primarily involves the system implementing steep slope safety mechanisms (e.g., automatic adjustment of feedforward torque based on predicted slope data), deceleration matching, and torque correction based on preset intelligent algorithms (e.g., Kalman filtering, vehicle dynamics models, etc.), ultimately achieving the optimal torque result and entering the execution phase. The execution phase primarily involves the system generating and sending control commands to the corresponding motors or other auxiliary devices based on the optimal torque result, i.e., the coasting energy recovery control strategy. While the motors are executing the coasting torque, the system monitors vehicle smoothness and dynamically adjusts parameters when wheel speed discrepancies or mode switching issues occur.

[0135] Figure 3 A specific flow chart of a coasting energy recovery control method provided in this application is as follows: Figure 3 As shown, the method includes:

[0136] 1. Activation determination: The activation determination period for coasting energy recovery can be 10 milliseconds. The system will continuously monitor the following five key conditions:

[0137] (1) Accelerator pedal status: The basic condition is triggered when the accelerator pedal is fully released (opening degree 0%);

[0138] (2) Brake pedal status: When the opening is less than 10%, the coasting recovery is maintained;

[0139] (3) Vehicle speed requirement: Start control when the vehicle speed is higher than 5 km / h;

[0140] (4) Battery temperature protection: The operating range is -10 degrees Celsius to 55 degrees Celsius;

[0141] (5) Motor temperature protection: Winding temperature is lower than the safety threshold of 150 degrees Celsius;

[0142] When the above five conditions are met at the same time, the energy recovery main control loop is activated.

[0143] 2. Real-time fusion of dynamic parameters: The cycle can be a high-speed cycle of 5 milliseconds. The method includes:

[0144] (1) Dynamic calculation of vehicle weight:

[0145] Drive-condition mass inversion: Deduct wind resistance and rolling torque from the motor output torque (wind resistance is dynamically calculated using air density, drag coefficient, frontal area, and the square of vehicle speed), then divide by the product of tire radius and real-time acceleration to obtain an instantaneous mass estimate.

[0146] Coasting mass calculation: Actual braking force is calculated based on brake fluid pressure, added with the vehicle's gravity component in the slope direction, and then divided by the deceleration value measured by the inertial measurement unit.

[0147] Multi-source data fusion: The mass estimates of driving and coasting conditions, along with the signals from the four-wheel suspension pressure sensors, are input into the Kalman filter for fusion and noise reduction, outputting an optimized vehicle weight (corresponding to the target vehicle weight) with an error of less than three percent.

[0148] (2) Slope perception fusion:

[0149] Multi-source data collection: such as vehicle acceleration and current motor torque;

[0150] Direct measurement: Slope sensors collect slope information in real time;

[0151] Trend prediction: Based on the vehicle acceleration, current vehicle weight, and current motor torque, the slope change trend (corresponding to the predicted slope data) in the next 500 milliseconds is extrapolated.

[0152] 3. Torque dual-loop dynamic generation: Its cycle can be a high-speed cycle of 10 milliseconds. The method includes:

[0153] (1) Feedforward control channel:

[0154] The benchmark torque is solved in real time based on the vehicle dynamics model, which includes four core components:

[0155] ① Target inertia term: vehicle weight multiplied by the preset deceleration multiplied by the tire radius;

[0156] ② Slope compensation: vehicle weight multiplied by gravity acceleration multiplied by the sine of the slope multiplied by the tire radius;

[0157] ③ Wind resistance component: Calculate the resistance based on air density, drag coefficient, frontal area and square of vehicle speed and convert it into tire torque;

[0158] ④ Rolling resistance component: Calculates friction resistance torque based on vehicle weight, slope cosine value, and rolling resistance coefficient;

[0159] Finally, two percent of the motor transmission loss is deducted to form the feedforward torque output.

[0160] (2) Feedback compensation channel:

[0161] Deviation processing: Real-time comparison of the difference between the preset deceleration and the actual deceleration

[0162] Synthetic demand torque: After the feedforward reference value and the feedback compensation amount are superimposed, the output is limited by the safety boundary module.

[0163] 4. Execution allocation and mode switching: The cycle can be a high-speed cycle of 10 milliseconds. The method includes:

[0164] (1) Mechanical and electrical collaborative allocation mechanism:

[0165] When the required torque does not exceed the maximum recovery capacity of the motor, all of it is allocated to the energy recovery system;

[0166] When the required torque exceeds the motor's capacity, the motor outputs the recovery torque at its maximum capacity;

[0167] The difference is compensated by a mechanical braking system with a response time of less than 20 milliseconds.

[0168] (2) Driving mode seamless transition:

[0169] Trigger condition: accelerator pedal opening exceeds 2%;

[0170] Transition strategy:

[0171] The energy recovery torque is linearly decayed from 100% to zero through the S-shaped curve;

[0172] The driving torque increases linearly from zero to 100%;

[0173] The torque change rate is forced to be limited to no more than 1000Nm per second.

[0174] Low Adhesion Optimization:

[0175] When the electronic stability system detects that the wheel speed difference exceeds 0.2m / s, the maximum regenerative torque is limited to 20% of the actual calculation.

[0176] 5. Real-time security protection: This method can be embedded in the entire cycle and includes:

[0177] (1) Triple operation boundary protection:

[0178] Motor temperature limit:

[0179] Full torque output is allowed below 100 degrees Celsius;

[0180] In the range of 100 to 150 degrees Celsius, the torque is reduced by 1% for every 1 degree Celsius increase in temperature.

[0181] When the temperature exceeds 150 degrees Celsius, the output torque is limited to 50% of the rated value.

[0182] Battery State of Charge (SOC) Limits:

[0183] When the battery level is above 95%, the maximum regenerative torque drops to half of the design value;

[0184] When the battery level is between 90% and 95%, 80% of the torque can be output;

[0185] At the same time, according to the maximum pulse power allowed by the battery, the recovery torque is limited so that the actual recovery power is less than or equal to the maximum pulse power allowed by the battery.

[0186] Battery temperature limit: If the battery is under low temperature conditions, such as -20℃ to 0℃, 50% torque output is allowed.

[0187] (2) Fault degradation strategy:

[0188] Sensor failure response:

[0189] When the slope signal is abnormal: If the vehicle continues to decelerate and the deceleration is greater than 0.15g, it is judged as uphill, otherwise it is downhill;

[0190] When the vehicle weight signal is lost: the effective average value of the last ten minutes is activated.

[0191] 6. Slow cycle parameter optimization: The cycle can be 100 milliseconds. The method includes:

[0192] Vehicle weight estimation optimization: Dynamically adjust Kalman filter noise parameters according to vehicle speed.

[0193] In one example, the coasting energy recovery control method can be integrated into the vehicle control unit (VCU), which is connected to the CAN bus of the electric vehicle. The VCU obtains real-time data of the electric vehicle and determines whether to enter the coasting energy feedback mode. If so, it enters the real-time control module to perform the final feedback torque calculation. The feedback torque is calculated and, after correction of the safety protection boundary, is sent to the motor controller MCU (Motor Control Unit), which then sends it to the drive motor.

[0194] The coasting energy recovery control method provided in the embodiment of the present application can improve the accuracy of coasting energy recovery control and thus improve energy recovery efficiency through the full-process control logic of preset deceleration, dynamic solution of vehicle weight / slope, motor torque adjustment, and deceleration verification, the longitudinal dynamics algorithm and noise filtering implementation method for reversely inferring vehicle weight through the load of the main drive motor, the weighted fusion method and sliding window prediction model of slope sensor data and navigation elevation prediction, the composite output rules and anti-saturation constraints of the feedforward reference torque calculation model and feedback compensation amount, as well as the torque weight dynamic allocation strategy and S-curve transition function implementation based on working condition identification.

[0195] Figure 4 This is a structural diagram of a coasting energy recovery control system provided by this application, such as Figure 4 As shown, the coasting energy recovery control system 40 provided in this embodiment includes:

[0196] An acquisition module 401 is used to acquire the speed, actual deceleration, multi-source quality observation parameters and dynamic state parameters of the electric vehicle;

[0197] Processing module 402 is configured to determine a feedforward torque based on vehicle speed, a preset deceleration, a target vehicle weight, and predicted slope data, wherein the target vehicle weight is obtained by fusing and denoising multi-source mass observation parameters using a Kalman filter, and the predicted slope data is obtained by predicting a slope trend based on dynamic state parameters and the target vehicle weight;

[0198] A first determining module 403 is configured to determine a feedback compensation torque according to a difference between a preset deceleration and an actual deceleration;

[0199] An obtaining module 404 is used to linearly superimpose the feedforward torque and the feedback compensation torque to obtain the required braking torque;

[0200] The second determining module 405 is configured to determine a coasting energy recovery control strategy for the electric vehicle according to the required braking torque and the maximum recovery capability of the motor.

[0201] In one possible implementation, the processing module 402 can also be used to: determine the target inertia item based on the vehicle speed and the preset deceleration; determine the slope compensation item based on the target vehicle weight and the predicted slope data; determine the wind resistance component and the tire torque corresponding to the wind resistance component based on the vehicle speed and the wind resistance characteristic parameters; determine the rolling resistance component and the friction resistance torque corresponding to the rolling resistance component based on the target vehicle weight, the predicted slope data and the rolling resistance characteristic parameters; wherein the feedforward torque includes the target inertia item, the slope compensation item, the tire torque and the friction resistance torque.

[0202] In one possible implementation, the acquisition module 401 can also be used to: obtain the motor output torque, rolling torque, wind resistance characteristic parameters, vehicle acceleration and tire radius of the electric vehicle; determine the wind resistance torque based on the wind resistance characteristic parameters and the vehicle speed; update the motor output torque based on the rolling torque and wind resistance torque to obtain the target motor output torque; and determine the first mass estimate under the driving condition based on the target motor output torque, vehicle acceleration and tire radius.

[0203] In a possible implementation, the acquisition module 401 may also be used to: acquire the braking force and gravity component of the electric vehicle; and determine a second mass estimate under a coasting condition based on the braking force, gravity component, and actual deceleration.

[0204] In one possible implementation, the second determination module 405 can also be used to: when the required braking torque is less than or equal to the maximum recovery torque corresponding to the maximum recovery capacity of the motor, determine that the coasting energy recovery control strategy of the electric vehicle is the motor energy recovery mode; when the required braking torque is greater than the maximum recovery torque corresponding to the maximum recovery capacity of the motor, determine that the coasting energy recovery control strategy of the electric vehicle is the electromechanical cooperative energy recovery mode.

[0205] In one possible implementation, the second determination module 405 can also be used to: adjust the required braking torque according to preset safety protection requirements to obtain a target required braking torque, the safety protection requirements including the adjustment requirements of the battery temperature, battery state of charge and motor temperature for the required braking torque; accordingly, determine the coasting energy recovery control strategy of the electric vehicle according to the required braking torque and the maximum recovery capacity of the motor, including: determining the coasting energy recovery control strategy of the electric vehicle according to the target required braking torque and the maximum recovery capacity of the motor.

[0206] In one possible implementation, the acquisition module 401 can also be used to: obtain the accelerator pedal status, brake pedal status, vehicle speed, battery temperature and motor temperature of the electric vehicle; when the accelerator pedal status, brake pedal status, vehicle speed, battery temperature and motor temperature meet the preset energy recovery control conditions, trigger the energy recovery mode, and execute the steps of obtaining the vehicle speed, actual deceleration, multi-source quality observation parameters and dynamic state parameters of the electric vehicle.

[0207] In one possible implementation, the second determination module 405 can also be used to: determine the accelerator pedal opening and wheel speed difference of the electric vehicle; when the wheel speed difference exceeds a preset difference threshold, adjust the recovery torque in the coasting energy recovery control strategy to a preset proportion of the required braking torque; when the accelerator pedal opening meets the preset opening requirement, based on a preset transition strategy, switch the electric vehicle from energy recovery mode to driving mode.

[0208] The coasting energy recovery control system 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.

[0209] The present application also provides a vehicle, comprising a vehicle body and a vehicle controller, the vehicle controller being configured to execute the method in the above embodiment. The specific implementation process of the vehicle controller can be found in the above method embodiment, and its implementation principles and technical effects are similar, so this embodiment will not be further described here.

[0210] Figure 5 This is a schematic diagram of the structure of the electronic device provided in this application, which may be a vehicle controller. Figure 5 As shown, the electronic device 50 provided in this embodiment includes: at least one processor 501 and a memory 502. Optionally, the device 50 further includes a communication component 503. The processor 501, the memory 502 and the communication component 503 are connected via a bus 504.

[0211] In a specific implementation process, at least one processor 501 executes the computer-executable instructions stored in the memory 502, so that the at least one processor 501 performs the above method.

[0212] The specific implementation process of the processor 501 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.

[0213] 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 (ASIC), 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 in the processor.

[0214] The memory may include a high-speed memory (Random Access Memory, RAM), and may also include a non-volatile memory (NVM), such as at least one disk memory.

[0215] The 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 classified 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.

[0216] The present application also provides a computer program product, including a computer program, which implements the above method when executed by a processor.

[0217] 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.

[0218] The above-mentioned readable storage medium can 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 memory, flash memory, magnetic disk or optical disk. The readable storage medium can be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0219] 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 a device as discrete components.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] If the function is implemented in the form of 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 part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling 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 present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, and other media that can store program code.

[0224] 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.

[0225] 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 speed, actual deceleration, multi-source quality observation parameters and dynamic state parameters of the electric vehicle; Determining a feedforward torque based on the vehicle speed, the preset deceleration, the target vehicle weight, and predicted slope data, wherein the target vehicle weight is obtained by fusing and denoising the multi-source mass observation parameters using a Kalman filter, and the predicted slope data is obtained by predicting a slope trend based on the dynamic state parameters and the target vehicle weight; determining a feedback compensation torque according to a difference between the preset deceleration and the actual deceleration; Linearly superimposing the feedforward torque and the feedback compensation torque to obtain a required braking torque; A coasting energy recovery control strategy of the electric vehicle is determined according to the required braking torque and the maximum recovery capacity of the motor.

2. The method according to claim 1, characterized in that The determining of the feedforward torque based on the vehicle speed, the preset deceleration, the target vehicle weight and the predicted slope data includes: determining a target inertia term based on the target vehicle weight and the preset deceleration; determining a slope compensation item based on the target vehicle weight and the predicted slope data; determining a wind resistance component and a tire torque corresponding to the wind resistance component based on the vehicle speed and the wind resistance characteristic parameter; determining a rolling resistance component and a friction resistance torque corresponding to the rolling resistance component based on the target vehicle weight, the predicted slope data, and a rolling resistance characteristic parameter; A feedforward torque is determined based on the target inertia term, the slope compensation term, the tire torque, and the friction resistance torque.

3. The method according to claim 1, characterized in that The multi-source mass observation parameters include a first mass estimate under a driving condition, a second mass estimate under a coasting condition, and measurement data from a suspension pressure sensor. The dynamic state parameters include a current slope, vehicle acceleration, and current motor torque.

4. The method according to claim 3, characterized in that The method further comprises: Obtaining motor output torque, rolling resistance torque, wind resistance characteristic parameters, vehicle acceleration and tire radius of the electric vehicle; determining a wind resistance torque according to the wind resistance characteristic parameter and the vehicle speed; updating the motor output torque according to the rolling resistance torque and the wind resistance torque to obtain a target motor output torque; A first mass estimation under the driving condition is determined based on the target motor output torque, the vehicle acceleration, and the tire radius.

5. The method according to claim 3, characterized in that The method further comprises: obtaining a braking force and a gravity component of the electric vehicle; A second mass estimation in the coasting condition is determined based on the braking force, the gravity component, and the actual deceleration.

6. The method according to claim 1, characterized in that The step of determining a coasting energy recovery control strategy for the electric vehicle based on the required braking torque and the maximum recovery capacity of the motor includes: When the required braking torque is less than or equal to the maximum recovery torque corresponding to the maximum recovery capacity of the motor, determining that the coasting energy recovery control strategy of the electric vehicle is a motor energy recovery mode; When the required braking torque is greater than the maximum recovery torque corresponding to the maximum recovery capacity of the motor, the coasting energy recovery control strategy of the electric vehicle is determined to be an electromechanical cooperative energy recovery mode.

7. The method according to any one of claims 1 to 6, characterized in that The method further comprises: Adjusting the required braking torque according to preset safety protection requirements to obtain a target required braking torque, wherein the safety protection requirements include adjustment requirements of the required braking torque based on battery temperature, battery state of charge, and motor temperature; Accordingly, determining the coasting energy recovery control strategy of the electric vehicle according to the required braking torque and the maximum recovery capacity of the motor includes: A coasting energy recovery control strategy of the electric vehicle is determined according to the target required braking torque and the maximum recovery capacity of the motor.

8. The method according to any one of claims 1 to 6, characterized in that Before obtaining the vehicle speed, actual deceleration, multi-source quality observation parameters and dynamic state parameters of the electric vehicle, the method further includes: Obtaining an accelerator pedal state, a brake pedal state, a vehicle speed, a battery temperature, and a motor temperature of the electric vehicle; When the accelerator pedal state, the brake pedal state, the vehicle speed, the battery temperature and the motor temperature meet the preset energy recovery control conditions, the energy recovery mode is triggered and the steps of obtaining the vehicle speed, actual deceleration, multi-source quality observation parameters and dynamic state parameters of the electric vehicle are executed.

9. The method according to claim 8, characterized in that After determining the coasting energy recovery control strategy of the electric vehicle according to the required braking torque and the maximum recovery capacity of the motor, the method further includes: determining an accelerator pedal opening and a wheel speed difference of the electric vehicle; When the wheel speed difference exceeds a preset difference threshold, adjusting the regenerative torque in the coasting energy regeneration control strategy to a preset proportion of the required braking torque; When the accelerator pedal opening meets a preset opening requirement, the electric vehicle is switched from the energy recovery mode to the driving mode based on a preset transition strategy.

10. A coasting energy recovery control system, characterized in that: include: An acquisition module is used to obtain the speed, actual deceleration, multi-source quality observation parameters and dynamic state parameters of the electric vehicle; a processing module configured to determine a feedforward torque based on the vehicle speed, a preset deceleration, a target vehicle weight, and predicted slope data, wherein the target vehicle weight is obtained by fusing and denoising the multi-source mass observation parameters using a Kalman filter, and the predicted slope data is obtained by predicting a slope trend based on the dynamic state parameters and the target vehicle weight; a first determining module, configured to determine a feedback compensation torque according to a difference between the preset deceleration and the actual deceleration; An obtaining module, configured to linearly superimpose the feedforward torque and the feedback compensation torque to obtain a required braking torque; The second determination module is configured to determine a coasting energy recovery control strategy for the electric vehicle according to the required braking torque and the maximum recovery capability of the motor.

11. A vehicle, characterized in that: The vehicle comprises a vehicle body and a vehicle controller, wherein the vehicle controller is used to execute the method according to any one of claims 1 to 9.

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