Coasting energy recovery control method, system and vehicle

Abstract

The application provides a coasting energy recovery control method, system and vehicle, and relates to the technical field of electric vehicles. The method comprises the following steps: acquiring the vehicle speed, actual deceleration, multi-source mass observation parameter and dynamic state parameter of the electric vehicle; determining 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 parameter based on Kalman filtering, and the predicted slope data is obtained by predicting the slope trend based on the dynamic state parameter and the target vehicle weight; determining the feedback compensation torque according to the difference between the preset deceleration and the actual deceleration; linearly superimposing the feedforward torque and the feedback compensation torque to obtain the required braking torque; and 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 application is used to improve the accuracy of the coasting energy recovery control, thereby improving the energy recovery efficiency.

Description

Technical Field

[0001] This application relates to the field of electric vehicle technology, and in particular to a coasting energy recovery control method, system and vehicle. Background Technology

[0002] With the increasing severity of the energy crisis and environmental pollution, improving the energy efficiency and range of electric vehicles has become a key focus of industry research. Coasting energy recovery, as an important component of electric vehicle energy-saving technology, effectively extends the vehicle's range and reduces energy waste by recovering kinetic energy and converting it into electrical energy during coasting or braking. However, due to the complex operating conditions of electric vehicles, higher demands are placed on coasting energy recovery control technology.

[0003] Currently, the recuperation control of electric vehicles is mainly based on single-parameter control or fixed recovery modes. Single-parameter control typically adjusts the recovery torque according to 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 variable operating conditions, vehicle speed, as a single parameter, is difficult to comprehensively and accurately reflect the actual kinetic energy and recovery potential of the electric vehicle during operation. Fixed recovery modes adjust the recovery intensity according to the user's 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 operating conditions make it difficult for users to predict and set an appropriate recovery intensity in advance.

[0004] Therefore, existing methods still suffer from inaccurate control when faced with complex and ever-changing operating conditions, resulting in low energy recovery efficiency. Summary of the Invention

[0005] The coasting energy recovery control method, system, and vehicle provided in this application are intended to solve the problem that existing control methods still suffer from inaccurate control when facing complex and ever-changing operating conditions, resulting in low energy recovery efficiency.

[0006] In a first aspect, embodiments of this application provide a method for controlling coasting energy recovery, including:

[0007] Acquire the vehicle speed, actual deceleration, multi-source mass observation parameters, and dynamic state parameters of electric vehicles;

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

[0009] The feedback compensation torque is determined based on the difference between the preset deceleration and the actual deceleration.

[0010] The required braking torque is obtained by linearly superimposing the feedforward torque and the feedback compensation torque.

[0011] Based on the required braking torque and the maximum recovery capacity of the motor, determine the coasting energy recovery control strategy for electric vehicles.

[0012] In one possible implementation, the feedforward torque is determined based on vehicle speed, preset deceleration, target vehicle weight, and predicted gradient data, including:

[0013] Based on the target vehicle weight and preset deceleration, determine the target inertial term;

[0014] Based on the target vehicle weight and predicted slope data, determine the slope compensation item;

[0015] Based on vehicle speed and wind resistance characteristics, the wind resistance component and the corresponding tire torque are determined.

[0016] Based on the target vehicle weight, predicted slope data, and rolling resistance characteristic parameters, the rolling resistance component and the corresponding frictional resistance torque are determined.

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

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

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

[0020] To obtain the motor output torque, rolling torque, wind resistance characteristics, vehicle acceleration, and tire radius of electric vehicles;

[0021] Determine the drag torque based on the wind resistance characteristics and vehicle speed;

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

[0023] Based on the target motor output torque, vehicle acceleration, and tire radius, the first mass estimate under driving conditions is determined.

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

[0025] Obtain the braking force and gravity components of electric vehicles;

[0026] The second mass estimate under the gliding condition is determined based on the braking force, the gravitational component, and the actual deceleration.

[0027] In one possible implementation, a coasting energy recovery control strategy for the electric vehicle is determined based on the required braking torque and the maximum regenerative braking capacity of the motor, including:

[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, the coasting energy recovery control strategy of the electric vehicle is determined to be the 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] Based on 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 battery temperature, battery state of charge and motor temperature on the required braking torque.

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

[0033] Based on the target braking torque requirement and the maximum recovery capacity of the motor, determine the coasting energy recovery control strategy for electric vehicles.

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

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

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

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

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

[0039] When the wheel speed difference exceeds the preset difference threshold, the recovery torque in the coasting energy recovery control strategy will be adjusted to a preset ratio of the required braking torque.

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

[0041] Secondly, embodiments of this application provide a coasting energy recovery control system, comprising:

[0042] The acquisition module is used to acquire the vehicle speed, actual deceleration, multi-source mass observation parameters, and dynamic state parameters of the electric vehicle.

[0043] The processing module is used to 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 based on Kalman filtering, and the predicted slope data is obtained by predicting the slope trend based on dynamic state parameters and target vehicle weight.

[0044] The first determining module is used to determine the feedback compensation torque based on the difference between the preset deceleration and the actual deceleration;

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

[0046] The second determining module is used to 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.

[0047] Thirdly, embodiments of this application provide a vehicle, including a vehicle body and a vehicle controller, the vehicle controller being used to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0048] Fourthly, embodiments of this application provide an electronic device, including: a memory and a processor;

[0049] The memory stores the instructions that the computer executes;

[0050] The processor executes computer execution instructions stored in memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0051] Fifthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed, are used to implement the first aspect and / or various possible implementations of the first aspect.

[0052] In a sixth aspect, embodiments of this application provide a computer program product, including a computer program that, when executed, implements the first aspect and / or various possible implementations of the first aspect.

[0053] The coasting energy recovery control method, system, and vehicle provided in this application acquire 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. The target vehicle weight is obtained by fusing and denoising the multi-source mass observation parameters using 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; 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 acquiring the multi-source mass observation parameters of the electric vehicle and using a Kalman filtering algorithm for fusion and denoising, a more accurate target vehicle weight is obtained. Simultaneously, based on the slope trend prediction using the dynamic state parameters and the target vehicle weight, the control strategy can adapt to changes in road conditions in advance, which helps 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 precise, ensuring the stability and accuracy of the braking process, and avoiding the problem of low recovery efficiency due to inaccurate control. Attached Figure Description

[0054] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0055] Figure 1 A schematic flowchart of a gliding energy recovery control method provided in this application;

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

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

[0058] Figure 4 This application provides a schematic diagram of the structure of a gliding energy recovery control system;

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

[0060] The accompanying drawings have illustrated specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to specific embodiments. Detailed Implementation

[0061] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of systems and methods consistent with some aspects of this application as detailed in the appended claims.

[0062] In existing technologies, the recuperation control of electric vehicles is mainly based on single-parameter control or fixed recovery modes. Single-parameter control typically adjusts the recovery torque according to 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 facing complex and variable operating conditions, vehicle speed, as a single parameter, is difficult to comprehensively and accurately reflect the actual kinetic energy and recovery potential of the electric vehicle during operation. Fixed recovery modes adjust the recovery intensity according to the user's preset intensity (e.g., high, medium, low). Although this mode provides users with a certain degree of flexibility, in actual driving, the complexity and variability of operating conditions make it difficult for users to predict and set an 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 facing complex and variable operating conditions. Based on this, some researchers have proposed an energy recovery control method based on random slope. This method dynamically adjusts the energy recovery intensity by feeding back the random slope information of the actual road in real time, in order to achieve the optimal recovery effect. This method sets multiple energy recovery modes and determines the energy recovery state based on vehicle speed, accelerator pedal, and brake pedal opening. Simultaneously, it determines the corresponding energy recovery power based on the current slope for more refined control. While this method improves energy recovery efficiency and adaptability to some extent, significant differences in actual vehicle weight between unloaded and fully loaded states mean that the actual deceleration of the vehicle fluctuates drastically at the same slope and speed. This causes the determined recovery power to fail to maintain consistency in driving experience when vehicle weight changes, still affecting control accuracy. Furthermore, this method relies on a preset slope-power mapping table, which cannot dynamically adjust control values, limiting control accuracy and robustness. Additionally, a single mapping table is difficult to adapt to diverse scenarios, resulting in a lack of user customization. Therefore, existing methods still suffer from inaccurate control and low energy recovery efficiency when facing complex and changing operating conditions.

[0063] To address the aforementioned issues, this application provides a coasting energy recovery control method, system, and vehicle. By acquiring multi-source mass observation parameters of the electric vehicle and using a Kalman filter algorithm for fusion and noise reduction, a more accurate target vehicle weight is obtained. Simultaneously, based on dynamic state parameters and slope trend prediction of the target vehicle weight, the control strategy can adapt to changes in road conditions in advance, 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, ensuring the stability and accuracy of the braking process, and avoiding low recovery efficiency due to inaccurate control. Therefore, a feedforward torque is derived based on vehicle speed, preset deceleration, target vehicle weight, and predicted slope data. Combined with feedback compensation torque, the final required braking torque is obtained through linear superposition, providing precise control commands 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 technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will be described below with reference to the accompanying drawings.

[0065] The execution entity of the coasting energy recovery control method provided in this application embodiment can be a computing device such as a server or server cluster. The server can be a mobile phone, computer, tablet, or other device. This application embodiment does not impose any particular restrictions on the implementation method of the execution entity, as long as the execution entity can obtain the electric vehicle's speed, actual deceleration, multi-source mass observation parameters, and dynamic state parameters; determine the feedforward torque based on the vehicle speed, preset deceleration, target vehicle weight, and predicted slope data, where the target vehicle weight is obtained by fusing and denoising the multi-source mass observation parameters using 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; 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 for the electric vehicle based on the required braking torque and the motor's maximum recovery capacity.

[0066] Figure 1 This is a flowchart illustrating a coasting energy recovery control method provided in this application. The executing entity of this method can be a server storing the coasting energy recovery control method or other servers; this embodiment does not impose any particular limitation. Figure 1 As shown, the method may include:

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

[0068] Vehicle speed refers to the current speed of the electric vehicle, which is usually obtained in real time through the vehicle's onboard speed sensor. Actual deceleration refers to the rate at which the electric vehicle decelerates during actual driving, measured by acceleration sensors and other methods.

[0069] Multi-source mass observation parameters are used to determine vehicle weight through multi-source fusion technology. These parameters may include, but are not limited to, factors affecting vehicle weight such as the first mass estimate under driving conditions, the second mass estimate under coasting conditions, and measurement data from suspension pressure sensors. They can be obtained comprehensively through multiple sensors and data fusion techniques.

[0070] Dynamic state parameters are used to predict the slope change trend over a future period (e.g., the next 500 milliseconds) and 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 dynamics model or through direct measurement, such as the current road inclination angle measured by a slope sensor, the vehicle acceleration obtained by an inertial measurement unit, and the real-time output motor torque fed back by the motor controller.

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

[0072] In this step, the preset deceleration refers to a desired deceleration value pre-set by the user or the implementing device (such as a system storing coasting energy recovery control methods) to control the vehicle's deceleration process. The preset deceleration can be set based on various factors, including but not limited to: driving model (e.g., economy mode, sport mode), road conditions (e.g., flat roads, uphill, downhill), vehicle status (e.g., battery charge, load), or user preferences. When the user sets the preset deceleration, a preset deceleration setting interface can be provided, allowing the user to set it through multiple selections or custom values, thereby achieving a linear and personalized coasting experience. By reasonably setting the preset deceleration, an optimal balance can be found between energy recovery efficiency, driving comfort, and safety.

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

[0074] Here, Kalman filtering can refer to the Kalman filter itself. When fusing and denoising multi-source quality observation parameters based on Kalman filtering, the output error can be preset to ensure the accuracy of the target vehicle weight; for example, the target vehicle weight can be set to have an output error of less than 3%. Optionally, a vehicle weight estimation optimization mechanism can be set: dynamically adjusting the noise parameters of the Kalman filter according to the vehicle speed to improve the accuracy of the target vehicle weight.

[0075] Slope trend prediction refers to using weighted fusion methods and pre-built sliding window prediction models to predict slope change trends over a future period. This allows for the early identification of steep inclines, enabling pre-compensation in torque calculations. For example, torque output can be increased before approaching an incline to ensure the vehicle can climb smoothly and powerfully. By predicting slope data, the torque required by the vehicle at different inclines can be calculated more accurately, improving the accuracy of torque calculations and enhancing the vehicle's climbing performance and driving experience. Optionally, a physics-based sliding window prediction model can be constructed using vehicle dynamics equations to back-calculate slope changes in real time, or a data-driven sliding window prediction model can be built using time-series networks such as LSTM / Transformer to predict slope change trends.

[0076] S103. Determine the feedback compensation torque based on the difference between the preset deceleration and the actual deceleration.

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

[0078] S104. The feedforward torque and the feedback compensation torque are linearly superimposed to obtain the required braking torque.

[0079] S105. Determine the coasting energy recovery control strategy for electric vehicles based on the required braking torque and the maximum recovery capacity of the motor.

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

[0081] Coasting energy recovery control strategies can include operating modes and control parameters of motors and other auxiliary equipment (such as braking torque magnitude, duration, etc.) to optimize energy recovery efficiency.

[0082] The coasting energy recovery control method provided in this application derives a feedforward torque from vehicle speed, preset deceleration, target vehicle weight, and predicted gradient data. This feedforward torque is then combined with feedback compensation torque and linearly superimposed to obtain the final required braking torque. This provides precise control commands for energy recovery, enabling the energy recovery strategy generated based on the required braking torque to maximize energy recovery during braking, thus improving the accuracy and efficiency of coasting energy recovery control. Furthermore, by predicting gradient trends based on dynamic state parameters and target vehicle weight, the control strategy can adapt to different road conditions and vehicle states, improving driving experience and safety, especially in complex and changing road conditions.

[0083] Based on the above embodiments, the method for determining feedforward torque based on vehicle speed, preset deceleration, target vehicle weight, and predicted slope data may include: determining a target inertia term based on the target vehicle weight and preset deceleration; determining a slope compensation term based on the target vehicle weight and predicted slope data; determining a wind resistance component and the corresponding tire torque based on vehicle speed and wind resistance characteristic parameters; determining a rolling resistance component and the corresponding frictional resistance torque based on the target vehicle weight, predicted slope data, and rolling resistance characteristic parameters; and determining the feedforward torque based on the target inertia term, slope compensation term, tire torque, and frictional resistance torque.

[0084] In this embodiment, it should be noted that the parameters mentioned above are key parameters for determining the target inertia term, slope compensation term, wind resistance component, tire torque, rolling resistance component, and frictional resistance torque, respectively. This does not mean that the parameters mentioned above can be used to determine the torque. In some examples, in order to ensure the accuracy and comprehensiveness of the torque calculation, it is also necessary to consider other influencing factors.

[0085] Furthermore, the target inertia term can be used to describe the impact of the inertial force generated by the vehicle's deceleration on the braking torque demand. When determining the target inertia term, in order to accurately convert the inertial force into torque demand, the tire radius is also included. For example, the target inertia term is obtained by multiplying the target vehicle weight by a preset deceleration and then by the tire radius.

[0086] The slope compensation term can be used to compensate for the additional braking torque required due to changes in road slope. In determining the slope compensation term, in addition to the target vehicle weight and predicted slope data, gravitational acceleration and tire radius are also included. The slope sine value needs to be determined based on the predicted slope data. Then, the slope compensation term is obtained by multiplying the target vehicle weight by gravitational acceleration, then by the slope sine value, and finally by the tire radius.

[0087] Aerodynamic drag parameters describe the resistance a vehicle experiences while driving through the air, such as air density, drag coefficient, and frontal area. The drag component represents the effect of air resistance on braking torque. Tire torque, corresponding to the drag component, is the additional torque required from the electric motor due to air resistance. For example, the drag component is calculated based on air density, drag coefficient, frontal area, and the square of vehicle speed, and then converted into tire torque.

[0088] Rolling resistance parameters describe the resistance characteristics experienced by a vehicle tire during rolling, such as the rolling resistance coefficient. The rolling resistance component represents the impact of the rolling resistance between the tire and the ground on braking torque during vehicle operation. Frictional resistance torque, corresponding to the rolling resistance component, is the additional torque required from the motor due to rolling resistance. For example, the cosine value of the slope is determined based on predicted slope data, and the rolling resistance component and frictional resistance torque are calculated based on vehicle weight, the cosine value of the slope, and the rolling resistance coefficient.

[0089] By considering the impact of wind resistance and rolling resistance on braking torque, the control strategy can better adapt to different driving environments and vehicle states, improving the accuracy of torque calculation under different driving conditions. At the same time, calculating the target inertia term, slope compensation term, tire torque, and friction resistance torque separately can more accurately reflect the braking torque requirements of the vehicle under different driving conditions, providing more comprehensive data support for the formulation of energy recovery strategies and helping to improve the accuracy and efficiency of energy recovery.

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

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

[0092] The second mass estimate refers to the vehicle's mass value estimated using a dynamic model or related algorithms when the electric vehicle is in a coasting state (i.e., without pressing the accelerator or brake, the vehicle is coasting freely). It can be obtained through comprehensive calculations based on parameters such as vehicle deceleration and braking force.

[0093] The measurement data from suspension pressure sensors refers to vehicle mass-related data obtained directly from pressure sensors installed on the vehicle's suspension system. This data reflects the vehicle's current actual load condition.

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

[0095] By using multi-source mass observation parameters, the actual mass of the vehicle can be estimated more accurately, and the target vehicle weight can be dynamically determined, which helps to improve the accuracy of subsequent torque calculation and energy recovery strategies. Dynamic state parameters enable the control strategy 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: acquiring the motor output torque, rolling resistance 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 resistance 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] Among these, motor output torque refers to the actual torque value currently output by the electric vehicle's motor, which is the power source driving the vehicle forward. Rolling resistance torque is the torque corresponding to the rolling resistance generated by the tires contacting the ground during vehicle movement. Wind resistance torque is the torque corresponding to the air resistance experienced by the vehicle during movement.

[0098] In one example, the first mass estimate is obtained by subtracting the wind resistance torque and rolling resistance torque (the wind resistance torque is dynamically calculated using air density, drag coefficient, frontal area, and the square of vehicle speed) from the motor output torque, and then dividing by the product of the tire radius and the real-time vehicle acceleration.

[0099] By acquiring key parameters such as the motor output torque and rolling resistance torque of electric vehicles in real time to determine the first mass estimate under driving conditions, we can not only improve the real-time performance and accuracy of target vehicle weight estimation, but also ensure that the motor output meets the needs of vehicle driving, while avoiding unnecessary energy waste.

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

[0101] Braking force refers to the force generated by the braking system that resists the movement of the vehicle during braking, and can be calculated from the brake fluid pressure. The gravitational component refers to the component of the vehicle's weight along the slope direction.

[0102] In some examples, the actual braking force is calculated based on the brake fluid pressure, and after adding the component of the vehicle's weight in the slope direction, it is divided by the measured deceleration value of the inertial measurement unit to obtain the second mass estimate.

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

[0104] Based on the above embodiments, the 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 the coasting energy recovery control strategy of the electric vehicle as 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, determining the coasting energy recovery control strategy of the electric vehicle as the electromechanical cooperative energy recovery mode.

[0105] Among them, the motor energy recovery mode refers to a braking mode in which energy is recovered solely by the motor 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 in which the motor and the mechanical braking system work together to provide braking torque when the required braking torque is greater than the motor's maximum recovery capacity.

[0106] Furthermore, when the required braking torque does not exceed the motor's maximum regenerative braking capacity, it is all allocated 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 the maximum recoverable torque; 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 400 Nm (the motor's maximum recovery capacity is 300 Nm), the motor is placed in maximum energy recovery mode, outputting the maximum recoverable torque within its capacity range, and the difference (400 Nm - 300 Nm = 100 Nm) is calculated in real time. This difference is precisely compensated by the mechanical braking system within 20 ms, forming a seamless hybrid braking. The compensation process can employ pressure-torque closed-loop control to ensure that the total output torque strictly matches the required value.

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

[0109] Based on the above embodiments, the method may further include: adjusting the required braking torque according to preset safety protection requirements to obtain the target required braking torque, wherein the safety protection requirements include the adjustment requirements of battery temperature, battery state of charge and motor temperature on 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, safety protection requirements can refer to a series of constraints that ensure the safe operation of the electric vehicle during braking and energy recovery. The target braking torque requirement is the braking torque value obtained by adjusting the original braking torque requirement according to the safety protection requirements, serving as the basis for actual braking and energy recovery control. For example, when the motor temperature is in the range of 100°C to 150°C, the required braking torque is reduced by 1% for every 1°C increase. Alternatively, when the battery state of charge is between 90% and 95%, 80% of the required braking torque output is allowed.

[0111] In one example, the required braking torque is 1000 Nm, but the maximum recovery torque corresponding to the maximum allowable discharge current of the battery is only 900 Nm. In this case, the required braking torque needs to be adjusted to 900 Nm, so that the final coasting energy recovery control strategy can be determined based on the braking torque of 900 Nm.

[0112] It should be noted that adjusting the target braking torque based on safety requirements can be done before determining the electric vehicle's coasting energy recovery control strategy, based on the required braking torque and the motor's maximum regenerative braking 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 they meet the safety requirements. If not, further adjustments are necessary to ensure the safety and stability of the electric vehicle during braking.

[0113] Optionally, safety protection requirements may also include a fault degradation strategy, which involves monitoring sensor data and automatically supplementing the lost data based on the fault degradation strategy if the data is lost. For example, if the slope signal is abnormal, if the vehicle continues to decelerate and the deceleration is greater than 0.15g, it is determined to be uphill, otherwise it is downhill, where g is the acceleration due to gravity. If the vehicle weight signal is lost, the effective average value of the most recent ten minutes is used.

[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 modifying 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 acquiring the vehicle speed, actual deceleration, multi-source mass observation parameters, and dynamic state parameters of the electric vehicle, the method may further include: acquiring the accelerator pedal state, brake pedal state, vehicle speed, battery temperature, and motor temperature of the electric vehicle; when the accelerator pedal state, brake pedal state, vehicle speed, battery temperature, and motor temperature meet preset energy recovery control conditions, triggering the energy recovery mode, and executing the steps of acquiring the vehicle speed, actual deceleration, multi-source mass observation parameters, and dynamic state parameters of the electric vehicle.

[0116] The accelerator pedal status refers to the current position or state of the accelerator pedal in an electric vehicle, reflecting the driver's intention or need to accelerate the vehicle. Similarly, the brake pedal status refers to the current position or state of the brake pedal in an electric vehicle, reflecting the driver's intention or need to brake the vehicle.

[0117] Energy recovery control conditions can refer to a series of preset conditions used to determine whether the energy recovery mode can be activated, including restrictions on the range of parameters 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 all five of the following conditions are met simultaneously:

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

[0120] 2. Brake pedal status: Maintain coasting recovery when the opening is less than 10%;

[0121] 3. Vehicle speed requirement: Start control when the speed exceeds 5 km / h;

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

[0123] 5. Motor temperature protection: The winding temperature is below the safety threshold of 150℃.

[0124] By acquiring and judging five key parameters, such as the accelerator pedal status, intelligent triggering of the energy recovery mode is achieved. This 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] Based on the above embodiments, 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 ratio 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, accelerator pedal opening refers to the degree to which the accelerator pedal is depressed, typically expressed as a percentage. Wheel speed difference refers to the speed difference between the wheels of an electric vehicle, or the difference between the vehicle's actual speed and its desired speed, used to determine the stability of the vehicle's driving state.

[0127] The preset difference threshold is a pre-defined limit for wheel speed difference. When the actual wheel speed difference exceeds this threshold, the vehicle's driving state is considered potentially unstable, and the energy recovery strategy needs to be adjusted. For example, when a wheel speed difference exceeding 0.2 m / s is detected, the braking torque will be limited to 20% (pre-defined percentage) of the actual calculated (demanded braking torque).

[0128] Preset accelerator pedal opening requirements refer to specific requirements for accelerator pedal opening. When these requirements are met, it is assumed that the driver has a clear intention to accelerate, and the vehicle needs to switch to drive mode. For example, an accelerator pedal opening exceeding 2%. Preset transition strategies refer to a preset control strategy used to smoothly switch an electric vehicle from energy recovery mode to drive mode, ensuring a smooth and safe transition process.

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

[0130] 1. The energy recovery torque is linearly reduced from 100% to zero through an S-curve.

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

[0132] 3. The torque change rate is forcibly limited to no more than 1000 Nm per second.

[0133] By increasing the monitoring of accelerator pedal opening and wheel speed difference, and corresponding recovery torque adjustment and mode switching strategies, the vehicle stability is ensured by adjusting the recovery torque when the vehicle's driving state is unstable; 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 electric vehicle coasting energy recovery control.

[0134] The execution process of the coasting energy recovery control method is described in detail below through some embodiments, taking the coasting energy recovery control system as the executing entity of the method. Figure 2 A schematic diagram illustrating the execution process of a coasting energy recovery control method provided in this application is shown below. Figure 2 As shown, the execution process includes three stages: data acquisition, control calculation, and execution. The data acquisition stage 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 the vehicle weight (i.e., the target vehicle weight), and provide slope feedback (i.e., the predicted slope data). Based on these four parameters—preset deceleration, vehicle speed, target vehicle weight, and predicted slope data—the system proceeds to the control calculation stage for further processing. The control calculation stage primarily involves the system using preset intelligent algorithms (such as Kalman filtering and vehicle dynamics models) to implement steep slope safety mechanisms (e.g., automatically adjusting feedforward torque based on predicted slope data), deceleration matching, and torque correction, ultimately obtaining the optimal torque result and proceeding to the execution stage. The execution stage primarily involves the system generating and sending control commands to the corresponding motor or other auxiliary equipment based on the optimal torque result, i.e., the coasting energy recovery control strategy. While the motor executes the coasting torque, the system monitors vehicle ride comfort and dynamically adjusts parameters when wheel speed differences or mode switching issues arise.

[0135] Figure 3 A schematic diagram of a specific process for a gliding energy recovery control method provided in this application is shown below. 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 states:

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

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

[0139] (3) Vehicle speed requirement: Start control when the speed exceeds 5 km / h;

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

[0141] (5) Motor temperature protection: The winding temperature is below the safety threshold of 150 degrees Celsius;

[0142] When all five conditions above are met, the main control cycle for energy recovery is activated.

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

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

[0145] Drive condition quality back-calculation: Subtract the wind resistance and rolling resistance torque from the motor output torque (wind resistance is dynamically calculated by air density, wind resistance coefficient, frontal area and vehicle speed square), and then divide by the product of tire radius and real-time acceleration to obtain the instantaneous quality estimate;

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

[0147] Multi-source data fusion: The mass estimates of driving and coasting conditions, together with the signals from the four-wheel suspension pressure sensors, are input into a Kalman filter for fusion and noise reduction, and the output is an optimized vehicle weight value (corresponding to the target vehicle weight) with an error of less than 3%.

[0148] (2) Slope perception fusion:

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

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

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

[0152] 3. Dynamic generation of dual-ring torque: Its period can be a high-speed period of 10 milliseconds. This method includes:

[0153] (1) Feedforward control channel:

[0154] The baseline torque is calculated in real time based on the vehicle dynamics model, and includes four core components:

[0155] ①Target inertia term: Vehicle weight multiplied by preset deceleration and then multiplied by tire radius;

[0156] ② Slope compensation: Vehicle weight multiplied by gravitational acceleration, then multiplied by the sine of the slope, and then multiplied by the tire radius;

[0157] ③ Drag component: The drag is calculated based on air density, drag coefficient, frontal area and the square of vehicle speed and converted into tire torque;

[0158] ④ Rolling resistance component: The frictional resistance torque is calculated based on the vehicle weight, the cosine value of the slope, and the rolling resistance coefficient;

[0159] Finally, after deducting 2% of the motor transmission loss, a feedforward torque output is generated.

[0160] (2) Feedback and compensation channel:

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

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

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

[0164] (1) Electromechanical Coordination Allocation Mechanism:

[0165] When the required torque does not exceed the motor's maximum recovery capacity, 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 the mechanical braking system, with a response time of less than 20 milliseconds.

[0168] (2) Seamless transition between driving modes:

[0169] Triggering condition: Accelerator pedal opening exceeds 2%;

[0170] Transition strategy:

[0171] The energy recovery torque is linearly reduced from 100% to zero using an S-shaped curve.

[0172] The drive torque increases linearly from zero to 100%.

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

[0174] Low adhesion optimization:

[0175] When the electronic stability system detects a wheel speed difference exceeding 0.2 m / s, it will limit the maximum recovery torque to 20 percent of the actual calculated value.

[0176] 5. Real-time security protection: Its cycle can be embedded throughout the entire lifecycle, and the method includes:

[0177] (1) Triple operational boundary protection:

[0178] Motor temperature limit:

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

[0180] For every 1 degree Celsius increase in the 100-150 degree Celsius range, torque decreases by one percent.

[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) Limitations:

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

[0184] When the battery level is between 90% and 95%, 80% torque output is allowed.

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

[0186] Battery temperature limit: Under low battery conditions, such as -20°C to 0°C, 50% torque output is allowed.

[0187] (2) Fault degradation strategy:

[0188] Sensor failure handling:

[0189] When the gradient signal is abnormal: if the vehicle continues to decelerate and the deceleration is greater than 0.15g, it is determined to be uphill; otherwise, it is downhill.

[0190] When the vehicle weight signal is lost: Use the effective average value of the most recent ten minutes.

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

[0192] Vehicle weight estimation optimization: Dynamically adjust the noise parameters of the Kalman filter based on 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 electric vehicle's CAN bus. The VCU acquires real-time data from the electric vehicle and determines whether to enter the coasting energy recovery mode. If so, it enters the real-time control module to perform the final recovery torque calculation. After the calculated recovery torque is corrected by the safety protection boundary, the torque is sent to the motor control unit (MCU), which then sends it to the drive motor.

[0194] The coasting energy recovery control method provided in this application improves the accuracy of coasting energy recovery control and thus improves energy recovery efficiency through a full-process control logic that includes preset deceleration, dynamic calculation of vehicle weight / gradient, motor torque adjustment, and deceleration verification. It also utilizes a longitudinal dynamics algorithm that uses the main drive motor load to infer vehicle weight, a noise filtering implementation, a weighted fusion method of gradient sensor data and navigation elevation prediction, a sliding window prediction model, a composite output rule and anti-saturation constraint condition for the feedforward reference torque calculation model and feedback compensation, and a dynamic torque weight allocation strategy based on operating condition identification and an S-curve transition function.

[0195] Figure 4 This application provides a schematic diagram of the structure of a gliding energy recovery control system, as shown below. Figure 4 As shown, the coasting energy recovery control system 40 provided in this embodiment includes:

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

[0197] Processing module 402 is used to 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 based on Kalman filtering, and the predicted slope data is obtained by predicting the slope trend based on dynamic state parameters and target vehicle weight.

[0198] The first determining module 403 is used to determine the feedback compensation torque based on the difference between the preset deceleration and the actual deceleration;

[0199] Module 404 is obtained, which 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 used to 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.

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

[0202] In one possible implementation, the acquisition module 401 can also be used to: acquire the motor output torque, rolling resistance 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 vehicle speed; update the motor output torque based on the rolling resistance 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 one possible implementation, the acquisition module 401 can also be used to: acquire the braking force and gravity component of the electric vehicle; and determine a second mass estimate under the coasting condition based on the braking force, gravity component, and actual deceleration.

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

[0205] In one possible implementation, the second determining module 405 can also be used to: adjust the required braking torque according to preset safety protection requirements to obtain the target required braking torque, wherein the safety protection requirements include the adjustment requirements of battery temperature, battery state of charge and motor temperature on 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: acquire the accelerator pedal state, brake pedal state, vehicle speed, battery temperature, and motor temperature of the electric vehicle; when the accelerator pedal state, brake pedal state, vehicle speed, battery temperature, and motor temperature meet the preset energy recovery control conditions, trigger the energy recovery mode and execute the steps of acquiring the vehicle speed, actual deceleration, multi-source mass observation parameters, and dynamic state parameters of the electric vehicle.

[0207] In one possible implementation, the second determining 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 recovered torque in the coasting energy recovery control strategy to a preset ratio of the required braking torque; when the accelerator pedal opening meets the preset opening requirement, switch the electric vehicle from the energy recovery mode to the driving mode based on a preset transition strategy.

[0208] The gliding energy recovery control system provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0209] This application also provides a vehicle, including a vehicle body and a vehicle controller, which is used to execute the methods in the above embodiments. The specific implementation process of the vehicle controller can be found in the above method embodiments, and its implementation principle and technical effects are similar; therefore, it will not be repeated 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, memory 502, and communication component 503 are connected via a bus 504.

[0211] In a specific implementation, at least one processor 501 executes computer execution instructions stored in memory 502, causing at least one processor 501 to perform the above-described method.

[0212] The specific implementation process of processor 501 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0213] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0214] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[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, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0216] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0217] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0218] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage 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 can be any available medium accessible to a general-purpose or special-purpose computer.

[0219] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0220] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0221] The units described as separate components may or may not be physically separate. The 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 the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0222] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0223] 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 this invention, or the part that contributes to the prior art, or a part 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 to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0224] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to 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; and 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 other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A method for controlling gliding energy recovery, characterized in that, include: Acquire the vehicle speed, actual deceleration, multi-source mass observation parameters, and dynamic state parameters of electric vehicles; Based on the vehicle speed, preset deceleration, target vehicle weight, and predicted slope data, the feedforward torque is determined. The target vehicle weight is obtained by fusing and denoising the multi-source mass observation parameters using Kalman filtering. The predicted slope data is obtained by predicting the slope trend based on the dynamic state parameters and the target vehicle weight. The feedback compensation torque is determined based on the difference between the preset deceleration and the actual deceleration; The required braking torque is obtained by linearly superimposing the feedforward torque and the feedback compensation 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 is determined; The step of determining the feedforward torque based on the vehicle speed, preset deceleration, target vehicle weight, and predicted gradient data includes: Based on the target vehicle weight and the preset deceleration, the target inertial term is determined; Based on the target vehicle weight and the predicted slope data, a slope compensation item is determined; Based on the vehicle speed and wind resistance characteristics, the wind resistance component and the tire torque corresponding to the wind resistance component are determined. Based on the target vehicle weight, the predicted slope data, and the rolling resistance characteristic parameters, the rolling resistance component and the frictional resistance torque corresponding to the rolling resistance component are determined. The feedforward torque is determined based on the target inertia term, the slope compensation term, the tire torque, and the frictional resistance torque.

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

3. The method according to claim 2, characterized in that, The method further includes: The motor output torque, rolling resistance torque, wind resistance characteristics, vehicle acceleration, and tire radius of the electric vehicle are obtained. The drag torque is determined based on the aforementioned drag characteristic parameters and the vehicle speed. The motor output torque is updated based on the rolling resistance torque and the wind resistance torque to obtain the target motor output torque; Based on the target motor output torque, the vehicle acceleration, and the tire radius, a first mass estimate under the driving condition is determined.

4. The method according to claim 2, characterized in that, The method further includes: Obtain the braking force and gravity components of the electric vehicle; Based on the braking force, the gravitational component, and the actual deceleration, a second mass estimate is determined for the gliding condition.

5. The method according to claim 1, characterized in that, The step of determining the 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, the coasting energy recovery control strategy of the electric vehicle is determined to be 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, the coasting energy recovery control strategy of the electric vehicle is determined to be the electromechanical cooperative energy recovery mode.

6. The method according to any one of claims 1-5, characterized in that, The method further includes: 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 battery temperature, battery state of charge and motor temperature on the required braking torque. Accordingly, determining the coasting energy recovery control strategy for the electric vehicle based on the required braking torque and the maximum recovery capacity of the motor includes: Based on the target braking torque requirement and the maximum regenerative braking capacity of the motor, the coasting energy recovery control strategy of the electric vehicle is determined.

7. The method according to any one of claims 1-5, characterized in that, Before acquiring the vehicle speed, actual deceleration, multi-source mass observation parameters, and dynamic state parameters of the electric vehicle, the method further includes: The accelerator pedal status, brake pedal status, vehicle speed, battery temperature, and motor temperature of the electric vehicle are obtained. 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 acquiring the electric vehicle's speed, actual deceleration, multi-source mass observation parameters, and dynamic state parameters are executed.

8. The method according to claim 7, characterized in that, 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: Determine the accelerator pedal opening and wheel speed difference of the electric vehicle; When the wheel speed difference exceeds a preset difference threshold, the recovery torque in the coasting energy recovery control strategy is adjusted to a preset ratio of the required braking torque; When the accelerator pedal opening meets the preset opening requirement, the electric vehicle is switched from the energy recovery mode to the drive mode based on the preset transition strategy.

9. A gliding energy recovery control system, characterized in that, include: The acquisition module is used to acquire the vehicle speed, actual deceleration, multi-source mass observation parameters, and dynamic state parameters of the electric vehicle. The processing module is used to determine the feedforward torque based on the vehicle speed, preset deceleration, target vehicle weight, and predicted slope data. The target vehicle weight is obtained by fusing and denoising the multi-source mass observation parameters using 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. The step of determining the feedforward torque based on the vehicle speed, preset deceleration, target vehicle weight, and predicted slope data includes: determining a target inertia term based on the target vehicle weight and the preset deceleration; determining a slope compensation term based on the target vehicle weight and the predicted slope data; determining a wind resistance component and the tire torque corresponding to the wind resistance component based on the vehicle speed and wind resistance characteristic parameters; determining a rolling resistance component and the frictional resistance torque corresponding to the rolling resistance component based on the target vehicle weight, the predicted slope data, and rolling resistance characteristic parameters; and determining the feedforward torque based on the target inertia term, the slope compensation term, the tire torque, and the frictional resistance torque. A first determining module is used to determine the feedback compensation torque based on the difference between the preset deceleration and the actual deceleration. The module is used to linearly superimpose the feedforward torque and the feedback compensation torque to obtain the required braking torque; The second determining module is used to 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.

10. A vehicle, characterized in that, It includes a vehicle body and a vehicle controller, the vehicle controller being used to perform the method as described in any one of claims 1-8.

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