Coasting energy recovery control method, device, controller and vehicle

Abstract

The application provides a coasting energy recovery control method and device, a controller and a vehicle, and relates to the technical field of vehicle driving. The method comprises the following steps: obtaining the battery state of charge, the charging power and the power system parameters of a target vehicle; determining the maximum wheel end negative torque of the target vehicle and the maximum coasting deceleration corresponding to the maximum wheel end negative torque according to the battery state of charge, the charging power and the power system parameters; determining the target deceleration according to a deceleration reference table, the maximum coasting deceleration, the vehicle weight and the vehicle speed of the target vehicle, the deceleration reference table being used for querying the coasting deceleration under different driving conditions, the driving conditions comprising: time period, vehicle weight, slope and vehicle speed, and the deceleration reference table being determined by correcting the target deceleration in the historical energy recovery process based on a self-learning mechanism; and controlling the motor of the target vehicle to apply brake torque according to the target deceleration, so as to realize energy recovery. The application improves the control accuracy and the energy recovery efficiency.

Description

Technical Field

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

[0002] With the rapid development of vehicle driving technology, coasting energy recovery technology, as one of the key means to improve vehicle energy efficiency, is widely used in various types of vehicles. This technology maximizes energy utilization by intelligently controlling the intensity of energy recovery during coasting (i.e., non-active acceleration or braking), thereby extending the vehicle's driving range and reducing energy consumption.

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

[0004] However, both manual adjustment and cloud-based automatic control methods suffer from insufficient control precision during coasting energy recovery, which in turn affects energy recovery efficiency. Summary of the Invention

[0005] The coasting energy recovery control method, device, controller, and vehicle provided in this application are intended to solve the problem that existing methods have insufficient control accuracy during coasting energy recovery control, which in turn affects energy recovery efficiency.

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

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

[0008] Based on the battery state of charge, charging power and power system parameters, determine the maximum wheel-end negative torque of the target vehicle and the maximum slip deceleration corresponding to the maximum wheel-end negative torque.

[0009] The target deceleration is determined based on the deceleration reference table, the maximum coasting deceleration, and the vehicle weight and speed of the target vehicle. The deceleration reference table is used to query the coasting deceleration under different driving conditions, including: time period, vehicle weight, slope, and vehicle speed. The deceleration reference table is determined by correcting the target deceleration in the historical energy recovery process based on a self-learning mechanism.

[0010] The target vehicle's motor is controlled to apply braking torque based on the target deceleration in order to achieve energy recovery.

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

[0012] In one possible implementation, during the energy recovery process, acquiring the actual deceleration and target deceleration of the target vehicle under the current driving conditions includes: during the energy recovery process, determining whether the target vehicle meets the self-learning conditions, the self-learning conditions including the activation of the coasting energy recovery function of the target vehicle, the operating state and slip ratio meeting preset requirements, and the accelerator pedal opening being zero for a preset time; when the target vehicle meets the self-learning conditions, acquiring the actual deceleration and target deceleration of the target vehicle under the current driving conditions.

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

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

[0015] In one possible implementation, determining the target deceleration based on a deceleration reference table, the maximum coasting deceleration, and the target vehicle's weight and speed includes: determining an initial deceleration from the deceleration reference table based on the target vehicle's weight and speed, as well as the time and gradient of the target vehicle; determining the initial deceleration as the target deceleration when the initial deceleration is less than or equal to the maximum coasting deceleration; and determining the maximum coasting deceleration as the target deceleration when the initial deceleration is greater than the maximum coasting deceleration.

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

[0017] Secondly, embodiments of this application provide a coasting energy recovery control device, comprising:

[0018] The acquisition module is used to acquire the target vehicle's battery state of charge, charging power, and powertrain parameters.

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

[0020] The processing module is used to determine the target deceleration based on the deceleration reference table, the maximum coasting deceleration, and the vehicle weight and speed of the target vehicle. The deceleration reference table is used to query the coasting deceleration under different driving conditions, including time period, vehicle weight, slope, and vehicle speed. The deceleration reference table is determined based on the target deceleration in the historical energy recovery process through a self-learning mechanism.

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

[0022] Thirdly, embodiments of this application provide a controller, including: a memory and a processor;

[0023] The memory stores instructions that the computer executes;

[0024] 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.

[0025] Fourthly, embodiments of this application provide a vehicle, including a vehicle body and a controller, the controller being used to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0026] 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.

[0027] 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.

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

[0029] 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.

[0030] Figure 1 A schematic flowchart of the gliding energy recovery control method provided in this application;

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

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

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

[0034] The accompanying drawings illustrate 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 particular embodiments. Detailed Implementation

[0035] 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 denote 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 apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0036] In existing technologies, manual adjustment requires the driver to judge and adjust the intensity of coasting energy recovery in real time based on road conditions and vehicle speed. This process involves subjectivity and lag in human judgment. The driver's reaction speed and judgment accuracy are affected by various factors, such as driving experience and distraction, which leads to untimely intervention on vehicle speed and insufficient precision in adjusting the recovery intensity. The energy during coasting may not be recovered optimally. At the same time, the driver spends a lot of energy frequently intervening in the intensity of coasting recovery, which is not conducive to safe driving.

[0037] While cloud-based algorithm-based automatic control can predict coasting deceleration and adjust recovery intensity, the process of building a training algorithm in the cloud, applying it on the vehicle, making adjustments on the vehicle, and then feeding back to the cloud for iterative training presents several challenges. These include a large amount of input information, algorithmic complexity, high computational load, slow iteration speed, and massive computational resource consumption. This can prevent the system from adapting promptly to changes in road conditions and vehicle status, resulting in inflexible and inefficient adjustments to recovery intensity, thus impacting energy recovery efficiency. Furthermore, communication latency between the cloud and the vehicle can also affect the real-time performance and accuracy of the control.

[0038] Therefore, both manual adjustment and cloud-based automatic control methods suffer from insufficient control precision during coasting energy recovery, which in turn affects energy recovery efficiency.

[0039] To address the aforementioned issues, this application provides a coasting energy recovery control method, device, controller, and vehicle. Based on the target vehicle's battery state of charge, charging power, and powertrain parameters, it can accurately calculate the maximum wheel-end negative torque and corresponding maximum coasting deceleration that the vehicle can apply under the current conditions. Combined with a deceleration reference table, a target deceleration is determined. This table provides refined coasting deceleration queries based on various driving conditions such as time period, vehicle weight, gradient, and vehicle speed, enabling optimal control under different driving environments. Simultaneously, the table utilizes a self-learning mechanism to collect and analyze deceleration data during the energy recovery process for correction and optimization, allowing the deceleration reference table to adapt to different driving habits and road condition changes, thereby continuously improving control accuracy and energy recovery efficiency. Finally, by precisely controlling the motor to apply braking torque through the target deceleration, efficient energy recovery during coasting is achieved, ensuring both driving comfort and safety while improving vehicle energy efficiency.

[0040] 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 now be described with reference to the accompanying drawings.

[0041] 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 target vehicle's battery state of charge, charging power, and power system parameters; determine the target vehicle's maximum wheel-end negative torque and the corresponding maximum coasting deceleration based on the battery state of charge, charging power, and power system parameters; determine the target deceleration based on the deceleration reference table, the maximum coasting deceleration, and the target vehicle's weight and speed. The deceleration reference table is used to query the coasting deceleration under different driving conditions, including: time period, vehicle weight, gradient, and vehicle speed. The deceleration reference table is determined based on a self-learning mechanism that corrects the target deceleration during historical energy recovery processes; and apply braking torque to the target vehicle's motor according to the target deceleration to achieve energy recovery.

[0042] Figure 1 A flowchart illustrating a gliding energy recovery control method provided in this application is shown below. Figure 1 As shown, the method may include:

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

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

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

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

[0047] S102. Based on the battery state of charge, charging power, and power system parameters, determine the maximum wheel-end negative torque of the target vehicle and the maximum slip deceleration corresponding to the maximum wheel-end negative torque.

[0048] Specifically, when the regenerative braking power in the motor's external characteristics exceeds the battery's charging power, the motor's maximum regenerative braking torque may need to be adjusted to ensure that the actual recovered energy does not exceed the battery's capacity. When the State of Charge (SOC) is close to full, to avoid overcharging and damaging the battery, it may be unable to accept more energy, which could limit the use of regenerative braking torque and thus limit the magnitude of the maximum wheel-end negative torque. Therefore, the battery's state of charge and charging power can be used to adjust the motor's regenerative braking torque when determining the target vehicle's maximum wheel-end negative torque, ensuring that the target vehicle's maximum wheel-end negative torque meets both actual driving needs and battery safety.

[0049] The maximum wheel-end negative torque refers to the maximum braking torque that the motor can provide when the vehicle is coasting, used for deceleration and energy recovery. Furthermore, by comprehensively considering the battery status, charging power, and power system parameters, the maximum braking torque that the target vehicle can apply under current conditions, as well as the resulting maximum coasting deceleration, can be accurately calculated, providing a reference for subsequently determining the target deceleration.

[0050] S103. Determine the target deceleration based on the deceleration reference table, the maximum coasting deceleration, and the vehicle weight and speed of the target vehicle. The deceleration reference table is used to query the coasting deceleration under different driving conditions. Driving conditions include: time period, vehicle weight, slope, and vehicle speed. The deceleration reference table is determined based on the target deceleration in the historical energy recovery process through a self-learning mechanism.

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

[0052] To reduce computational resources and improve response speed, various factors in driving conditions can be divided into intervals. Time periods can be based on factors such as traffic flow and driving habits, with a focus on peak hours. Vehicle weight affects inertia, braking performance, and energy recovery potential; therefore, vehicle weight can be categorized as light, medium, and heavy vehicles, or more specific weight ranges can be used. Gradient is a crucial factor affecting vehicle coasting and energy recovery; it can be categorized as flat roads, slight uphill (downhill), moderate uphill (downhill), and steep uphill (downhill), or more specific gradient ranges can be used. Vehicle speed is a key factor influencing deceleration selection and energy recovery effectiveness. Significant differences in braking performance and energy recovery potential exist at different speeds; therefore, multiple speed ranges (speed segments) can be defined, such as low-speed (e.g., 0-30 km / h), medium-speed (e.g., 30-60 km / h), and high-speed (e.g., above 60 km / h). Within each speed range, smaller speed intervals can be further subdivided to more accurately determine the target deceleration. Thus, based on the different combinations of time periods, vehicle weight, gradient, and speed, multiple driving conditions are created, each corresponding to a coasting deceleration value.

[0053] Based on the current time period, vehicle weight, gradient, and speed, the deceleration benchmark table is consulted to find the corresponding coasting deceleration as the target deceleration. The deceleration benchmark table is continuously optimized through historical data and a self-learning mechanism. Simultaneously, by imposing numerical constraints on the target deceleration based on the maximum coasting deceleration, a target deceleration that meets energy recovery requirements while ensuring driving comfort and safety can be determined, helping to maximize energy recovery and achieve smooth vehicle deceleration during coasting.

[0054] S104. Control the motor of the target vehicle to apply braking torque according to the target deceleration in order to achieve energy recovery.

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

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

[0057] The coasting energy recovery control method provided in this application, based on the target vehicle's battery state of charge, charging power, and power system parameters, can accurately calculate the maximum wheel-end negative torque that the vehicle can apply in the current state, as well as the corresponding maximum coasting deceleration, providing a data foundation for subsequent precise control. Combined with a deceleration reference table, a target deceleration is determined. This table provides refined coasting deceleration queries based on various driving conditions such as time period, vehicle weight, gradient, and vehicle speed, enabling rapid determination of the most suitable coasting deceleration under different driving environments, thereby achieving refined control. Simultaneously, this table utilizes a self-learning mechanism to correct and optimize the deceleration data during the energy recovery process, allowing the deceleration reference table to adapt to different driving habits and road condition changes, thereby continuously improving control accuracy and energy recovery efficiency. Finally, by precisely controlling the motor to apply braking torque through the target deceleration, energy can be recovered to the maximum extent possible while ensuring driving comfort and safety.

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

[0059] In this embodiment, actual deceleration refers to the deceleration value actually achieved by the vehicle during actual coasting. Iterative correction refers to the process of repeatedly acquiring the actual value and comparing it with the target value under the current driving conditions, and adjusting the target value accordingly to gradually bring the two closer together. Furthermore, the correction condition can satisfy that a single correction is less than 0.1, and small-step iterations are used to prevent fluctuations. For example, 25% of the difference between the actual coasting deceleration and the target deceleration is taken as the correction amount. By limiting the correction amount of each iteration to a small range, fluctuations or instability in the self-learning process due to excessive correction amounts can be prevented.

[0060] The preset convergence condition can refer to a judgment criterion set during the iterative correction process. Through multiple iterations, it gradually approaches an optimal or stable deceleration value, making the difference between the actual gliding deceleration and the target deceleration smaller and smaller until the preset accuracy requirement is met.

[0061] Linear regression is a statistical analysis method used to fit an optimal straight line or curve based on the distribution of data points, revealing the linear relationship between variables. The coasting deceleration distribution curve, obtained through linear regression based on the first corrected deceleration under multiple driving conditions, describes the change in coasting deceleration with driving conditions, providing a reference for subsequent energy recovery control. Updating the deceleration reference table can involve adding new data points or correcting existing data points to make the reference table more closely reflect actual driving conditions.

[0062] Furthermore, the actual deceleration can include multiple collected deceleration values. For example, when different driving conditions are determined through different vehicle speed segments (speed ranges), the actual deceleration includes multiple deceleration values ​​within the current speed range. In this case, the difference between decelerations is the difference between the average of the multiple deceleration values ​​within the current speed range and the target deceleration. Figure 2 A schematic diagram of a gliding deceleration distribution curve provided in this application, as shown below. Figure 2 As shown, when different driving conditions are determined through different vehicle speed ranges, the coasting deceleration distribution curve is a curve showing the change of coasting deceleration with vehicle speed. In the figure, the horizontal axis represents vehicle speed and the vertical axis represents coasting deceleration. Different horizontal dashed line segments correspond to different vehicle speed ranges. The dots on each horizontal dashed line segment correspond to the first corrected deceleration under the corresponding vehicle speed range. By determining the deceleration through the vehicle speed under different driving conditions, energy recovery can be achieved.

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

[0064] In some examples, through multiple iterative corrections, the learning process continues until the difference between the actual deceleration learned in this iteration and the target deceleration recorded in the previous iteration is less than 25% of the target deceleration recorded in the previous iteration. This indicates that the deceleration has stabilized, and the learning process terminates, with no further recording of the current result. If the difference is greater than or equal to 25%, it indicates a significant change in deceleration, possibly due to changes in road conditions, vehicle status, or environmental factors. In this case, the system will record a new deceleration value and make corrections based on this new value to adapt to the new driving conditions.

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

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

[0067] Based on the above embodiments, the method for obtaining the actual deceleration and target deceleration of the target vehicle under the current driving conditions during the energy recovery process may include: determining whether the target vehicle meets the self-learning conditions during the energy recovery process, the self-learning conditions including the activation of the coasting energy recovery function of the target vehicle, the operation status and slip ratio meeting preset requirements, and the accelerator pedal opening being zero for a preset time; when the target vehicle meets the self-learning conditions, obtaining the actual deceleration and target deceleration of the target vehicle under the current driving conditions.

[0068] The operating status refers to whether the vehicle has any malfunctions during operation. Slip ratio refers to the degree of wheel slippage relative to the road surface; it's a parameter describing the contact state between the vehicle's tires and the road surface. Preset requirements are used to determine whether the vehicle is in a safe driving condition; for example, the operating status indicates that the vehicle has no malfunctions and the slip ratio is less than 15%.

[0069] In one example, during energy recovery, the system first checks whether the coasting energy recovery function is activated, then monitors the vehicle's operating status and slip ratio to ensure they meet preset self-learning requirements. Simultaneously, it checks if the accelerator pedal opening is zero and monitors this continuously for a preset period to ensure the vehicle is in a stable coasting state. Further, the self-learning conditions are: coasting energy recovery function activated, vehicle without faults, slip ratio less than 15% (exiting self-learning if it exceeds this), accelerator pedal at zero, and maintained for a minimum calibration time (0.5s). Within a certain speed range, regardless of whether acceleration is achieved by pressing the accelerator or brake, releasing the accelerator triggers learning. Releasing the accelerator ignores the frequency and depth of accelerator and brake inputs, as well as their sequence with coasting conditions; the focus is on this speed range. The presence of a coasting condition indicates a coasting intention within that range. The combination of accelerator and brake inputs is intended to achieve this coasting acceleration; therefore, only the actual deceleration needs to be recorded.

[0070] By setting self-learning conditions, it is possible to ensure that self-learning occurs at the appropriate time, thereby avoiding inaccurate learning results due to inappropriate conditions (such as unstable vehicle status or inactive coasting energy recovery function), which in turn affects the accuracy of coasting energy recovery control and energy recovery efficiency.

[0071] Based on the above embodiments, before applying braking torque to the motor of the target vehicle according to the first corrected deceleration to achieve energy recovery, the method may further include: determining the maximum and minimum deceleration of the target vehicle under different vehicle weights and speeds based on the battery state of charge, charging power, and power system parameters; determining a correction range for the target deceleration based on the maximum and minimum deceleration of the target vehicle under different vehicle weights and speeds; adjusting the first corrected deceleration when it does not meet the correction range to obtain an adjusted first corrected deceleration; correspondingly, applying braking torque to the motor of the target vehicle according to the first corrected deceleration to achieve energy recovery includes: applying braking torque to the motor of the target vehicle according to the adjusted first corrected deceleration to achieve energy recovery.

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

[0073] The correction range can be determined based on the maximum and minimum decelerations, and is used to verify and adjust the first corrected deceleration. This range can be adjusted according to actual needs to ensure that the first corrected deceleration is reasonable and feasible within this range. The adjustment method can be linear adjustment, proportional adjustment, or other suitable adjustment methods; no particular limitation is made here.

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

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

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

[0077] Rear axle ratio refers to the gear ratio of the transmission gears in the rear axle of a vehicle, and is also used to change the vehicle's transmission ratio and driving force. Vehicle weight refers to the vehicle's curb weight, including the weight of the vehicle itself and the weight of its cargo. Tire radius refers to the effective radius of a tire under standard tire pressure and load, affecting the vehicle's speed and deceleration.

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

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

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

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

[0082] For example, based on the target vehicle's weight, speed, current time (considering traffic congestion or changes in driving habits over time), and gradient information, the corresponding initial deceleration value is retrieved from the deceleration reference table, which contains deceleration data for different combinations of vehicle weight, speed, time, and gradient. Then, by comparing the initial deceleration with the maximum coasting deceleration, it is ensured that the target deceleration conforms to the vehicle's actual driving conditions without exceeding the maximum coasting deceleration the vehicle can achieve.

[0083] By taking into account actual driving conditions such as time and gradient, the initial deceleration of the vehicle can be determined more accurately, and the deceleration can be checked and adjusted based on the maximum coasting deceleration, thereby ensuring that the target deceleration can adapt to different driving environments and driving needs, and achieve efficient energy recovery.

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

[0085] In this embodiment, wheel speed difference refers to the difference in rotational speed between the left and right wheels of the vehicle, typically used to reflect the dynamic characteristics of the vehicle during cornering. Steering angle refers to the angle of deflection of the steering wheel or front wheels relative to the straight-line direction of travel, used to control the vehicle's direction of travel. Cornering radius refers to the radius of curvature of the trajectory of the vehicle during cornering, determining the severity of the turn. A preset speed threshold is a set speed value used to determine whether the vehicle is traveling at high or low speed, allowing for the selection of different methods to determine the cornering radius; for example, setting the speed threshold to 30 km / h.

[0086] For example, vehicle sensors can acquire real-time rotational speed information of the left and right wheels and calculate the wheel speed difference; simultaneously, steering angle information can be obtained through steering wheel sensors or front wheel sensors. When the vehicle speed is greater than or equal to a preset speed threshold, the turning radius during the turning process is calculated based on the wheel speed difference and the vehicle dynamics model to account for the dynamic characteristics of the vehicle turning at high speeds. When the vehicle speed is less than the preset speed threshold, the turning radius is calculated through geometric relationships based on the steering angle and vehicle geometric parameters (such as front track and wheelbase) to reflect the turning situation at low speeds.

[0087] The process of correcting the target deceleration based on the curve radius involves selecting an appropriate method based on the specific vehicle and driving conditions. For example, for autonomous vehicles or advanced driver assistance systems (ADAS), simulation analysis based on vehicle dynamics models or methods based on empirical formulas can be used; while for traditional vehicles or manual driving situations, manual adjustment methods based on driver experience can be employed. Furthermore, the empirical formula can be established based on a large amount of experimental data or actual driving data, describing the deceleration value that should be adjusted to maintain vehicle stability and safety under different curve radii.

[0088] By acquiring wheel speed difference and steering angle information in real time and determining the corner radius accordingly, the dynamic characteristics of the vehicle during cornering can be estimated more accurately. This allows for correction of the target deceleration, making coasting energy recovery more adaptable to different cornering situations and driving needs. It can solve the problems of inaccurate control and low efficiency caused by insufficient consideration of the vehicle's dynamic characteristics during cornering.

[0089] Based on the above embodiments, the following example illustrates the coasting energy recovery control method, which may include:

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

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

[0092] 3. Data acquisition and correction.

[0093] 3.1 Preset Values: Based on experience, preset coasting deceleration values ​​are established for different time periods, vehicle weights, gradients, and speed ranges, especially for the morning, noon, and evening periods when traffic conditions significantly impact vehicle speed. A deceleration baseline table is obtained, as shown in Table 1. Time periods are divided into: 7:00-9:00, 11:00-13:00, 17:00-19:00, and other time periods; vehicle weight is divided into empty and heavily loaded cases; gradient is divided into: 10-30, 5-10, 2-5, -2 - 2, -5 - -2, -10 - -5, -30 - -10; and speed ranges are divided into: 0-10, 10-20, 20-30, 30-40, 40-50, 50-70, and 70-90. Based on the combination of different time periods, vehicle weights, gradients, and speed ranges, multiple driving conditions are formed, with each condition corresponding to a coasting deceleration value. It should be noted that the initial table was set based on human experience, and the table used each time thereafter is the table updated in the previous self-learning process.

[0094] Table 1 Deceleration Reference Table

[0095]

[0096] 3.2 Curve-based correction: When the vehicle speed is higher than 30km / h (which can be adjusted in practice), the curve radius is calculated based on the wheel speed difference. When the vehicle speed is lower than 30km / h, the curve radius is determined based on the recorded steering angle, and the deceleration is corrected according to the turning radius.

[0097] 4. Self-learning: Drivers' expected coasting deceleration varies significantly across different driving scenarios. For example, road conditions can differ greatly at different times of the same road segment at the same speed, leading to substantial differences in drivers' expected coasting deceleration at the same speed over different time periods. Because drivers' expected deceleration differs, the coasting deceleration is adjusted based on driving behavior analysis within specific driving scenarios.

[0098] 4.1 Self-learning conditions are met:

[0099] 1. The vehicle has no malfunctions; 2. The coasting energy recovery switch is activated; 3. The slip ratio is less than or equal to 15%. When it is greater than 15%, the state learning ends; 4. The accelerator pedal is at zero and maintained for the minimum calibration time (0.5s). Within the speed range, regardless of whether the accelerator and brake are pressed, as long as the accelerator is released, the learning is triggered. Releasing the accelerator will indicate a coasting intention in that range. Just pay attention to the actual deceleration.

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

[0101] During the correction process, the correction range can be limited based on the maximum and minimum deceleration values ​​of the vehicle under different vehicle weights and speeds, thereby avoiding overcorrection and improving correction accuracy.

[0102] 4.3 Iteration: The decelerations learned and corrected from multiple speed segments are processed using linear regression and updated in the deceleration baseline table. This value can be called upon the next trigger of coasting deceleration self-learning (e.g., ...). Figure 2 (The shown is the gliding deceleration distribution curve), repeat 4.2.

[0103] 4.4 Learning Termination and Re-triggering: Accumulated deceleration is calculated. If the gliding deceleration occurs on the same route, within the same speed range, and during the same time period each day, it will converge to a small range. This includes multiple iterations until the difference between the current learned deceleration and the previous deceleration is less than 25% of the previous recorded value. In this case, no record is made. If the deviation is greater than 25% of the previous recorded value, it is recorded and corrected again.

[0104] The coasting energy recovery control method provided in this application establishes a deceleration reference table based on the influence of different vehicle speed ranges, vehicle weight, gradient, and traffic conditions at different times. Based on the curve radius and a self-learning mechanism, the deceleration reference table is continuously updated, making the coasting deceleration more consistent with actual driving conditions and the driver's expected needs. Thus, utilizing the vehicle's existing signals and resources, and considering the significant differences in traffic conditions at different times during actual driving, which lead to varying vehicle speeds and different expectations for coasting deceleration, the method performs self-learning on the vehicle's coasting deceleration to achieve automatic coasting deceleration control. This reduces braking usage, maximizes energy recovery, and ultimately achieves further energy-saving effects.

[0105] Figure 3 A schematic diagram of the gliding energy recovery control device provided in this application is shown below. Figure 3 As shown, the gliding energy recovery control device 30 provided in this embodiment includes:

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

[0107] The determination module 302 is used to determine the maximum wheel-end negative torque of the target vehicle and the maximum slip deceleration corresponding to the maximum wheel-end negative torque based on the battery state of charge, charging power and power system parameters.

[0108] The processing module 303 is used to determine the target deceleration based on the deceleration reference table, the maximum coasting deceleration, and the vehicle weight and speed of the target vehicle. The deceleration reference table is used to query the coasting deceleration under different driving conditions. The driving conditions include: time period, vehicle weight, slope and vehicle speed. The deceleration reference table is determined based on the target deceleration in the historical energy recovery process through a self-learning mechanism.

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

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

[0111] In one possible implementation, the processing module 303 can also be used to: determine whether the target vehicle meets the self-learning conditions during the energy recovery process. The self-learning conditions include the activation of the coasting energy recovery function of the target vehicle, the operation status and slip ratio meeting preset requirements, and the accelerator pedal opening being zero for a preset time. When the target vehicle meets the self-learning conditions, the actual deceleration and target deceleration of the target vehicle under the current driving conditions are obtained.

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

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

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

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

[0116] The gliding energy recovery control device 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.

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

[0118] In a specific implementation, at least one processor 401 executes computer execution instructions stored in memory 402, causing at least one processor 401 to perform the above-described method.

[0119] The specific implementation process of processor 401 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0120] 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.

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

[0122] 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.

[0123] This application also provides a vehicle, including a vehicle body and a controller, the controller being used to execute the methods described in the above embodiments. The specific implementation process of the controller can be found in the above method embodiments, as its implementation principle and technical effects are similar, and will not be repeated here.

[0124] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0125] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of 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.

[0132] 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.

[0133] 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: Obtain the target vehicle's battery state of charge, charging power, and powertrain parameters; Based on the battery state of charge, the charging power, and the power system parameters, determine the maximum wheel-end negative torque of the target vehicle and the maximum slip deceleration corresponding to the maximum wheel-end negative torque. The target deceleration is determined based on the deceleration reference table, the maximum coasting deceleration, and the vehicle weight and speed of the target vehicle. The deceleration reference table is used to query the coasting deceleration under different driving conditions. The driving conditions include: time period, vehicle weight, slope, and vehicle speed. The deceleration reference table is determined by correcting the target deceleration in the historical energy recovery process based on a self-learning mechanism. The target vehicle's motor is controlled to apply braking torque based on the target deceleration in order to achieve energy recovery; During the energy recovery process, the actual deceleration and target deceleration of the target vehicle under the current driving conditions are obtained; Based on the difference between the actual deceleration and the target deceleration, the target deceleration under the current driving conditions is iteratively corrected until the difference between the actual deceleration and the target deceleration meets the preset convergence condition, and the first corrected deceleration is obtained. The target vehicle's motor is controlled to apply braking torque according to the first corrected deceleration to achieve energy recovery; After obtaining the first corrected deceleration under multiple driving conditions, linear regression processing is performed on the first corrected deceleration under the multiple driving conditions to generate a coasting deceleration distribution curve. The deceleration reference table is updated based on the gliding deceleration distribution curve.

2. The method according to claim 1, characterized in that, The process of energy recovery, including obtaining the actual deceleration and target deceleration of the target vehicle under current driving conditions, includes: During the energy recovery process, it is determined whether the target vehicle meets the self-learning conditions. The self-learning conditions include the activation of the coasting energy recovery function of the target vehicle, the operation status and slip ratio meeting preset requirements, and the accelerator pedal opening being zero for a preset time. When the target vehicle meets the self-learning conditions, the actual deceleration and target deceleration of the target vehicle under the current driving conditions are obtained.

3. The method according to claim 1, characterized in that, Before applying braking torque to the motor of the target vehicle according to the first corrected deceleration to achieve energy recovery, the method further includes: Based on the battery state of charge, the charging power, and the power system parameters, determine the maximum and minimum deceleration of the target vehicle under different vehicle weights and speeds. Based on the maximum and minimum deceleration of the target vehicle under different vehicle weights and speeds, the correction range of the target deceleration is determined; When the first corrected deceleration does not meet the correction range, the first corrected deceleration is adjusted to obtain the adjusted first corrected deceleration; Correspondingly, The step of controlling the motor of the target vehicle to apply braking torque according to the first corrected deceleration to achieve energy recovery includes: The target vehicle's motor is controlled to apply braking torque according to the adjusted first corrected deceleration to achieve energy recovery.

4. The method according to any one of claims 1-3, characterized in that, The power system parameters include at least the motor external characteristics, vehicle speed, transmission ratio, rear axle ratio, vehicle weight, and tire radius; The step of determining the maximum wheel-end negative torque of the target vehicle and the maximum slip deceleration corresponding to the maximum wheel-end negative torque based on the battery state of charge, the charging power, and the power system parameters includes: The maximum wheel-end negative torque of the target vehicle is determined based on the battery state of charge, the charging power, and the motor external characteristics, transmission ratio, and rear axle ratio in the powertrain parameters. The maximum slip deceleration corresponding to the maximum wheel-end negative torque is determined based on the maximum wheel-end negative torque and the vehicle speed, vehicle weight, and tire radius in the power system parameters.

5. The method according to any one of claims 1-3, characterized in that, Determining the target deceleration based on the deceleration reference table, the maximum coasting deceleration, and the weight and speed of the target vehicle includes: The initial deceleration is determined from the deceleration reference table based on the target vehicle's weight and speed, as well as the time and gradient of the target vehicle. When the initial deceleration is less than or equal to the maximum gliding deceleration, the initial deceleration is determined as the target deceleration; When the initial deceleration is greater than the maximum gliding deceleration, the maximum gliding deceleration is determined as the target deceleration.

6. The method according to any one of claims 1-3, characterized in that, After determining the target deceleration based on the deceleration reference table, the maximum coasting deceleration, and the weight and speed of the target vehicle, the method further includes: Determine the wheel speed difference and steering angle of the target vehicle; When the target vehicle's speed is greater than or equal to a preset speed threshold, the curve radius is determined based on the wheel speed difference. When the speed of the target vehicle is less than a preset speed threshold, the curve radius is determined based on the steering angle; Based on the curve radius, the target deceleration is corrected to obtain a second corrected deceleration; Correspondingly, The step of controlling the motor of the target vehicle to apply braking torque according to the target deceleration to achieve energy recovery includes: The target vehicle's motor is controlled to apply braking torque according to the second corrected deceleration control in order to achieve energy recovery.

7. A gliding energy recovery control device, characterized in that, include: The acquisition module is used to acquire the target vehicle's battery state of charge, charging power, and powertrain parameters. The determination module is used to determine the maximum wheel-end negative torque of the target vehicle and the maximum slip deceleration corresponding to the maximum wheel-end negative torque based on the battery state of charge, the charging power and the power system parameters. The processing module is used to determine the target deceleration based on the deceleration reference table, the maximum coasting deceleration, and the vehicle weight and speed of the target vehicle. The deceleration reference table is used to query the coasting deceleration under different driving conditions. The driving conditions include: time period, vehicle weight, slope, and vehicle speed. The deceleration reference table is determined based on a self-learning mechanism to correct the target deceleration in the historical energy recovery process. The control module is used to control the motor of the target vehicle to apply braking torque according to the target deceleration in order to achieve energy recovery; The processing module is also used for: During the energy recovery process, the actual deceleration and target deceleration of the target vehicle under the current driving conditions are obtained; Based on the difference between the actual deceleration and the target deceleration, the target deceleration under the current driving conditions is iteratively corrected until the difference between the actual deceleration and the target deceleration meets the preset convergence condition, and the first corrected deceleration is obtained. The target vehicle's motor is controlled to apply braking torque according to the first corrected deceleration to achieve energy recovery; After obtaining the first corrected deceleration under multiple driving conditions, linear regression processing is performed on the first corrected deceleration under the multiple driving conditions to generate a coasting deceleration distribution curve. The deceleration reference table is updated based on the gliding deceleration distribution curve.

8. A controller, characterized in that, include: Memory, processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the method as described in any one of claims 1-6.

9. A vehicle, characterized in that, It includes a vehicle body and a controller, the controller being used to perform the method as described in any one of claims 1-6.

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