Intelligent energy feedback adjusting method for regenerative braking system

Abstract

The invention relates to the technical field of braking energy feedback, in particular to an intelligent energy feedback adjusting method for a regenerative braking system, and the method comprises the steps: collecting energy feedback related data in real time, sequentially carrying out denoising and normalization processing on the energy feedback related data, carrying out feature extraction on the processed data, and outputting energy feedback feature data; the kinetic energy of the vehicle is calculated in real time, and meanwhile the braking force requirement and the maximum recoverable braking force are calculated in real time; inputting the energy feedback characteristic data into a machine learning model, and predicting and outputting a regenerative braking force distribution coefficient; and according to the predicted regenerative braking force distribution coefficient, distribution control is conducted on regenerative braking force and mechanical braking force, and the energy recovery amount is calculated.

Description

Background Art

[0002] Regenerative braking system is a key energy recovery technology in electric and hybrid vehicles. Its purpose is to convert the vehicle's kinetic energy into electrical energy and store it in the battery when the vehicle decelerates or brakes, so as to improve energy utilization efficiency, extend driving range, and reduce brake wear.

[0003] The traditional braking system generates braking force through friction, converts kinetic energy into heat energy, and finally dissipates it into the environment. This energy loss makes the energy in the vehicle braking process completely unusable. The regenerative braking system, on the other hand, converts kinetic energy into electrical energy through the operation of the motor, and feeds this energy back to the battery system for subsequent use, which greatly improves the energy utilization rate and vehicle endurance.

[0004] Although regenerative braking systems have made significant progress, there are still some technical challenges to overcome. During regenerative braking, the size of the recovered braking force needs to be adjusted in real time according to multiple factors such as vehicle status, driver demand and battery status to ensure a balance between recovery efficiency and braking safety; the effect of regenerative braking and traditional mechanical braking need to be highly coordinated to ensure that the total braking force meets the driver's needs. This involves the coordinated work between multiple control systems; the state of charge (SOC), temperature and life of the battery are all key factors that need to be considered in the regenerative braking system. When the battery is close to full charge, energy cannot be recovered, which may lead to overcharging or battery damage. In addition, excessively high or low battery temperature will also have an adverse effect on the energy recovery process. Therefore, it is necessary to propose an intelligent energy feedback regulation method for regenerative braking systems to overcome the above technical problems. Summary of the invention

[0005] The main purpose of the present invention is to provide an intelligent energy feedback regulation method for a regenerative braking system, which effectively solves the above-mentioned problems mentioned in the background technology.

[0006] The technical solution of the present invention is as follows:

[0007] An intelligent energy feedback regulation method for a regenerative braking system is proposed, the method comprising the following steps:

[0008] S1. Real-time collection of energy feedback related data, denoising and normalization of the energy feedback related data, feature extraction of the processed data, and output of energy feedback feature data;

[0009] S2. Real-time calculation of vehicle kinetic energy, braking force requirement and maximum recoverable braking force;

[0010] S3, inputting the energy feedback characteristic data into the machine learning model, predicting and outputting the regenerative braking force distribution coefficient;

[0011] S4. Based on the predicted regenerative braking force distribution coefficient, the regenerative braking force and the mechanical braking force are distributed and controlled, and the energy recovery amount is calculated.

[0012] A further improvement of the present invention is that the energy feedback related data in S1 includes: vehicle speed v(t), brake pedal position p b (t), battery state of charge SOC(t), motor efficiency Road slope θ(t), ambient temperature T env (t), tire-ground friction coefficient μ(t) and vehicle mass m, where t is the time node.

[0013] A further improvement of the present invention is that the specific formula for calculating the braking force requirement in S2 is:

[0014] F brack (t) = k p ×p b (t);

[0015] Among them, F brack (t) is the braking force requirement, k p is the brake pedal pressure conversion factor.

[0016] A further improvement of the present invention is that the specific formula for calculating the maximum recoverable braking force in S2 is:

[0017] P regen-max (t)=μ(t)×m×g×cos(θ(t));

[0018] Among them, P regen-max (t) is the maximum recoverable braking force.

[0019] A further improvement of the present invention is that the specific content of S3 is: combining the energy feedback feature data into the form of feature vectors, taking the set of all feature vectors as the input of the machine learning model, the machine learning model takes the regenerative braking force distribution coefficient predicted by each group of feature vectors as the output, and takes the actual regenerative braking force distribution coefficient corresponding to each group of feature vectors as the prediction target, training the machine learning model until the sum of the prediction accuracies reaches convergence, then stopping the training and outputting the regenerative braking force distribution coefficient α(t).

[0020] A further improvement of the present invention is that the specific formula for controlling the distribution of the regenerative braking force and the mechanical braking force in S4 is:

[0021] F regen (t)=α(t)×Fbrack (t);

[0022] F mechanical (t) = F brack (t)-F regen (t);

[0023] Among them, F regen (t) is the regenerative braking force, F mechanical (t) is the mechanical braking force.

[0024] A further improvement of the present invention is that the specific formula for calculating the energy recovery amount in S4 is:

[0025]

[0026] Among them, E regen (t) is the energy recovery amount.

[0027] The technical effects of the present invention are as follows:

[0028] An intelligent energy feedback regulation method for a regenerative braking system is constructed. The present invention collects energy feedback related data in real time, performs denoising and normalization processing on the energy feedback related data in turn, extracts features from the processed data, and outputs energy feedback feature data; calculates the vehicle kinetic energy in real time, and simultaneously calculates the braking force demand and the maximum recoverable braking force in real time; inputs the energy feedback feature data into a machine learning model, predicts and outputs the regenerative braking force distribution coefficient; and controls the distribution of the regenerative braking force and the mechanical braking force according to the predicted regenerative braking force distribution coefficient, and calculates the energy recovery amount. Dynamic adjustments can be made according to the real-time collected vehicle operating status to optimize the energy recovery efficiency. The introduction of machine learning improves the intelligence level of the control system, thereby achieving the optimal strategy in different driving scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0030] Figure 1 The figure is a flow chart of an intelligent energy feedback regulation method for a regenerative braking system according to the present invention. DETAILED DESCRIPTION

[0031] The present invention aims to propose an intelligent energy feedback regulation method for a regenerative braking system, which collects energy feedback related data in real time, performs denoising and normalization processing on the energy feedback related data in turn, extracts features from the processed data, and outputs energy feedback feature data; calculates the vehicle kinetic energy in real time, and at the same time calculates the braking force demand and the maximum recoverable braking force in real time; inputs the energy feedback feature data into a machine learning model, predicts and outputs the regenerative braking force distribution coefficient; according to the predicted regenerative braking force distribution coefficient, distributes and controls the regenerative braking force and the mechanical braking force, and calculates the energy recovery amount. Dynamic adjustments can be made according to the real-time collected vehicle operating status to optimize the energy recovery efficiency. The introduction of machine learning improves the intelligence level of the control system, thereby achieving the optimal strategy in different driving scenarios.

[0032] Embodiment 1:

[0033] This embodiment proposes an intelligent energy feedback adjustment method for a regenerative braking system. Figure 1 The following specific steps are shown:

[0034] S1. Real-time collection of energy feedback related data, denoising and normalization of the energy feedback related data, feature extraction of the processed data, and output of energy feedback feature data;

[0035] S2. Real-time calculation of vehicle kinetic energy, braking force requirement and maximum recoverable braking force;

[0036] S3, inputting the energy feedback characteristic data into the machine learning model, predicting and outputting the regenerative braking force distribution coefficient;

[0037] S4. Based on the predicted regenerative braking force distribution coefficient, the regenerative braking force and the mechanical braking force are distributed and controlled, and the energy recovery amount is calculated.

[0038] In this embodiment, the energy feedback related data in S1 include: vehicle speed v(t), brake pedal position p b (t), battery state of charge SOC(t), motor efficiency Road slope θ(t), ambient temperature T env (t), tire-ground friction coefficient μ(t) and vehicle mass m, where t is the time node.

[0039] In this embodiment, the specific formula for calculating the braking force requirement in S2 is:

[0040] F brack (t) = k p ×p b (t);

[0041] Among them, F brack (t) is the braking force requirement, k p is the brake pedal pressure conversion factor.

[0042] In this embodiment, the specific formula for calculating the maximum recoverable braking force in S2 is:

[0043] P regen-max (t)=μ(t)×m×g×cos(θ(t));

[0044] Among them, P regen-max (t) is the maximum recoverable braking force.

[0045] In this embodiment, the specific content of S3 is: combining the energy feedback feature data into the form of feature vectors, taking the set of all feature vectors as the input of the machine learning model, the machine learning model takes the regenerative braking force distribution coefficient predicted by each group of feature vectors as the output, and takes the actual regenerative braking force distribution coefficient corresponding to each group of feature vectors as the prediction target, and trains the machine learning model until the sum of the prediction accuracies reaches convergence, then stops the training, and outputs the regenerative braking force distribution coefficient α(t).

[0046] In this embodiment, the specific formula for controlling the distribution of the regenerative braking force and the mechanical braking force in S4 is:

[0047] F regen (t)=α(t)×F brack (t);

[0048] F mechanical (t) = F brack (t)-F regen (t);

[0049] Among them, F regen (t) is the regenerative braking force, F mechanical (t) is the mechanical braking force.

[0050] In this embodiment, the specific formula for calculating the energy recovery amount in S4 is:

[0051]

[0052] Among them, E regen (t) is the energy recovery amount.

[0053] Embodiment 2:

[0054] This embodiment provides an electronic device, including: a processor and a memory, wherein the memory stores a computer program that can be called by the processor; the processor executes the above-mentioned intelligent energy feedback regulation method for a regenerative braking system by calling the computer program stored in the memory.

[0055] The electronic device may have relatively large differences due to different configurations or performances, and may include one or more processors (Central Processing Units, CPU) and one or more memories, wherein the memory stores at least one computer program, which is loaded and executed by the processor to implement a smart energy feedback regulation method for a regenerative braking system provided by the above method embodiment. The electronic device may also include other components for implementing the functions of the device, for example, the electronic device may also have components such as a wired or wireless network interface and an input and output interface for data input and output. This embodiment will not be described in detail here.

[0056] Those skilled in the art know that the present invention can be implemented as a system, method or computer program product. Therefore, the present disclosure can be specifically implemented in the following forms, namely: it can be complete hardware, it can be complete software (including firmware, resident software, microcode, etc.), and it can also be a combination of hardware and software, which is generally referred to as "circuit", "module" or "system" herein. In addition, in some embodiments, the present invention can also be implemented in the form of a computer program product in one or more computer-readable media, and the computer-readable medium contains computer-readable program code.

[0057] Any combination of one or more computer-readable media may be used. A computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, apparatus, or device.

[0058] The present invention is described with reference to flowcharts and block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process or block in the flowchart and block diagram, as well as the combination of processes and blocks in the flowchart or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts. Figure 1 Process or multiple processes and boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0059] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 Process or multiple processes and boxes Figure 1 The steps for the functions specified in one or more boxes.

[0060] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the enlightenment of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the purpose of the present invention and the claims, which all fall within the protection of the present invention.

Claims

1. An intelligent energy feedback regulation method for a regenerative braking system, characterized in that: The specific steps include: S1. Real-time collection of energy feedback related data, denoising and normalization of the energy feedback related data, feature extraction of the processed data, and output of energy feedback feature data; S2. Real-time calculation of vehicle kinetic energy, braking force requirement and maximum recoverable braking force; S3, inputting the energy feedback characteristic data into the machine learning model, predicting and outputting the regenerative braking force distribution coefficient; S4. Based on the predicted regenerative braking force distribution coefficient, the regenerative braking force and the mechanical braking force are distributed and controlled, and the energy recovery amount is calculated.

2. The intelligent energy feedback regulation method for a regenerative braking system according to claim 1, characterized in that: The energy feedback related data in S1 include: vehicle speed v(t), brake pedal position p b (t), battery state of charge SOC(t), motor efficiency Road slope θ(t), ambient temperature T env (t), tire-ground friction coefficient μ(t) and vehicle mass m, where t is the time node.

3. The intelligent energy feedback regulation method for a regenerative braking system according to claim 2, characterized in that: The specific formula for calculating the braking force requirement in S2 is: F brack (t)=k p ×p b (t); Among them, F brack (t) is the braking force requirement, k p is the brake pedal pressure conversion factor.

4. The intelligent energy feedback regulation method for a regenerative braking system according to claim 3, characterized in that: The specific formula for calculating the maximum recoverable braking force in S2 is: P regen-max (t)=μ(t)×m×g×cos(θ(t)); Among them, P regen-max (t) is the maximum recoverable braking force.

5. The intelligent energy feedback regulation method for a regenerative braking system according to claim 4, characterized in that: The specific content of S3 is: combining the energy feedback feature data into the form of feature vectors, taking the set of all feature vectors as the input of the machine learning model, the machine learning model taking the regenerative braking force distribution coefficient predicted by each set of feature vectors as the output, taking the actual regenerative braking force distribution coefficient corresponding to each set of feature vectors as the prediction target, training the machine learning model until the sum of the prediction accuracies reaches convergence, stopping the training, and outputting the regenerative braking force distribution coefficient α(t).

6. The intelligent energy feedback regulation method for a regenerative braking system according to claim 5, characterized in that: The specific formula for controlling the distribution of the regenerative braking force and the mechanical braking force in S4 is: F regen (t)=α(t)×F brack (t); F mechanical (t)=F brack (t)-F regen (t); Among them, F regen (t) is the regenerative braking force, F mechanical (t) is the mechanical braking force.

7. The intelligent energy feedback regulation method for a regenerative braking system according to claim 6, characterized in that: The specific formula for calculating the energy recovery amount in S4 is: Among them, E regen (t) is the energy recovery amount.

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