Energy balancing method for multi-electric braking system of drive-by-wire chassis of electric carrying equipment

Abstract

The invention discloses an energy balancing method for multiple electric braking systems of a drive-by-wire chassis of electric carrying equipment, which comprises the following steps of: 1, firstly, acquiring passing vehicle speeds, braking requirements and residual electric quantity data of different vehicles at different time under the same road environment by utilizing a vehicle-road cloud platform, and fusing historical data and vehicle real-time data; 2, designing a braking energy dynamic balance algorithm based on the predicted value of the required braking energy obtained in the step 1, and calculating and dynamically adjusting the energy recovery ratio of the EMB system and the electric driving system in real time through an optimization algorithm; and 3, on the basis of the step 2, energy recovery data in the braking process are collected and processed in real time by integrating a sensor network and cloud data analysis, and real-time data and historical data are combined. Accurate prediction of the energy demand of the vehicle under different braking working conditions is realized, and the perspectiveness and adaptability of the prediction model are improved through the self-updating energy demand prediction method.

Description

Technical Field

[0001] The present invention relates to the technical field of control of a wire-controlled chassis of an electric vehicle, and in particular to an energy balancing method for a multi-electric braking system of a wire-controlled chassis of an electric vehicle. Background Art

[0002] With the continuous advancement of electric vehicle technology, the limitations of traditional hydraulic or pneumatic brake systems in terms of energy efficiency improvement and energy recovery are gradually becoming apparent. Although existing electric vehicles can recover some energy through regenerative braking of the electric drive system, there is still much room for improvement in overall energy utilization efficiency. With the development of wire-controlled chassis and electromagnetic braking (EMB) technology, multi-electric braking systems are gradually becoming a reality. This system not only includes the regenerative braking function of the electric drive system, but also introduces the energy recovery capability of the EMB system, which is expected to significantly improve the recovery efficiency of braking energy.

[0003] However, the above existing multi-electric brake system faces the following potential problems in practical application:

[0004] First, existing research mainly focuses on the optimization design of energy recovery for a single system, and fails to fully consider the coordination and control of multiple braking systems during the energy recovery process. For example, the Chinese invention patent application number is CN201910426185.5, entitled "A pure electric vehicle braking energy recovery system and its working method"; and the Chinese invention patent application number is CN201810527092.7, entitled "A braking energy recovery system and energy-saving vehicle", both of which optimize the vehicle braking energy recovery system and improve energy utilization efficiency, but ignore the problem of balanced energy distribution among multiple systems, and fail to recover the kinetic energy loss caused by braking to the greatest extent.

[0005] Second, in the process of energy recovery, existing research has failed to propose an effective multi-system collaborative control strategy, which may lead to unbalanced energy recovery in practical applications. This imbalance not only affects the overall energy efficiency of the system, but also may reduce the stability and safety of the braking system, especially under complex working conditions, which may lead to a significant decline in system performance. For example, the Chinese invention patent application number is CN201510025850.1, and the name is "A segmented composite braking system for electric vehicles and its energy recovery method". It turns on the pure motor braking or electro-hydraulic braking mode according to the strength of the braking signal, but does not coordinate the control of multiple braking systems. The complex control strategy may lead to inconsistent braking response and affect the effect of energy recovery. In addition, the energy conversion efficiency of different braking systems may be different, and the overall efficiency may be lower than expected.

[0006] Therefore, how to effectively coordinate the energy recovery of the EMB system and the electric drive system in the multi-electric braking system and develop a strategy that can achieve balanced energy distribution to maximize the recovery efficiency of braking energy has become a key technical issue that currently restricts the large-scale application of multi-electric braking systems in electric transportation equipment. Summary of the invention

[0007] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0008] In view of the problems existing in the energy balancing method of the multi-electric braking system of the wire-controlled chassis of the above-mentioned existing electric transport equipment, the present invention is proposed.

[0009] Therefore, the purpose of the present invention is to provide an energy balancing method for a multi-electric brake system of a wire-controlled chassis of an electric vehicle, which solves the problem in the prior art that when performing energy recovery and system collaborative control, it is difficult to effectively coordinate the energy distribution between the EMB system and the electric drive system, resulting in uneven energy recovery efficiency and unsatisfactory overall system control effect when some system functions fail. By optimizing the energy distribution and collaborative control strategy among multiple systems, the present invention can achieve more efficient and stable brake energy recovery under complex working conditions and improve the overall performance of the multi-electric brake system.

[0010] In order to solve the above technical problems, the present invention provides the following technical solutions: a method for balancing the energy of a multi-electric braking system of a wire-controlled chassis of an electric vehicle, comprising the following steps:

[0011] Step 1: First, the vehicle speed, braking demand and remaining power data of different vehicles at different times under the same road environment are obtained by the vehicle-road cloud platform. By integrating historical data with real-time vehicle data, a dynamic self-updating energy demand prediction method is designed to predict the energy demand of vehicles in real time under different braking conditions, thereby improving the accuracy and foresight of the prediction.

[0012] Step 2: Based on the predicted value of required braking energy obtained in step 1, a dynamic braking energy balancing algorithm is designed. The energy recovery ratio of the EMB system and the electric drive system is dynamically adjusted in real time through the optimization algorithm, so as to continuously maintain efficient energy recovery in changing working conditions and keep the system in the best working state at all times.

[0013] Step 3: Based on step 2, the energy recovery data during braking is collected and processed in real time through integrated sensor networks and cloud data analysis. The real-time data is combined with historical data and fed back to the central control unit to dynamically optimize the energy distribution strategy to ensure that under different road conditions and working conditions, the coordinated optimization of energy recovery efficiency and braking effect between the EMB system and the electric drive system reaches the optimal balance, thereby realizing intelligent energy recovery management and braking efficiency control.

[0014] As a preferred solution of the energy balancing method of the multi-electric braking system of the wire-controlled chassis of the electric vehicle equipment of the present invention, the step 1 specifically includes:

[0015] 1.1) Data acquisition and preprocessing: Using the vehicle-road cloud platform, we collect the speed v of different vehicles passing through different time periods under the same road environment. i (t), braking demand B i (t) and remaining power E i (t) Data: through data cleaning and noise reduction, outliers are removed and various data are standardized to meet the input requirements of subsequent algorithms;

[0016] 1.2) Based on the feature extraction and historical data fusion in 1.1), the key feature vector F is extracted from the preprocessed data in 1.1) using the feature extraction method. hist ={f1,f2,…,f n}, and based on the time series analysis model, the extracted features are used to predict the energy demand trend in the future. hist (t), the prediction formula is:

[0017]

[0018] Among them, w i The feature f i The weight of energy demand is calculated and a preliminary prediction model of energy demand is established by integrating historical data.

[0019] 1.3) Based on the prediction model in 1.2), real-time data integration and dynamic self-update are performed: Based on the energy demand prediction model constructed in 1.2), the current operation data of the vehicle is collected in real time. real (t), and compare it with the historical data predicted value D hist (t) Fusion: Use Bayesian or Kalman filter dynamic update method to adjust the energy demand prediction value D in real time real (t), in order to adapt to the dynamic changes of actual working conditions, the formula is as follows:

[0020] D real (t) = α·D hist (t)+β·Freal (t)

[0021] Among them, α and β are weight coefficients, which reflect the fusion ratio of historical data and real-time data.

[0022] As a preferred solution of the energy balancing method of the multi-electric brake system of the wire-controlled chassis of the electric vehicle equipment of the present invention, the step 2 specifically includes:

[0023] 2.1) Based on the energy demand forecast value in 1.3), optimal allocation is performed: using the energy demand forecast value D updated in real time in 1.3) real (t), the energy distribution ratio λ(t) between the EMB system and the electric drive system is calculated through an optimization algorithm such as the Lagrange multiplier method. The goal is to minimize the energy loss L(t), and the formula is:

[0024] minL(t)=min[λ(t)·E EMB (t)+(1-λ(t))·E ED (t)]

[0025] Among them, E EMB (t) and E ED (t) are the energy consumption of the EMB system and the electric drive system, respectively;

[0026] 2.2) Based on the allocation ratio in 2.1), dynamic adjustment is performed: the allocation ratio λ(t) is updated in real time according to changes in road conditions and working conditions. The dynamic programming method is used to ensure the continuous high efficiency of the system in energy recovery. The formula for the adjustment strategy is:

[0027]

[0028] Among them, γ is the learning rate;

[0029] 2.3) Based on the adjustment strategy in 2.2), performance evaluation and feedback control are performed: the energy recovery efficiency η(t) and the braking effect B(t) are calculated to evaluate the effectiveness of the current energy distribution; if the evaluation result does not meet expectations, the distribution parameters are re-optimized through the feedback mechanism to ensure that the system is in the best state. The calculation formula for the recovery efficiency is:

[0030]

[0031] As a preferred solution of the energy balancing method of the multi-electric brake system of the wire-controlled chassis of the electric vehicle equipment of the present invention, the step three specifically includes:

[0032] 3.1) Based on the feedback mechanism in 2.3), real-time collection and processing of sensor data: using the integrated sensor network, real-time acquisition of energy recovery data E during brakingrec (t) and real-time working condition data C(t), which are quickly processed and analyzed through the cloud to ensure their accuracy and timeliness;

[0033] 3.2) Based on the data processing results in 3.1), perform collaborative analysis of historical and real-time data: combine the real-time data with the historical features F extracted in 1.2) hist Conduct collaborative analysis and use machine learning algorithms such as random forests or support vector machines to continuously optimize energy allocation strategies F opt (t) to ensure the optimal energy recovery and balance state of the system under different working conditions. The formula is:

[0034] F opt (t) = ML(F real (t),F hist )

[0035] Among them, ML represents machine learning model;

[0036] 3.3) Based on the optimization strategy in 3.2), dynamic optimization and feedback control are performed to convert the optimization strategy F after collaborative analysis into opt (t) Feedback to the central control unit to adjust the energy distribution ratio λ between the EMB system and the electric drive system in real time opt (t), to ensure the stability and optimal energy recovery of the system under various working conditions. The optimization formula is:

[0037]

[0038] Among them, δ is the feedback gain coefficient, which is used to adjust the optimization step.

[0039] As a preferred solution of the energy balancing method of the multi-electric braking system of the wire-controlled chassis of the electric vehicle equipment of the present invention, the energy balancing method of the multi-electric braking system of the wire-controlled chassis of the electric vehicle equipment is based on the multi-electric braking system of the wire-controlled chassis of the electric vehicle equipment, including: a central single-stage reduction drive axle, a disc EMB braking system, a brake coordination control unit, a brake pressure sensor system, a power supply unit, a battery energy management system, and a communication and bus system;

[0040] The central single-stage reduction drive axle is connected to the differential through a main reducer, the differential is connected to the drive wheel through a half shaft, and the main reducer changes the transmission direction of the torque through a bevel gear pair and transmits the torque to the differential;

[0041] The disc EMB brake system is connected to the central single-stage reduction drive axle through a brake caliper bracket, and a brake caliper is connected to the brake caliper bracket to provide the necessary braking force;

[0042] The brake cooperative control unit is connected to the disc EMB brake system and the brake pressure sensor system through the communication and bus system, and is used to receive sensor signals in real time and regulate the braking process;

[0043] The brake pressure sensor system is connected to the brake cooperative control unit through sensor wiring, monitors the brake pressure and feeds back data for precise control of the brake function;

[0044] The power supply unit is connected to the battery energy management system through a power harness to ensure power supply to the entire system, and distributes power to various components through a power distribution module;

[0045] The battery energy management system is connected to the power supply unit through a signal transmission line to manage the charge and discharge status of the battery, and is connected to the brake cooperative control unit through a communication and bus system to ensure efficient use of electrical energy;

[0046] The communication and bus system is connected to the brake cooperative control unit, brake pressure sensor system, disc EMB brake system and battery energy management system through a data bus to ensure real-time data exchange and coordinated control between the components.

[0047] As a preferred solution of the energy balancing method of the multi-electric braking system of the wire-controlled chassis of the electric vehicle described in the present invention, the central single-stage reduction drive axle is also connected to the external frame through adjusting bolts to facilitate precise adjustment during installation.

[0048] As a preferred solution of the energy balancing method of the multi-electric braking system of the wire-controlled chassis of the electric vehicle equipment described in the present invention, the disc EMB braking system is provided with a brake disc, and the brake disc is fixed on the central single-stage reduction drive axle by a locating pin to ensure the stability of the braking force transmission.

[0049] As a preferred solution of the energy balancing method of the multi-electric braking system of the wire-controlled chassis of the electric vehicle equipment described in the present invention, the power supply unit is connected to the battery energy management system through the power supply monitoring module to provide real-time monitoring and protection functions for the battery status.

[0050] Beneficial effects of the present invention:

[0051] 1) The present invention not only takes into account the dynamic data fusion based on the vehicle-road-cloud platform, and realizes the accurate prediction of the energy demand of the vehicle under different braking conditions, but also improves the foresight and adaptability of the prediction model through the self-updating energy demand prediction method, thereby maintaining efficient energy management under different road conditions and time conditions.

[0052] 2) The present invention introduces an energy balancing algorithm based on dynamic self-updating energy demand prediction, which realizes real-time dynamic adjustment of the energy recovery ratio of the EMB system and the electric drive system. Not only can the overall energy recovery efficiency be maximized under normal working conditions, but the system can also maintain efficient operation when the working conditions change, ensuring that the system's energy recovery and braking effects are always in the best state.

[0053] 3) The present invention designs a real-time feedback and dynamic optimization mechanism by integrating sensor networks and cloud data analysis. This mechanism can combine the real-time collected energy recovery data with historical data to continuously optimize the energy allocation strategy, ensuring the coordinated optimization of the EMB system and the electric drive system under different road conditions and working conditions. In this way, even under complex and changeable road conditions, the optimal balance between energy management and braking effect can be ensured, significantly improving the intelligence and dynamic response capabilities of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0055] Figure 1 This is a structural diagram of the multi-electric brake system of the wire-controlled chassis of the electric vehicle equipment of the present invention;

[0056] Figure 2 This is a schematic diagram of the energy balancing method of the multi-electric braking system of the present invention. DETAILED DESCRIPTION

[0057] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0058] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0059] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0060] Secondly, the present invention is described in detail with reference to the schematic diagram. When describing the embodiments of the present invention in detail, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0061] Reference Figure 1-2 , provides an energy balancing method for a multi-electric brake system of a wire-controlled chassis of an electric vehicle, comprising the following steps:

[0062] Step 1: First, the vehicle speed, braking demand and remaining power data of different vehicles at different times under the same road environment are obtained by the vehicle-road cloud platform. By integrating historical data with real-time vehicle data, a dynamic self-updating energy demand prediction method is designed to predict the energy demand of vehicles in real time under different braking conditions, thereby improving the accuracy and foresight of the prediction.

[0063] Step 2: Based on the predicted value of required braking energy obtained in step 1, a dynamic braking energy balancing algorithm is designed. The energy recovery ratio of the EMB system and the electric drive system is dynamically adjusted in real time through the optimization algorithm, so as to continuously maintain efficient energy recovery in changing working conditions and keep the system in the best working state at all times.

[0064] Step 3: Based on step 2, the energy recovery data during braking is collected and processed in real time through integrated sensor networks and cloud data analysis. The real-time data is combined with historical data and fed back to the central control unit to dynamically optimize the energy distribution strategy to ensure that under different road conditions and working conditions, the coordinated optimization of energy recovery efficiency and braking effect between the EMB system and the electric drive system reaches the optimal balance, thereby realizing intelligent energy recovery management and braking efficiency control.

[0065] Among them, step one specifically includes:

[0066] 1.1) Data acquisition and preprocessing: Using the vehicle-road cloud platform, we collect the speed v of different vehicles passing through different time periods under the same road environment. i (t), braking demand B i (t) and remaining power E i (t) Data: through data cleaning and noise reduction, outliers are removed and various data are standardized to meet the input requirements of subsequent algorithms;

[0067] 1.2) Based on the feature extraction and historical data fusion in 1.1), the key feature vector F is extracted from the preprocessed data in 1.1) using the feature extraction method. hist ={f1,f2,…,f n}, and based on the time series analysis model, the extracted features are used to predict the energy demand trend in the future. hist (t), the prediction formula is:

[0068]

[0069] Among them, w i The feature f i The weight of energy demand is calculated and a preliminary prediction model of energy demand is established by integrating historical data.

[0070] 1.3) Based on the prediction model in 1.2), real-time data integration and dynamic self-update are performed: Based on the energy demand prediction model constructed in 1.2), the current operation data of the vehicle is collected in real time. real (t), and compare it with the historical data predicted value D hist (t) Fusion: Use Bayesian or Kalman filter dynamic update method to adjust the energy demand prediction value D in real time real (t), in order to adapt to the dynamic changes of actual working conditions, the formula is as follows:

[0071] D real (t) = α·D hist (t)+β·F real (t)

[0072] Among them, α and β are weight coefficients, which reflect the fusion ratio of historical data and real-time data.

[0073] Among them, step two specifically includes:

[0074] 2.1) Based on the energy demand forecast value in 1.3), optimal allocation is performed: using the energy demand forecast value D updated in real time in 1.3) real (t), the energy distribution ratio λ(t) between the EMB system and the electric drive system is calculated through an optimization algorithm such as the Lagrange multiplier method. The goal is to minimize the energy loss L(t), and the formula is:

[0075] minL(t)=min[λ(t)·E EMB (t)+(1-λ(t))·E ED (t)]

[0076] Among them, E EMB (t) and E ED (t) are the energy consumption of the EMB system and the electric drive system, respectively;

[0077] 2.2) Based on the allocation ratio in 2.1), dynamic adjustment is performed: the allocation ratio λ(t) is updated in real time according to changes in road conditions and working conditions. The dynamic programming method is used to ensure the continuous high efficiency of the system in energy recovery. The formula for the adjustment strategy is:

[0078]

[0079] Among them, γ is the learning rate;

[0080] 2.3) Based on the adjustment strategy in 2.2), performance evaluation and feedback control are performed: the energy recovery efficiency η(t) and the braking effect B(t) are calculated to evaluate the effectiveness of the current energy distribution; if the evaluation result does not meet expectations, the distribution parameters are re-optimized through the feedback mechanism to ensure that the system is in the best state. The calculation formula for the recovery efficiency is:

[0081]

[0082] Furthermore, step three specifically includes:

[0083] 3.1) Based on the feedback mechanism in 2.3), real-time collection and processing of sensor data: using the integrated sensor network, real-time acquisition of energy recovery data E during braking rec (t) and real-time working condition data C(t), which are quickly processed and analyzed through the cloud to ensure their accuracy and timeliness;

[0084] 3.2) Based on the data processing results in 3.1), perform collaborative analysis of historical and real-time data: combine the real-time data with the historical features F extracted in 1.2) hist Conduct collaborative analysis and use machine learning algorithms such as random forests or support vector machines to continuously optimize energy allocation strategies F opt (t) to ensure the optimal energy recovery and balance state of the system under different working conditions. The formula is:

[0085] F opt (t) = ML(F real (t),F hist )

[0086] Among them, ML represents machine learning model;

[0087] 3.3) Based on the optimization strategy in 3.2), dynamic optimization and feedback control are performed to convert the optimization strategy F after collaborative analysis into opt (t) Feedback to the central control unit to adjust the energy distribution ratio λ between the EMB system and the electric drive system in real time opt (t), to ensure the stability and optimal energy recovery of the system under various working conditions. The optimization formula is:

[0088]

[0089] Among them, δ is the feedback gain coefficient, which is used to adjust the optimization step.

[0090] Specifically, the energy balancing method of the electric vehicle equipment wire-controlled chassis multi-electric brake system is based on the electric vehicle equipment wire-controlled chassis multi-electric brake system, including: a central single-stage reduction drive axle 15, a disc EMB brake system 4, a brake coordination control unit 2, a brake pressure sensor system 6, a power supply unit 1, a battery energy management system 12 and a communication and bus system 13;

[0091] The central single-stage reduction drive axle 15 is connected to the differential 16 through the main reducer 14. The differential 16 is connected to the driving wheel through the half shaft 17. The main reducer 14 changes the transmission direction of the torque through the bevel gear pair and transmits the torque to the differential.

[0092] The disc EMB brake system 4 is connected to the central single-stage reduction drive axle 15 through the brake caliper bracket 7, and the brake caliper 5 is connected to the brake caliper bracket 7 to provide the necessary braking force;

[0093] The brake cooperative control unit 2 is connected to the disc EMB brake system 4 and the brake pressure sensor system 6 via the communication and bus system 13, and is used to receive sensor signals in real time and regulate the braking process;

[0094] The brake pressure sensor system 6 is connected to the brake cooperative control unit 2 through sensor wiring to monitor the brake pressure and feedback data for precise control of the brake function;

[0095] The power supply unit 1 is connected to the battery energy management system 12 through the power harness 11 to ensure the power supply of the entire system and distribute the power to each component through the power distribution module;

[0096] The battery energy management system 12 is connected to the power supply unit 1 through the signal transmission line 3 to manage the charging and discharging status of the battery, and is connected to the brake cooperative control unit 2 through the communication and bus system 13 to ensure efficient use of electric energy;

[0097] The communication and bus system 13 is connected to the brake cooperative control unit 2, the brake pressure sensor system 6, the disc EMB brake system 4 and the battery energy management system 12 via a data bus to ensure real-time data exchange and coordinated control between the components.

[0098] Among them, the central single-stage reduction drive axle 15 is also connected to the external frame through adjusting bolts to facilitate precise adjustment during installation. The disc EMB brake system 4 is provided with a brake disc 9, and the brake disc 9 is fixed to the central single-stage reduction drive axle 15 through a locating pin 8 to ensure the stability of braking force transmission; the power supply unit 1 is connected to the battery energy management system 12 through the power monitoring module 10 to provide real-time monitoring and protection functions for the battery status.

[0099] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for balancing the energy of a multi-electric brake system of a wire-controlled chassis of an electric vehicle, characterized in that: The following steps are involved: Step 1: First, the vehicle speed, braking demand and remaining power data of different vehicles at different times under the same road environment are obtained by the vehicle-road cloud platform. By integrating historical data with real-time vehicle data, a dynamic self-updating energy demand prediction method is designed to predict the energy demand of vehicles in real time under different braking conditions, thereby improving the accuracy and foresight of the prediction. Step 2: Based on the predicted value of required braking energy obtained in step 1, a dynamic braking energy balancing algorithm is designed. The energy recovery ratio of the EMB system and the electric drive system is dynamically adjusted in real time through the optimization algorithm, so as to continuously maintain efficient energy recovery in changing working conditions and keep the system in the best working state at all times. Step 3: Based on step 2, the energy recovery data during braking is collected and processed in real time through integrated sensor networks and cloud data analysis. The real-time data is combined with historical data and fed back to the central control unit to dynamically optimize the energy distribution strategy to ensure that under different road conditions and working conditions, the coordinated optimization of energy recovery efficiency and braking effect between the EMB system and the electric drive system reaches the optimal balance, thereby realizing intelligent energy recovery management and braking efficiency control.

2. The energy balancing method of the multi-electric brake system of the electric vehicle control-by-wire chassis according to claim 1 is characterized by: The step 1 specifically includes: 1.1) Data acquisition and preprocessing: Using the vehicle-road cloud platform, we collect the speed v of different vehicles passing through different time periods under the same road environment. i (t), braking demand B i (t) and remaining power E i (t) Data: through data cleaning and noise reduction, outliers are removed and various data are standardized to meet the input requirements of subsequent algorithms; 1.2) Based on the feature extraction and historical data fusion in 1.1), the key feature vector F is extracted from the preprocessed data in 1.1) using the feature extraction method. hist ={f1,f2,…,f n }, and based on the time series analysis model, the extracted features are used to predict the energy demand trend in the future. hist (t), the prediction formula is: Among them, w i The feature f i The weight of energy demand is used to establish a preliminary prediction model of energy demand by integrating historical data. 1.3) Based on the prediction model in 1.2), real-time data integration and dynamic self-update are performed: Based on the energy demand prediction model constructed in 1.2), the current operation data of the vehicle is collected in real time. real (t), and compare it with the historical data predicted value D hist (t) Fusion: Use Bayesian or Kalman filter dynamic update method to adjust the energy demand prediction value D in real time real (t), in order to adapt to the dynamic changes of actual working conditions, the formula is as follows: D real (t)=α·D hist (t)+β·F real (t) Among them, α and β are weight coefficients, which reflect the fusion ratio of historical data and real-time data.

3. The energy balancing method of the multi-electric brake system of the electric vehicle control-by-wire chassis according to claim 2 is characterized in that: The step 2 specifically includes: 2.1) Based on the energy demand forecast value in 1.3), optimal allocation is performed: using the energy demand forecast value D updated in real time in 1.3) real (t), the energy distribution ratio λ(t) between the EMB system and the electric drive system is calculated through an optimization algorithm such as the Lagrange multiplier method. The goal is to minimize the energy loss L(t), and the formula is: minL(t)=min[λ(t)·E EMB (t)+(1-λ(t))·E ED (t)] Among them, E EMB (t) and E ED (t) are the energy consumption of the EMB system and the electric drive system, respectively; 2.2) Based on the allocation ratio in 2.1), dynamic adjustment is performed: the allocation ratio λ(t) is updated in real time according to changes in road conditions and working conditions. The dynamic programming method is used to ensure the continuous high efficiency of the system in energy recovery. The formula for the adjustment strategy is: Among them, γ is the learning rate; 2.3) Based on the adjustment strategy in 2.2), performance evaluation and feedback control are performed: the energy recovery efficiency η(t) and the braking effect B(t) are calculated to evaluate the effectiveness of the current energy distribution; if the evaluation result does not meet expectations, the distribution parameters are re-optimized through the feedback mechanism to ensure that the system is in the best state. The calculation formula for the recovery efficiency is:

4. The energy balancing method of the multi-electric brake system of the wire-controlled chassis of the electric vehicle according to claim 3 is characterized by: The step three specifically includes: 3.1) Based on the feedback mechanism in 2.3), real-time collection and processing of sensor data: using the integrated sensor network, real-time acquisition of energy recovery data E during braking rec (t) and real-time working condition data C(t), which are quickly processed and analyzed through the cloud to ensure their accuracy and timeliness; 3.2) Based on the data processing results in 3.1), perform collaborative analysis of historical and real-time data: combine the real-time data with the historical features F extracted in 1.2) hist Conduct collaborative analysis and use machine learning algorithms such as random forests or support vector machines to continuously optimize energy allocation strategies F opt (t) to ensure the optimal energy recovery and balance state of the system under different working conditions. The formula is: F opt (t)=ML(F real (t),F hist ) Among them, ML represents machine learning model; 3.3) Based on the optimization strategy in 3.2), dynamic optimization and feedback control are performed to convert the optimization strategy F after collaborative analysis into opt (t) Feedback to the central control unit to adjust the energy distribution ratio λ between the EMB system and the electric drive system in real time opt (t), to ensure the stability and optimal energy recovery of the system under various working conditions. The optimization formula is: Among them, δ is the feedback gain coefficient, which is used to adjust the optimization step.

5. The energy balancing method of the multi-electric brake system of the electric vehicle control-by-wire chassis according to claim 1 is characterized by: The energy balancing method of the multi-electric brake system of the wire-controlled chassis of an electric vehicle is based on the multi-electric brake system of the wire-controlled chassis of an electric vehicle, and comprises: a central single-stage reduction drive axle (15), a disc EMB brake system (4), a brake coordination control unit (2), a brake pressure sensor system (6), a power supply unit (1), a battery energy management system (12), and a communication and bus system (13); The central single-stage reduction drive axle (15) is connected to a differential (16) via a main reducer (14); the differential (16) is connected to a drive wheel via a half shaft (17); the main reducer (14) changes the transmission direction of the torque via a bevel gear pair and transmits the torque to the differential; The disc-type EMB brake system (4) is connected to the central single-stage reduction drive axle (15) via a brake caliper bracket (7), and a brake caliper (5) is connected to the brake caliper bracket (7) to provide necessary braking force; The brake cooperative control unit (2) is connected to the disc EMB brake system (4) and the brake pressure sensor system (6) via a communication and bus system (13) and is used to receive sensor signals in real time and regulate the braking process; The brake pressure sensor system (6) is connected to the brake cooperative control unit (2) via sensor wiring, monitors the brake pressure and feeds back data for precise control of the brake function; The power supply unit (1) is connected to the battery energy management system (12) via a power harness (11) to ensure power supply to the entire system, and distributes power to various components via a power distribution module; The battery energy management system (12) is connected to the power supply unit (1) via a signal transmission line (3) to manage the charging and discharging state of the battery, and is connected to the brake cooperative control unit (2) via a communication and bus system (13) to ensure efficient use of electric energy; The communication and bus system (13) is connected to the brake cooperative control unit (2), the brake pressure sensor system (6), the disc EMB brake system (4) and the battery energy management system (12) via a data bus, thereby ensuring real-time data exchange and coordinated control between the various components.

6. The energy balancing method of the multi-electric brake system of the electric vehicle control-by-wire chassis according to claim 5 is characterized in that: The central single-stage reduction drive axle (15) is also connected to the external vehicle frame via adjusting bolts to facilitate accurate adjustment during installation.

7. The method for balancing the energy of a multi-electric brake system of a wire-controlled chassis of an electric vehicle according to claim 5, characterized in that: The disc-type EMB brake system (4) is provided with a brake disc (9), and the brake disc (9) is fixed on a central single-stage reduction drive axle (15) via a positioning pin (8) to ensure the stability of the transmission of the braking force.

8. The method for balancing the energy of a multi-electric brake system of a wire-controlled chassis of an electric vehicle according to claim 5, characterized in that: The power supply unit (1) is connected to the battery energy management system (12) via a power supply monitoring module (10), providing real-time monitoring and protection functions for the battery status.

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