An energy equalization method for an electrically powered, carrier equipment, drive-by-wire chassis, multi-electric braking system

Abstract

The application discloses an energy equalization method for an electrically driven vehicle line control chassis multi-energy braking system, comprising the following steps: step one: first, obtaining the through vehicle speed, braking demand and residual power data of different vehicles at different times under the same road environment by using a vehicle-road cloud platform, and fusing historical data and real-time vehicle data; step two: based on the demand braking energy prediction value obtained in step one, designing a braking energy dynamic equalization algorithm, and real-time calculating the energy recovery ratio of an EMB system and an electric drive system by using an optimization algorithm; and step three: on the basis of step two, integrating a sensor network and cloud data analysis, real-time collecting and processing the energy recovery data in the braking process, and combining the real-time data with historical data. The application realizes accurate prediction of the energy demand of a vehicle under different braking conditions, and improves the foresight and adaptability of a prediction model through a self-updating energy demand prediction method.

Description

Technical Field

[0001] This invention relates to the technical field of drive-by-wire chassis control for electric transport equipment, and more particularly to an energy balancing method for a multi-electric braking system of drive-by-wire chassis for electric transport equipment. Background Technology

[0002] With the continuous advancement of electric vehicle technology, the limitations of traditional hydraulic or pneumatic braking systems in terms of energy efficiency improvement and energy recovery are becoming increasingly apparent. Although existing electric vehicles can recover some energy through regenerative braking of the electric drive system, there is still considerable room for improvement in overall energy utilization efficiency. With the development of drive-by-wire chassis and electromagnetic braking (EMB) technology, multi-electric braking systems are gradually becoming a reality. These systems not only include the regenerative braking function of the electric drive system but also incorporate the energy recovery capabilities of the EMB system, thus potentially significantly improving braking energy recovery efficiency.

[0003] However, the existing multi-electric braking systems mentioned above face the following potential problems in practical applications:

[0004] First, existing research mainly focuses on optimizing the energy recovery design of a single system, failing to fully consider the coordinated control issues of multiple braking systems during the energy recovery process. For example, Chinese invention patent applications CN201910426185.5, entitled "A Braking Energy Recovery System for Pure Electric Vehicles and Its Working Method," and CN201810527092.7, entitled "A Braking Energy Recovery System and Energy-Saving Vehicle," both optimize the vehicle's braking energy recovery system and improve energy utilization efficiency, but they neglect the issue of balancing energy distribution among multiple systems, failing to recover the kinetic energy loss caused by braking to the greatest extent possible.

[0005] Second, existing research has failed to propose effective multi-system coordinated control strategies during energy recovery, potentially leading to uneven energy recovery in practical applications. This imbalance not only affects the overall energy efficiency of the system but may also reduce the stability and safety of the braking system, especially under complex operating conditions, potentially causing a significant decline in system performance. For example, Chinese invention patent application CN201510025850.1, entitled "A Segmented Composite Braking System for Electric Vehicles and Its Energy Recovery Method," activates either pure electric motor braking or electro-hydraulic braking mode based on the strength of the braking signal, but fails to coordinate the control of multiple braking systems. The complex control strategy may lead to inconsistent braking responses, affecting the effectiveness of energy recovery. Furthermore, the energy conversion efficiency of different braking systems may vary, 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 a multi-electric braking system, and develop a strategy that can achieve balanced energy distribution to maximize the efficiency of braking energy recovery, has become a key technical problem restricting 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 outline some aspects of embodiments of the present invention and to briefly describe some preferred embodiments. Simplifications or omissions may be made in this section, as well as in the abstract and title of this application, to avoid obscuring the purpose of these documents; however, such simplifications or omissions should not be construed as limiting the scope of the 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 electric transport equipment, the present invention is proposed.

[0009] Therefore, the purpose of this invention is to provide an energy balancing method for a multi-electric braking system on a drive-by-wire chassis of an electric vehicle. This method addresses the problem in existing technologies where, during energy recovery and system coordinated control, the difficulty in effectively coordinating energy distribution between the EMB system and the electric drive system leads to uneven energy recovery efficiency and unsatisfactory overall system control performance when some system functions fail. This invention optimizes the energy distribution and coordinated control strategies among multiple systems, enabling more efficient and stable braking energy recovery under complex operating conditions and improving the overall performance of the multi-electric braking system.

[0010] To solve the above-mentioned technical problems, the present invention provides the following technical solution: an energy balancing method for a multi-electric braking system of a drive-by-wire chassis for electric transport equipment, comprising the following steps:

[0011] Step 1: First, using the vehicle-road cloud platform to obtain data on vehicle speed, braking demand, and remaining battery power of different vehicles at different times under the same road environment, a dynamic self-updating energy demand prediction method is designed by integrating historical data with real-time vehicle data. This method can 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 braking energy demand obtained in Step 1, design a dynamic braking energy balancing algorithm. Through optimization algorithm, dynamically adjust the energy recovery ratio of the EMB system and the electric drive system in real time to maintain efficient energy recovery under changing operating conditions, so that the system is always in the best working state.

[0013] Step 3: Building on Step 2, by integrating sensor networks and cloud data analysis, energy recovery data during braking is collected and processed in real time. Real-time data is combined with historical data and fed back to the central control unit to dynamically optimize the energy distribution strategy. This ensures that the energy recovery efficiency and braking effect between the EMB system and the electric drive system reach the optimal balance under different road conditions and operating conditions, thereby realizing intelligent energy recovery management and braking performance control.

[0014] As a preferred embodiment of the energy balancing method for the multi-electric braking system of the electric vehicle drive chassis described in this invention, step one specifically includes:

[0015] 1.1) Data Acquisition and Preprocessing: Using the vehicle-road cloud platform, collect data on vehicle speeds (v) at different times and for different vehicles under the same road conditions. i (t), Braking demand B i (t) and remaining electricity E i (t) data, through data cleaning and noise reduction, outliers are removed and all data are standardized to meet the input requirements of subsequent algorithms;

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

[0017]

[0018] Among them, w i For feature f i By merging historical data, a preliminary predictive model for energy demand is established based on the weights of the data.

[0019] 1.3) Based on the prediction model in 1.2), perform real-time data fusion and dynamic self-updating: Based on the energy demand prediction model built in 1.2), collect real-time vehicle operating data F. real (t), and compare it with the historical data prediction value D. hist (t) Fusion; using Bayesian or Kalman filtering dynamic update methods, the energy demand forecast value D is adjusted in real time. real (t) is used to adapt to the dynamic changes in actual working conditions, and the formula is as follows:

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

[0021] Here, α and β are weighting coefficients, reflecting the fusion ratio of historical data and real-time data.

[0022] As a preferred embodiment of the energy balancing method for the multi-electric braking system of the electric vehicle drive chassis described in this invention, step two specifically includes:

[0023] 2.1) Optimal allocation based on the energy demand forecast value in 1.3): Utilize the real-time updated energy demand forecast value D in 1.3). real The energy distribution ratio λ(t) between the EMB system and the electric drive system is calculated using optimization algorithms such as the Lagrange multiplier method. The objective is to minimize the energy loss L(t), as shown in the formula:

[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) represents 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 operating conditions. Dynamic programming is used to ensure the system's continuous high efficiency in energy recovery. The formula for the adjustment strategy is:

[0027]

[0028] Where γ is the learning rate;

[0029] 2.3) Based on the adjustment strategy in 2.2), perform performance evaluation and feedback control: calculate the energy recovery efficiency η(t) and braking effect B(t) to evaluate the effectiveness of the current energy allocation; if the evaluation results do not meet expectations, re-optimize the allocation parameters through the feedback mechanism to ensure the system is in the optimal state. The formula for calculating the recovery efficiency is:

[0030]

[0031] As a preferred embodiment of the energy balancing method for the multi-electric braking system of the electric vehicle drive chassis described in this invention, step three specifically includes:

[0032] 3.1) Based on the feedback mechanism in 2.3), real-time acquisition and processing of sensor data is performed: using an integrated sensor network, energy recovery data E during the braking process is acquired in real time.rec The data (t) and real-time operating condition data C(t) are processed and analyzed quickly in the cloud to ensure their accuracy and timeliness;

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

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

[0035] Where ML stands for machine learning model;

[0036] 3.3) Based on the optimization strategy in 3.2), dynamic optimization and feedback control are performed, and the optimization strategy F after collaborative analysis is applied. opt (t) Feedback is sent 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 of the system and optimal energy recovery under various operating conditions, the optimization formula is:

[0037]

[0038] Where δ is the feedback gain coefficient, used to adjust the optimization pace.

[0039] As a preferred embodiment of the energy balancing method for the multi-electric braking system of the electric vehicle drive-by-wire chassis described in this invention, the energy balancing method for the multi-electric braking system of the electric vehicle drive-by-wire chassis is based on the multi-electric braking system of the electric vehicle drive-by-wire chassis, including: a central single-stage reduction drive axle, a disc EMB braking system, a braking coordination control unit, a braking 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 the main reducer. The differential is connected to the drive wheel through the half shaft. The main reducer changes the direction of torque transmission through the bevel gear pair and transmits the torque to the differential.

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

[0042] The braking coordination control unit is connected to the disc EMB braking system and the braking pressure sensor system through a 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 coordination control unit via sensor wiring to monitor brake pressure and feed back data for precise control of the braking function.

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

[0045] The battery energy management system is connected to the power supply unit via a signal transmission line to manage the charging and discharging status of the battery, and is connected to the braking coordination control unit via a communication and bus system to ensure efficient use of electrical energy.

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

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

[0048] As a preferred embodiment of the energy balancing method for the multi-electric braking system of the electric vehicle drive chassis described in this invention, the disc EMB braking system is provided with a brake disc, which is fixed to the central single-stage reduction drive axle by a positioning pin to ensure the stability of braking force transmission.

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

[0050] The beneficial effects of this invention are:

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

[0052] 2) This invention introduces an energy balancing algorithm based on dynamic self-updating energy demand prediction, enabling real-time dynamic adjustment of the energy recovery ratio between the EMB system and the electric drive system. This not only maximizes overall energy recovery efficiency under normal operating conditions but also maintains efficient system operation even under changing conditions, ensuring that the system's energy recovery and braking performance are always at their best.

[0053] 3) This invention integrates sensor networks and cloud data analysis to design a real-time feedback and dynamic optimization mechanism. This mechanism combines real-time energy recovery data with historical data to continuously optimize the energy allocation strategy, ensuring coordinated optimization of the EMB system and electric drive system under different road conditions and operating conditions. Thus, even under complex and changing road conditions, it ensures an optimal balance between energy management and braking performance, significantly improving the system's intelligence and dynamic response capabilities. Attached Figure Description

[0054] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein:

[0055] Figure 1 This is a structural diagram of the multi-electric braking system of the electric vehicle chassis controlled by wire, which is a structural diagram of the present invention.

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

[0057] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0058] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the 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 phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0060] Secondly, the present invention is described in detail with reference to the schematic diagrams. When detailing the embodiments of the present invention, for ease of explanation, the cross-sectional views illustrating the device structure may be partially enlarged, not according to the usual scale. Furthermore, the schematic diagrams are merely examples and should not limit the scope of protection of the present invention. In addition, actual fabrication should include three-dimensional spatial dimensions of length, width, and depth.

[0061] Reference Figure 1-2 A method for energy balancing of a multi-electric braking system in a drive-by-wire chassis of an electric transport vehicle is provided, comprising the following steps:

[0062] Step 1: First, using the vehicle-road cloud platform to obtain data on vehicle speed, braking demand, and remaining battery power of different vehicles at different times under the same road environment, a dynamic self-updating energy demand prediction method is designed by integrating historical data with real-time vehicle data. This method can 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 braking energy demand obtained in Step 1, design a dynamic braking energy balancing algorithm. Through optimization algorithm, dynamically adjust the energy recovery ratio of the EMB system and the electric drive system in real time to maintain efficient energy recovery under changing operating conditions, so that the system is always in the best working state.

[0064] Step 3: Building on Step 2, by integrating sensor networks and cloud data analysis, energy recovery data during braking is collected and processed in real time. Real-time data is combined with historical data and fed back to the central control unit to dynamically optimize the energy distribution strategy. This ensures that the energy recovery efficiency and braking effect between the EMB system and the electric drive system reach the optimal balance under different road conditions and operating conditions, thereby realizing intelligent energy recovery management and braking performance control.

[0065] Step one specifically includes:

[0066] 1.1) Data Acquisition and Preprocessing: Using the vehicle-road cloud platform, collect data on vehicle speeds (v) at different times and for different vehicles under the same road conditions. i (t), Braking demand B i (t) and remaining electricity E i (t) data, through data cleaning and noise reduction, outliers are removed and all data are standardized to meet the input requirements of subsequent algorithms;

[0067] 1.2) Feature extraction and historical data fusion based on 1.1): From the preprocessed data in 1.1), key feature vectors F are extracted using feature extraction methods. hist ={f1,f2,…,f nBased on a time series analysis model, the extracted features are used to predict the energy demand trend over a future period. hist (t), the prediction formula is:

[0068]

[0069] Among them, w i For feature f i By merging historical data, a preliminary predictive model for energy demand is established based on the weights of the data.

[0070] 1.3) Based on the prediction model in 1.2), perform real-time data fusion and dynamic self-updating: Based on the energy demand prediction model built in 1.2), collect real-time vehicle operating data F. real (t), and compare it with the historical data prediction value D. hist (t) Fusion; using Bayesian or Kalman filtering dynamic update methods, the energy demand forecast value D is adjusted in real time. real (t) is used to adapt to the dynamic changes in actual working conditions, and the formula is as follows:

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

[0072] Here, α and β are weighting coefficients, reflecting the fusion ratio of historical data and real-time data.

[0073] Step two specifically includes:

[0074] 2.1) Optimal allocation based on the energy demand forecast value in 1.3): Utilize the real-time updated energy demand forecast value D in 1.3). real The energy distribution ratio λ(t) between the EMB system and the electric drive system is calculated using optimization algorithms such as the Lagrange multiplier method. The objective is to minimize the energy loss L(t), as shown in the formula:

[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) represents 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 operating conditions. Dynamic programming is used to ensure the system's continuous high efficiency in energy recovery. The formula for the adjustment strategy is:

[0078]

[0079] Where γ is the learning rate;

[0080] 2.3) Based on the adjustment strategy in 2.2), perform performance evaluation and feedback control: calculate the energy recovery efficiency η(t) and braking effect B(t) to evaluate the effectiveness of the current energy allocation; if the evaluation results do not meet expectations, re-optimize the allocation parameters through the feedback mechanism to ensure the system is in the optimal state. The formula for calculating the recovery efficiency is:

[0081]

[0082] Furthermore, step three specifically includes:

[0083] 3.1) Based on the feedback mechanism in 2.3), real-time acquisition and processing of sensor data is performed: using an integrated sensor network, energy recovery data E during the braking process is acquired in real time. rec The data (t) and real-time operating condition data C(t) are processed and analyzed quickly in the cloud to ensure their accuracy and timeliness;

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

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

[0086] Where ML stands for machine learning model;

[0087] 3.3) Based on the optimization strategy in 3.2), dynamic optimization and feedback control are performed, and the optimization strategy F after collaborative analysis is applied. opt (t) Feedback is sent 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 of the system and optimal energy recovery under various operating conditions, the optimization formula is:

[0088]

[0089] Where δ is the feedback gain coefficient, used to adjust the optimization pace.

[0090] Specifically, the energy balancing method for the multi-electric braking system of the electric vehicle chassis is based on the multi-electric braking system of the electric vehicle chassis, including: a central single-stage reduction drive axle 15, a disc EMB braking system 4, a braking coordination control unit 2, a braking 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 drive wheel through the half shaft 17. The main reducer 14 changes the direction of torque transmission through the bevel gear pair and transmits the torque to the differential.

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

[0093] The brake coordination 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 coordination control unit 2 via sensor wiring to monitor brake pressure and feed back data for precise control of the braking 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 distributes 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 via the signal transmission line 3 to manage the charging and discharging state of the battery, and is connected to the braking coordination control unit 2 via the communication and bus system 13 to ensure the efficient use of electrical energy.

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

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

[0099] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for energy balancing of a multi-electric braking system on a drive-by-wire chassis of an electric transport vehicle, characterized in that, The method comprises the following steps: Step one: first, using the same road environment under different time, different vehicle speed, braking demand and residual capacity data obtained by vehicle-road cloud platform, through the fusion of historical data and real-time data of vehicle, the dynamic self-updating energy demand prediction method is designed, the energy demand of vehicle is predicted in real time under different braking conditions, the accuracy and forward-looking of prediction is improved; The step one specifically comprises: 1.1) Data acquisition and preprocessing: Using the vehicle-road cloud platform, collect the passing speed of different vehicles at different time periods and in the same road environment , braking demand , and remaining power data. Through data cleaning and noise reduction processing, eliminate outliers and standardize each data to meet the input requirements of subsequent algorithms; 1.2) Feature extraction based on 1.1) and historical data fusion: from the data preprocessed in 1.1), the key feature vector is extracted by using feature extraction method , and based on the time series analysis model, the energy demand trend in the future period is predicted by using the extracted features , the prediction formula is: ； wherein, characterized in that the weight, through the fusion of historical data, the establishment of energy demand preliminary prediction model; 1.3) Based on the prediction model in 1.2), fusion and dynamic self-update of real-time data: on the basis of the energy demand preliminary prediction model constructed in 1.2), the current running data of the vehicle is collected in real time , and is fused with the historical data energy demand trend ; the Bayesian or Kalman filtering dynamic updating method is adopted to adjust the energy demand prediction value in real time to adapt to the dynamic changes of the actual working conditions, and the formula is as follows: ； wherein, and are weight coefficients, reflecting the fusion ratio of historical data and real-time data; Step two: based on the energy demand prediction value obtained in step one, a dynamic balancing algorithm for braking energy is designed, and the energy recovery ratio of the EMB system and the electric drive system is calculated in real time through the optimization algorithm, so that the efficient energy recovery is continuously maintained in the changing working conditions, and the system is always in the best working state; The step two specifically comprises: 2.1) Based on the energy demand prediction value in 1.3), optimal distribution is performed: using the real-time updated energy demand prediction value in 1.3) , the energy distribution ratio between the EMB system and the electric drive system is calculated by an optimization algorithm such as the Lagrange multiplier method , the goal is to minimize energy loss , the formula is: ； wherein, and respectively the energy consumption of the EMB system and the electric drive system; 2.2) Based on the allocation ratio in 2.1), dynamic adjustment is made: according to the changes of road and working conditions, the allocation ratio is updated in real time By dynamic programming method, the system ensures continuous high efficiency in energy recovery, and the formula of adjustment strategy is: ； wherein, is the learning rate; 2.3) Based on the adjustment strategy in 2.2), performance evaluation and feedback control: Calculate the energy recovery efficiency and braking effect , evaluate the effectiveness of the current energy allocation; if the evaluation result does not meet the expectation, re-optimize the allocation parameters through the feedback mechanism to ensure that the system is in the best state, and the calculation formula of energy recovery efficiency is: ； wherein, represents the energy recovery data during braking; represents the total energy input data; Step three: on the basis of step two, through the integration of sensor network and cloud data analysis, the energy recovery data in the braking process is collected and processed in real time, the real-time data is combined with the historical data, and the central control unit is fed back, the energy distribution strategy is dynamically optimized, and the energy recovery efficiency and braking effect between the EMB system and the electric drive system are optimized to the optimal balance state under different road conditions and working conditions, so that the intelligent energy recovery management and braking efficiency control are realized.

2. The electrically powered ground equipment-by-wire chassis multi-electric braking system energy equalization method of claim 1, wherein: The step three specifically comprises: 3.1) Based on the feedback mechanism in 2.3), real-time acquisition and processing of sensor data: using an integrated sensor network, real-time acquisition of energy recovery data during braking and real-time operating data , through cloud rapid processing and analysis of these data, to ensure its accuracy and timeliness; 3.2) Based on the data processing results in 3.1), collaborative analysis of historical and real-time data is carried out: real-time data is combined with the historical feature key feature vector extracted in 1.2) Collaborative analysis is carried out, and machine learning algorithms such as random forest or support vector machine are used to continuously optimize energy allocation strategies to ensure the best energy recovery and balance state of the system under different working conditions, and the optimized strategy after collaborative analysis The formula is: ； wherein, represents a machine learning model; 3.3) Based on the optimization strategy in 3.2), dynamic optimization and feedback control are carried out to feed back the optimized strategy after collaborative analysis to the central control unit to adjust the energy distribution ratio of the EMB system and the electric drive system in real time , ensuring the stability and optimal energy recovery of the system under various working conditions, and the optimization formula is: ​ ； wherein is a feedback gain coefficient used to adjust the optimization step.

3. The electrically powered ground equipment-by-wire chassis multi-electric braking system energy equalization method of claim 1, wherein: The energy balancing method of the electrically variable transmission chassis drive-by-wire chassis multi-electric braking system is based on the electrically variable transmission chassis drive-by-wire chassis multi-electric braking system, which comprises a central single reduction drive axle (15), a disc type EMB braking system (4), a braking cooperative 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 reduction drive axle (15) is connected with the differential (16) through the main reducer (14), and the differential (16) is connected with 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; The disc type EMB braking system (4) is connected with the central single reduction drive axle (15) through the brake caliper support (7), and the brake caliper (5) is connected on the brake caliper support (7) to provide the necessary braking force; The braking cooperative control unit (2) is connected with the disc type EMB braking system (4) and the brake pressure sensor system (6) through the communication and bus system (13), which is used for receiving sensor signals in real time and controlling the braking process; The brake pressure sensor system (6) is connected with the braking cooperative control unit (2) through the sensor wiring, monitors the brake pressure and feeds back the data, and is used for accurately controlling the braking function; The power supply unit (1) is connected with the battery energy management system (12) through the power line bundle (11), ensures the power supply of the whole system, and distributes the power to each component through the power distribution module; The battery energy management system (12) is connected with the power supply unit (1) through the signal transmission line (3), manages the charging and discharging state of the battery, and is connected with the braking cooperative control unit (2) through the communication and bus system (13), to ensure the efficient use of electric energy. The communication and bus system (13) is connected with 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) through a data bus, ensuring real-time data exchange and coordinated control between the components.

4. The electrically powered, x-by-wire, chassis, multi-electric-brake system energy equalization method of claim 3, wherein: The central single-stage reduction drive axle (15) is further connected with the external frame through adjusting bolts, so as to facilitate accurate adjustment during installation.

5. The electrically powered, x-by-wire, chassis, multi-electric-brake system energy equalization method of claim 4, wherein: The disc EMB brake system (4) is provided with a brake disc (9), and the brake disc (9) is fixed on the central single-stage reduction drive axle (15) through a positioning pin (8), so as to ensure the stability of brake force transmission.

6. The electrically powered, x-by-wire, chassis, multi-electric-brake system energy equalization method of claim 5, wherein: The power supply unit (1) is connected with the battery energy management system (12) through a power supply monitoring module (10), providing real-time monitoring and protection functions for the battery state.

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