A vehicle energy management method for pure electric commercial vehicles

Abstract

The present invention discloses a method for energy management of a pure electric commercial vehicle. In response to the deficiencies in the prior art, the present invention proposes a new energy management strategy that combines the advantages of a rule-based strategy with a strategy based on optimal control. By introducing a hybrid strategy that combines rolling time domain optimization with rule control, the present invention can improve the optimality of energy distribution and the adaptability of the system while ensuring real-time performance. In particular, when the battery energy is low, the optimal control strategy can effectively improve the economy of the system, overcoming the limitations of traditional rule-based strategies that have poor adaptability and insufficient economy under complex working conditions. In addition, the present invention also achieves dynamic coordination between vehicle energy management and driver needs by introducing a human-computer interaction module, further improving the driving experience and the effect of energy efficiency management.

Description

Technical Field

[0001] The present invention belongs to the technical field of energy management of new energy vehicles, and specifically relates to a method for energy management of a pure electric commercial vehicle. Background Art

[0002] Pure electric vehicles offer significant advantages in environmental friendliness and energy efficiency, but their widespread adoption still faces several technical challenges, particularly in energy management. Energy management involves optimizing the processes of energy acquisition, storage, distribution, and recovery to improve the economic efficiency and efficiency of the entire system. In electric vehicles, proper energy management not only optimizes battery charging and discharging, extending driving range, but also significantly improves overall energy efficiency, reducing energy consumption and costs.

[0003] Currently, electric vehicle energy management strategies primarily rely on information such as the battery's real-time charge and discharge capabilities, the usage status of vehicle accessories, the vehicle's driving conditions, and the driver's operating intentions. These strategies dynamically allocate energy across multiple onboard power modules, including the electric motor, battery thermal management system, and low-voltage power system. The goal is to achieve energy conservation, improve driving range, and further enhance driving economy.

[0004] Existing pure electric vehicle energy management strategies can generally be divided into two categories: Rule-Based Energy Management Strategy (RB-EMS) and Optimal Control-based Energy Management Strategy (OC-EMS).

[0005] Existing technology 1: rule-based energy management strategy

[0006] Technical Solution

[0007] A rule-based energy management strategy involves technicians or experts defining energy allocation rules based on the vehicle's power performance requirements, the operating characteristics and high-efficiency ranges of various components, the characteristics of driving conditions, and extensive engineering experience. This approach typically adjusts the power distribution of various modules in real time according to a set of preset rules to meet the vehicle's energy needs under different operating conditions. This strategy is simple to implement and requires relatively low controller computing power, making it one of the more mature control methods currently in use.

[0008] 2. Disadvantages

[0009] Although rule-based energy management strategies are widely used, they also have some significant disadvantages:

[0010] Energy allocation schemes lack optimality: Regularized strategies are usually designed based on experience and specific operating conditions. They cannot provide the optimal energy allocation scheme under all operating conditions and often fail to fully tap the system's maximum potential.

[0011] Difficulty in parameter calibration: This strategy requires calibration of numerous parameters based on different driving environments, operating conditions, and actual vehicle status, increasing the complexity and maintenance costs of practical applications.

[0012] Insufficient adaptability: For complex and changing driving environments and complex power system architectures, rule-based strategies cannot be adjusted in real time, have poor adaptability, and are unable to cope with dynamically changing energy demands.

[0013] Existing technology 2: Energy management strategy based on optimal control

[0014] Technical Solution

[0015] Energy management strategies based on optimal control establish a mathematical model of the vehicle's powertrain and solve a constrained optimal control problem to achieve optimal energy economy and power allocation. This approach models factors such as vehicle driving conditions and the status of vehicle system equipment, sets optimization objectives, introduces constraints, and uses direct or indirect methods to iteratively approximate the optimal solution, ultimately achieving the optimal energy allocation strategy. This approach can more accurately meet predetermined energy economy and power allocation targets.

[0016] 2. Disadvantages

[0017] Although the energy management strategy based on optimal control can provide a more accurate energy management solution, its application also has the following major problems:

[0018] High computational complexity: As constraints and state dimensions increase, solving the optimal control problem involves a large number of complex calculations, resulting in a significant increase in the amount of computation, which in turn affects the system's operational efficiency and real-time performance, making it difficult to meet the requirements of real-time allocation and regulation of vehicle energy systems.

[0019] Easy to fall into local optimality: The optimal solution obtained based on the optimal control algorithm may be a local optimal solution rather than a global optimal solution, and thus cannot adapt well to all driving conditions and dynamically changing energy requirements.

[0020] In summary, the currently proposed pure electric vehicle energy management strategies mainly focus on optimizing the power conversion efficiency or real-time optimization of energy loss of a few important power-consuming modules. Although this strategy design method can improve the economic efficiency of important power-consuming modules, it does not coordinate the energy consumption relationship of other vehicle power-consuming modules such as the battery thermal management system, cabin thermal management system, and low-voltage power system. Summary of the Invention

[0021] The present invention aims to overcome the shortcomings of the existing technology and provide a pure electric commercial vehicle whole vehicle energy management method that integrates rolling time domain optimization and rule control. The method can dynamically adjust power distribution according to different driving conditions, improve the adaptability of the system under complex working conditions, and ensure that the system stability can be maintained when the optimal control strategy fails.

[0022] To solve the above technical problems, the technical method adopted by the present invention is as follows: The present invention discloses a vehicle energy management method for a pure electric commercial vehicle, comprising the following steps:

[0023] S1. Obtain the driver's required power information through the driving demand acquisition module, including:

[0024] The KNN algorithm is used to predict vehicle speed in the next 1-3 seconds, and the GADPC graph theory-density peak clustering algorithm is used to identify the driver's acceleration intention and calculate the driving power demand.

[0025] Identify braking intensity based on fuzzy inference rules and calculate braking power requirements in combination with braking force distribution strategy;

[0026] Read the real-time power requirements of the comfort power module and the non-comfort power module;

[0027] S2. Use the battery status detection module to evaluate the battery's maximum charge and discharge capacity and the current recovered power.

[0028] S3. Dynamically select control strategies through energy management optimization allocation module:

[0029] When the battery SOC is ≥ 30% or the optimal control module fails, a rule-based power allocation strategy is adopted;

[0030] When the battery SOC is less than 30%, a rolling-horizon optimization model is established with the driving power response ω1, braking power response ω2, and power response ω3 of the power module as optimization variables to solve the optimal power allocation solution.

[0031] S4. Provide the driver with suggestions for adjusting the comfortable power module through the human-computer interaction module, and dynamically adjust the power distribution based on the driver's feedback.

[0032] Furthermore, the driver acceleration intention recognition includes performing cluster analysis on an offline data set using a GADPC graph theory-density peak clustering algorithm, wherein the data set includes an accelerator pedal opening, an accelerator pedal opening change rate, a vehicle speed, and a corresponding acceleration;

[0033] By calculating the distance between the test sample and the cluster center point, combined with the adjustment coefficient and distance correction coefficient, the real-time acceleration demand is predicted.

[0034] Furthermore, the GADPC graph theory-density peak clustering algorithm includes the following steps:

[0035] SA1. Select state quantity , control quantity As sample data, good data that can better describe normal urban road driving conditions are collected and screened offline as training data sets, including is the accelerator pedal opening, is the rate of change of the accelerator pedal opening, v is the vehicle speed, a is the corresponding acceleration, and the local density of data point i is calculated. and distance ;

[0036] Where, the cutoff distance d c The neighborhood cutoff distance of the manually set data point i is taken as a value that can make the number of neighborhood points of each data point account for 1%-2% of the total number of data points. ;X ik 、X jk is the state element of the data point, and n is the number of states;

[0037] SA2. Add endpoints based on coordinates and edge lines based on cutoff distances, construct a sparse graph, and filter suspicious cluster centers.

[0038] SA3. Determine the final cluster center through cut point and bridge detection;

[0039] SA4. The dataset screening criterion is that the proportion of edge points and outliers does not exceed 2%.

[0040] Furthermore, in step S1, the step of identifying the braking intensity based on fuzzy inference rules includes:

[0041] Define the membership function of the brake pedal output voltage, the output voltage change rate, and the fuzzy linguistic variables of the braking intensity;

[0042] Reasoning is performed according to the braking intensity recognition inference rules, and the braking intensity z is output through defuzzification using the center of gravity method.

[0043] Furthermore, the objective function of the rolling horizon optimization model is: ;Where, t i , The time interval is t c The discrete moment, t n is the time step at the nth moment, P s is the safety power requirement, P1 is the motor drive power, P2 is the motor braking power, and P3 is the power of other on-board electrical modules. The objective function has the following constraints: ;

[0044] Among them, ω1 is the driving power responsiveness, ω2 is the braking power responsiveness, and ω3 is the power responsiveness of the power module. is the driving mode's influence coefficient on driving power, is the driving mode's influence coefficient on braking power, is the driving mode's influence coefficient on the vehicle's power consumption module, is the maximum charging power allowed by the motor, determined by the current performance of the battery. is the maximum discharge power allowed by the motor determined by the current performance of the battery, k gen is the adjustment coefficient of the motor’s recovered power, P gen The power recovered by the motor in the previous time step.

[0045] Furthermore, the driving modes include:

[0046] Sports mode, satisfying , so that the motor drive power demand is met first;

[0047] Comfort mode, satisfying , so that the demand for comfortable power modules can be met first;

[0048] Economic model, satisfying , so that the energy recovery power demand is met first.

[0049] Furthermore, in step S3, the rule-based power allocation strategy includes: SB1. Power allocation in braking or coasting state:

[0050] When the vehicle is in braking or coasting state, the motor enters the electric feedback braking state, and the following power distribution rules are used: , where k gen P is the current power adjustment parameter of the motor. gen P is the power recovered by the motor in the previous time step. 2_r Predict the braking power required for the motor, P 3_r Predict the required power for other on-board power modules, P S For safety demand power, is the maximum charging power allowed by the motor, determined by the current performance of the battery. is the maximum discharge power allowed by the motor, determined by the current performance of the battery. , where P STS and P BS are the rated power of the wire-controlled steering system and the wire-controlled brake system, P safe Reserve power for battery safety;

[0051] when When satisfying 、 、 、 Under the constraints, 3_r The power consumption modules in the system meet the requirements of each module in turn according to the priority of BMS, BTMS, PTMS, and DCS. BMS_min 、P BTMS_min 、P PTMS_min 、P DCS_min Both are the minimum power required to maintain the operation of the power module; P BMS Allocate power to BMS, P BTMS Allocate power to BTMS, P PTMS Allocate power to PTMS, P DCS Allocate power to DCS, P BMS _r is the power required by BMS, P BTMS _r is the power demanded by BTMS, P PTMS _r is the power required by PTMS, P DCS _r Power required by DCS;

[0052] when In the case of , the vehicle controller adjusts the electric braking torque proportional coefficient to dynamically adjust the braking power P2 to meet the constraint conditions;

[0053] SB2. Power distribution in driving mode: When the vehicle is in driving mode, the motor is in operation and the power distribution rules are as follows: Where, P 1_r Predict the required drive power for the motor. When the required power meets the constraint, power allocation is performed based on the required power. If it does not meet the constraint, adjustments are made as follows:

[0054] when In the case of ,in The driving power adjustment coefficient affected by the driving mode is set to , comfort mode is , the economic model is ; in satisfaction 、 、 、 Where P BMS_min 、P BTMS_min 、P PTMS_min 、P DCS_min Under the condition that the minimum power required to maintain the operation of the power module is the constraint condition, 3_rThe power consumption module requirements in the system are met in the order of priority of BMS, BTMS, PTMS and DCS until Re-established.

[0055] Furthermore, in step S2, the battery status detection module calculates the SOC using the Thevenin equivalent circuit model combined with the ampere-hour integration method, and uses the formula: Determine the maximum power of battery charging and discharging;

[0056] Where n s Indicates the number of battery cells connected in series in the battery pack, n p Indicates the number of parallel battery cells in the battery pack, U t,max and U t,min are the voltage thresholds of the battery during charge and discharge, P chgmin is the maximum charging power allowed by the motor determined by the current performance of the battery, P dismax is the maximum discharge power allowed by the motor, determined by the current performance of the battery. is the maximum charging current of the battery pack, is the maximum discharge current of the battery pack;

[0057] future Maximum allowable charge and discharge current after time , k is the serial number of the single battery, is the maximum charging current of the kth single cell, is the maximum discharge current of the kth single cell, is the SOC measurement time period, s k (t) is the battery SOC at time t, is the battery coulombic efficiency, C max is the rated capacity of the battery, S max is the maximum SOC value, S min is the minimum SOC value;

[0058] The maximum charge and discharge current of the entire battery pack .

[0059] Furthermore, the human-computer interaction module provides suggestions based on the following conditions:

[0060] When SOC>30% and there is insufficient power in the power module, it prompts to reduce the comfortable power demand;

[0061] When SOC<30% and ω1<1.0, the system prompts to switch to economy mode;

[0062] When SOC < 30% and ω3 < 1.0, it is recommended to shut down PTMS or DCS.

[0063] Furthermore, the comfort power module includes a cabin thermal management module and a low-voltage power system, and the non-comfort power module includes a battery management system and a battery thermal management system.

[0064] Beneficial effects:

[0065] By combining rolling time domain optimization with rule-based control, accurately identifying the driver's intentions, designing a human-computer interaction module, and dynamically selecting a control strategy based on the battery status, the present invention comprehensively improves the optimality, adaptability, real-time performance, user experience, and fault tolerance of the energy management strategy of pure electric commercial vehicles.

[0066] Specifically, the advantages include:

[0067] 1. Improving Optimal Energy Allocation: This invention utilizes a rolling horizon (RHC) algorithm to dynamically adjust the power allocation strategy, comprehensively considering the impact of driving conditions over the next 1-3 seconds on vehicle energy flow. When the battery SOC is high, rule-based control is employed to ensure real-time performance; when the SOC is low, optimal control is employed to improve efficiency. This strategy adjusts power allocation in real time based on predicted driving conditions, ensuring optimal energy efficiency under varying driving conditions.

[0068] 2. Enhanced System Adaptability and Robustness: This invention incorporates a graph-theory-based density peak clustering algorithm (GADPC) and a fuzzy-inference braking intention recognition algorithm to accurately identify the driver's acceleration and braking intentions. Furthermore, a human-machine interaction module is designed to provide the driver with feedback on power adjustment recommendations from the comfort power module, forming a closed loop of "optimization recommendations - manual decision-making - dynamic adjustment." By accurately identifying the driver's intentions and providing real-time feedback and adjustments, the system can adapt to complex and changing driving environments, enhancing the driving experience while ensuring the stability and effectiveness of the energy management strategy.

[0069] 3. Improved Computational Efficiency and Real-Time Performance: This invention reduces computational complexity by adopting a rule-based control strategy when the SOC is high. At lower SOC levels, it switches to a rolling-horizon optimal control strategy to improve economic efficiency. By dynamically selecting the control strategy based on battery status, it balances computational efficiency and energy economy, meets real-time requirements, and ensures stable system operation under various operating conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 This is a framework diagram of the vehicle energy management strategy in Example 1 of the present invention;

[0071] Figure 2 This is a schematic diagram of the vehicle power system framework, energy flow, and control signal transmission in Example 1 of the present invention;

[0072] Figure 3This is a flow chart of the KNN vehicle speed prediction algorithm in Example 1 of the present invention;

[0073] Figure 4 Flowchart of the GADPC algorithm in Example 1 of the present invention;

[0074] Figure 5 : is the membership function image of the brake pedal output voltage in Example 1 of the present invention;

[0075] Figure 6 : is the membership function image of the brake pedal output voltage change rate in Example 1 of the present invention;

[0076] Figure 7 The braking intensity membership function image in Example 1 of the present invention;

[0077] Figure 8 Schematic diagram of the Thevenin battery model in Example 1 of the present invention. DETAILED DESCRIPTION

[0078] The present invention will be described in further detail below with reference to the accompanying drawings and specific implementation methods.

[0079] Example 1

[0080] A method for energy management of a pure electric commercial vehicle comprises the following steps:

[0081] S1. Obtain the driver's required power information through the driving demand acquisition module, including:

[0082] The KNN algorithm is used to predict vehicle speed in the next 1-3 seconds, and the GADPC graph theory-density peak clustering algorithm is used to identify the driver's acceleration intention and calculate the driving power demand.

[0083] Identify braking intensity based on fuzzy inference rules and calculate braking power requirements in combination with braking force distribution strategy;

[0084] Read the real-time power requirements of the comfort power module and the non-comfort power module;

[0085] S2. Use the battery status detection module to evaluate the battery's maximum charge and discharge capacity and the current recovered power.

[0086] S3. Dynamically select control strategies through energy management optimization allocation module:

[0087] When the battery SOC is ≥ 30% or the optimal control module fails, a rule-based power allocation strategy is adopted;

[0088] When the battery SOC is less than 30%, a rolling-horizon optimization model is established with the driving power response ω1, braking power response ω2, and power response ω3 of the power module as optimization variables to solve the optimal power allocation solution.

[0089] S4. Provide the driver with suggestions for adjusting the power comfort module through the human-computer interaction module, and dynamically adjust the power distribution based on the driver's feedback.

[0090] Among them, such as Figure 1 As shown in the figure, the vehicle energy management system mainly includes four core modules: driving demand acquisition module, battery status detection module, energy management optimization allocation module and human-computer interaction module.

[0091] The Driving Demand Acquisition Module analyzes the driver's driving needs based on past and future driving conditions, the driver's use of the accelerator and decelerator pedals, and the human-machine interface module. These requirements primarily include vehicle drive power, braking power, and comfort power requirements. The vehicle's drive power requirement is calculated based on past driving conditions and the driver's accelerator pedal operation; the vehicle's braking power requirement is calculated based on the driver's brake pedal operation and the vehicle's braking force distribution strategy; and the comfort power requirement is calculated based on the driver's commands on the human-machine interface. Finally, this module outputs this driving power demand information to the energy management optimization and allocation module.

[0092] The battery status detection module estimates the battery's maximum charge and discharge capacity based on the battery model and current battery status. It also calculates and estimates the battery's current recovered power based on current vehicle driving information. Finally, this module transmits this estimated battery information to the energy management optimization and allocation module.

[0093] The Energy Management Optimization and Allocation Module receives information from the Driving Demand Acquisition Module and the Battery Status Detection Module and transmits it to the Rule-Based Power Allocation Module and the Optimal Control-Based Power Allocation Module, respectively, for processing. The resulting adjustments to the motor and battery management systems are then transmitted to the motor controller and battery management system, respectively, for power control. The Comfort Power Module's adjustment recommendations are then transmitted to the Human-Machine Interaction Module.

[0094] The human-computer interaction module, based on the adjustment results and adjustment suggestion information obtained by the energy management optimization and allocation module, reminds the driver and makes operational suggestions, and further adjusts the power distribution of the cabin thermal management system and the low-voltage power system based on the driver's decision.

[0095] The vehicle electrical system mainly includes electric motors, vehicle control unit VCU (Vehicle Control Unit), braking system BS (Brake System), steering system STS (Steering System), battery management module BMS (Battery Management System), battery thermal management module BTMS (Battery Thermal Management System), cabin thermal management module PTMS (Passenger Thermal Management Syestem) and DC / DC low-voltage power system DCS (Direct Current System) and other power modules. Among them, the electric motor power is controlled by the motor controller, the BS power is controlled by the electric brake control unit, the STS power is controlled by the steering system main controller, the BMS and BTMS power are controlled by the BMS, the PTMS power is controlled by the air conditioning automatic control system, and the DCS power is controlled by the DC / DC control management module. BMS, PTMS, BTMS and DCS participate in power optimization and distribution, and PTMS and DCS are comfort power components. The accelerator pedal information and brake pedal information are collected by the A / D acquisition module, and the transmission information is collected by the transmission controller. Figure 2 The figure shows the framework of the vehicle's electrical system, energy flow, and control signal transmission diagram.

[0096] Preferably, the driving demand collection module is divided into three main parts: driving power demand collection, braking power demand collection and on-board electrical component demand collection.

[0097] 1. Drive power demand collection

[0098] The driving power demand collection part is divided into three parts: vehicle speed prediction, acceleration demand analysis and power calculation.

[0099] Vehicle Speed Prediction: This uses a KNN algorithm to predict vehicle speed within the next 1-3 seconds. By integrating GIS and GPS information with onboard sensors, it calculates driving conditions (such as slope, adhesion coefficient, and temperature) over a short period of time to predict vehicle speed.

[0100] Acceleration demand calculation: The GADPC graph theory-density peak clustering algorithm is used to perform offline cluster analysis on data such as the accelerator pedal opening rate change rate and vehicle speed to identify the driver's acceleration intention.

[0101] Power calculation: The required driving force is calculated based on the longitudinal vehicle dynamics model, and the required motor drive power is further derived.

[0102] 2. Braking power demand collection

[0103] Braking intensity requirement calculation: A fuzzy control strategy is used to process the brake pedal output voltage and its rate of change to calculate the required braking intensity.

[0104] Calculation of the maximum braking torque of the motor: Calculate the maximum braking torque of the motor based on the maximum electric braking force limit of the motor and the maximum charging current limit of the battery.

[0105] Braking force distribution strategy: When the battery SOC is lower than 90%, the motor feedback braking and mechanical braking force are distributed according to the set ratio.

[0106] Motor feedback braking regenerative power calculation: Calculates the motor's regenerative power based on the braking intensity and motor braking torque.

[0107] 3. Demand collection for other vehicle-mounted power modules

[0108] Non-comfort power module: includes the power required by the battery management system (BMS) and the battery thermal management system (BTMS).

[0109] Comfort power module: includes the power requirements of the cabin thermal management module (PTMS) and the low-voltage power system (DCS).

[0110] More preferably, the following contents are also included:

[0111] (1) Vehicle speed prediction: The vehicle speed is predicted using the KNN (K-nearest neighbor) algorithm. The specific process is as follows:

[0112] Collect the state quantity of each state including the road slope in advance offline , road adhesion coefficient , ambient temperature T , target variable vehicle speed v The historical information is used as the training data set In actual driving, the road slope, road adhesion coefficient and ambient temperature in the short term (usually 1-3 seconds) are predicted by combining Geographic Information System (GIS) and Global Positioning System (GPS) information with vehicle-mounted multi-sensor fusion data. T As a test sample, calculate its Euclidean distance with each training sample, that is, ,in is the training sample point, Then sort the training samples in ascending order according to the distance, select the nearest k training samples, and calculate the average speed of these k training samples. , the result is the predicted vehicle speed. Figure 3 This is the flow chart of the KNN vehicle speed prediction algorithm.

[0113] Driver acceleration demand calculation: Offline classification of driving status: Select state quantity , the control quantity is a As sample data, good data that can better describe normal urban road driving conditions are collected and screened offline as training data sets, where is the accelerator pedal opening, is the accelerator pedal opening rate, v is the vehicle speed, a is the corresponding acceleration. GADPC graph theory-density peak clustering algorithm is used to perform cluster analysis on the data set. First, calculate each data point i With other data points j The local density , where the cutoff distance d c Manually set data points i The neighborhood cutoff distance is usually set to a value that makes the number of neighborhood points of each data point account for 1%-2% of the total number of data points. Then calculate the distance between points , where x ik 、x jk is the state element of the data point, n is the number of states. Then add endpoints based on the coordinates and add edge lines based on the cutoff distance, and construct a sparse graph based on the endpoints and edges. Next, calculate the gamma value of each point , and displayed in descending order on the gamma graph, according to Calculate the turning angle of each point on the gamma map And thus construct the angle map, where a, b, c are the gamma map 、 、 The length of each side of a triangle connected by three points, selected from the angle diagram The point before the 180° transition (the turning angle will eventually increase to 180° as the point sequence in the turning angle diagram increases) is used as the suspicious cluster center point. The final cluster center point is determined by screening based on whether the connection path between the points in the cluster to which the suspicious center point belongs includes cut points and bridges. Then, each point is looped through and aggregated to nearby points with higher density and stronger connectivity. Finally, edge points are detected with low centrality and outliers are detected with low centrality and long distance values. The number of edge points and outliers does not exceed 2% of the total number as the evaluation standard for a good data set. The number of clusters is obtained. N and the corresponding cluster center value set The overall algorithm flow chart is as follows: Figure 4 .

[0114] (2) Online acceleration prediction: During the real-time operation of the vehicle, the VCU obtains test data points by collecting the current driver's operation of the accelerator pedal and the vehicle speed. , classify the state and calculate the distance between the test sample and the center point of the cluster, and finally obtain the required acceleration under the driving state. The calculation formula is as follows:

[0115] in , 、 、 、 are the required acceleration, accelerator pedal opening, accelerator pedal opening rate, and vehicle speed of the test sample, respectively. , 、 、 、 are the acceleration, accelerator pedal opening, accelerator pedal opening rate, and vehicle speed at the cluster center, respectively. It is an adjustment coefficient formulated according to actual conditions, with a value range of , is the distance correction factor, , is the Euclidean distance to the test data point The acceleration of the most recent training sample point.

[0116] (3) Power calculation: The power demand of the vehicle can be calculated by the longitudinal vehicle dynamics model, and the driving force F t The calculation formula is as follows: ;

[0117] Where F t 、F W 、F i 、F j They are rolling resistance, air resistance, slope resistance and acceleration resistance, among which F f The calculation formula is as follows: ;

[0118] Where M is the mass of the vehicle, g is the acceleration of gravity, and f is the rolling resistance coefficient. The slope of the road in front of the vehicle is measured by GPS, GIS and multi-sensor fusion information;

[0119] F W The calculation formula is as follows: ;

[0120] Where C d is the air resistance coefficient, A is the vehicle's frontal area, is the air density, v is the predicted vehicle speed obtained in the vehicle speed prediction step;

[0121] F i The calculation formula is as follows: ;

[0122] F j The calculation formula is as follows: Where is the rotational mass conversion factor.

[0123] The required torque can be obtained from the driving force: Where T tq is the motor torque, r is the wheel radius, i g is the gearbox transmission ratio, i0 is the main reduction gearbox transmission ratio, For transmission system efficiency.

[0124] In summary, the motor's predicted drive power requirement is: ; Where n is the motor speed.

[0125] More preferably,

[0126] (1) The calculation of the braking intensity requirement includes the following steps: the vehicle controller VCU uses a fuzzy control strategy to process the input brake pedal output voltage U and the brake pedal output voltage change rate dU, and outputs the required braking intensity Z. The specific method of the fuzzy control strategy is as follows:

[0127] Taking a 5V rated voltage brake pressure sensor as an example, the brake pedal output voltage is [0,5], the unit is volt (V), and the fuzzy input language is U{small (US), smaller (UNS), medium (UM), larger (UNB), large (UB)}. The membership function uses a triangular function, and the function graph is as follows: Figure 5 As shown. The function expression is as follows:

[0128] ;

[0129] ;

[0130] ;

[0131] ;

[0132] ;

[0133] The brake pedal output voltage change rate dU has a domain of [0,70], a unit of volts per second (V / s), and the fuzzy input language is dU{small (DUS), smaller (DUNS), medium (DUM), larger (DUNB), larger (DUB)}. The membership function uses a triangular function, and the function graph is as follows: Figure 6 As shown. The function expression is as follows:

[0134] ;

[0135] ;

[0136] ;

[0137] ;

[0138] ;

[0139] Braking intensity Z, domain is [0,1], fuzzy input language is {small (ZS), smaller (ZNS), medium (ZM), larger (ZNB), large (ZB)}. The membership function is a triangular function, the function graph is as follows Figure 7 As shown. The function expression is as follows: ; ; ; ; ;

[0140] The fuzzy inference rules are shown in Table 1. Table 1 Inference rules for braking intensity identification

[0141]

[0142] After obtaining the fuzzy expression of the braking intensity, defuzzification is performed. Here, the defuzzification method adopts the centroid method, and its calculation method is as follows: Suppose the set of output variables z is , ;

[0143] Where z is the output variable of the control quantity, and p is the variable output number of Z (p=1,2,…,5); is the fuzzy membership function.

[0144] (2) Maximum braking torque of the motor

[0145] Maximum electric braking force F reg_max , and its calculation formula is: ;

[0146] Where i g is the gearbox transmission ratio, i0 is the main reducer reduction ratio, is the transmission efficiency from the motor to the wheel, r is the wheel radius, F reg_max is the maximum motor braking torque. Limited by the motor peak torque and the battery maximum charging current, the maximum braking torque that the motor can provide is the smaller of the two, that is:

[0147] ;

[0148] ;

[0149] ;

[0150] Where n is the motor speed; n0 is the minimum motor speed for braking energy recovery determined by the motor efficiency; T m_mot T is the maximum braking torque that the motor can provide when the motor speed is n; m_max is the peak braking torque of the motor; P m_max is the peak braking power of the motor; T m_bat is the maximum braking torque of the motor determined by the maximum charging power of the battery when the motor speed is n; The maximum allowable charging power of the battery calculated by the battery status assessment module; For battery charging efficiency.

[0151] (3) Braking force distribution strategy

[0152] When the battery SOC is below 90%, motor regenerative braking is allowed to intervene in the braking system. This pure electric commercial vehicle uses a parallel-structured brake energy recovery system, in which mechanical braking force and electric braking force exist simultaneously and are distributed according to a certain ratio. When the driver presses the brake pedal to a certain degree, the pedal pressure sensor converts the pedal signal into an electrical signal and inputs it into the fuzzy controller. The fuzzy controller outputs the required braking intensity and sends a command to the motor to generate the corresponding braking force. The total front axle braking force is calculated as: ; Where G is the vehicle weight, b is the distance between the rear axle and the vehicle center of mass, h g is the height of the center of mass, and L is the wheelbase.

[0153] The total braking force on the rear axle is calculated as: ;Where a is the distance between the front axle and the vehicle's center of mass.

[0154] The calculation formula for the motor torque required for the corresponding axle is: ; where k is the rate of change of total braking force with braking intensity z, z is braking intensity, F reg_max The maximum electric braking force.

[0155] The corresponding motor braking torque is: Where T reg is the motor braking torque.

[0156] (4) Calculation of motor feedback braking recovery power

[0157] The estimated electric feedback braking recovery power from the above three links is: ;In the formula is the power generation efficiency of the motor, n N is the rated speed of the motor.

[0158] Therefore, the motor's predicted braking power requirement is P 2_r =P reg .

[0159] Further preferably, the non-comfort power modules are the power required by BMS and BTMS respectively, and the VCU reads the current power required by the two, which are P BMS_r With P BTMS_r .

[0160] The comfort power modules are the power required by PTMS and DCS respectively. VCU can read the driver's operation of the comfort power module from the human-machine interface, and obtain the power P required by each through the air conditioning automatic control system and DC / DC low-voltage power management module. PTMS_r With P DCS_r .

[0161] Total demand for other on-board power modules P 3_r The calculation formula is: .

[0162] Further preferably, the battery status evaluation module is divided into two parts: battery peak charge and discharge capacity and current battery recovered power.

[0163] (1) Battery peak charge and discharge capacity

[0164] The battery peak charge and discharge capability is divided into three parts: battery SOC state calculation, battery maximum allowable current calculation, and peak discharge charging power calculation.

[0165] Battery SOC calculation

[0166] Commonly used equivalent circuit models for electric vehicle simulation include the Rint model, PNGV model, Thevenin model, and second-order RC model. Considering that the Rint model is too simple, while the PNGV model and second-order RC model are too complex to be used in practical engineering applications, this method uses the Thevenin model to model and identify the parameters of the battery module of the pure electric commercial vehicle under study. The schematic diagram of the Thevenin battery model is shown in the figure. Figure 8 shown.

[0167] The mathematical model of the Thevenin battery model is: ;

[0168] Where U is the output voltage, I is the load current, and U rc is the dynamic branch voltage, R0 is the internal resistance, and the above model parameters can be identified by HPPC (compound pulse power test).

[0169] Combined with the ampere-hour integral method The SOC after time is calculated: ;

[0170] In the formula is the Coulomb efficiency parameter of the current, C t is the rated capacity of the battery, i k is the current of the battery cell.

[0171] Calculation of maximum allowable battery current: Future The maximum allowable charge and discharge current after time can be expressed as: ;

[0172] In the formula is the maximum charging current of the battery, is the maximum discharge current of the battery, S max is the maximum SOC value, S min is the minimum SOC value.

[0173] The maximum charge and discharge current of the entire battery pack is: ;

[0174] Peak discharge charging power calculation: Through the above calculation, we can get the future After a certain time, the maximum charge and discharge power of the battery pack is: ;

[0175] Where n s Indicates the number of battery cells connected in series in the battery pack, n p Indicates the number of parallel battery cells in the battery pack, U t,max and U t,min are the voltage thresholds of the battery during charging and discharging respectively.

[0176] (2) The current battery has recovered power

[0177] Vehicle controller recording time The vehicle speed before and collect the current vehicle speed through sensors , and then calculate the vehicle acceleration information for each period of time: ;

[0178] When the measured When it is less than zero, the slope of the road where the passing vehicle is traveling is ignored and the braking force F t The calculation formula is as follows: ;

[0179] Where F f 、F w 、F i 、F j They are rolling resistance, air resistance and acceleration resistance, among which F f The calculation formula is as follows: F f =Mgf;

[0180] Where M is the mass of the vehicle, g is the acceleration of gravity, and f is the rolling resistance coefficient;

[0181] F w The calculation formula is as follows: ;

[0182] Where C d is the air resistance coefficient, A is the vehicle's frontal area, is the air density, v is the vehicle speed at time t;

[0183] F w The calculation formula is as follows: ;In the formula is the rotational mass conversion factor.

[0184] The motor torque required for braking can be obtained from the driving force: ;

[0185] Where T is the motor torque required for braking, r is the wheel radius, i g is the gearbox transmission ratio, i0 is the main reduction gearbox transmission ratio, is the transmission system efficiency, and k is the ratio of the motor braking force to the total braking force set by the vehicle controller.

[0186] From the motor model, we can know that the torque of the motor regenerative braking is: ;

[0187] Where T d is the actual torque of the motor, n N is the rated speed of the motor, n is the motor speed, and the calculation formula is: , where R is the wheel radius.

[0188] In summary, based on the models of permanent magnet brushless DC motors and permanent magnet synchronous motors commonly used in electric vehicles on the market, it can be seen that the motor drive and braking characteristics have a certain consistency. The difference lies in the difference in power generation efficiency and electric output efficiency. Therefore, the current recovered power of the motor is: ;In the formula is the motor power generation efficiency.

[0189] Furthermore, the energy optimization and regulation module is divided into two parts: a rule-based power allocation module and an optimal control-based power allocation module. When the battery SOC is above 30% or the optimal control module fails for some reason, the rule-based power allocation module takes over power allocation. When the battery SOC is below 30%, the optimal control-based power allocation module takes over power allocation.

[0190] 1. Power allocation rules:

[0191] The power distribution rules are divided into two types: braking and driving.

[0192] (1) Braking or coasting state

[0193] When the vehicle is in braking or coasting state, the motor is in electric feedback braking state, P1=0, so the following strategy is adopted: ;

[0194] Where k gen P is the current power adjustment parameter of the motor. 2_r is the power required for motor braking as mentioned above, P 3_r is the power required by other power modules as mentioned above, P s The safety power requirement is calculated as follows: .

[0195] Among them, P STS and P BS are the calibrated rated power of the wire-controlled steering system and the wire-controlled brake system, respectively. Since the power consumed by both accounts for a small proportion of the total vehicle power and both have a significant impact on vehicle driving safety, their power changes and distribution are not considered; P safe Power is reserved for battery safety, and safety rules are formulated for the battery based on the material properties, packaging and manufacturing process, as well as usage properties including temperature, current, voltage, external pressure, service life, and health status of the battery used in this vehicle.

[0196] When the required power meets the above constraints, power allocation is performed according to the required power; if the constraints cannot be met, two cases are discussed.

[0197] against In the case of satisfying the following constraints, 3_r The power consumption modules in the system meet the requirements of each module in turn according to the priority of BMS, BTMS, PTMS, and DCS:

[0198] ;

[0199] ;

[0200] ;

[0201] ;

[0202] Where P BMS_min 、P BTMS_min 、P PTMS_min 、P DCS_min Both are the minimum power required to maintain the operation of the power module, P BMS Allocate power to BMS, PBTMS Allocate power to BTMS, P PTMS Allocate power to PTMS, P DCS Allocate power to DCS, P BMS _r is the power required by BMS, P BTMS _r is the power demanded by BTMS, P PTMS _r is the power required by PTMS, P DCS _r Requires power for DCS.

[0203] against In this case, the vehicle controller adjusts the electric braking torque proportional coefficient k so that the braking power P2 is dynamically adjusted to meet the constraint conditions.

[0204] (2) Driving status

[0205] When the vehicle is in driving state, the motor is in working state, P2=0, so the following strategy is adopted:

[0206] ;

[0207] When the required power meets the constraint, power allocation is performed according to the required power; if the constraint cannot be met, two cases are discussed.

[0208] against In the case of ,in The driving power adjustment coefficient affected by the driving mode can be set to , comfort mode is , the economic model is Under the following constraints, 3_r The power consumption module requirements in the system are met in the order of priority of BMS, BTMS, PTMS and DCS until Re-established.

[0209] ;

[0210] ;

[0211] ;

[0212] ;

[0213] Where P BMS_min 、P BTMS_min 、P PTMS_min 、P DCS_min Both are the minimum power required to maintain the operation of the power module, P BMS Allocate power to BMS, PBTMS Allocate power to BTMS, P PTMS Allocate power to PTMS, P DCS Allocate power to DCS, P BMS _r is the power required by BMS, P BTMS _r is the power demanded by BTMS, P PTMS _r is the power required by PTMS, P DCS _r Requires power for DCS.

[0214] against Considering that the power consumed by the drive motor and the power module should be much greater than the current recovered power of the motor after the coefficient adjustment, this situation is not considered.

[0215] 2. Power distribution optimization control part:

[0216] The drive power response , Braking power responsiveness , power response of other vehicle-mounted power modules As the decision variable to be optimized, the following rolling horizon optimization problem is established:

[0217] Where t i , The time interval is t c The discrete moment, t n is the time step at the nth moment, P s is the safety power requirement, P1 is the motor drive power, P2 is the motor braking power, and P3 is the power of other on-board electrical modules. The objective function has the following constraints: ;

[0218] Where ω1, ω2, and ω3 are the power response of each power module. is the driving mode's influence coefficient on driving power, is the driving mode's influence coefficient on braking power, k is the driving mode's influence coefficient on the vehicle's electrical module power, gen is the adjustment coefficient of the motor’s recovered power, P gen is the power recovered by the motor in the previous time step, is the maximum charging power allowed by the motor, determined by the current performance of the battery. The maximum discharge power allowed by the motor is determined by the current performance of the battery. When the power value is negative, the battery is in the charging state; when it is positive, the battery is in the discharging state. Therefore, the minimum value is the maximum charging power. The VCU controls the power every cycle. Solve the rolling time domain optimization problem at the current time t1, obtain the optimal power response control sequence in the prediction time domain, and convert the response at time t1 to 、 、 As the power responsiveness control strategy at the current moment, the control variables at other moments are discarded.

[0219] After t c After time t2, the VCU solves the rolling time domain optimization problem at time t2 and repeats the above operations.

[0220] When ω1<1, a command is issued to the motor controller to allocate the motor drive power ;

[0221] When ω2<1, the vehicle controller adjusts the electric braking torque proportional coefficient k and sends a command to the motor controller to adjust the electric braking torque required by the braking system so that the allocated motor braking power ;

[0222] When ω3<1, the following allocation scheme information is given to the human-computer interaction module so that the power of other on-board power modules is allocated , under the following constraints, the power requirements of each module are met in order according to the priority of BMS, BTMS, PTMS, and DCS:

[0223] ;

[0224] ;

[0225] ;

[0226] ;

[0227] ;

[0228] The impact of different driving modes on parameters is as follows:

[0229] When in sports mode, , so that the motor drive power demand is met first, you can refer to the setting 、 、 ;

[0230] When in comfort mode, , so that the demand for comfortable power modules can be met first, which can be set as 、 、 ;

[0231] When in economy mode, , so that the energy recovery power demand is met first, which can be set as 、 、 .

[0232] The parameter settings of the above three modes should all meet the following parameter conditions: ;

[0233] Preferably, the human-machine interface (HMI) receives the power adjustment information transmitted by the energy optimization and regulation module through the vehicle controller and feeds it back to the driver for decision making.

[0234] When the battery SOC is greater than 30%, if the power distribution rules calculate that the power demand of the electrical components cannot be met, the HMI performs the following operations: when in braking or coasting state, it reminds the driver to reduce the power demand of the comfort power module according to the comfort power module adjustment strategy provided by the energy optimization and regulation module; when in acceleration state, it informs the driver that the motor drive power has been limited and reminds him to reduce the power demand.

[0235] When the battery SOC is less than 30%, if ω1<1, the HMI prompts the driver that the motor drive power has been limited and recommends that the driver reduce power demand and select economy mode; if ω2<1, the HMI recommends that the driver appropriately take coasting or slow braking operations to reduce the intensity of energy recovery; if ω3<1, the HMI recommends that the driver reduce the use demand of the comfort power module or turn off the comfort power module according to the adjustment plan of the energy optimization and regulation module.

[0236] Example 2: A pure electric commercial vehicle energy management system

[0237] A vehicle energy management system for a pure electric commercial vehicle, comprising:

[0238] Driving demand collection module, used to obtain driving power demand, braking power demand and comfort power module power demand;

[0239] Battery status detection module, used to evaluate the battery's maximum charge and discharge capacity and the current recovered power;

[0240] Energy management optimization allocation module, configured to dynamically switch rule control and rolling time domain optimization control based on battery SOC;

[0241] A human-computer interaction module, used to provide power adjustment suggestions to the driver and receive feedback instructions;

[0242] The system realizes data interaction and control signal transmission between modules through the vehicle controller (VCU).

[0243] Preferably, the driving demand acquisition module includes:

[0244] The vehicle speed prediction unit uses the KNN algorithm to predict the vehicle speed in the next 1-3 seconds;

[0245] Acceleration intention recognition unit, using GADPC graph theory-density peak clustering algorithm to classify driver needs;

[0246] The braking intention recognition unit uses a fuzzy controller to output the braking intensity z.

[0247] Preferably, the energy management optimization allocation module includes:

[0248] A rule control unit predefines the power allocation priority under braking / driving states;

[0249] Rolling time domain optimization unit, with power response 、 、 are decision variables, and solve the multi-objective optimization problem.

[0250] Preferably, the human-computer interaction module includes:

[0251] Display unit for displaying power allocation suggestions and SOC status;

[0252] The input unit is used to receive the driver's adjustment instructions for the comfort power module.

[0253] Preferably, the battery status detection module includes:

[0254] Thevenin equivalent circuit model unit for real-time calculation of battery terminal voltage and SOC;

[0255] Peak power calculation unit, based on the battery's maximum charge and discharge current and voltage threshold output , where n s Indicates the number of battery cells connected in series in the battery pack, n p Indicates the number of parallel battery cells in the battery pack, U t,max and U t,min are the voltage thresholds of the battery during charging and discharging respectively.

[0256] In response to the shortcomings of the existing technology, the present invention proposes a new energy management strategy that combines the advantages of rule-based energy management methods and optimal control-based strategies. By introducing a hybrid strategy that combines rolling time domain optimization with rule-based control, the present invention can improve the optimality of energy distribution and the adaptability of the system while ensuring real-time performance. In particular, when the battery SOC is low, the system economy can be improved through the optimal control strategy, avoiding the shortcomings of existing rule-based strategies under complex working conditions. In addition, the present invention also realizes the dynamic coordination of vehicle energy management and driver needs by introducing a human-computer interaction module, further improving the driving experience and energy efficiency management.

[0257] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the foregoing embodiments. The foregoing embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for energy management of a pure electric commercial vehicle, characterized in that: The following steps are involved: S1. Obtain the driver's required power information through the driving demand acquisition module, including The KNN algorithm is used to predict vehicle speed in the next 1-3 seconds, and the GADPC graph theory-density peak clustering algorithm is used to identify the driver's acceleration intention and calculate the driving power demand. Identify braking intensity based on fuzzy inference rules and calculate braking power requirements in combination with braking force distribution strategy; Read the real-time power requirements of the comfort power module and the non-comfort power module; S2. Evaluate the battery's maximum charge and discharge capacity and the current recovered power through the battery status detection module; S3. Dynamically select control strategies through energy management optimization allocation module: When the battery SOC is ≥30% or the optimal control module fails, a rule-based power allocation strategy is adopted; When the battery SOC is less than 30%, a rolling-horizon optimization model is established with the driving power response ω1, braking power response ω2, and power response ω3 of the power module as optimization variables to solve the optimal power allocation solution. S4. Provide the driver with suggestions for adjusting the power comfort module through the human-computer interaction module, and dynamically adjust the power distribution based on the driver's feedback.

2. The method for energy management of a pure electric commercial vehicle according to claim 1, characterized in that: Identifying the driver's acceleration intention includes performing cluster analysis on an offline data set using a GADPC graph theory-density peak clustering algorithm, the data set including accelerator pedal opening, accelerator pedal opening rate of change, vehicle speed, and corresponding acceleration; By calculating the distance between the test sample and the cluster center point, combined with the adjustment coefficient and distance correction coefficient, the real-time acceleration demand is predicted.

3. The method for energy management of a pure electric commercial vehicle according to claim 2, characterized in that: The GADPC graph theory-density peak clustering algorithm includes the following steps: SA1. Select state quantity , control quantity As sample data, good data that can better describe normal urban road driving conditions are collected and screened offline as training data sets, including is the accelerator pedal opening, is the rate of change of the accelerator pedal opening, v is the vehicle speed, a is the corresponding acceleration, and the local density of data point i is calculated. and distance ; Where, the cutoff distance d c The neighborhood cutoff distance of the manually set data point i is taken as a value that can make the number of neighborhood points of each data point account for 1%-2% of the total number of data points. ;X ik 、X jk is the state element of the data point, and n is the number of states; SA2. Add endpoints based on coordinates and edge lines based on cutoff distances, construct a sparse graph, and filter suspicious cluster centers. SA3. Determine the final cluster center through cut point and bridge detection; SA4. The dataset screening criterion is that the proportion of edge points and outliers does not exceed 2%.

4. The method for energy management of a pure electric commercial vehicle according to claim 1, characterized in that: In step S1, the step of identifying the braking intensity based on fuzzy inference rules includes: Define the membership function of the brake pedal output voltage, the output voltage change rate, and the fuzzy linguistic variables of the braking intensity; Reasoning is performed according to the braking intensity recognition inference rules, and the braking intensity z is output through defuzzification using the center of gravity method.

5. The method for energy management of a pure electric commercial vehicle according to claim 1, characterized in that: The objective function of the rolling horizon optimization model is: Where, t i , The time interval is t c The discrete moment, t n is the time step at the nth moment, P s is the safety power requirement, P1 is the motor drive power, P2 is the motor braking power, and P3 is the power of other on-board electrical modules. The objective function has the following constraints: ; Among them, ω1 is the driving power responsiveness, ω2 is the braking power responsiveness, and ω3 is the power responsiveness of the power module. is the driving mode's influence coefficient on driving power, is the driving mode's influence coefficient on braking power, is the driving mode's influence coefficient on the vehicle's power consumption module, is the maximum charging power allowed by the motor, determined by the current performance of the battery. is the maximum discharge power allowed by the motor determined by the current performance of the battery, k gen is the adjustment coefficient of the motor's recovered power, P gen The power recovered by the motor in the previous time step.

6. The method for energy management of a pure electric commercial vehicle according to claim 5, characterized in that: The driving modes include: Sports mode, satisfying , so that the motor drive power demand is met first; Comfort mode, satisfying , so that the demand for comfortable power modules can be met first; Economic model, satisfying , so that the energy recovery power demand is met first.

7. The method for energy management of a pure electric commercial vehicle according to claim 1, characterized in that: In step S3, the rule-based power allocation strategy includes: SB1. Power distribution during braking or coasting: When the vehicle is in braking or coasting state, the motor enters the electric feedback braking state, and the following power distribution rules are used: , where k gen P is the current power adjustment parameter of the motor. gen P is the power recovered by the motor in the previous time step. 2_r Predict the braking power required for the motor, P 3_r Predict the required power for other on-board power modules, P S For safety demand power, is the maximum charging power allowed by the motor, determined by the current performance of the battery. is the maximum discharge power allowed by the motor, determined by the current performance of the battery. , where P STS and P BS are the rated power of the wire-controlled steering system and the wire-controlled brake system, P safe Reserve power for battery safety; when When satisfying 、 、 、 Under the constraints, 3_r The power consumption modules in the system meet the requirements of each module in turn according to the priority of BMS, BTMS, PTMS, and DCS. BMS_min 、P BTMS_min 、P PTMS_min 、P DCS_min Both are the minimum power required to maintain the operation of the power module; P BMS Allocate power to BMS, P BTMS Allocate power to BTMS, P PTMS Allocate power to PTMS, P DCS Allocate power to DCS, P BMS _r is the power required by BMS, P BTMS _r is the power demanded by BTMS, P PTMS _r is the power required by PTMS, P DCS _r Power required by DCS; when In the case of , the vehicle controller adjusts the electric braking torque proportional coefficient to dynamically adjust the braking power P2 to meet the constraint conditions; SB2. Power distribution in driving mode: When the vehicle is in driving mode, the motor is in operation and the power distribution rules are as follows: Where, P 1_r Predict the required drive power for the motor. When the required power meets the constraint, power allocation is performed based on the required power. If it does not meet the constraint, adjustments are made as follows: when In the case of ,in The driving power adjustment coefficient affected by the driving mode is set to , comfort mode is , the economic model is ; in satisfaction 、 、 、 Where P BMS_min 、P BTMS_min 、P PTMS_min 、P DCS_min Under the condition that the minimum power required to maintain the operation of the power module is the constraint condition, 3_r The power consumption module requirements in the system are met in the order of priority of BMS, BTMS, PTMS and DCS until Re-established.

8. The method for energy management of a pure electric commercial vehicle according to claim 1, characterized in that: In step S2, the battery status detection module calculates the SOC using the Thevenin equivalent circuit model combined with the ampere-hour integration method, and uses the formula: Determine the maximum power of battery charging and discharging; Where n s Indicates the number of battery cells connected in series in the battery pack, n p Indicates the number of parallel battery cells in the battery pack, U t,max and U t,min are the voltage thresholds of the battery during charge and discharge, is the maximum charging power allowed by the motor, determined by the current performance of the battery. is the maximum discharge power allowed by the motor, determined by the current performance of the battery. is the maximum charging current of the battery pack, is the maximum discharge current of the battery pack; future Maximum allowable charge and discharge current after time , k is the serial number of the single battery, is the maximum charging current of the kth single cell, is the maximum discharge current of the kth single cell, is the SOC measurement time period, s k (t) is the battery SOC at time t, is the battery coulombic efficiency, C max is the rated capacity of the battery, S max is the maximum SOC value, S min is the minimum SOC value; The maximum charge and discharge current of the entire battery pack .

9. The method for energy management of a pure electric commercial vehicle according to claim 1, characterized in that: The human-computer interaction module provides suggestions based on the following conditions: When SOC>30% and there is insufficient power in the power module, it prompts to reduce the comfortable power demand; When SOC<30% and ω1<1.0, the system prompts to switch to economy mode; When SOC < 30% and ω3 < 1.0, it is recommended to shut down PTMS or DCS.

10. The method for energy management of a pure electric commercial vehicle according to claim 1, characterized in that: The comfort power module includes a cabin thermal management module and a low-voltage power system, and the non-comfort power module includes a battery management system and a battery thermal management system.

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