Power distribution method for redundant electric drive system

Abstract

A power distribution method for a redundant electric drive system. The method comprises: on the basis of drive objectives on shafts and states and parameters of batteries and electric motors, determining constraint levels of the batteries, so as to set the maximum power of each battery pack; calculating an optimal power configuration of each battery pack, and on the basis of the optimal power configuration, setting a power matrix, a disturbance term and a target power vector; determining the constraint level of each electric motor, and on the basis of the constraint level, adjusting the power matrix and the disturbance term; on the basis of a full-rank condition for the power matrix and a rationality condition for a power distribution ratio, determining whether a rational power ratio can be obtained; if the conditions are not met, removing constraints layer by layer in ascending order until the conditions are met, otherwise, notifying an upper-layer controller or an upper-layer control module for processing; and on the basis of the obtained power ratio, calculating an output power of each electric motor. The method provides a general method for optimizing the power distribution of a redundant electric drive system under constraint conditions of different levels, thereby simplifying design and development, shortening development cycles and reducing costs; and calculation costs are low, and the method can be implemented in a real-time control system.

Description

Redundant electric drive system power allocation method [Technical Field]

[0001] This invention relates to a power distribution method for redundant electric drive systems. [Background Technology]

[0002] Distributed electric drive systems can be used in various vehicles, such as airplanes, automobiles, ships, and rail vehicles. Their advantages lie in whether the inherent redundancy of the distributed electric drive system can be utilized to fully improve the driving efficiency, safety, and reliability of the transportation system. For example, multiple electric motors are connected to multiple batteries as redundant power sources to drive a shaft that serves as the output terminal.

[0003] The applicant filed an application on September 5, 2022, with publication number CN115431794A, entitled "Control Architecture of Distributed Electric Drive System," in which the power distribution module adopts a redundant design. A redundant electric drive system refers to an electric drive system with redundant characteristics, composed of multiple motors and multiple batteries connected by a pre-designed electrical connection architecture. These motors and batteries form a power conduction network to achieve the driving objective. While the redundant electric drive system is electrically distributed, it is not necessarily distributed physically; it can be centralized. The driven object, i.e., the upper-level system, can be distributed, i.e., having multiple independent driving objectives, or centralized, with only one driving objective. Because the system has redundancy, even if some driving components fail or are restricted, as long as the control degrees of freedom are still greater than the control objective, multiple different power distribution schemes can be used to achieve the same driving objective. Thus, this power conduction network can not only achieve fault-tolerant control but also achieve optimal drive control with efficiency balance and minimum loss as its objectives. This application aims to propose an easily implemented method for optimizing the power distribution of a redundant electric drive system, enabling optimal fault-tolerant control of the redundant electric drive system online with minimal computational cost.

[0004] Chinese patent CN113541299A proposes a load power distribution method, device, and parallel redundant uninterruptible power supply system. Although it proposes a load power distribution method, this method only addresses how to control all uninterruptible power supply devices to equally share the load power when all devices are in battery power mode. It lacks fault-tolerance strategies and optimization calculation measures, making it fundamentally unsuitable for the current demands of high efficiency, safety, and reliability in transportation electrification applications. It is also unsuitable for the power distribution of the redundant electric drive system addressed in this invention. If this method is used to control the electric drive system, it will result in poor reliability and safety, low efficiency, and inability to adjust power distribution when one or more electric drive units fail or are limited. Furthermore, it cannot consider the priority of various factors to optimize the overall system performance in real time during operation, failing to meet the needs of critical fields such as electric vehicles and aerospace. [Summary of the Invention]

[0005] The purpose of this invention is to address the lack of online optimal fault-tolerant control for redundant electric drive systems in existing technologies. It proposes a power allocation method for redundant electric drive systems that can operate online in real time, aiming to improve the reliability and efficiency of electric drive systems. By optimizing the collaborative work of multiple electric drive units (motors and batteries) in real time, dynamic power allocation among motors is achieved under various constraints. Furthermore, when one or more electric drive units fail or become limited, the system can automatically adjust the power allocation to ensure that the remaining units can continue to operate efficiently and stably. This not only improves the system's fault tolerance but also extends the service life of the equipment, making it suitable for critical fields such as electric vehicles and aerospace.

[0006] To achieve the above objectives, this invention proposes a power allocation method for a redundant electric drive system, comprising the following steps:

[0007] a) Obtain axis output requirements: Obtain axis output requirements from the upper-level controller or control module;

[0008] b) Obtain the status parameters of each battery and motor: Obtain the status and various parameters of the battery and motor from the front-end control module;

[0009] c) Calculate the maximum output power of each battery pack: Determine the constraint level of each battery and calculate the maximum output power of each battery pack;

[0010] d) Set the maximum output power of each motor: Determine the constraint level of each motor and set the maximum output power of each motor;

[0011] e) Determine if the motor meets the requirements: Determine if the current motor can meet the on-axis output requirements under the established constraints. If not, remove the motor constraints layer by layer from low to high until the on-axis output requirements are met. Otherwise, report to the upper-level controller or control module: Some on-axis output requirements cannot be met and the target needs to be adjusted.

[0012] f) Determine if the power allocation ratio is reasonable: Calculate the power allocation ratio and determine if it is reasonable. If it is not reasonable, remove the battery constraints layer by layer from low to high until the reasonable conditions are met. Otherwise, report to the upper-level controller or control module: the output requirements on some axes cannot be met and the target needs to be adjusted.

[0013] g) Calculate the motor output power: Calculate the output power of each motor according to the power ratio.

[0014] Preferably, in step c), when calculating the maximum output power of each battery pack, the output power of each battery pack is adjusted according to the battery parameter settings within the maximum power range to achieve global optimization; and the power matrix p, disturbance term p0, and target power vector A are set according to the optimal power configuration of the battery pack.

[0015] Preferably, in step e), it is determined whether the current motor meets the on-axis output requirements under the established constraints. This is done by determining whether the power matrix p is full rank. If it is not full rank, it indicates that the motor has too many constraints and cannot achieve the on-axis output target. In this case, the motor constraints are removed layer by layer from low to high until the full rank condition is met.

[0016] Preferably, in step f), the power allocation ratio is calculated by matrix calculation to obtain the allocation control vector r = p. T (pp T ) -1 (A-p0).

[0017] Preferably, in step f), the determination of whether the power allocation ratio is reasonable can be made by judging whether the calculated power allocation ratio is within a certain reasonable range, such as [0,1]. If it is not within this range, it indicates that the battery constraints are too many. Then, the battery constraints are removed layer by layer from low to high until all power allocation ratios are within the selected range.

[0018] Preferably, the system involved in this method comprises n battery packs, where battery pack i is composed of h i The system consists of several batteries connected in parallel, i = 1, 2, ..., n; m motors; and l driven targets are controlled. The specific steps are as follows:

[0019] A) Obtain l driving targets A from the upper-level controller or control module. i i = 1, 2, ..., l; obtain the status and parameters of each battery and motor from the front-end control program; initialize the program to assign initial values ​​to each control variable;

[0020] B) Determine the respective constraint level p based on the state of each battery. k k = 1, 2, ..., a; the smaller k is, the higher the constraint level, that is, the higher the priority to satisfy; at the same time, under the premise of satisfying the on-axis power requirements, the constraint level is adjusted from high to low to the optimal level, and the maximum power that each battery can provide is determined.

[0021] C) Determine the maximum output power of each battery pack based on the constraint level of each battery and the combination method of each battery pack;

[0022] D) Based on the SoH health status parameters of each battery obtained from the front-end control program. ij , i=1,2,…,n, j=1,2,…,h i and the state of charge (SOC) of the battery pack i For i = 1, 2, ..., n, the output power of the battery pack is adjusted and optimized to meet the on-axis power requirements within the maximum output power of each battery pack, while balancing the health and state of charge of each battery pack.

[0023] E) Determine the respective constraint level P based on the status of each motor. k k = 1, 2, ..., b; the smaller k is, the higher the constraint level, that is, the higher the priority to satisfy; at the same time, under the premise of satisfying the power demand on the shaft, the constraint level is adjusted from high to low to the optimal level, and the maximum power that each motor can provide is determined.

[0024] F) Determine the output power of the constrained motor; then set the power matrix p and disturbance term p0 according to the constrained motor; p is an l×(mn) matrix, and p0 is an l-dimensional term vector; and use the full-rank condition of the power matrix p to determine whether the motor constraints can meet the power requirements of each axis. If p is not full-rank, it indicates that there are too many motor constraints, so abandon the lowest motor constraint condition layer by layer, that is, retain the higher-level motor constraints as much as possible, until a full-rank p is obtained, and use it to calculate the allocation control vector r = p T (pp T ) -1 (A-p0),

[0025] G) Determine the power allocation ratio. j = 1, 2, ..., mn, whether it is within a reasonable range, i.e. [0, 1]. If there is a power allocation ratio that is not within a reasonable range, it indicates that there are too many battery constraints. Then, abandon the current lowest battery constraint layer by layer, i.e. retain the higher level battery constraints as much as possible, until all calculated power allocation ratios are within a reasonable range.

[0026] H) Based on the power conduction matrix R mxnCalculate m r ji The value of , i∈{1,2,…,m}, j∈{1,2,…,n}, and determined by r ji The output power of all unconstrained motors is calculated and combined with the output power of constrained motors obtained in F), and then transmitted to the lower-level controller in the form of a command to control each motor to reach the required power, thereby satisfying the power demand on all shafts while achieving an optimal balance between the battery and the motor.

[0027] Preferably, the battery's constraint level p k k = 1, 2, ..., a, representing a levels, which can be divided into: Level 1 is failure, the highest level, i.e., physical hardware limitation; Level 2 is limitation, i.e., mandatory under non-emergency circumstances; Levels 3 to a-1 are different levels of optimization; Level a is complete freedom; in order of priority from high to low: p 1 >p 2 >p 3 >…>p a-1 >p a .

[0028] Preferably, the constraint level P of the motor k k = 1, 2, ..., b, representing b levels, which can be divided into: Level 1 is failure, the highest level, i.e., physical hardware limitation; Level 2 is limitation, i.e., mandatory under non-emergency circumstances; Levels 3 to b-1 are different levels of optimization; Level b is completely free; ranked from highest to lowest priority as follows: P 1 >P 2 >P 3 >…>P b-1 >P b .

[0029] As a preferred option, the dimensions of the power matrix p and the allocation control vector r are reduced from m to mn, thereby reducing the computational load.

[0030] Preferably, the power matrix p consists of 0 and the optimized power of each battery pack, and the disturbance term p0 consists of 0 and the optimized power of each battery pack and the power of the constrained motor.

[0031] The beneficial effects of this invention are as follows: This invention solves the power allocation optimization problem of redundant electric drive systems under different priority constraints, improving the reliability and efficiency of electric drive systems. By optimizing the collaborative work of multiple electric drive units (motors and batteries) in real time, dynamic power allocation among motors is achieved under various constraints: based on factors such as motor efficiency, thermal management, battery health status, battery charge balance, and load demand, and considering the priority level of each factor, the overall system performance is optimized; moreover, when one or more electric drive units fail or are limited, the system can automatically adjust the power allocation to ensure that the remaining units can continue to operate efficiently and stably. This method not only improves the fault tolerance of the system but also extends the service life of the equipment, making it suitable for key fields such as electric vehicles and aerospace.

[0032] First, this method is designed for online real-time operation, has low computational cost, and can be implemented in existing real-time control systems; rather than an optimization method that is only suitable for offline operation.

[0033] Secondly, it can handle different levels of constraints on the battery motor, including multiple layers of soft constraints, rather than the single-level hard constraints typically found in optimization problems. Furthermore, each layer of soft constraints allows for dynamic changes; new constraints can be added, existing constraints can be removed, or existing constraints can be modified based on actual conditions. When all constraints cannot be satisfied, this method adjusts the lowest level of the satisfyable constraints to address the safety and reliability requirements at different levels, while simultaneously pursuing the highest efficiency and optimal balance of the battery motor, maximizing the lifespan, reliability, and driving range of the electric drive system.

[0034] Third, even if some drive components fail, as long as the integrity of the power transmission network is maintained, that is, if there is no situation where the on-axis requirements cannot be met, there is no need for the intervention of the upper-level controller or upper-level control module. In other words, it is transparent to the upper-level control design, simplifying the design of the upper-level controller or upper-level control module.

[0035] Fourth, this invention provides a systematic approach that offers a general solution for all redundant electric drive systems, rather than requiring the design of a specific algorithm for each particular structure. This versatility simplifies the design and development process and reduces development cycle and cost.

[0036] Fifth, this method, combined with motor control, ensures that the power optimization process produces no unevenness in the output on the shaft.

[0037] The features and advantages of the present invention will be described in detail through embodiments and in conjunction with the accompanying drawings. [Attached Image Description]

[0038] Figure 1 is a general flowchart of the power allocation method for redundant electric drive system of the present invention;

[0039] Figure 2 is a flowchart of the initialization module in step A of the power allocation method for redundant electric drive system of the present invention.

[0040] Figure 3 is a flowchart of the battery constraint level determination module in step B of the redundant electric drive system power allocation method of the present invention.

[0041] Figure 4 is a flowchart of the battery pack power boundary establishment module in step C of the redundant electric drive system power allocation method of the present invention.

[0042] Figure 5 is a flowchart of the battery pack power optimization module in step D of the redundant electric drive system power allocation method of the present invention.

[0043] Figure 6 is a flowchart of the motor constraint level and power boundary establishment module in step E of the redundant electric drive system power allocation method of the present invention.

[0044] Figure 7 is a flowchart of step F, motor constraint annealing reset and module flow, in the power allocation method of the redundant electric drive system of the present invention.

[0045] Figure 8 is a flowchart of the battery constraint annealing reset module in step G of the redundant electric drive system power allocation method of the present invention.

[0046] Figure 9 is a flowchart of the power allocation ratio and motor output power calculation module in step H of the redundant electric drive system power allocation method of the present invention.

[0047] Figure 10 is a hardware connection diagram of Embodiment 1 of the present invention;

[0048] Figure 11 is a hardware connection diagram of Embodiment 2 of the present invention;

[0049] Figure 12 is a hardware connection diagram of Embodiment 3 of the present invention.

Detailed Implementation Methods

[0050] Referring to Figure 1, the power allocation method for a redundant electric drive system obtains the on-axis output requirements from the upper-level controller or control module. Here, the controller refers to additional hardware that transmits information via communication; the control module refers to a software module that transmits information within the program. The system obtains the battery and motor status and parameters from the front-end control module. It determines the constraint level of each battery and calculates the maximum output power of each battery pack. Within the maximum power range, it adjusts the output power of each battery pack according to the battery parameter settings to achieve global optimization. Based on the optimal power configuration of the battery packs, it sets the power matrix p, disturbance term p0, and target power vector A. It determines the constraint level of each motor and sets the maximum output power of each motor. Based on the set motor constraints, it adjusts the power matrix p and disturbance term p0. It checks if the power matrix p is full rank; if not, it removes constraints layer by layer from low to high until the full rank condition is met. Otherwise, it alarms the upper-level controller or control module: some on-axis output requirements cannot be met, and the target needs adjustment. Finally, it calculates the allocation control vector r = p. T (pp T ) -1 (A-p0); Determine if the power allocation ratio is reasonable. If not, remove constraints layer by layer from low to high until reasonable conditions are met. Otherwise, issue an alarm to the upper-level controller or control module: the output requirements on some axes cannot be met and the target needs to be adjusted. Calculate the output power of each motor based on the final power allocation ratio.

[0051] A redundant electric drive system containing n batteries and m motors needs to control l drive targets. First, an n×m matrix is ​​used to describe the power transfer from the batteries to the motors, as shown in Table 1.

[0052] Table 1

[0053] In Table 1, [P1,P2,…,P] m ] T This represents the power achieved by m motors, [p1, p2, ..., p n ] T r represents the power provided by n batteries. ij Let i = 1, 2, ..., n, j = 1, 2, ..., m, representing the power distribution ratio of battery i to motor j. The power transfer matrix from battery to motor is R = [r ij ] n×m Thus R T p = P.

[0054] The motor power satisfies the constraint MP = A, where A = [A1, A2, ..., A l ] T Let l represent the driving targets, and M be an l×m motor connection matrix; the battery power constraint satisfies R[1]. m =[1]n That is, r i1 +r i2 +…+r im =1, i=1,2,…,n; the power conduction network of this redundant electric drive system can MR T p = A is used to describe this.

[0055] In the event of system failures and limitations, some values ​​in P and p are fixed, while some values ​​in R are... ij The values ​​will be set to zero, so our goal is, under dynamic constraints, for the dynamically refreshed P, p, and R, to determine the remaining free r in R. ij Find the optimal solution to satisfy driving objective A; if for R n×m Each element in the matrix attempts to find the optimal value by taking different values ​​within the entire search space. This would be a complex and computationally expensive process. Such an indirect method is clearly not suitable for the needs of online real-time computing and cannot be implemented in applications with high real-time controllability requirements, such as aviation, aerospace, vehicles, and ships.

[0056] Therefore, we propose a novel direct matrix operation method to quickly obtain the optimal solution. At the same time, this method can simplify the power conduction network of the system according to the actual electrical connection architecture, so as to reduce the system complexity and corresponding computational cost by using some sparse matrix processing methods. Moreover, by utilizing some related redundancy, the dimension of the power conduction network, i.e. the dimension of the R matrix, can be reduced, thereby further reducing the system complexity and corresponding computational cost.

[0057] The entire electric propulsion system consists of n independent battery packs, each of which can have one or more batteries connected in parallel, and m independent motors that jointly drive l drive shafts. Based on the actual electrical connection topology, the power transmission matrix can be in the following form. This form is only for description and not a definitive instance. The power transmission matrix is ​​a typical n×m matrix as shown in Table 2. The difference between Table 2 and Table 1 is that Table 1 is a truly general form, where each power distribution ratio in the n×m power transmission matrix is ​​a free variable. However, the actual power transmission matrix must conform to a specific electrical connection architecture, meaning that some elements in the power transmission matrix are 0, indicating no electrical connection between the corresponding motor and battery. The general form in Table 1 can be transformed into the specific structural form in Table 2.

[0058] Table 2

[0059] It can be represented as: P M =Rp B , where P M =[P1 P2…P m ] T , Power allocation matrix R mxn It is an m×n matrix, in the following form:

[0060] Right now

[0061] The topology connecting the motor and drive shaft can be represented as: A = D lxm P M Where A = [A1 A2…A l ] T D l×m It is an l×m matrix, with the form D l×m = [0 1 1 0…0 0; 0 0 1 1…0 0; …; 1 0 0 0…0 1]; Thus, the power transfer matrix from the battery to the drive shaft can be expressed as: A = DR T p B Rewrite the power allocation matrix R as a power ratio vector r, containing all power ratio values ​​r with independent degrees of freedom. ij ,by Construct the battery power matrix p, and an interference vector p0 generated by the non-independent power ratio. The power conduction matrix above can then be rewritten as: pr = A - p0. Following the previous logical flow, first calculate the appropriate... The battery power matrix p is constructed, and then the battery power matrix p and the disturbance vector p0 are reset according to the motor constraints. Then, the power ratio vector r = p is calculated using the power vector A on the drive shaft obtained from the upper controller or control module. T (pp T ) -1 (A-p0), and adjust the constraint level according to the full rank condition and the reasonable ratio condition, so as to optimize the power ratio, that is, optimize the power transmission matrix R; here GB i This is battery pack number i, which can be composed of multiple batteries connected in parallel. Let's define battery pack number i as having h... i It consists of several batteries connected in parallel, note h. i For any i = 1, 2, ..., n, the power is expressed as p. ij , i=1,2,…,n, j=1,2,…,h i Based on the aforementioned drive power transmission matrix, the allocation control vector r is determined, in the form r = [r 12 r 13 r 14 r 25 r 26 …r n-1m-2 r n-1m-1 r nm rn1 Considering the constraint that the total battery allocation is 100%, the control variable r can be simplified by reducing it from an m-dimensional vector to an mn-dimensional vector; after obtaining the allocation control ratio r... ij Then, the corresponding P can be obtained. j When the motor operates at a given power P j During operation, each battery pack will also operate at a set... Providing electrical energy ensures that the entire electric drive system operates optimally while satisfying constraints at each level, and r ij P j , The system updates in real time as the operating conditions and constraints change, thus enabling a real-time optimization process.

[0062] Figures 2 to 9 show the specific implementation flow of steps A to H.

[0063] When the battery constraint level is p 1 Or the motor constraint level is P 1 The fact that the full-rank condition and the reasonable power distribution ratio condition cannot be met indicates that the power on at least one axis is insufficient. Combined with the overall insufficient power of the battery, this means... When the power on the shaft cannot meet the requirements, i.e. when the battery emergency mode or motor emergency mode is set, it has exceeded the control range of the controller at this level. The upper-level controller must adjust the load on the shaft to redistribute and optimize it under the premise of safe operation of the whole machine.

[0064] Power conduction MR in this invention T p=A also applies to the process of generating electricity from a shaft-driven motor to charge a battery. It is only necessary to transform all the discharge-related parameters into charging-related parameters, and the power processed is also transformed from motor drive power and battery discharge power into motor generation power and battery charging power. The method proposed above can be extended to the generation mode, that is, the motor works in generator mode and the battery works in charging mode. Thus, under the constraints and limitations of the motor and battery, the electrical energy generated by the generator is distributed to different batteries in real time according to the power ratio.

[0065] Example 1:

[0066] Referring to Figure 10, taking this redundant electric drive system as an example, we will illustrate the proposed method. The system has four drive shafts, eight motors, and eight batteries. The drive shafts are designated as first drive shaft A1, second drive shaft A2, third drive shaft A3, and fourth drive shaft A4. The motors are designated as first motor M1, second motor M2, third motor M3, fourth motor M4, fifth motor M5, sixth motor M6, seventh motor M7, and eighth motor M8. The batteries are designated as first battery B1, second battery B2, third battery B3, fourth battery B4, fifth battery B5, sixth battery B6, seventh battery B7, and eighth battery B8. Each drive shaft is driven by two independent motors, and each motor is powered by two independent batteries connected in parallel to a battery pack. Each battery pack simultaneously powers two motors. In the figure, thick lines represent mechanical connections, and thin lines represent electrical connections.

[0067] The system needs to meet the power requirements of the four drive shafts [A1, A2, A3, A4]. T Furthermore, the operating power of each motor is dynamically and optimally allocated under the respective power constraints of the motor and the battery. The motor power is represented by P, which in this example is [P1, P2, P3, P4, P5, P6, P7, P8]. T The battery power is represented by p, which in this example is [p1, p2, p3, p4, p5, p6, p7, p8]. T The power distribution ratio of battery n to motor m is expressed as r. nm , n=1,2,…,8,m=1,2,…,8;The power transmission matrix from battery to motor in this example is shown in Table 3.

[0068] Table 3

[0069] In Table 3, most of the r nm Since n = 1, 2, ..., 8 and m = 1, 2, ..., 8 have no electrical connections, they can be directly set to zero. Thus, the motor constraints in this example are:

[0070] P1+P2=A1 P3+P4=A2 P5+P6=A3 P7+P8=A4 (1)

[0071] In this example, the battery power constraint can be written as:

[0072] r 12 ×p1+r 22 ×p2=P2 r 13 ×p1+r 23 ×p2=P3 r 34 ×p3+r 44 ×p4=P4 r 35 ×p3+r 45×p4=P5 r 56 ×p5+r 66 ×p6=P6 r 57 ×p5+r 67 ×p6=P7 r 78 ×p7+r 88 ×p8=P8 r 71 ×p7+r 81 ×p8=P1 (2)

[0073] Where, r nm satisfy:

[0074] r 12 +r 13 =1 r 22 +r 23 =1 r 34 +r 35 =1 r 44 +r 45 =1 r 56 +r 57 =1 r 66 +r 67 =1 r 78 +r 71 =1 r 88 +r 81 =1 (3)

[0075] The limitations on motor and battery power can be categorized into different levels, such as Level 1 being mandatory, Level 2 being preferred, and Level 3 being completely free, respectively denoted as P. 1 P 2 P 3 , and p 1 p 2 p 3 We set the motor's priority to be higher than the battery's. In other words, when considering priority limits, the order from highest to lowest priority is: P 1 =p 1 >P 2 >p 2 >P 3 >p 3 The general rule is to set b restriction levels for the motor and a restriction levels for the battery, with the lowest level being unrestricted.

[0076] The problem we need to solve is to find a suitable power allocation ratio r under the constraints listed above, namely the motor power constraints (1) and battery power constraints (2) and (3), and the constraints from the upper-level strategy. 12 ,r 13 ,r 22 ,r23 ,r 34 ,r 35 ,r 44 ,r 45 ,r 56 ,r 57 ,r 66 ,r 67 ,r 78 ,r 71 ,r 88 ,r 81 .

[0077] Since the two batteries in the example above are connected in parallel, the power conduction matrix above can be simplified to Table 4.

[0078] Table 4

[0079] In Table 4, GB1 is a battery pack of size 1, consisting of batteries of size 1 and 2 connected in parallel; GB3 is a battery pack of size 3, consisting of batteries of size 3 and 4 connected in parallel; GB5 is a battery pack of size 5, consisting of batteries of size 5 and 6 connected in parallel; and GB7 is a battery pack of size 7, consisting of batteries of size 7 and 8 connected in parallel. For simplicity, we define... j = 1, 2, ..., 4

[0080] Based on the simplified drive power transmission matrix, determine the allocation control vector r = [r 12 r 13 r 34 r 35 r 56 r 57 r 78 r 71 The number of control variables has been reduced from 16 to 8. However, in this case, due to (3) in the battery constraint, the number of control variables can be further reduced to 4 to simplify the process. Therefore, the allocation control vector can be rewritten as r = [r 12 r 34 r 56 r 78 1], where 1 is a constant used to represent r 13 =1-r 12 The constraint, i.e., r 13 Since they are not free control variables, the actual allocation control vector contains only four free control variables and one constant generated by a constraint.

[0081] Based on different failure and constraint conditions, the above problem is rewritten to suit a new method for describing direct matrix operations; we define a battery power matrix p, the determination of which is set by the current failure tolerance and optimization constraint strategy.

[0082] The drive target is obtained from the upper-level control module; in this example, it is the power requirement for the four drive shafts, defined as a 4-dimensional vector A = [A1, A2, A3, A4]. T Obtain battery parameters from the battery management module, such as state of charge (SOC) and maximum output power (p). max_d Maximum input power p max_c Discharge impedance, charging impedance, maximum discharge current I max_d Maximum charging current I max_c And so on, as well as the battery's current state of health (SoH). Battery parameters such as discharge impedance, SOC, and SoH define the current limitations of the battery. For example, if battery pack 1 fails, then p1 = 0, or battery pack 1 needs to limit its power to p. 1_lim Then the choice of p1 must be less than p 1_lim And set the limit level, and then, based on the battery parameters above, freely select the battery pack power p within the allowable space. 12 p 34 p 56 p 78 The value of p is given by the condition that p is satisfied. 12 +p 34 +p 56 +p 78 =A1+A2+A3+A4; To simplify the following description, define j = 1, 2, ..., 4, theoretically there can be infinitely many j. The combinations can be chosen, but the actual implementation needs to generate a reasonable set according to a certain logical process.

[0083] Based on the constraint p obtained from the superior control program 1 p 2 , ..., p k Calculate the power limit p for each battery. i_lim ,i=1,2,…,8; In this example, we take k=4 as an example to illustrate one flow of implementing the algorithm logic, namely p 1 p 2 p 3 p 4 , where p i 1 =0, i=1,2,…,8,p i 4 =p i_max i = 1, 2, ..., 8, i.e., unconstrained, p i_max The physical upper limit is obtained by the front-end control program; this example can be directly generalized to the case where k>4.

[0084] The specific implementation steps in this embodiment are as follows:

[0085] Step a): Obtain four driving targets A from the upper-level controller or control module. i , i = 1, 2, ..., 4; obtain the maximum power p of each battery under each limit level from the front-end control program. i k , i = 1, 2, ..., 8, k = 1, 2, 3, 4, where p i 1 =0, p i 3 =p i_max i = 1, 2, ..., 8, p i_max This represents the maximum power of battery i; the battery constraint level is marked as p. 3 The maximum power P of each motor at each limit level is obtained from the front-end control program. i k i = 1, 2, ..., 8, k = 1, 2, 3, 4; Mark the motor constraint level as P. 3 The internal resistance R of the battery is obtained from the front-end control program. i i = 1, 2, ..., 8; the state of charge (SOC) of the battery pack is obtained from the front-end control program. j j = 1, 2, ..., 4, the battery's health state SoH i , i = 1, 2, ..., 8;

[0086] Step b1): p i_lim =p i_max , i = 1, 2, ..., 8;

[0087] Step b2): Determine if battery i is faulty, i = 1, 2, ..., 8; if yes, proceed to step b3); if no, continue processing the next battery until all batteries are processed, then proceed to step b4.

[0088] Step b3): p i_lim =p i 1 And mark battery i as faulty, report to the upper controller; then return to step b2);

[0089] Step b4): Determine ∑ 8 i＝1 p i_lim <∑ 4 i＝1 A i If the condition is true, proceed to step b5); if not, proceed to step b6.

[0090] Step b5): Send an alarm to the upper-level controller stating that the shaft power exceeds the battery limit and adjust the shaft output power; mark the battery constraint level as p. 1After setting the battery emergency mode, proceed to step c1);

[0091] Step b6): Determine if the constraint level is p 1 If yes, proceed to step c1); if no, proceed to step b7.

[0092] Step b7): Determine if battery i is not faulty and is restricted, i = 1, 2, ..., 8; if yes, proceed to step b8); if no, continue processing the next battery until all batteries are processed and then proceed to step b9.

[0093] Step b8): p i_lim_tem =p i_lim p i_lim =p i 2 Then return to step b7);

[0094] Step b9): Determine ∑ 8 i＝1 p i_lim <∑ 4 i＝1 A i If the condition is met, proceed to step b10; otherwise, proceed to step b14.

[0095] Step b10): An alarm is sent to the upper-level controller, indicating that the power demand on the shaft is too high. The output power on the shaft is adjusted, and a battery damage alarm is triggered. Then proceed to step b11).

[0096] Step b11): Determine if battery i is not faulty and is restricted, i = 1, 2, ..., 8; if yes, proceed to step b12); if no, continue processing the next battery until all batteries are processed and then return to step b13.

[0097] Step b12): p i_lim =p i_lim_tem Then return to step b11);

[0098] Step b13): Mark the battery constraint level as p 2 Then proceed to step c1);

[0099] Step b14): Determine if the constraint level is p. 2 If yes, proceed to step c1); if no, proceed to step b15.

[0100] Step b15): Determine whether battery i is not faulty, not restricted, and optimized, i = 1, 2, ..., 8; if yes, proceed to step b16); if no, continue processing the next battery until all batteries are completed and then proceed to step b17.

[0101] Step b16): p i_lim_tem =p i_lim p i_lim =p i 3 Then return to step b15);

[0102] Step b17): Determine ∑ 8 i＝1 p i_lim <∑ 4 i＝1 A i Is it true? If it is true, proceed to step b18); if it is not true, proceed to step b22.

[0103] Step b18): Report to the upper-level controller, the report content is to optimize the power demand on the shaft, adjust the output power on the shaft, and trigger a battery damage alarm; proceed to step b19);

[0104] Step b19): Determine whether battery i is not faulty, not restricted, and optimized, i = 1, 2, ..., 8; if yes, proceed to step b20); if no, continue processing the next battery until all batteries are completed and then proceed to step b21.

[0105] Step b20): p i_lim =p i_lim_tem Then return to step b19);

[0106] Step b21): Mark the battery constraint level as p 3 Then proceed to step c1);

[0107] Step b22): Determine if the battery constraint level is p. 3 If yes, proceed to step c1); if no, proceed to step b23).

[0108] Step b23): Determine whether battery i is not faulty, not restricted, and optimized, i = 1, 2, ..., 8; if yes, proceed to step b24); if no, continue processing the next battery until all batteries are completed and then proceed to step b25.

[0109] Step b24): p i_lim_tem =p i_lim p i_lim =p i 3 Then return to step b23);

[0110] Step b25): Determine ∑ 8 i＝1 p i_lim <∑ 4i＝1 A i Is it true? If true, proceed to step b26); if false, proceed to step c1.

[0111] Step b26): Report to the upper-level controller, stating: Optimize the power demand on the shaft and adjust the output power on the shaft; then proceed to step b27);

[0112] Step b27): Determine whether battery i is not faulty, not restricted, and optimized, i = 1, 2, ..., 8; if yes, proceed to step b28); if no, continue processing the next battery until all batteries are completed and then proceed to step b29.

[0113] Step b28): p i_lim =p i_lim_tem Then return to step b27);

[0114] Step b29): Mark the constraint level as p 3 Then proceed to step c1);

[0115] Step c1): Determine whether battery 2j-1 in battery pack j has been marked as invalid; if yes, proceed to step c2); if no, proceed to step c3.

[0116] Step c2): Determine whether battery 2j in battery pack j has been marked as faulty; if so, then Proceed to step c3); if not, then Then proceed to step c3);

[0117] Step c3): Determine whether battery 2j in battery pack j has been marked as faulty; if so, then Then proceed to step c4); if not, then j = 1, 2, ..., 4; then proceed to step c4);

[0118] Step c4): Determine if it is in battery emergency mode; if so, then Proceed to step g1); if not, proceed to step d1.

[0119] Step d1): Calculate the average battery power p avg :p avg =∑ 4 i＝1 A i / 4; with the SOC of battery pack j j , j = 1, 2, ..., 4, calculate the mean SOC avg :∑ 4 j＝1 SOC j / 4; SoH of the worst cell in battery pack j i Let i = 1, 2, ..., 8, and mark the SOH of battery pack j. j =min(SoH 2j-1 SoH 2j ), j = 1, 2, ..., 4; Calculate the mean SOH value. avg :SOH avg =∑ 4 j＝1 SOH j / 4;

[0120] Step d2): Determine |SOC j -SOC avg |Is it greater than the set threshold? If yes, proceed to step d3); if no, proceed to step d4.

[0121] Step d3): (SOC j -SOC avg )×Cap j ×V j ×rate cb ], Then proceed to step d5);

[0122] Step d4): Determine |SOH j -SOH avg Is it greater than the set threshold? If so, then... If not, then dp j =0, Then proceed to step d5);

[0123] Step d5): Calculate the total power adjustment value dp sum =∑ 4 j＝1 dp j Then determine dp sum Check if =0 is true. If true, proceed to step e1); if false, proceed to step d6.

[0124] Step d6): Determine dp sum >0 is true; if true, proceed to step d7); if false, proceed to step d8.

[0125] Step d7): Calculate the adjustment deviation: dp j_mrgn Is the condition equal to 0 true? If true, adjust the bias. j =0; if not true, then calculate the adjustment bias: biasj =min[max 4 k＝1 (SOC k )+dSOC ad -SOC j ,dp j_mrgn / dp sum ]; Calculate the adjustment ratio: rate j_ad =bias j / ∑ 4 j＝1 bias j dp j =dp j +rate j_ad ×dp sum Then proceed to step d9);

[0126] Step d8): Calculate the adjustment deviation: bias j =min[SOC j -min 4 k＝1 (SOC k )-dSOC ad [,0];Calculate the adjustment ratio: rate j_ad =bias j / ∑ 4 j＝1 bias j dp j =dp j +rate j_ad ×dp sum Then proceed to step d9);

[0127] Step d9): Add the adjustment value to p avg : Then proceed to step e1);

[0128] Step e1): P i_lim =P i_max , i = 1, 2, ..., 8;

[0129] Step e2): Determine if motor i is faulty, i = 1, 2, ..., 8; if yes, proceed to step e3); if no, proceed to step e4.

[0130] Step e3): P i_lim =P i 1 And mark motor i as constrained, report motor i failure to the upper controller, and proceed to step e4);

[0131] Step e4): Determine ∑ 8i＝1 P i_lim <∑ 4 i＝1 A i If the condition is met, an alarm should be sent to the higher-level controller: the power on the shaft exceeds the motor's upper limit, and the output power P on the shaft should be adjusted. i =P i_lim The constraint level is marked as P. 1 Set the motor to emergency mode and return to the end of the process; if this fails, proceed to step e5.

[0132] Step e5): Determine if the motor constraint level is P. 1 If yes, proceed to step f1); if no, proceed to step e6.

[0133] Step e6): Determine if motor i is not faulty and is restricted, i = 1, 2, ..., 8; if so, then P i_lim_tem =P i_lim P i_lim =P i 2 If not, proceed to step e7);

[0134] Step e7): Determine ∑ 8 i＝1 P i_lim <∑ 4 i＝1 A i Is it true? If yes, proceed to step e8); if no, proceed to step e9.

[0135] Step e8): Alarm to the upper-level controller: excessive power demand on the shaft, adjust the output power on the shaft, motor damage alarm; and determine whether motor i is not faulty and is restricted, i = 1, 2, ..., 8; if so, then P i_lim =P i_lim_tem Mark the motor constraint level as P. 2 Then proceed to step f1); if not, mark the motor constraint level as P. 2 Then, proceed to step f1);

[0136] Step e9): Determine if the constraint level is P. 2 If yes, proceed to step f1); if no, proceed to step e10.

[0137] Step e10): Determine if motor i is not faulty, not restricted, and optimized, i = 1, 2, ..., 8; if so, then P i_lim_tem =P i_lim P i_lim =P i3 If the motor i is marked as constrained, proceed to step e11); otherwise, proceed directly to step e11.

[0138] Step e11): Determine ∑ 8 i＝1 P i_lim <∑ 4 i＝1 A i Is it true? If it is true, proceed to step e12); if it is not true, proceed directly to step f1.

[0139] Step e12): Report to the upper-level controller: optimize the power demand on the shaft, adjust the output power value on the shaft, and the motor efficiency loss value; and determine whether motor i is not faulty, not limited, and optimized, i = 1, 2, ..., 8; if so, then P i_lim =P i_lim_tem Mark the motor constraint level as P. 3 Then proceed to step f1); if not, mark the motor constraint level as P. 3 Then, proceed to step f1);

[0140] Step f1): Determine if motor i is constrained; if yes, proceed to step f2); if no, continue to the next motor until all motors have been determined, then proceed to step f5.

[0141] Step f2): Determine P i_lim ≥P i 4 Does it hold true? If it does, then P i =P i 4 Then proceed to step f3); if this is not true, then P i =P i_lim Then proceed to step f3);

[0142] Step f3): Based on P of the i-th motor i Set p and p0;

[0143] Step f4): Count the restricted motors count = count + 1; then continue to the next motor until all motors have been checked, then proceed to step f5);

[0144] Step f5): Determine if count = 0 is true; if true, then P1 = P1 for the first motor. 4 Set p and p0, and mark motor 1 as constrained; then proceed to step f6); if not, proceed directly to step f6.

[0145] Step f6): Determine if rank(p) is equal to 4; if yes, proceed to step f8); if no, proceed to step f7.

[0146] Step f7): Determine if the motor constraint level is P. 1 If yes, then send an alarm to the upper-level controller: the power demand on the shaft is too high, adjust the output power on the shaft, set the motor to emergency mode, and then end; if no, then check the current motor constraint level and adjust the current motor constraint level P. k Promoted to P k-1 Then return to step e1);

[0147] Step f8): Using matrix p and vector p0, the power requirement vector A on the shaft is obtained from the upper-level controller. By solving the equation p×r=A-p0, the allocation control vector r=p -1 (A-p0); then proceed to step g1);

[0148] Step g1): Determine j = 1, 2, ..., 4, is it within the range [0, 1]? If yes, proceed to step h); if no, proceed to step g2).

[0149] Step g2): Determine if the battery constraint level is p. 1 If yes, then issue an alarm to the higher-level controller: the shaft power demand is too high, adjust the shaft output power, set the battery emergency mode, and then end; if no, then check the current battery constraint level and adjust the current battery constraint level p. k Promoted to p k-1 Then return to step b1);

[0150] Step h): Determine if motor i is constrained; if not, then in r 2j-1 Then end; if so, end the process directly and return.

[0151] Example 2:

[0152] Referring to Figure 11, the system has six independent drive shafts, each driven by two independent motors. Each motor is powered by two independent batteries connected in parallel to a battery pack. Each battery pack simultaneously powers three motors: the first battery pack (batteries B1 and B2) powers motors M2, M4, and M6; the second battery pack (batteries B3 and B4) powers motors M3, M5, and M7; and the third battery pack (batteries B5 and B6) powers motors M6, M7, M8, and M9. A M C Power supply; the fourth battery pack (batteries B7 and B8) supplies power to motors M9 and M... BM1 is the power supply unit; after simplifying the parallel battery connection, the power transmission matrix from the battery pack to the motor is shown in Table 5.

[0153] Table 5

[0154] Following a method similar to Example 1, the power transmission matrix from the battery pack to the motor is constructed and simplified to obtain the motor power constraint in this example:

[0155] P1+P2=A1 P3+P4=A2 P5+P6=A3 P7+P8=A4 P9+P A =A5 P B +P C =A6 (4)

[0156] The battery power constraint can be written as:

[0157] Where, r nm satisfy:

[0158] r 12 +r 14 +r 16 =1 r 33 +r 35 +r 37 =1 r 58 +r 5A +r 5C =1 r 79 +r 7B +r 71 =1 (6)

[0159] The constraints on motor power and battery power can each be divided into different levels, such as level 1 being mandatory, level 2 being preferred, and level 3 being completely free, respectively denoted as P. 1 P 2 P 3 , and p 1 p 2 p 3 We can set the motor's priority to be higher than the battery's. In other words, when considering priority limits, the order from highest to lowest priority is: P 1 =p 1 >P 2 >p 2 >P 3 >p 3 Similarly, the priority of the battery can be set to be higher than that of the motor. This does not hinder the implementation of this method and should be regarded as a direct extension of the present invention. In general, b restriction levels can be set for the motor and a restriction levels for the battery. The lowest level can be set to an unrestricted case.

[0160] Because of (6) in the battery constraint, the number of independent control variables is reduced to 8, i.e., r 12 ,r 14 ,r 33 ,r 35 ,r 58 ,r 5A ,r 79 ,r 7B The problem we need to solve is to find a suitable power allocation ratio r under the constraints of motor power (4) and battery power (5) and (6) listed above, and under the constraints from the upper-level strategy. 12 ,r 14 ,r 33 ,r 35 ,r 58 ,r 5A ,r 79 ,r 7B .

[0161] The power requirements of the six drive shafts are obtained from the upper-level control module as the drive target, defined as a 6-dimensional vector A = [A1, A2, A3, A4, A5, A6]. T Determine the allocation control vector r = [r 12 r 14 r 33 r 35 r 58 r 5A r 79 r 7B Then, based on different failure tolerance and optimization constraint strategies, the battery power matrix p and the disturbance vector p0 generated by constraint (3) are determined, so that p×r=A-p0. Since p is a 6×8 matrix and p0 is a 6-dimensional vector, as follows:

[0162] Therefore, r = p T (pp T ) -1 (A-p0).

[0163] Obtain battery parameters from the battery management module, such as state of charge (SOC) and maximum output power (p). max_d Maximum input power p max_c Discharge impedance, charging impedance, maximum discharge current I max_d Maximum charging current I max_c And so on, as well as the battery's current state of health (SOC), the battery's current limitations are defined by battery parameters such as discharge impedance, SOC, and SOC. For example, if battery #1 fails, then p1 = 0, or battery #1 needs to have its power limited to p. 1_limThen the choice of p1 must be less than p 1_lim Then, a limit level is set, and based on the battery parameters above, the battery power can be freely selected within the allowable range. The value of , as long as it satisfies For specific implementation steps, please refer to steps a) to h) in the invention content section, and select according to the designed process. The value of p is then determined based on the constraint level, full rank, and rationality conditions of each motor. The power matrix p and disturbance term p0 are then determined, thereby obtaining the allocation control vector r = p0. T (pp T ) -1 (A-p0), and thereby calculate the output power of each motor; such logic can be implemented in different processes. This invention is not only for this process a) to h), but also includes protection for different processes that implement the same logic.

[0164] Example 3:

[0165] Referring to Figure 12, the system has six independent drive shafts, each driven by two independent motors. Each motor is powered by two independent batteries connected in parallel to a battery pack. Each battery pack simultaneously powers three motors. The first battery pack (batteries B1 and B2) powers motors M2, M6, and M7. A Power supply; the second battery pack (batteries B3, B4) supplies power to motors M3, M7, and M. B Power supply; the third battery pack (batteries B5 and B6) supplies power to motors M4, M8, and M. C Power supply; the fourth battery pack (batteries B7 and B8) supplies power to motors M5, M9, and M1; after simplifying the parallel batteries, the power transmission matrix from the battery pack to the motor is shown in Table 6.

[0166] Table 6

[0167] In this example, the power transfer matrix from the battery pack to the motor is simplified using a method similar to that in Examples 1 and 2, resulting in the following motor power constraint:

[0168] P1+P2=A1 P3+P4=A2 P5+P6=A3 P7+P8=A4 P9+P A =A5 P B +P C =A6 (7)

[0169] In this example, the battery power constraint can be written as:

[0170] Where, r nm satisfy:

[0171] r 12 +r 16 +r 1A =1 r 33 +r 37 +r 3B =1 r 54 +r 58 +r 5C =1 r 75 +r 79 +r 71 =1 (9)

[0172] The limitations on motor and battery power can be categorized into different levels, such as Level 1 being mandatory, Level 2 being preferred, and Level 3 being completely free, respectively denoted as P. 1 P 2 P 3 , and p 1 p 2 p 3 We can set the motor's priority to be higher than the battery's. In other words, when considering priority limits, the order from highest to lowest priority is: P 1 =p 1 >P 2 >p 2 >P 3 >p 3 Similarly, the priority of the battery can be set to be higher than that of the motor. This does not hinder the implementation of this method and should be regarded as a direct extension of the present invention. In general, b restriction levels can be set for the motor and a restriction levels for the battery. The lowest level can be set to an unrestricted case.

[0173] Because of (9) in the battery power constraint, the number of independent control variables is reduced to 8, i.e., r 12 ,r 16 ,r 33 ,r 37 ,r 54 ,r 58 ,r 75 ,r 79 The problem we need to solve is to find a suitable power allocation ratio r under the constraints of motor power (7) and battery power (8) and (9) listed above, and under the constraints from the upper-level strategy. 12 ,r 16 ,r 33 ,r 37 ,r 54 ,r 58 ,r 75 ,r 79 .

[0174] The power requirements of the six drive shafts are obtained from the upper-level control module as the drive target, defined as a 6-dimensional vector A = [A1, A2, A3, A4, A5, A6]. T Determine the allocation control vector r = [r 12 r 16 r 33 r 37 r 54 r 58 r 75 r 79 Then, based on different failure tolerance and optimization constraint strategies, the battery power matrix p and the disturbance vector p0 generated by constraint (3) are determined, so that p×r=A-p0. Since p is a 6×8 matrix and p0 is a 6-dimensional vector, as follows:

[0175] Therefore, r = p T (pp T ) -1 (A-p0).

[0176] Obtain battery parameters from the battery management module, such as state of charge (SOC) and maximum output power (p). max_d Maximum input power p max_c Discharge impedance, charging impedance, maximum discharge current I max_d Maximum charging current I max_c And so on, as well as the battery's current state of health (SOC), the battery's current limitations are defined by battery parameters such as discharge impedance, SOC, and SOC. For example, if battery #1 fails, then p1 = 0, or battery #1 needs to have its power limited to p. 1_lim Then the choice of p1 must be less than p 1_lim Then, a limit level is set, and based on the battery parameters above, the battery power can be freely selected within the allowable range. The value of , as long as it satisfies For specific implementation steps, please refer to steps a) to h) in the invention content section, and select according to the designed process. The value of p is then determined based on the constraint level, full rank, and rationality conditions of each motor. The power matrix p and disturbance term p0 are then determined, thereby obtaining the allocation control vector r = p0. T (pp T ) -1 (A-p0), and thereby calculate the output power of each motor; such logic can be implemented in different processes. This invention is not only for this process a) to h), but also includes protection for different processes that implement the same logic.

[0177] It should be noted that the above embodiments are illustrative of the present invention and not intended to limit the present invention. Any simple modifications to the present invention are within the scope of protection of the present invention.

Claims

1. A power allocation method for a redundant electric drive system, characterized in that: Includes the following steps: a) Obtain axis output requirements: Obtain axis output requirements from the upper-level controller or control module; b) Obtain the status parameters of each battery and motor: Obtain the status and various parameters of the battery and motor from the front-end control module; c) Calculate the maximum output power of each battery pack: Determine the constraint level of each battery and calculate the maximum output power of each battery pack; d) Set the maximum output power of each motor: Determine the constraint level of each motor and set the maximum output power of each motor; e) Determine if the motor meets the requirements: Determine if the current motor can meet the on-axis output requirements under the established constraints. If not, remove the motor constraints layer by layer from low to high until the on-axis output requirements are met. Otherwise, report to the upper-level controller or control module: Some on-axis output requirements cannot be met and the target needs to be adjusted. f) Determine if the power allocation ratio is reasonable: Calculate the power allocation ratio and determine if it is reasonable. If it is not reasonable, remove the battery constraints layer by layer from low to high until the reasonable conditions are met. Otherwise, report to the upper-level controller or control module: the output requirements on some axes cannot be met and the target needs to be adjusted. g) Calculate the motor output power: Calculate the output power of each motor according to the power ratio.

2. The power allocation method for a redundant electric drive system as described in claim 1, characterized in that: When calculating the maximum output power of each battery pack in step c), within the maximum power range, the output power of each battery pack is adjusted according to the battery parameter settings to achieve global optimization; based on the optimal power configuration of the battery pack, the power matrix p, the disturbance term p0, and the target power vector A are set.

3. The power allocation method for a redundant electric drive system as described in claim 1, characterized in that: In step e), it is determined whether the current motor meets the on-axis output requirements under the established constraints. This is done by determining whether the power matrix p is full rank. If it is not full rank, it indicates that the motor has too many constraints and cannot achieve the on-axis output target. In this case, the motor constraints are removed layer by layer from low to high until the full rank condition is met.

4. A power allocation method for a redundant electric drive system as described in any one of claims 1 to 3, characterized in that: In step f), the power allocation ratio is calculated by matrix calculation to obtain the allocation control vector r = p. T (pp T ) -1 (A-p0).

5. The power allocation method for a redundant electric drive system as described in claim 4, characterized in that: In step f), it is determined whether the power allocation ratio is reasonable. This can be done by judging whether the calculated power allocation ratio is within a certain reasonable range, such as [0,1]. If it is not within this range, it indicates that there are too many battery constraints. Then, the battery constraints are removed layer by layer from low to high until all power allocation ratios are within the selected range.

6. The power allocation method for a redundant electric drive system as described in claim 1, characterized in that: The system involved in this method contains n battery packs, where battery pack i is composed of h i The system consists of several batteries connected in parallel, i = 1, 2, ..., n; m motors; and l driven targets are controlled. The specific steps are as follows: A) Obtain l driving targets A from the upper-level controller or control module. i i = 1, 2, ..., l; obtain the status and parameters of each battery and motor from the front-end control program; initialize the program to assign initial values ​​to each control variable; B) Determine the respective constraint level p based on the state of each battery. k k = 1, 2, ..., a; the smaller k is, the higher the constraint level, that is, the higher the priority to satisfy; at the same time, under the premise of satisfying the on-axis power requirements, the constraint level is adjusted from high to low to the optimal level, and the maximum power that each battery can provide is determined. C) Determine the maximum output power of each battery pack based on the constraint level of each battery and the combination method of each battery pack; D) Based on the SoH health status parameters of each battery obtained from the front-end control program. ij , i=1,2,…,n, j=1,2,…,h i and the state of charge (SOC) of the battery pack i For i = 1, 2, ..., n, the output power of the battery pack is adjusted and optimized to meet the on-axis power requirements within the maximum output power of each battery pack, while balancing the health and state of charge of each battery pack. E) Determine the respective constraint level P based on the status of each motor. k k = 1, 2, ..., b; the smaller k is, the higher the constraint level, that is, the higher the priority to satisfy; at the same time, under the premise of satisfying the power demand on the shaft, the constraint level is adjusted from high to low to the optimal level, and the maximum power that each motor can provide is determined. F) Determine the output power of the constrained motor; then set the power matrix p and disturbance term p0 according to the constrained motor. p It is an l×(mn) matrix, and p0 is an l-dimensional term vector. The full-rank condition of the power matrix p is used to determine whether the motor constraints can meet the power requirements of each axis. If p is not full-rank, it indicates that there are too many motor constraints. Therefore, the lowest motor constraint condition is abandoned layer by layer, that is, the higher-level motor constraints are retained as much as possible, until a full-rank p is obtained, and the allocation control vector r = p is calculated based on this. T (pp T ) -1 (A-p0), G) Determine the power allocation ratio. j = 1, 2, ..., mn, whether it is within a reasonable range, i.e. [0, 1]. If there is a power allocation ratio that is not within a reasonable range, it indicates that there are too many battery constraints. Then, abandon the current lowest battery constraint layer by layer, i.e. retain the higher level battery constraints as much as possible, until all calculated power allocation ratios are within a reasonable range. H) Based on the power conduction matrix R mxn Calculate m r ji The value of , i∈{1,2,…,m}, j∈{1,2,…,n}, and determined by r ji The output power of all unconstrained motors is calculated and combined with the output power of constrained motors obtained in F), and then transmitted to the lower-level controller in the form of a command to control each motor to reach the required power, thereby satisfying the power demand on all shafts while achieving an optimal balance between the battery and the motor.

7. The power allocation method for a redundant electric drive system as described in claim 6, characterized in that: Battery constraint level p k k = 1, 2, ..., a, representing a levels, which can be divided into: Level 1 is failure, the highest level, i.e., physical hardware limitation; Level 2 is limitation, i.e., mandatory under non-emergency circumstances; Levels 3 to a-1 are different levels of optimization; Level a is complete freedom; in order of priority from high to low: p 1 >p 2 >p 3 >…>p a-1 >p a .

8. A power allocation method for a redundant electric drive system as described in claim 6, characterized in that: Motor constraint level P k k = 1, 2, ..., b, representing b levels, which can be divided into: Level 1 is failure, the highest level, i.e., physical hardware limitation; Level 2 is limitation, i.e., mandatory under non-emergency circumstances; Levels 3 to b-1 are different levels of optimization; Level b is completely free; ranked from highest to lowest priority as follows: P 1 >P 2 >P 3 >…>P b-1 >P b .

9. A power allocation method for a redundant electric drive system as described in claim 6, characterized in that: The dimensions of the power matrix p and the allocation control vector r are reduced from m to mn, thus reducing the computational load.

10. A power allocation method for a redundant electric drive system as described in any one of claims 6 to 9, characterized in that: The power matrix p consists of 0 and the optimized power of each battery pack, and the disturbance term p0 consists of 0 and the combination of the optimized power of each battery pack and the power of the constrained motor.

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