A battery pack balancing control method, device and storage medium

Abstract

The present invention relates to the technical field of battery management, and discloses a battery pack balancing control method, device and storage medium. The method includes the following steps: establishing a simulation model of the balanced circuit to be controlled; establishing a control model of a fuzzy closed-loop balanced control system including the simulation model of the balanced circuit according to the adopted fuzzy control scheme; taking the goal of ensuring that the balanced current of the control model remains within the desired range while minimizing the running time required for the control model to reach balance, and using a swarm intelligence algorithm to optimize the domain of the fuzzy controller in the fuzzy closed-loop balanced control system; using the optimized fuzzy closed-loop balanced control system to control the battery pack. This battery pack balancing control method can design a fuzzy control scheme with high adaptability to the balanced circuit to be controlled without relying on expert experience, improve the balanced control effect on the battery pack, and extend the service life of the batteries in the battery pack.

Description

Technical Field

[0001] The present invention relates to the technical field of battery management, and particularly to a battery pack balancing control method, device, and storage medium. Background Art

[0002] With the development of battery technology, battery packs composed of lithium-ion batteries are widely used in electric vehicles, energy storage systems, and intelligent products. With the long-term use of the battery pack, the batteries in the battery pack are prone to problems such as material aging and capacity attenuation. Moreover, due to inevitable individual differences between the single batteries in the battery pack, the battery management system (BMS) needs to monitor the working conditions of each battery, regulate the energy in the batteries through a balancing method, narrow the energy gap between the batteries to extend the operating life of the battery, and ensure the safe and stable operation of the battery system.

[0003] Using fuzzy control to achieve battery balancing control is an effective method. However, due to the fact that the parameter setting of fuzzy control depends on expert experience, etc., the design process is complex, the control effect of the same fuzzy control scheme varies greatly for different battery packs, the transferability of the fuzzy control scheme is poor, and it is difficult to apply. Therefore, it is necessary to design a new battery pack balancing control method to reduce the design difficulty of fuzzy control parameters. Summary of the Invention

[0004] In order to overcome the deficiencies of the prior art, the purpose of the present invention is to provide a battery pack balancing control method, which can reduce the parameter design difficulty of the fuzzy control scheme, improve the adaptability of the fuzzy control scheme to the battery pack, and enhance the balancing effect of the battery pack.

[0005] To solve the above problems, the technical solution adopted by the present invention is as follows: A battery pack balancing control method includes the following steps:

[0006] Establish a simulation model of the balanced circuit to be controlled;

[0007] According to the adopted fuzzy control scheme, establish a control model of a fuzzy closed-loop balanced control system including the simulation model of the balanced circuit;

[0008] With the goal of ensuring that the balanced current of the control model remains within the desired range while minimizing the running time required for the control model to reach balance, use a swarm intelligence algorithm to optimize the domain of the fuzzy controller in the fuzzy closed-loop balanced control system;

[0009] Control the battery pack using the optimized fuzzy closed-loop balanced control system.

[0010] Compared with the prior art, the beneficial effects of the present invention are as follows: The control effect of the fuzzy control scheme is simulated by using the control model of the fuzzy closed-loop equalization control system established according to the simulation model of the equalization circuit to be controlled, and the universe of discourse of the fuzzy controller is optimized by using the swarm intelligence algorithm according to the simulation effect, so as to reduce the design difficulty of the parameters of the fuzzy control scheme. The universe of discourse of the fuzzy control optimized by the swarm intelligence algorithm has a higher adaptability to the equalization circuit to be controlled, so as to improve the equalization effect on the battery pack.

[0011] In the above battery pack equalization control method, in the step of optimizing the universe of discourse of the fuzzy controller in the fuzzy closed-loop equalization control system by using the swarm intelligence algorithm with the goal of ensuring that the equalization current of the control model remains within the desired range while minimizing the running time required for the control model to reach equalization, the particle swarm optimization algorithm is used to optimize the universe of discourse of the fuzzy controller in the fuzzy closed-loop equalization control system.

[0012] In the above battery pack equalization control method, in the step of optimizing the universe of discourse of the fuzzy controller in the fuzzy closed-loop equalization control system by using the swarm intelligence algorithm with the goal of ensuring that the equalization current of the control model remains within the desired range while minimizing the running time required for the control model to reach equalization, the fitness function f(x) of the particle is as shown in the following formula:

[0013]

[0014] In the formula, T(x) is the running time function required for the control model to reach equalization, I(x) is the equalization current function of the control model, I max is the maximum value of the desired range of the equalization current, I min is the minimum value of the desired range of the equalization current, and c is a constant coefficient.

[0015] A storage medium stores a computer program, and when the computer program is called and executed by a processor, the above battery pack equalization control method is implemented.

[0016] An optimization device for a battery pack equalization control system includes: an equalization circuit simulation module for establishing a simulation model of the equalization circuit and simulating the dynamic process of the equalization circuit through the simulation model; a fuzzy closed-loop control module for performing equalization control on the equalization circuit simulation module according to the SOC value and voltage value of the battery pack simulated by the equalization circuit simulation module; and an optimization module for optimizing the universe of discourse of the fuzzy control module in the fuzzy closed-loop control module by using the swarm intelligence algorithm to minimize the running time required to reach equalization while keeping the equalization current simulated by the equalization circuit simulation module within the desired range.

[0017] The optimization device of the above battery pack equalization control system is characterized in that the fuzzy closed-loop control module includes: an SOC fuzzy control module for outputting a corresponding SOC desired current value according to the SOC values of the batteries in the battery pack simulated by the equalization circuit simulation module and the average SOC value of all the batteries in the battery pack; a voltage fuzzy control module for outputting a corresponding voltage desired current value according to the voltage values of the batteries in the battery pack simulated by the equalization circuit simulation module and the average voltage value of all the batteries in the battery pack; a weight fuzzy control module for outputting an SOC control weight and a voltage control weight according to the SOC value and the voltage desired current value, and calculating a desired equalization current value according to the SOC desired current value, the voltage desired current value, the SOC control weight and the voltage control weight; a closed-loop control module for performing closed-loop control on the equalization circuit simulation module according to the actual equalization current value simulated by the equalization circuit simulation module and the desired equalization current value.

[0018] For the optimization device of the above battery pack equalization control system, the optimization module uses the particle swarm optimization algorithm to optimize the domain of the fuzzy controller in the fuzzy closed-loop control module, and the optimization module calculates the fitness of the particle through the fitness function f’(x) shown as follows:

[0019]

[0020] In the formula, T’(x) is the running time function required for the equalization circuit simulation module to reach equalization, I’(x) is the equalization current function of the equalization circuit simulation module, I max is the maximum value of the desired equalization current range, I min is the minimum value of the desired equalization current range, and c is a constant coefficient.

[0021] A battery pack equalization control device includes: an equalization circuit for controlling the charging and discharging processes of each battery in the battery pack; a monitoring circuit for monitoring the voltage value and SOC value of the battery pack, the SOC value and voltage value of each battery in the battery pack, and the actual equalization current value of the equalization circuit; a fuzzy closed-loop controller, the domain of the fuzzy controller in which is the domain optimized by the optimization device of the above battery pack equalization control system, and the fuzzy closed-loop controller is used to control the equalization circuit according to the parameters of the battery pack and the equalization circuit fed back by the monitoring circuit, so that the electric energy of each battery in the battery pack reaches equalization.

[0022] The above battery pack equalization control device, the fuzzy closed-loop controller includes: an SOC fuzzy controller for outputting a corresponding SOC desired current value according to the SOC values of the batteries in the battery pack and the average SOC value of all the batteries in the battery pack; a voltage fuzzy controller for outputting a corresponding voltage desired current value according to the voltage values of the batteries in the battery pack and the average voltage value of all the batteries in the battery pack; a weight fuzzy controller for outputting an SOC control weight and a voltage control weight according to the SOC value and the voltage desired current value, and calculating a desired equalization current value according to the SOC desired current value, the voltage desired current value, the SOC control weight and the voltage control weight; a closed-loop controller for performing closed-loop control on the equalization circuit according to the actual equalization current value of the equalization circuit and the desired equalization current value.

[0023] The above battery pack equalization control device, the equalization circuit is connected layer by layer by a plurality of Buck-Boost circuits. The Buck-Boost circuit includes two serially connected MOS transistors and a power inductor. Freewheeling diodes are provided between the source and drain of the two MOS transistors. The gates of the two MOS transistors are both connected to the closed-loop controller. The first end of the power inductor is connected to the connection node of the two MOS transistors. The second end of the power inductor forms a first interface and a second interface with the two ends of the serial structure formed by serially connecting the two MOS transistors respectively. The first interface and the second interface of the Buck-Boost circuit on the upper layer are respectively connected to two Buck-Boost circuits on the lower layer. The first interface and the second interface of the Buck-Boost circuit on the last layer are respectively connected to two batteries in the battery pack.

[0024] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. Description of the Drawings

[0025] Figure 1 It is a flowchart of the battery pack equalization control method according to an embodiment of the present invention.

[0026] Figure 2 It is a flowchart of the particle swarm optimization algorithm according to an embodiment of the present invention.

[0027] Figure 3 It is a principle block diagram of the battery equalization control device according to an embodiment of the present invention.

[0028] Figure 4 It is a principle block diagram of the fuzzy closed-loop controller according to an embodiment of the present invention.

[0029] Figure 5 It is a schematic diagram of the equalization circuit according to an embodiment of the present invention.

[0030] Figure 6The static equalization effect diagram of the battery pack before optimization.

[0031] Figure 7 The static equalization effect diagram of the battery pack after optimization.

[0032] Figure 8 The equalization effect diagram of the battery pack in the charging state before optimization.

[0033] Figure 9 The equalization effect diagram of the battery pack in the charging state after optimization.

[0034] Figure 10 The equalization effect diagram of the battery pack in the discharging state before optimization.

[0035] Figure 11 The equalization effect diagram of the battery pack in the discharging state after optimization.

[0036] Figure 12 The equalization current of adjacent batteries in the battery pack before optimization.

[0037] Figure 13 The equalization current of adjacent batteries in the battery pack after optimization. Detailed implementation manners

[0038] The embodiments of the present invention will be described in detail below. Referring to Figure 1 , the embodiments of the present invention provide a battery pack equalization control method, including the following steps:

[0039] Establish a simulation model of the equalization circuit to be controlled;

[0040] Establish a control model of a fuzzy closed-loop equalization control system including the simulation model of the equalization circuit;

[0041] Taking the goal of ensuring that the equalization current of the control model remains within the desired range while minimizing the running time required for the control model to reach equalization, use a swarm intelligence algorithm to optimize the domain of the fuzzy controller in the fuzzy closed-loop equalization control system;

[0042] Control the battery pack using the optimized fuzzy closed-loop equalization control system.

[0043] The battery pack balancing control method according to the embodiment of the present invention optimizes the universe of discourse in the fuzzy closed-loop scheme by using a swarm intelligence algorithm, reducing the design difficulty of the fuzzy control scheme. And by establishing a simulation model for the balancing circuit to be controlled, taking the fuzzy balancing control effect of the simulation model of the balancing circuit with the optimized universe of discourse each time as feedback, the adaptability between the universe of discourse optimized by the swarm intelligence algorithm and the balancing circuit to be controlled is higher, thereby improving the balancing effect of the battery pack. The battery pack balancing control method according to the embodiment of the present invention can optimize the fuzzy control scheme of any balancing circuit to a fuzzy control scheme with higher adaptability to the target balancing circuit without relying on expert experience, reducing the design difficulty of the fuzzy control scheme for battery pack balancing control.

[0044] It can be understood that the swarm intelligence algorithm can adopt an ant colony algorithm, a genetic algorithm, a particle swarm optimization algorithm, etc. In this embodiment, the particle swarm optimization algorithm is used to optimize the universe of discourse of the fuzzy control scheme. Referring to Figure 4 , taking the fuzzy control strategy shown in the figure as an example, it includes an SOC fuzzy controller, a voltage fuzzy controller, a weight fuzzy controller, and a closed-loop controller. The SOC fuzzy controller outputs an SOC desired current value according to the detected actual state of charge (SOC) values of each battery in the battery pack and the average SOC value of all batteries in the battery pack; the voltage fuzzy controller is used to output a voltage desired current value according to the voltage of each battery in the battery pack and the average voltage value of all batteries in the battery pack; the weight fuzzy controller outputs an SOC control weight and a voltage control weight according to the SOC value and the voltage desired current value of the battery pack, and calculates the desired balancing current according to the SOC desired current value, the voltage desired current value, the SOC control weight, and the voltage control weight; the closed-loop controller then performs closed-loop control on the balancing circuit according to the desired balancing current and the actual balancing current in the balancing circuit, making the actual balancing current value approach the desired balancing current value.

[0045] Among them, the interval settings of the fuzzy sets of the SOC fuzzy controller and the voltage fuzzy controller are the same. The fuzzy set interval of the deviation value between the average value and the actual value is divided into seven subsets: negative large (NL), negative medium (NM), negative small (NS), zero (Z), positive small (PS), positive medium (PM), and positive large (PL). The fuzzy set interval of the mean value is set to three subsets: low (L), medium (M), and high (H). The fuzzy set interval of the expected current value of the output is also divided into seven subsets: negative large (NL), negative medium (NM), negative small (NS), zero (Z), positive small (PS), positive medium (PM), and positive large (PL). The original domain of the SOC deviation value is {-20, -13, -6, 0, 6, 13, 20}, the original domain of the SOC average value is {2.5, 2.85, 3.3, 3.65}, the original domain of the voltage deviation value is {-0.2, -0.13, -0.06, 0, 0.06, 0.13, 0.2}, the original domain of the voltage average value is {2.5, 2.85, 3.3, 3.65}, and the original domains of the SOC expected current value and the voltage expected current value are {-6, -4, -2, 0, 2, 4, 6}.

[0046] In the weight fuzzy controller, the fuzzy set interval of the SOC value of the battery pack is divided into three subsets: low (L), medium (M), and high (H). The fuzzy set interval of the voltage expected current value is divided into seven subsets: negative large (NL), negative medium (NM), negative small (NS), zero (Z), positive small (PS), positive medium (PM), and positive large (PL). The fuzzy set interval of the SOC control weight and the voltage control weight is divided into five subsets: small weight (WS), relatively small weight (WL), medium weight (WN), relatively large weight (WM), and large weight (WH). The initial value of the domain of the SOC value of the battery pack is {0, 10, 95, 100}, the initial value of the domain of the voltage expected current is {-10, -5, -1, 0, 1, 5, 10}, and the initial values of the domains of the SOC control weight and the voltage control weight are both {0, 0.25, 0.5, 0.75, 1}. The weight fuzzy controller calculates the expected equalization current value through the following formula:

[0047] (1)

[0048] In the formula, I * is the expected equalization current value, and are the SOC control weight and the voltage control weight output by the weight fuzzy controller respectively, and i soc and i v are the SOC expected current value and the voltage expected current value input to the weight fuzzy controller respectively.

[0049] Refer to Figure 2, the steps of the particle swarm optimization algorithm are shown in the figure. In this embodiment, the center points and boundary points of the membership functions of the SOC fuzzy controller and the voltage fuzzy controller are used as variables to form the position coordinates of the particles. Let the universe of discourse of the average SOC be {0, x 1 , x 2 , 100}, the universe of discourse of the SOC deviation value be {-20, x 3 , x 4 , 0, x 5 , x 6 , 20}, the universe of discourse of the average voltage be {2.5, x 7 , x 8 , 3.65}, the universe of discourse of the voltage deviation value be {-0.2, x 9 , x 10 , 0, x 11 , x 12 , 0.2}. The position coordinates of the particles are composed of twelve variables in these four universes of discourse. The optimal particle position is found through the particle swarm optimization algorithm. After each update, the variables of each particle are written into the SOC fuzzy controller and the voltage fuzzy controller in the control model of the established fuzzy closed-loop equalization control system for the control simulation of the equalization circuit, and the running time function T(x) and the equalization current function I(x) required for the equalization of the simulation model of the equalization circuit are calculated according to the running time and equalization current required for the equalization of each particle corresponding simulation model. The fitness of the position where each particle is located is calculated based on the running time and equalization current, and the position and speed of the particle are updated according to the fitness value. The fitness function f(x) is shown as follows:

[0050] (2)

[0051] In the formula, I max is the maximum value of the expected range of the equalization current, I min is the minimum value of the expected range of the equalization current, and c is a constant coefficient. By adopting this fitness function, the fitness value of the particle with a smaller running time and the equalization current falling within the expected range of the equalization current will be larger. Updating the speed and position of each particle according to this fitness can make the particles in the particle swarm approach the position of the optimal solution in the solution space, so that the universes of discourse of the SOC fuzzy controller and the voltage fuzzy controller more suitable for the target equalization circuit can be obtained. In this embodiment, the population size of the particle swarm is set to 5, the cognitive parameter and the social parameter of each particle are both 1.5, and the maximum number of genetic generations is 50. The particle with the optimal fitness value at the maximum number of iterations is taken as the optimal solution. It can be understood that in the actual optimization process, the parameters of the particle swarm algorithm can be adjusted according to the number of variables, the size of the solution space, etc.

[0052] Refer to Figures 6 to 11, by respectively adopting an unoptimized fuzzy control scheme and a fuzzy control scheme optimized by the control method of the embodiment of the present invention for the equalization circuits with the same parameters, the operation times required for the battery packs before optimization to reach equalization in the static state, charging state, and discharging state are 584 s, 492.7 s, and 613.6 s respectively, while the operation times required for the optimized battery packs to reach equalization in the static state, charging state, and discharging state are 426.8 s, 431.6 s, and 446.2 s respectively. Referring to Figure 12 and 13 , compared with before optimization, the range of the equalization current between adjacent batteries after optimization can better stabilize within the desired range. Thus, it can be seen that compared with before optimization, the optimized fuzzy control scheme has better operation time required for equalization and stability of the equalization current, and has a better equalization control effect.

[0053] Based on the same inventive concept, an embodiment of the present invention further provides a computer-readable storage medium, in which a computer program is stored. When the computer program is called and executed by a processor, the above-mentioned battery pack equalization control method can be implemented.

[0054] The storage medium can be a non-volatile computer-readable storage medium, which can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules, such as flash memory, hard disk, multimedia card, card-type memory, random access memory (Random Access Memory, RAM), static random access memory (Static Random Access Memory, SRAM), programmable read-only memory (Programmable Read Only Memory, PROM), read-only memory (Read Only Memory, ROM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, EEPROM), magnetic memory, magnetic disk, optical disc, etc. The storage medium is any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The storage medium in the embodiment of the present application can also be a circuit or any other device capable of implementing a storage function, used to store program instructions and / or data.

[0055] In some possible implementation manners, each aspect of the battery pack equalization control method provided by the present invention can also be implemented in the form of a program product, which includes program code. When the program product runs on a device, the program code is used to cause the control device to execute the steps in the battery pack equalization control method according to various exemplary embodiments of the present application described above in this specification.

[0056] Based on the same inventive concept, an embodiment of the present invention further provides an optimization device for a battery pack balancing control system, including a balancing circuit simulation module, a fuzzy closed-loop control module, and an optimization module. The balancing circuit simulation module is used to establish a simulation model of the balancing circuit and simulate the dynamic process of the balancing circuit through the simulation model. The fuzzy closed-loop control module is used to perform balancing control on the balancing circuit simulation module according to the SOC value and voltage value of the battery pack simulated by the balancing circuit simulation module. The optimization module is used to optimize the universe of discourse of the fuzzy controller in the fuzzy closed-loop control module by using a swarm intelligence algorithm, so as to minimize the running time required to achieve balancing while making the balancing current simulated by the balancing circuit simulation module within the expected range.

[0057] In this embodiment, the fuzzy closed-loop control module includes an SOC fuzzy control module, a voltage fuzzy control module, a weight fuzzy control module, and a closed-loop control module. The SOC fuzzy control module is used to output a corresponding SOC expected current value according to the SOC value of each battery in the battery pack simulated by the balancing circuit simulation module and the average SOC value of all batteries in the battery pack. The voltage fuzzy control module is used to output a corresponding voltage expected current value according to the voltage value of each battery in the battery pack simulated by the balancing circuit simulation module and the average voltage value of all batteries in the battery pack. The weight fuzzy control module is used to output an SOC control weight and a voltage control weight according to the SOC value and the voltage expected current value, and calculate an expected balancing current value according to the SOC expected current value, the voltage expected current value, the SOC control weight, and the voltage control weight. The closed-loop control module is used to perform closed-loop control on the balancing circuit simulation module according to the actual balancing current value and the expected balancing current value simulated by the balancing circuit simulation module.

[0058] Among them, the optimization module uses the particle swarm optimization algorithm to optimize the universe of discourse of the SOC fuzzy control module and the voltage fuzzy control module in the fuzzy closed-loop control module. The optimization module uses the fitness function f’(x) shown in the following formula to calculate the fitness value of the particle, so as to guide the update of the position and speed of the particle:

[0059] (3)

[0060] In the formula, T’(x) is the running time function required for the balancing circuit simulation module to reach balance, and I’(x) is the balancing current function of the balancing circuit simulation module.

[0061] Refer to Figure 3, based on the same inventive concept, an embodiment of the present invention further provides a battery pack equalization control device, including an equalization circuit, a monitoring circuit, and a fuzzy closed-loop controller. The equalization circuit is used to control the charging and discharging processes of each battery in the battery pack. The monitoring circuit is used to monitor the voltage value and SOC value of each battery in the battery pack, as well as the actual equalization current value of the equalization circuit. The universe of discourse of the fuzzy controller in the fuzzy closed-loop controller is optimized by the optimization device of the above battery pack equalization control system, and is used to control the equalization circuit according to the parameters of the battery pack and the equalization circuit fed back by the monitoring circuit, so that the electric energy of each battery in the battery pack reaches equilibrium.

[0062] Referring to Figure 4 , in this embodiment, the fuzzy closed-loop controller includes an SOC fuzzy controller, a voltage fuzzy controller, a weight fuzzy controller, and a closed-loop controller. The SOC fuzzy controller is used to output a corresponding SOC desired current value according to the SOC value of each battery in the battery pack and the average SOC value of all batteries in the battery pack. The voltage fuzzy controller is used to output a corresponding voltage desired current value according to the voltage value of each battery in the battery pack and the average voltage value of all batteries in the battery pack. The weight fuzzy controller is used to output an SOC control weight and a voltage control weight according to the SOC value and the voltage desired current value, and calculate a desired equalization current value according to the SOC desired current value, the voltage desired current value, the SOC control weight, and the voltage control weight. The closed-loop controller is used to perform closed-loop control on the equalization circuit according to the actual equalization current value and the desired equalization current value of the equalization current, so that the actual equalization current value of the equalization circuit approaches the desired equalization current value. In this embodiment, the universe of discourse of the SOC fuzzy controller and the voltage fuzzy controller is optimized by the particle swarm optimization algorithm. The SOC fuzzy controller, the voltage fuzzy controller, and the weight fuzzy controller all use a Mamdani-type fuzzy inference machine to establish an inference rule base, and the membership function uses a triangular function. The weight fuzzy controller can flexibly adjust the proportion of the desired equalization current calculated based on the SOC value and the desired equalization current calculated based on the voltage according to the SOC value and the voltage value of the battery, and can use the voltage of the battery as the main equalization variable when the voltage value changes rapidly, and use the SOC of the battery as the main equalization variable when the SOC value changes rapidly, so as to avoid excessive overshoot of the equalization current and adjust the electric energy of each battery in the battery pack to equilibrium as soon as possible.

[0063] Referring to Figure 5, in this embodiment, in order to improve the scalability of the balancing circuit and the flexibility of the design, the balancing circuit is implemented by a Buck-Boost circuit to achieve energy transfer between batteries, so as to adjust the electric energy of each battery in the battery pack to be balanced. Multiple Buck-Boost circuits are connected layer by layer. Each Buck-Boost circuit includes two series-connected MOS transistors and a power inductor. Freewheeling diodes are provided between the source and drain of the two MOS transistors. The gates of the MOS transistors in all Buck-Boost circuits are connected to a closed-loop controller and are controlled by the PWM control signal output by the closed-loop controller. The first end of the power inductor is connected to the connection node of the two MOS transistors, and the second end of the power inductor forms a first interface and a second interface with the two ends of the series structure formed by the two series-connected MOS transistors respectively. The first interface and the second interface of the Buck-Boost circuit in the upper layer are respectively connected to two Buck-Boost circuits in the lower layer, and the first interface and the second interface of the Buck-Boost circuit in the bottom layer are respectively connected to two batteries in the battery pack. By controlling the conduction and cut-off of the two MOS transistors in each Buck-Boost circuit, the electric energy in the battery with high electric energy is first charged into the power inductor, and then the electric energy in the power inductor is charged into the battery with low electric energy, completing the transfer of electric energy between batteries and realizing the balance of the electric energy of each battery in the battery pack. In this embodiment, the closed-loop controller uses a PID closed-loop control algorithm to control the conduction and cut-off of each MOS transistor, so that the balance current value between adjacent batteries approaches the expected balance current value output by the weighted fuzzy controller.

[0064] This application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, and the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate an apparatus for implementing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0065] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device, and the instruction device implements the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0066] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide for implementing the steps of the function specified in one process or multiple processes and / or boxes Figure 1 one process or multiple processes and / or boxes Figure 1 steps of the function specified in one box or multiple boxes.

[0067] In the description of the present invention, the meaning of "a number of" is one or more, the meaning of "multiple" is two or more, "greater than", "less than", "exceeding", etc. are understood as not including the number itself, "above", "below", "within", etc. are understood as including the number itself. If there is a description of "first" or "second", etc., it is only for the purpose of distinguishing technical features and cannot be understood as indicating or implying relative importance or implicitly indicating the quantity of the indicated technical features or implicitly indicating the sequence relationship of the indicated technical features.

[0068] In the description of the present invention, unless otherwise clearly defined, words such as "set", "installed", "connected", etc. should be understood in a broad sense, and those skilled in the art can reasonably determine the specific meaning of the above words in the present invention in combination with the specific content of the technical solution.

[0069] The above embodiments are only the preferred embodiments of the present invention and cannot be used to limit the scope of protection of the present invention. Any non-substantive changes and substitutions made by those skilled in the art on the basis of the present invention belong to the scope of protection required by the present invention.

Claims

1. A battery pack balancing control method, characterized in that: The steps include: Establish a simulation model of the controlled equalization circuit; Establishing a control model of a fuzzy closed-loop balancing control system including a simulation model of the balancing circuit according to the adopted fuzzy control scheme; The domain of the fuzzy controller in the fuzzy closed-loop balancing control system is optimized by using a swarm intelligence algorithm with the goal of ensuring that the balancing current of the control model is maintained within a desired range while minimizing the running time required for the control model to reach equilibrium; Controlling the battery pack using the optimized fuzzy closed-loop balancing control system; Wherein, in the step of optimizing the domain of the fuzzy controller in the fuzzy closed-loop balanced control system by using a swarm intelligence algorithm with the goal of ensuring that the balanced current of the control model is maintained within the desired range while minimizing the running time required for the control model to reach equilibrium, a particle swarm optimization algorithm is used to optimize the domain of the fuzzy controller in the fuzzy closed-loop balanced control system; In the step of optimizing the domain of the fuzzy controller in the fuzzy closed-loop balanced control system by using a swarm intelligence algorithm with the goal of ensuring that the balanced current of the control model is maintained within the desired range while minimizing the running time required for the control model to reach equilibrium, the fitness function f(x) of the particle is as follows: Where, T(x) is the running time function required for the control model to reach equilibrium, I(x) is the equilibrium current function of the control model, and I max is the maximum value of the expected range of balancing current, I min is the minimum value of the expected range of balanced current, and c is a constant coefficient.

2. A storage medium storing a computer program, characterized in that: When the computer program is executed and called by the processor, the battery pack balancing control method according to claim 1 is implemented.

3. An optimization device for a battery pack balancing control system, characterized in that: include: The equalizing circuit simulation module is used to establish a simulation model of the equalizing circuit and simulate the dynamic process of the equalizing circuit through the simulation model; A fuzzy closed-loop control module, used for performing balancing control on the balancing circuit simulation module according to the SOC value and voltage value of the battery pack simulated by the balancing circuit simulation module; An optimization module, used for optimizing the domain of the fuzzy control module in the fuzzy closed-loop control module by using a swarm intelligence algorithm, so that the balancing current simulated by the balancing circuit simulation module is within a desired range while minimizing the running time required to achieve balancing; The optimization module uses a particle swarm optimization algorithm to optimize the domain of the fuzzy control module in the fuzzy closed-loop control module. The optimization module calculates the fitness of the particles through the fitness function f'(x) shown below: Wherein, T'(x) is the running time function required for the equalization circuit simulation module to achieve the equalization, I'(x) is the equalization current function of the equalization circuit simulation module, and I max is the maximum value of the expected range of balancing current, I min is the minimum value of the expected range of balanced current, and c is a constant coefficient.

4. The optimization device for a battery pack balancing control system according to claim 3, characterized in that: The fuzzy closed-loop control module comprises: An SOC fuzzy control module, configured to output a corresponding SOC expected current value according to the SOC value of each battery in the battery pack simulated by the equalization circuit simulation module and the SOC average value of all batteries in the battery pack; A voltage fuzzy control module, used for outputting a corresponding voltage expected current value according to the voltage value of each battery in the battery pack simulated by the equalization circuit simulation module and the voltage average value of all batteries in the battery pack; A weight fuzzy control module, configured to output an SOC control weight and a voltage control weight according to the SOC value and the voltage expected current value, and calculate an expected balancing current value according to the SOC expected current value, the voltage expected current value, the SOC control weight and the voltage control weight; The closed-loop control module is used to perform closed-loop control on the balancing circuit simulation module according to the actual balancing current value simulated by the balancing circuit simulation module and the expected balancing current value.

5. A battery pack balancing control device, characterized in that: include: A balancing circuit, the balancing circuit is used to control the charging and discharging process of each battery in the battery pack; A monitoring circuit, used to monitor the SOC value and voltage value of each battery in the battery pack, and the actual balancing current value of the balancing circuit; A fuzzy closed-loop controller, wherein the domain of the fuzzy controller is the domain optimized by the optimization device of the battery pack balancing control system according to claim 3 or 4, and the fuzzy closed-loop controller is used to control the balancing circuit according to the parameters of the battery pack and the balancing circuit fed back by the monitoring circuit, so that the electrical energy of each battery in the battery pack is balanced.

6. The battery pack balancing control device according to claim 5, characterized in that: The fuzzy closed-loop controller comprises: An SOC fuzzy controller is used to output a corresponding SOC expected current value according to the SOC value of each battery in the battery pack and the SOC average value of all batteries in the battery pack; A voltage fuzzy controller, used for outputting a corresponding voltage expected current value according to the voltage value of each battery in the battery pack and the average voltage value of all batteries in the battery pack; A weighted fuzzy controller, configured to output an SOC control weight and a voltage control weight according to the SOC value and the voltage expected current value, and calculate an expected balancing current value according to the SOC expected current value, the voltage expected current value, the SOC control weight and the voltage control weight; A closed-loop controller is used to perform closed-loop control on the balancing current according to an actual balancing current value of the balancing circuit and the expected balancing current value.

7. The battery pack balancing control device according to claim 6, characterized in that: The balancing circuit is connected layer by layer by multiple Buck-Boost circuits. The Buck-Boost circuit includes two MOS tubes connected in series and a power inductor. A freewheeling diode is provided between the source and drain of the two MOS tubes. The gates of the two MOS tubes are connected to the closed-loop controller. The first end of the power inductor is connected to the connection node of the two MOS tubes. The second end of the power inductor forms a first interface and a second interface with the two ends of the series structure formed by the two MOS tubes connected in series. The first interface and the second interface of the Buck-Boost circuit of the upper layer are respectively connected to the two Buck-Boost circuits of the lower layer. The first interface and the second interface of the Buck-Boost circuit of the last layer are respectively connected to the two batteries in the battery pack.

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