Battery pack equalization control method and device and storage medium

Abstract

The invention relates to the technical field of battery management, and discloses a battery pack equalization control method and device and a storage medium, and the method comprises the following steps: building a simulation model of a controlled equalization circuit; establishing a control model of a fuzzy closed-loop equalization control system comprising the simulation model of the equalization circuit according to the adopted fuzzy control scheme; optimizing the discourse domain of a fuzzy controller in the fuzzy closed-loop equalization control system by adopting a swarm intelligence algorithm while ensuring that the equalization current of the control model is kept in an expected range and minimizing the operation time required by the control model to achieve equalization; and controlling the battery pack by using the optimized fuzzy closed-loop equalization control system. According to the battery pack equalization control method, a fuzzy control scheme with high adaptability to the controlled equalization circuit can be designed without depending on expert experience, the equalization control effect on the battery pack is improved, and the service life of the batteries in the battery pack is prolonged.

Description

Technical Field

[0001] The present invention relates to the technical field of battery management, and in particular 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 smart products. With the long-term use of battery packs, the batteries in the battery packs are prone to material aging and capacity attenuation. Since there must be some individual differences between the single cells in the battery pack, the battery management system (BMS) needs to monitor the working conditions of each battery, regulate the energy in the battery through balancing methods, narrow the energy gap between each battery to extend the battery's operating life, and ensure the safe and stable operation of the battery system.

[0003] Using fuzzy control to achieve battery balancing control is an effective method, but since the parameter setting of fuzzy control depends on expert experience, the design process is complicated, the control effect of the same fuzzy control scheme on different battery packs varies greatly, the fuzzy control scheme has poor mobility and is difficult to apply. Therefore, it is necessary to design a new battery pack balancing control method to reduce the difficulty of designing fuzzy control parameters. Summary of the invention

[0004] In order to overcome the deficiencies of the prior art, the object of the present invention is to provide a battery pack balancing control method, which can reduce the difficulty of parameter design of the fuzzy control scheme, improve the adaptability of the fuzzy control scheme to the battery pack, and enhance the balancing effect on 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 comprises the following steps: 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; The battery pack is controlled by utilizing the optimized fuzzy closed-loop balancing control system.

[0006] Compared with the prior art, the beneficial effect of the present invention is that the control effect of the fuzzy control scheme is simulated by using a control model of a fuzzy closed-loop balancing control system established according to a simulation model of the balancing circuit to be controlled, and the domain of the fuzzy controller is optimized according to the simulation effect by a swarm intelligence algorithm, thereby reducing the design difficulty of the parameters of the fuzzy control scheme. The domain of the fuzzy control optimized by the swarm intelligence algorithm is more compatible with the balancing circuit to be controlled, thereby improving the balancing effect on the battery pack.

[0007] In the above-mentioned battery pack balancing control method, the goal is to ensure that the balancing current of the control model is maintained within the desired range while minimizing the running time required for the control model to reach balance. In the step of using a swarm intelligence algorithm to optimize the domain of the fuzzy controller in the fuzzy closed-loop balancing control system, a particle swarm optimization algorithm is used to optimize the domain of the fuzzy controller in the fuzzy closed-loop balancing control system.

[0008] In the above-mentioned battery pack balancing control method, the goal is to ensure that the balancing current of the control model is kept within the desired range while minimizing the running time required for the control model to reach balancing. In the step of optimizing the domain of the fuzzy controller in the fuzzy closed-loop balancing control system using a swarm intelligence algorithm, the fitness function f(x) of the particle is as follows:

[0009] 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.

[0010] A storage medium stores a computer program, wherein the computer program implements the above-mentioned battery pack balancing control method when executed and called by a processor.

[0011] An optimization device for a battery pack balancing control system, comprising: a balancing circuit simulation module, used to establish a simulation model of the balancing circuit, and simulate the dynamic process of the balancing circuit through the simulation model; a fuzzy closed-loop control module, 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; and an optimization module, used to optimize the domain of the fuzzy control module in the fuzzy closed-loop control module using a swarm intelligence algorithm, so that the balancing current simulated by the balancing circuit simulation module is within the expected range while minimizing the operating time required to achieve balancing.

[0012] The above-mentioned optimization device for the battery pack balancing control system is characterized in that the fuzzy closed-loop control module includes: an SOC fuzzy control module, which is used to output the 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 SOC average value of all batteries in the battery pack; a voltage fuzzy control module, which is used to output the 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 voltage average value of all batteries in the battery pack; a weight fuzzy control module, which is used to output the SOC control weight and the voltage control weight according to the SOC value and the voltage expected current value, and calculate the 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 control module, which 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.

[0013] In the above-mentioned optimization device for the battery pack balancing control system, the optimization module uses a 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 particles through the fitness function f'(x) shown below:

[0014] 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.

[0015] A battery pack balancing control device comprises: a balancing circuit, wherein the balancing circuit is used to control the charging and discharging process of each battery in the battery pack; a monitoring circuit, wherein the voltage value and the SOC value of the battery pack, the SOC value and the 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 above-mentioned battery pack balancing control system, 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 electric energy of each battery in the battery pack is balanced.

[0016] The above-mentioned battery pack balancing control device, the fuzzy closed-loop controller includes: an SOC fuzzy controller, used to output the 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 to output the corresponding voltage expected current value according to the voltage value of each battery in the battery pack and the voltage average value of all batteries in the battery pack; a weight fuzzy controller, used to output the SOC control weight and the voltage control weight according to the SOC value and the voltage expected current value, and calculate the 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, used to perform closed-loop control on the balancing circuit according to the actual balancing current value of the balancing circuit and the expected balancing current value.

[0017] In the above-mentioned battery pack balancing control device, the balancing circuit is composed of multiple Buck-Boost circuits connected layer by layer, the Buck-Boost circuit includes two MOS tubes connected in series and a power inductor, a freewheeling diode is arranged 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 is respectively connected to the two ends of the series structure formed by the two MOS tubes in series to form a first interface and a second interface, 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 next layer, and 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.

[0018] The present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 The figure is a flow chart of a battery pack balancing control method according to an embodiment of the present invention.

[0020] Figure 2 Flow chart of a particle swarm optimization algorithm according to an embodiment of the present invention.

[0021] Figure 3 The figure is a principle block diagram of a battery balancing control device according to an embodiment of the present invention.

[0022] Figure 4 4 is a principle block diagram of a fuzzy closed-loop controller according to an embodiment of the present invention.

[0023] Figure 5 FIG. 4 is a schematic diagram of an equalization circuit according to an embodiment of the present invention.

[0024] Figure 6This is the static equalization effect diagram of the battery pack before optimization.

[0025] Figure 7 This is the static balancing effect diagram of the optimized battery pack.

[0026] Figure 8 This is a diagram of the equalization effect of the battery pack under charging status before optimization.

[0027] Fig. 9 This is a diagram of the equalization effect of the optimized battery pack under charging status.

[0028] Fig.10 This is a diagram of the equalization effect of the battery pack under discharge state before optimization.

[0029] Fig.11 This is a diagram of the equalization effect of the optimized battery pack under discharge state.

[0030] Fig.12 is the balancing current of adjacent batteries in the battery pack before optimization.

[0031] Fig.13 is the balancing current of adjacent batteries in the optimized battery pack. DETAILED DESCRIPTION

[0032] The embodiments of the present invention are described in detail below. Figure 1 , an embodiment of the present invention provides a battery pack balancing control method, comprising the following steps: 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; 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; The battery pack is controlled by utilizing the optimized fuzzy closed-loop balancing control system.

[0033] The battery pack balancing control method of the embodiment of the present invention uses a swarm intelligence algorithm to optimize the domain in the fuzzy closed-loop scheme, thereby reducing the design difficulty of the fuzzy control scheme. A simulation model is established for the balancing circuit that needs to be controlled, and the fuzzy balancing control effect of the simulation model of the balancing circuit after each optimization is used as feedback, so that the domain optimized by the swarm intelligence algorithm is more compatible with the balancing circuit that needs to be controlled, thereby improving the balancing effect on the battery pack. The battery pack balancing control method of the embodiment of the present invention can optimize the fuzzy control scheme of any balancing circuit to a fuzzy control scheme that is more compatible with the target balancing circuit without relying on expert experience, thereby reducing the design difficulty of the fuzzy control scheme for the battery pack balancing control.

[0034] It is understandable that the swarm intelligence algorithm may adopt an ant colony algorithm, a genetic algorithm or a particle swarm optimization algorithm. In this embodiment, a particle swarm optimization algorithm is used to optimize the domain of the fuzzy control scheme. Figure 4 , taking the fuzzy control strategy shown in the figure as an example, it includes SOC fuzzy controller, voltage fuzzy controller, weight fuzzy controller and closed-loop controller. The SOC fuzzy controller outputs the SOC expected current value according to the actual state of charge (SOC) value of each battery in the detected battery pack and the average SOC value of all batteries in the battery pack; the voltage fuzzy controller is used to output the voltage expected 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 the SOC control weight and the voltage control weight according to the SOC value and the voltage expected current value of the battery pack, and calculates the expected balancing current according to the SOC expected current value, the voltage expected current value, the SOC control weight and the voltage control weight; the closed-loop controller performs closed-loop control on the balancing circuit according to the expected balancing current and the actual balancing current in the balancing circuit, so that the actual balancing current value approaches the expected balancing current value.

[0035] 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 is set to three subsets: low (L), medium (M), and high (H). The interval of the fuzzy set 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 SOC deviation value is {-20, -13, -6, 0, 6, 13, 20}, the original domain of SOC average value is {2.5, 2.85, 3.3, 3.65}, the original domain of voltage deviation value is {-0.2, -0.13, -0.06, 0, 0.06, 0.13, 0.2}, the original domain of voltage average value is {2.5, 2.85, 3.3, 3.65}, the original domain of SOC expected current value and voltage expected current value is {-6, -4, -2, 0, 2, 4, 6}.

[0036] The interval of the fuzzy set of the SOC value of the battery pack in the weighted fuzzy controller is divided into three subsets: low (L), medium (M), and high (H). The interval of the fuzzy set 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 interval of the fuzzy set of the SOC control weight and the voltage control weight is divided into five subsets: small weight (WS), small weight (WL), medium weight (WN), 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 value of the domain of the SOC control weight and the voltage control weight is {0, 0.25, 0.5, 0.75, 1}. The weighted fuzzy controller calculates the expected balancing current value by the following formula:

[0037] In the formula, I * is the expected balancing current value, and are the SOC control weight and voltage control weight output by the weighted fuzzy controller, i soc and i v They are the SOC expected current value and voltage expected current value input to the weighted fuzzy controller respectively.

[0038] Reference Figure 2The steps of the particle swarm optimization algorithm are shown in the figure. In this embodiment, the center point and boundary point of the membership function of the SOC fuzzy controller and the voltage fuzzy controller are used as variables to form the position coordinates of the particles. Assume that the domain of the SOC average value is {0, x1, x2, 100}, the domain of the SOC deviation value is {-20, x3, x4, 0, x5, x6, 20}, the domain of the voltage average value is {2.5, x7, x8, 3.65}, and the domain of the voltage deviation value is {-0.2, x9, x 10 ,0,x 11 , x 12 , 0.2}, the position coordinates of the particles are composed of twelve variables in these four domains, and 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 voltage fuzzy controller in the control model of the established fuzzy closed-loop balancing control system to simulate the control of the balancing circuit, and the running time function T (X) and the balancing current function I (X) required for the simulation model of the balancing circuit to achieve equilibrium are used to calculate the running time and balancing current required for the simulation model corresponding to each particle to reach equilibrium, and then the fitness of each particle's position is calculated based on the running time and balancing current, and the particle's position and speed are updated according to the fitness value. The fitness function f (x) is shown as follows:

[0039] In the formula, I max is the maximum value of the expected range of balancing current, I min is the minimum value of the expected range of the balanced current, and c is a constant coefficient. By adopting this fitness function, the smaller the running time and the smaller the fitness value of the particle whose balanced current falls within the expected range of the balanced current, the greater the value. By updating the speed and position of each particle according to the fitness, the particles in the particle swarm can be moved closer to the location of the optimal solution in the solution space, thereby obtaining the domain of the SOC fuzzy controller and the voltage fuzzy controller that are more suitable for the target balanced circuit. In this embodiment, the population size of the particle swarm is set to 5, the cognitive parameter and social parameter of each particle are both 1.5, and the maximum genetic generation is 50, so that the particle with the best fitness value when the maximum number of iterations is reached is the optimal solution. It can be understood that the parameters of the particle swarm algorithm in the actual optimization process can be adjusted according to the number of variables, the size of the solution space, etc.

[0040] Reference Figures 6 to 11By using an unoptimized fuzzy control scheme and a fuzzy control scheme optimized by the control method of the present invention for the equalization circuit with the same parameters, the operating time required for the battery pack to reach equilibrium in the static state, charging state and discharging state before optimization is 584s, 492.7s and 613.6s respectively, and the operating time required for the battery pack to reach equilibrium in the static state, charging state and discharging state after optimization is 426.8s, 431.6s and 446.2s respectively. Fig.12 and 13 , the range of the balanced current between adjacent batteries after optimization is better than that before optimization, and the balanced current can be better stabilized within the expected range. It can be seen that the optimized fuzzy control scheme has better running time and stability of balanced current than before optimization, and has better balanced control effect.

[0041] 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 balancing control method can be implemented.

[0042] 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, disk, optical disk, 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 to this. The storage medium in the embodiment of the present application can also be a circuit or any other device that can realize the storage function, for storing program instructions and / or data.

[0043] In some possible implementations, various aspects of the battery pack balancing control method provided by the present invention can also be implemented in the form of a program product, which includes a program code. When the program product is run on a device, the program code is used to enable the control device to execute the steps of the battery pack balancing control method according to various exemplary embodiments of the present application described above in this specification.

[0044] Based on the same inventive concept, an embodiment of the present invention also 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 domain of the fuzzy controller in the fuzzy closed-loop control module using a swarm intelligence algorithm, so that the balancing current simulated by the balancing circuit simulation module is within the desired range while minimizing the running time required to achieve balancing.

[0045] In this embodiment, the fuzzy closed-loop control module includes a 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 the 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 SOC average value of all batteries in the battery pack. The voltage fuzzy control module is used to output the 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 voltage average value of all batteries in the battery pack. The weight fuzzy control module is used to output the SOC control weight and the voltage control weight according to the SOC value and the voltage expected current value, and calculate the 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.

[0046] The optimization module uses the particle swarm optimization algorithm to optimize the domain 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 to guide the update of the particle position and speed:

[0047] In the formula, T'(X) is a function of the running time required for the balancing circuit simulation module to achieve balancing, and I'(X) is a function of the balancing current of the balancing circuit simulation module.

[0048] Reference Figure 3Based on the same inventive concept, an embodiment of the present invention also provides a battery pack balancing control device, including a balancing circuit, a monitoring circuit and a fuzzy closed-loop controller. The balancing circuit is used to control the charging and discharging process 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 balancing current value of the balancing circuit. The domain of the fuzzy controller in the fuzzy closed-loop controller is optimized by the above-mentioned optimization device of the battery pack balancing control system, and 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 electric energy of each battery in the battery pack is balanced.

[0049] Reference Figure 4 In this embodiment, the fuzzy closed-loop controller includes a SOC fuzzy controller, a voltage fuzzy controller, a weight fuzzy controller and a closed-loop controller. The SOC fuzzy controller is used to output the 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. The voltage fuzzy controller is used to output the corresponding voltage expected current value according to the voltage value of each battery in the battery pack and the voltage average value of all batteries in the battery pack. The weight fuzzy controller is used to output the SOC control weight and the voltage control weight according to the SOC value and the voltage expected current value, and calculate the 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 controller is used to perform closed-loop control on the balancing circuit according to the actual balancing current value and the expected balancing current value of the balancing current, so that the actual balancing current value of the balancing circuit approaches the expected balancing current value. In this embodiment, the domains of the SOC fuzzy controller and the voltage fuzzy controller are optimized by the particle swarm optimization algorithm, and the SOC fuzzy controller, the voltage fuzzy controller and the weight fuzzy controller all use the Mamdani type fuzzy inference engine to establish the inference rule base, and the membership function all uses triangular functions. The weighted fuzzy controller can flexibly adjust the proportion of the expected balancing current calculated based on the SOC value and the expected balancing current calculated based on the voltage according to the SOC value and voltage value of the battery. It can use the battery voltage as the main balancing variable when the voltage value changes rapidly, and use the battery SOC as the main balancing variable when the SOC value changes rapidly, thereby adjusting the power of each battery in the battery pack to balance as quickly as possible while avoiding excessive overshoot of the balancing current.

[0050] Reference Figure 5In this embodiment, in order to improve the scalability and design flexibility of the balancing circuit, the balancing circuit adopts a Buck-Boost circuit to realize 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, and each Buck-Boost circuit includes two MOS tubes connected in series and a power inductor. A freewheeling diode is arranged between the source and drain of the two MOS tubes. The gates of the MOS tubes in all Buck-Boost circuits are connected to the closed-loop controller and 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 tubes, and the second end of the power inductor is connected to the two ends of the series structure formed by the two MOS tubes in series to form a first interface and a second interface. 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, and the first interface and the second interface of the Buck-Boost circuit of the bottom layer are respectively connected to the two batteries in the battery pack. By controlling the on and off of the two MOS tubes 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, so as to complete the transfer of electric energy between the batteries and realize the balance of 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 on and off of each MOS tube, so that the balanced current value between adjacent batteries approaches the expected balanced current value output by the weighted fuzzy controller.

[0051] The present application is described with reference to the flowcharts and / or block diagrams of the methods, apparatus (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the 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 a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0052] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0053] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0054] In the description of the present invention, "several" means one or more, "more" means two or more, "greater than", "less than", "exceed" etc. are understood to exclude the number itself, and "above", "below", "within" etc. are understood to include the number itself. If there is a description of "first" or "second", it is only used to distinguish the technical features, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features.

[0055] In the description of the present invention, unless otherwise clearly defined, terms such as setting, installing, connecting, etc. should be understood in a broad sense, and technicians in the relevant technical field can reasonably determine the specific meanings of the above terms in the present invention based on the specific content of the technical solution.

[0056] The above-mentioned embodiments are only preferred embodiments of the present invention and cannot be used to limit the scope of protection of the present invention. Any non-substantial changes and substitutions made by technicians in this field on the basis of the present invention shall fall within 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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