基于CCS-MPC的储能锂电池组均衡控制策略

doi:10.11930/j.issn.1004-9649.202407037

摘要/Abstract

摘要：

针对长时间充放电后锂电池模组之间荷电状态（state of charge，SOC）不一致的问题，传统集中式均衡电路存在均衡速度过低的缺陷，以对称式开关阵列、Boost变换器与LC准谐振电路作为均衡主电路，提出了一种基于连续集模型预测控制（continuous control set model predictive control，CCS-MPC）的均衡控制策略。首先，对均衡系统进行建模，构建离散状态空间方程；然后，根据状态方程设计多步模型预测算法，并以SOC预测值和参考值、变换器开关管当前输入和上一时刻输入之间的误差作为价值函数；最后，对价值函数进行二次规划，在线求解出一组控制最优解，并应用于均衡系统，通过动态调整占空比以控制均衡电流的大小。相较于单步预测，多步预测需要考虑被控量在多个周期内保持最优，可以保证在每个均衡周期内均衡器都能输出最优的均衡电流，有效防止均衡器失稳。仿真结果表明，所提模型预测算法实现了各电池组SOC一致，保证了均衡电流的稳定输出，相比常规PI算法缩短了17%的均衡时间。

关键词: 锂电池组, 荷电状态, 连续集模型预测控制, SOC一致, 多步预测

Abstract:

Aiming at the issue of inconsistent state of charge (SOC) among lithium battery modules after long-term charging and discharging, traditional centralized balancing circuits suffer from the drawback of low balancing speed. To address this, a balancing control strategy based on continuous control set model predictive control (CCS-MPC) is proposed, utilizing a symmetrical switch array, boost converter, and LC quasi-resonant circuit as the main balancing circuit. Firstly, the balancing system is modeled, and a discrete state-space equation is constructed. Subsequently, a multi-step model predictive algorithm is designed based on the state equation, with the value function defined by the error between the SOC predicted value and the reference value, as well as the difference between the current input and the previous input of the converter’s switching elements. Finally, a quadratic programming is applied to the value function to obtain an optimal control solution online, which is then implemented in the balancing system. By dynamically adjusting the duty cycle, the magnitude of the balancing current is controlled. Compared with single-step prediction, multi-step prediction requires considering the optimality of the controlled variables over multiple cycles, ensuring that the balancer can output the optimal balancing current in each balancing cycle and effectively preventing instability of the balancer. The simulation results show that compared with the conventional PI algorithm, the proposed model prediction algorithm achieves SOC consistency among battery modules, ensures stable output of balancing current, and reduces the balancing time by 17%.

Key words: lithium battery pack, state of charge, continuous set model predictive control, SOC consistency, multi-step prediction

周楷, 陶正顺, 潘庭龙, 许德智. 基于CCS-MPC的储能锂电池组均衡控制策略[J]. 中国电力, 2025, 58(7): 177-186.

ZHOU Kai, TAO Zhengshun, PAN Tinglong, XU Dezhi. Balancing Control Strategy for Energy Storage Lithium Battery Pack Based on CCS-MPC[J]. Electric Power, 2025, 58(7): 177-186.

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链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202407037

https://www.electricpower.com.cn/CN/Y2025/V58/I7/177

图/表 10

图 1 均衡电路拓扑

Fig.1 Balanced circuit topology

图 2 均衡电路的两种工作状态

Fig.2 Two operating states of the balanced circuit

图 3 B1与B3均衡时序图

Fig.3 Timing diagram for the balance between B1 and B3

图 4 串联谐振等效电路的拓扑结构与交流方波电源

Fig.4 Topological structure of the series resonant equivalent circuit and the AC square-wave power supply

图 5 基于CCS-MPC的均衡流程

Fig.5 Balanced flow chart based on CCS-MPC

表 1 仿真参数配置

Table 1 Simulation parameter configuration

参数名称		参数值
电池组额定容量Q/(A·h)		30
L_b/μH		600
C_b/μF		3000
L/μH		5
C/μF		12.6
LC变换器等效电阻R_LC/Ω		0.3
谐振周期T/μs		50

图 6 谐振状态下谐振电流i与电容电压VC

Fig.6 Resonant current i and capacitor voltage VC under resonant condition

图 7 CCS-MPC算法与PI算法均衡对比

Fig.7 Comparison of balancing performance between CCS-MPC algorithm and PI algorithm

表 3 锂电池组初始状态

Table 3 Initial state of the lithium battery pack

实验组数	S_OC1/%	S_OC2/%	S_OC3/%	S_OC4/%
一	82	76.3	68.2	66.0
二	78	73.6	68.4	65.0
三	70	62.1	51.3	47.1

图 8 CCS-MPC算法均衡效果

Fig.8 Balancing effect diagram of the CCS-MPC algorithm

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