[1]秦东晨,罗庆洲,杨俊杰,等.基于PSO-MPC的锂离子电池快速安全充电策略[J].郑州大学学报(工学版),2025,46(05):90-97.[doi:10.13705/j.issn.1671-6833.2025.05.005]
　QIN Dongchen,LUO Qingzhou,YANG Junjie,et al.Fast and Safe Charging Strategy for Lithium-ion Batteries Based on PSO-MPC[J].Journal of Zhengzhou University (Engineering Science),2025,46(05):90-97.[doi:10.13705/j.issn.1671-6833.2025.05.005]
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基于PSO-MPC的锂离子电池快速安全充电策略()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
46
期数:
2025年05期
页码:
90-97
栏目:
出版日期:
2025-08-10

文章信息/Info

Title:
Fast and Safe Charging Strategy for Lithium-ion Batteries Based on PSO-MPC
文章编号:
1671-6833(2025)05-0090-08
作者:
秦东晨;  罗庆洲;  杨俊杰;  陈江义;  武红霞
郑州大学 机械与动力工程学院,河南 郑州 450001
Author(s):
QIN Dongchen;  LUO Qingzhou;  YANG Junjie;  CHEN Jiangyi;  WU Hongxia
School of Mechanical and Power Engineering, Zhengzhou University, Zhengzhou 450001, China
关键词:
锂离子电池;  充电策略优化;  MPC;  电池性能;  电池安全;  电池容量衰减
Keywords:
lithium-ion battery;  charging strategy optimization;  MPC;  battery performance;  battery safety;  battery capacity decay
分类号:
TM912.9TP18
DOI:
10.13705/j.issn.1671-6833.2025.05.005
文献标志码:
A
摘要:
针对锂离子电池充电过程中速度缓慢、过度升温、析锂及过充等问题,提出了基于改进粒子群算法(PSO)的模型预测控制(MPC)充电策略。首先,建立了锂离子电池的等效电路-热-电化学-老化耦合模型,结合等效电路模型与电化学模型的优点,准确预测充电过程中的端电压、温度变化及老化机制(如SEI膜增长、活性材料损失和析锂导致的容量损失)。其次,对耦合模型离散化处理,构建充电的空间状态模型,并增加避免热失控、析锂及过充的安全约束。基于空间状态模型,预测充电系统未来状态,并构建描述充电时间及损耗的代价函数。最后,通过改进PSO算法求解最优充电电流序列,实现对充电过程的实时优化。MATLAB/Simulink联合仿真结果表明:该策略在显著缩短充电时间的同时,有效控制了电池温度、端电压及析锂过电势,避免了热失控、析锂和过充等安全问题。通过实验与3种传统充电策略对比,结果表明:该策略充电时间缩短约17.3%~61.1%,且平均每次充电的容量衰减量相对于额定容量降低7.6%~36%,可为锂电池充电优化提供新方法。
Abstract:
The issues of low charging speed, rapid temperature rise, lithium plating, and overcharging in lithiumion batteries were addressed in this study. A model predictive control (MPC) charging strategy based on an improved particle swarm optimization (PSO) algorithm was proposed.Firstly,an equivalent circuit-thermal-electrochemical-aging coupled model was established, combining the advantages of equivalent circuit and electrochemical models to accurately predict terminal voltage, temperature variations, and aging mechanisms (e. g., SEI film growth, active material loss, and capacity loss from lithium plating).Secondly,the coupled model was discretized to build a state-space model, with added safety constraints to prevent thermal runaway, lithium plating, and overcharging. Based on the state-space model, future battery states were predicted, and a cost function for charging time and energy loss was formulated.Finally, the improved PSO algorithm was used to solve for the optimal charging current sequence, enabling real-time charging optimization.MATLAB/Simulink simulations showed the strategy significantly reduced charging time while effectively controlling battery temperature, terminal voltage, and lithium plating overpotential, avoiding issues like thermal runaway, lithium plating and overcharging. Experimental comparisons with traditional strategies showed a reduction in charging time by 17.3% to 61.1% and capacity decay by 7.6% to 36%. This research provided a new direction for lithium-ion battery charging optimization.

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更新日期/Last Update: 2025-09-19