首页|基于多参数耦合模型的锂离子电池充电策略优化研究

基于多参数耦合模型的锂离子电池充电策略优化研究

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为满足电动汽车用户需求和保障锂电池使用安全,设计一项符合锂电池车载工作特性且安全高效的充电控制策略至关重要.基于多参数耦合模型开发了一种适用于车载锂电池的多级恒流充电策略,实现了锂离子电池在充电过程中充电时间、充电温升和充电能量损失的综合性能提升.首先,基于等效电路模型、Bernardi热模型和能量损失模型,建立了电-热-能量损失多参数耦合模型,实现了锂电池充电过程中电气特性、热特性以及能量损失特性的精确表征;其次,提出了考虑充电时间、温升和能量损失的多目标最优充电策略,并对温度和电池参数进行了限制,采用粒子群优化算法对多阶段恒流充电策略进行优化;最后,在实验室条件下与厂家确定的标准恒流充电方法进行了对比,验证了所开发的多阶段恒流充电控制策略性能.结果表明所提出的优化充电策略明显优于标准恒流充电策略,在充电过程电池最高温度仅增加约2.2 ℃的情况下,实现了充电时间缩短12.8%,充电过程中的能量损失减少19.1%.
Charging Optimization Method of Lithium-Ion Battery Based on Multi-parameter Coupling Model
To meet the needs of electric vehicle users and ensure the safety of lithium battery use,it is crucial to design a safe and efficient charging control strategy that conforms to the on-board working characteristics of lith-ium batteries.Therefore,this article develops a multi-stage constant current charging strategy suitable for auto-motive lithium batteries based on a multi-parameter coupling model,achieving comprehensive performance im-provement of lithium-ion batteries in terms of charging time,charging temperature rise,and charging energy loss during the charging process.Firstly,based on the equivalent circuit model,Bernardi thermal model,and energy loss model,a multi-parameter coupled model of electricity heat energy loss was established,achieving accurate characterization of electrical,thermal,and energy loss characteristics during the charging process of lithium batter-ies.Secondly,a multi-objective optimal charging strategy considering charging time,temperature rise,and energy loss was proposed,and temperature and battery parameters were limited.Particle swarm optimization algorithm was used to optimize the multi-stage constant current charging strategy.Finally,the performance of the devel-oped multi-stage constant current charging control strategy was verified by comparing it with the standard con-stant current charging method determined by the manufacturer under laboratory conditions.The results show that the proposed optimized charging strategy is significantly superior to the standard constant current charging strate-gy,achieving a 12.8%reduction in charging time and a 19.1%reduction in energy loss during the charging process when the maximum temperature of the battery only increases by about 2.2 ℃.

lithium-ion batteriesmulti-parameter coupling modelcharging strategyparticle swarm optimiza-tion(PSO)algorithm

申江卫、蒋宝良、张政、陈峥

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昆明理工大学交通工程学院,云南 昆明 650093

锂离子电池 多参数耦合模型 充电策略 粒子群优化算法

国家自然科学基金项目云南省基础研究计划项目昆明理工大学自然科学研究基金项目

52162051202301AT070423KK23202202021

2024

昆明理工大学学报(自然科学版)
昆明理工大学

昆明理工大学学报(自然科学版)

CSTPCD北大核心
影响因子:0.516
ISSN:1007-855X
年,卷(期):2024.49(5)