Capacity Consistency Prediction and Process Parameter Optimization of Lithium-Ion Battery based on Neural Network and Particle Swarm Optimization Algorithm,Advanced Theory and Simulations

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Capacity Consistency Prediction and Process Parameter Optimization of Lithium-Ion Battery based on Neural Network and Particle Swarm Optimization Algorithm
Advanced Theory and Simulations ( IF 2.9 ) Pub Date : 2023-05-23 , DOI: 10.1002/adts.202300125
Youjun Han, _{1,

2} , Hongyuan Yuan ₁ , Ying Shao ₂ , Jin Li ₂ , Xuejie Huang _{2,

3}

Affiliation

The grading capacity of lithium-ion battery is an important basis for evaluating battery quality. Aiming at the difficulty of determining the critical control factors and the threshold of the control parameters in the lithium-ion battery manufacturing process, a capacity prediction and process parameter optimization model of lithium-ion battery is proposed by combining the back propagation (BP) and particle swarm optimization (PSO) algorithms. First, the BP method is applied to establish the nonlinear mapping relationship between process data and grading capacity, which is regarded as the capacity consistency prediction model. Second, using the prediction model as fitness function and combining with PSO algorithm, the optimization model of process parameters is established. Finally, under the given initial process parameters from the lithium-ion battery pilot line, it is carried out to obtain the best process parameter formula. The results show that the BP method has an accurate capacity consistency prediction effect. Combined with PSO algorithm, the optimized process parameters are obtained, which significantly improves the capacity consistency of lithium-ion batteries. The results serve as an engineering application method to guide the selection and confirmation of process parameters at the battery design stage.

中文翻译：

基于神经网络和粒子群优化算法的锂离子电池容量一致性预测及工艺参数优化

锂离子电池的分级容量是评价电池质量的重要依据。针对锂离子电池制造过程中关键控制因素和控制参数阈值难以确定的问题，结合反向传播（BP）和粒子群优化（PSO）算法。首先，采用BP方法建立过程数据与分级能力之间的非线性映射关系，作为能力一致性预测模型。其次，以预测模型为适应度函数，结合PSO算法，建立工艺参数的优化模型。最后，在锂离子电池中试线给定的初始工艺参数下进行，得到最佳工艺参数公式。结果表明，BP方法具有准确的容量一致性预测效果。结合PSO算法，得到优化的工艺参数，显着提高了锂离子电池的容量一致性。研究结果可作为工程应用方法指导电池设计阶段工艺参数的选择和确定。显着提高了锂离子电池的容量一致性。研究结果可作为工程应用方法指导电池设计阶段工艺参数的选择和确定。显着提高了锂离子电池的容量一致性。研究结果可作为工程应用方法指导电池设计阶段工艺参数的选择和确定。

更新日期：2023-05-23

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