Capacity Consistency Prediction and Process Parameter Optimization of Lithium‐Ion Battery based on Neural Network and Particle Swarm Optimization Algorithm

• Published in: Advanced theory and simulations 2023-08, Vol.6 (8), p.n/a
• Main Authors: Han, Youjun, Yuan, Hongyuan, Shao, Ying, Li, Jin, Huang, Xuejie
• Format: Article
• Language: English
• Subjects: battery manufacturing process; consistency prediction; lithium‐ion battery consistency; optimization of process parameters
• ISSN: 2513-0390; 2513-0390
• DOI: 10.1002/adts.202300125

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. To improve, a capacity prediction and process parameter optimization model of lithium‐ion battery is proposed by combining the back propagation and particle swarm optimization algorithms. The results serve as an engineering application method to guide the selection and confirmation of process parameters at the battery design stage.