Parameter Identification for Electrochemical Models of Lithium-Ion Batteries Using Bayesian Optimization

• URL: http://arxiv.org/abs/2405.10750v1
• Date: Fri, 17 May 2024 12:59:15 GMT
• Title: Parameter Identification for Electrochemical Models of Lithium-Ion Batteries Using Bayesian Optimization
• Authors: Jianzong Pi, Samuel Filgueira da Silva, Mehmet Fatih Ozkan, Abhishek Gupta, Marcello Canova,
• Abstract summary: Gradient-based and metaheuristic optimization techniques are limited by their lack of robustness, high computational costs, and susceptibility to local minima. In this study, Bayesian Optimization is used for tuning the dynamic parameters of an electrochemical equivalent circuit battery model (E-ECM) for a nickel-manganese-cobalt (NMC)-graphite cell.
• Score: 5.637250168164636
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Efficient parameter identification of electrochemical models is crucial for accurate monitoring and control of lithium-ion cells. This process becomes challenging when applied to complex models that rely on a considerable number of interdependent parameters that affect the output response. Gradient-based and metaheuristic optimization techniques, although previously employed for this task, are limited by their lack of robustness, high computational costs, and susceptibility to local minima. In this study, Bayesian Optimization is used for tuning the dynamic parameters of an electrochemical equivalent circuit battery model (E-ECM) for a nickel-manganese-cobalt (NMC)-graphite cell. The performance of the Bayesian Optimization is compared with baseline methods based on gradient-based and metaheuristic approaches. The robustness of the parameter optimization method is tested by performing verification using an experimental drive cycle. The results indicate that Bayesian Optimization outperforms Gradient Descent and PSO optimization techniques, achieving reductions on average testing loss by 28.8% and 5.8%, respectively. Moreover, Bayesian optimization significantly reduces the variance in testing loss by 95.8% and 72.7%, respectively.

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