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.

翻译：电化学模型的高效参数辨识对于锂离子电池的精确监控与控制至关重要。当应用于依赖大量相互关联参数且这些参数影响输出响应的复杂模型时，该过程面临挑战。尽管梯度优化与元启发式优化技术此前被用于此任务，但其受限于鲁棒性不足、计算成本高昂且易陷入局部最优解。本研究采用贝叶斯优化对镍锰钴（NMC）-石墨电池的电化学等效电路模型（E-ECM）动态参数进行调优。将贝叶斯优化的性能与基于梯度和元启发式方法的基准方法进行对比。通过实验驱动循环验证参数优化方法的鲁棒性。结果表明，贝叶斯优化优于梯度下降与粒子群优化算法，平均测试损失分别降低28.8%和5.8%，同时测试损失方差分别显著降低95.8%和72.7%。