Combining Reduced-Order Model With Data-Driven Model for Parameter Estimation of Lithium-Ion Battery

过度拟合 估计理论 灵敏度(控制系统) 计算机科学 杠杆(统计) 替代模型 可观测性 数据驱动 数学优化 算法 机器学习 工程类 人工智能 数学 电子工程 人工神经网络 应用数学
作者
Zhong-Yi Shui,Xuhao Li,Yun Feng,Bing-Chuan Wang,Yong Wang
出处
期刊:IEEE Transactions on Industrial Electronics [Institute of Electrical and Electronics Engineers]
卷期号:70 (2): 1521-1531 被引量:28
标识
DOI:10.1109/tie.2022.3157980
摘要

The parameters of a lithium-ion battery are important to construct an effective battery management system. Parameter estimation assisted by the pseudo-two-dimensional (P2D) model is much more cost-effective than direct measurement methods. However, this is a nontrivial task, because the simulation of the P2D model is time-consuming. Alternatively, surrogate models such as reduced-order/data-driven models are often used to accelerate the parameter estimation process. Each category of surrogate models has its own strengths and weaknesses. Traditionally, reduced-order models run faster than data-driven models, while data-driven models are more accurate than reduced-order models. To leverage the complementary advantages of these two kinds of surrogate models, we make an interesting attempt to combine them compactly, thus proposing a two-phase surrogate model-assisted parameter estimation algorithm (TPSMA-PEAL). In the first phase, a fast reduced-order model is designed for parameter prescreening. In the second phase, a high-fidelity data-driven model is developed for fine estimation. In TPSMA-PEAL, except the time-consuming simulation, the other two challenges (i.e., the overfitting problem and the low observability of some parameters) are also considered from the perspective of optimization. Note that TPSMA-PEAL takes advantage of differential evolution and parameter sensitivity analysis to address them. Simulations and experiments verify that TPSMA-PEAL is efficient and accurate.
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