A simplified electro-chemical lithium-ion battery model applicable for in situ monitoring and online control

被引:20
|
作者
Gu, Yuxuan [1 ]
Wang, Jianxiao [2 ]
Chen, Yuanbo [1 ]
Xiao, Wei [1 ]
Deng, Zhongwei [3 ]
Chen, Qixin [1 ]
机构
[1] Tsinghua Univ, Dept Elect Engn, Beijing 100084, Peoples R China
[2] Peking Univ, Natl Engn Lab Big Data Anal & Applicat, Beijing 100084, Peoples R China
[3] Chongqing Univ, Coll Mech & Vehicle Engn, Chongqing 400044, Peoples R China
基金
中国国家自然科学基金;
关键词
Electro-chemical model; Lithium-ion battery; Model simplification; State estimation; SINGLE-PARTICLE MODEL; SOLID-PHASE DIFFUSION; REDUCED-ORDER MODEL; REDUCTION; DESIGN; STATE; EXTENSION;
D O I
10.1016/j.energy.2022.126192
中图分类号
O414.1 [热力学];
学科分类号
摘要
The penetration of lithium-ion batteries (LIBs) in transport, energy, and communication systems is increasing rapidly. A meticulous but simplified LIB model for non-uniform internal state monitoring and online control is sought in practice. Based on the pseudo-two-dimensional (P2D) model, a simplified electro-chemical model for LIBs is proposed. Specifically, a rigorous model of the non-uniform reaction rates inside the battery is derived. Sub-models that capture the non-uniformity of current densities, potentials and concentrations are developed synchronously. Time-variant parameters and a lumped thermal model are incorporated as well. A full-cycle simulation framework, including the discretization, initialization, stabilization and closed-loop correction methods, is designed for ease of online control. Numerical experiments on the widely used NCM and LFP 18650 batteries under standard charge and discharge protocols and dynamic protocols during the peak-shaving or regulation service are conducted for validation. Generally, the speed of the proposed model increases hundreds of times compared to the P2D model. The estimation accuracy of internal and external states increases around 10% to 100% compared to state-of-art electro-chemical models. In addition, the correction speed and accuracy of the closed-loop framework increase around ten times and around 100% respectively compared to the widely used ensemble Kalman filter.
引用
收藏
页数:19
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