Reduced-order electrochemical model for lithium-ion battery with domain decomposition and polynomial approximation methods

Energy ◽  
2021 ◽  
Vol 221 ◽  
pp. 119662
Author(s):  
Changlong Li ◽  
Naxin Cui ◽  
Chunyu Wang ◽  
Chenghui Zhang
2021 ◽  
Vol 57 (1) ◽  
pp. 1094-1104
Author(s):  
Yuntian Liu ◽  
Rui Ma ◽  
Shengzhao Pang ◽  
Liangcai Xu ◽  
Dongdong Zhao ◽  
...  

Energies ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 2120 ◽  
Author(s):  
Wei He ◽  
Michael Pecht ◽  
David Flynn ◽  
Fateme Dinmohammadi

State-of-charge (SOC) is one of the most critical parameters in battery management systems (BMSs). SOC is defined as the percentage of the remaining charge inside a battery to the full charge, and thus ranges from 0% to 100%. This percentage value provides important information to manufacturers about the performance of the battery and can help end-users identify when the battery must be recharged. Inaccurate estimation of the battery SOC may cause over-charge or over-discharge events with significant implications for system safety and reliability. Therefore, it is crucial to develop methods for improving the estimation accuracy of battery SOC. This paper presents an electrochemical model for lithium-ion battery SOC estimation involving the battery’s internal physical and chemical properties such as lithium concentrations. To solve the computationally complex solid-phase diffusion partial differential equations (PDEs) in the model, an efficient method based on projection with optimized basis functions is presented. Then, a novel moving-window filtering (MWF) algorithm is developed to improve the convergence rate of the state filters. The results show that the developed electrochemical model generates 20 times fewer equations compared with finite difference-based methods without losing accuracy. In addition, the proposed projection-based solution method is three times more efficient than the conventional state filtering methods such as Kalman filter.


2021 ◽  
Vol 70 (13) ◽  
pp. 138801-138801
Author(s):  
Li Tao ◽  
◽  
Cheng Xi-Ming ◽  
Hu Chen-Hua

Author(s):  
Zachary Salyer ◽  
Matilde D'Arpino ◽  
Marcello Canova

Abstract Aging models are necessary to accurately predict the SOH evolution in lithium ion battery systems when performing durability studies under realistic operatings, specifically considering time-varying storage, cycling, and environmental conditions, while being computationally efficient. This paper extends existing physics-based reduced-order capacity fade models that predict degradation resulting from the solid electrolyte interface (SEI) layer growth and loss of active material (LAM) in the graphite anode. Specifically, the physics of the degradation mechanisms and aging campaigns for various cell chemistries are reviewed to improve the model fidelity. Additionally, a new calibration procedure is established relying solely on capacity fade data and results are presented including extrapolation/validation for multiple chemistries. Finally, a condition is integrated to predict the onset of lithium plating. This allows the complete cell model to predict the incremental degradation under various operating conditions, including fast charging.


2019 ◽  
Vol 3 (1) ◽  
pp. 148-165 ◽  
Author(s):  
Wenxin Mei ◽  
Haodong Chen ◽  
Jinhua Sun ◽  
Qingsong Wang

Schematic of the lithium-ion battery and description of the P2D electrochemical model.


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