Journal
CURRENT OPINION IN ELECTROCHEMISTRY
Volume 20, Issue -, Pages 66-77Publisher
ELSEVIER
DOI: 10.1016/j.coelec.2020.02.026
Keywords
Electrochemical impedance spectroscopy (EIS); Lithium-ion battery (LIB); In situ EIS; Diagnostics; Machine learning
Funding
- 'Development of Safety and Cost Competitive Energy Storage System for Renewable Energy' of the New Energy and Industrial Technology Development Organization of Japan [P06004]
- 'Research & Development Initiative for Scientific Innovation of New Generation Batteries (RISING)' of the New Energy and Industrial Technology Development Organization of Japan [P09012]
- 'Research & Development Initiative for Scientific Innovation of New Generation Batteries 2 (RISING2)' of the New Energy and Industrial Technology Development Organization of Japan [P16001]
- 'Development of next-generation storage battery systems for 'smart communities' Saitama Leading Edge Project' of the Saitama Prefecture
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Electrochemical impedance spectroscopy has been widely used to understand the chemistry and physics of battery systems. This review covers electrochemical impedance spectroscopy used for the interpretation of impedance data of lithium-ion batteries (LIBs) from advanced equivalent circuit models to the mathematical model, which is developed by John Newman. In addition, as a method to realize an energy-sustainable society using diagnostics based on the combination of LIBs and electrochemical impedance spectroscopy, on-board diagnostics of battery packs are achieved based on an input signal generated by a power controller in a battery management system instead of the conventionally used frequency response analyzer. The diagnostic system is applicable to energy management systems which are installed in homes, buildings, and communities, accumulating the impedance data on state of health of LIBs. Finally, a future possibility regarding the diagnostics of battery packs coupled with the machine learning of impedance data is introduced.
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