Abstract
Power battery technology is essential to ensuring the overall performance and safety of electric vehicles. Non-invasive characteristic curve analysis (CCA) for lithium-ion batteries is of particular importance. CCA can provide characteristic data for further applications such as state estimation and thermal runaway warning without disassembling the batteries. This paper summarizes the characteristic curves consisting of incremental curve analysis, differential voltage analysis, and differential thermal voltammetry from the perspectives of exploring the aging mechanism of batteries and constructing the data-driven model. The process of quantitative analysis of battery aging mechanism is presented and the steps of constructing data-driven models are induced. Moreover, the recent progress and application of the main features and methodologies are discussed. Finally, the applicability of battery CCA is discussed by converting non-quantifiable battery information into transportable data covering macrostate and micro-reaction information. Combined with the cloud-based battery management platform, the above-mentioned battery characteristic curves could be used as a valuable dataset to upgrade the next-generation battery management system design.
| Original language | English |
|---|---|
| Pages (from-to) | 146-163 |
| Number of pages | 18 |
| Journal | Automotive Innovation |
| Volume | 5 |
| Issue number | 2 |
| Early online date | 15 Apr 2022 |
| DOIs | |
| Publication status | Published - Apr 2022 |
Bibliographical note
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Funder
The National Key Research and Development Program of China (2018YFB0104001-01) and National Natural Science Foundation of China (No. 52102470).Publisher Copyright:
© 2022, China Society of Automotive Engineers (China SAE).
Keywords
- Data-driven model
- Degradation analysis
- Lithium-ion
- Non-invasive characteristic
ASJC Scopus subject areas
- Automotive Engineering
