Projects per year
Lithium-ion battery aging mechanism analysis and health prognostics are of great significance for a smart battery management system to ensure safe and optimal use of the battery system. This paper provides a comprehensive review of aging mechanisms and the state-of-the-art health prognostic methods and summarizes the main challenges and research prospects for battery health prognostics. First, the complex relationships among aging mechanisms, aging modes, influencing factors, and aging types are reviewed and summarized. Then, the battery health prognostic methods are divided according to different time scales and objectives, which include the short-term state of health estimation, long-term end-of-life prediction, and degradation trajectory prediction, followed by a detailed review of each prognostic task and method. For consistency, we first provide a clear and concise description of each method, showing the similarities and peculiarities of these methods, and then review several representative works. After that, comparative evaluations are conducted. The main advantages and disadvantages of each prognostic task and prognostic method are analyzed in detail. Next, key challenges are presented by considering the specific characteristics of each prognostic task. Moreover, for each challenge, potential solutions are presented and discussed. These proposed potential solutions to the main challenges are beneficial and can be considered by researchers in their further studies. Finally, the future trends of battery health prognostics are discussed, and several new ideas for battery health prognostics are proposed.
|Journal||Energy and Environmental Science|
|Number of pages||34|
|Publication status||Published - 4 Jan 2023|
Bibliographical noteFunding Information:
This research was funded by the the Villum Foundation for Smart Battery project (No. 222860), the National Key Research and Development Program (No. 2022YFE0102700), and the National Natural Science Foundation of China (Grant No. 52111530194).
© 2023 The Royal Society of Chemistry.
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- 2 Active
State of Health Estimation and Prediction for Lithium-ion Batteries Based on Transfer Learning
Che, Y., Teodorescu, R. & Sui, X.
01/01/2022 → 31/12/2024
Project: PhD Project
CROSBAT: SMART BATTERY
Teodorescu, R., Stroe, D., Steffensen, B., Christensen, M. D., Sui, X., Vilsen, S. B., Che, Y., Zheng, Y., Bharadwaj, P., Kulkarni, A. & Weinreich, N. A.
01/09/2021 → 31/08/2027