Probabilistic neural network-based flexible estimation of lithium-ion battery capacity considering multidimensional charging habits

Qingbo Li, Jun Zhong, Jinqiao Du, Yong Yi, Jie Tian, Yan Li, Chunyan Lai, Taolin Lu and Jingying Xie

Energy, 2024, vol. 294, issue C

Abstract: As the adoption of electric vehicles (EVs) continues to rise due to increasing environmental concerns and policy support, the accurate estimation of battery capacity becomes crucial for efficient vehicle management and prolonging battery life. This study presents a framework for capacity estimation under real-world electric vehicle charging scenarios, reflecting a variety of driving habits. A novel CNN-LSTM neural network architecture featuring a probabilistic regression layer (CLPNN) is developed, utilizing short time interval data to provide accurate, reliable capacity and uncertainty estimates for diverse charging situations. The adaptability of the proposed method is evaluated by intercepting real-vehicle statistics to simulate charging events. We introduce two result fusion strategies for refining capacity estimations: the Trimmed Mean Strategy and the Uncertainty Screening Strategy. Their performances are meticulously compared to establish the most effective approach. Moreover, probabilistic layer transfer learning is employed to address the challenge of insufficient labeled data in the target domain, thereby improving the transfer and generalization performance of the model. The findings of this study underscore the potential of the proposed methods in accurately evaluating battery capacity and dealing with uncertainties in real-world EV operations.

Keywords: Lithium-ion battery; Capacity estimation; Probabilistic neural network; Multi-results fusion (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544224006534
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:294:y:2024:i:c:s0360544224006534

DOI: 10.1016/j.energy.2024.130881

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().