A multi-time-scale framework for state of energy and maximum available energy of lithium-ion battery under a wide operating temperature range.

Chen, Lei; Wang, Shunli; Jiang, Hong; Fernandez, Carlos

Abstract

Lithium-ion batteries are one of the best choices as energy storage devices for self-powered nodes in wireless sensor networks (WSN) due to their advantages of no memory effect, high energy density, long cycle life, and being pollution-free after being discarded, ensuring that the sensor nodes maintain high power operation for a long time. An improved co-estimation framework for SOE and maximum available energy has been established, considering the problem of maximum available energy decay caused by temperature and battery charge-discharge rate, which can update the maximum available energy in real-time and reduce SOE errors caused by fixed energy values. A multi-timescale SOE and maximum available energy co-estimation framework is proposed to address the asynchronous and coupled characteristics of maximum available energy and SOE estimation, effectively reducing the algorithm's computational complexity. The proposed algorithm is experimentally verified according to the designed dynamic stress test conditions of lithium-ion batteries in WSN nodes. The experimental results show that smaller time scales can provide more accurate and reliable maximum available energy correction and higher SOE estimation accuracy, but the computational time cost is higher than that of larger time scales. To balance SOE estimation accuracy and algorithm computational complexity, the appropriate time scale should be selected based on the SOE estimation accuracy and time cost in practical battery management system working conditions.

Citation

CHEN, L, WANG, S., JIANG, H. and FERNANDEZ, C. 2024. A multi-time-scale framework for state of energy and maximum available energy of lithium-ion battery under a wide operating temperature range. Applied energy [online], 355, article number 122225. Available from: https://doi.org/10.1016/j.apenergy.2023.122225

• Journal Article Type: Article
• Acceptance Date: Oct 30, 2023
• Online Publication Date: Nov 9, 2023
• Publication Date: Feb 1, 2024
• Deposit Date: Nov 23, 2023
• Publicly Available Date: Nov 10, 2024
• Journal: Applied energy
• Print ISSN: 0306-2619
• Electronic ISSN: 1872-9118
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 355
• Article Number: 122225
• DOI: https://doi.org/10.1016/j.apenergy.2023.122225
• Keywords: Lithium-ion batteries; Bi-polarization fractional-order equivalent circuit model; State of energy; Maximum available energy; Multi-timescale
• Public URL: https://rgu-repository.worktribe.com/output/2146674