Reduced order model-based observer design for online temperature distribution estimation in lithium-ion batteries

• Published in: Nonlinear dynamics Vol. 111; no. 4; pp. 3327 - 3344
• Main Authors: Fan, Bi, Zhuang, Yufen, Liu, Zhen, Gan, Min, Xu, Kangkang
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.02.2023; Springer Nature B.V
• Subjects: Artificial neural networks; Automotive Engineering; Classical Mechanics; Computer science; Control; Design; Dynamical Systems; Electric vehicles; Electrical measurement; Engineering; Environmental impact; Estimation; Genetic algorithms; Heat; Lithium; Lithium-ion batteries; Machine learning; Mechanical Engineering; Optimization; Original Paper; Partial differential equations; Real time; Rechargeable batteries; Reduced order models; Sensors; Temperature; Temperature distribution; Vibration; Working conditions; Lithium-ion batteries; Optimal sensor placement; State observer; Extreme learning machine; Spatiotemporal model
• ISSN: 0924-090X; 1573-269X
• DOI: 10.1007/s11071-022-08025-x

Time/space separation-based modeling methods have been widely researched for estimating lithium-ion battery (LIB) thermal dynamics. However, these methods have been developed in an offline environment and may not perform well in real-time application since the battery systems in electric vehicles (EVs) are usually subject to external disturbances. Furthermore, the onboard measurements of temperature are often corrupted by significant error. To address these problems, we present a reduced model-based observer design for online temperature distribution estimation in LIBs. First, an extreme learning machine (ELM)-based offline spatiotemporal model is constructed to approximate the thermal dynamics of LIB. Second, an adaptive reduced order observer is designed based on the offline model developed in the previous step. The offline model is then updated with the estimation results of the observer. As the performance of the estimator is highly related to the placement of sensors, a genetic algorithm (GA)-based integrated optimization strategy is also developed to determine the optimal sensor location for online estimation. Finally, the whole temperature distribution is estimated in real time using the observer, the measured voltage, current and the limited available temperature data. Two experiments on different batteries with different input currents verify the effectiveness of this developed model.