An Online Coestimation Framework for Coupled Electrothermal-Aging Key State of Lithium-Ion Batteries

Online acquisition of accurate battery electrical, thermal, and aging states is crucial for monitoring and management of batteries, especially in cloud-edge collaboration. In this paper, an online joint estimation method for state of charge (SOC), 3-dimensional state of temperature distribution (SOTD), and state of health (SOH) of batteries based on an electro-thermal-aging coupled model is proposed. Firstly, three well-chosen charging durations are leveraged as the aging features to achieve SOH estimation with the assistance of the extreme learning machine (ELM). Secondly, SOT is estimated through a lumped parameter thermal model (LPTM) and adaptive extended Kalman filter (AEKF), and SOTD is reconstructed using a well-designed fractal theory method (FTM) derived from the iterated function system (IFS) method and a Lindenmayer system (L-system), which ensures precise thermal state monitoring. Then, based on the parameter updating mechanism, the parameters of the equivalent circuit model (ECM) are adjusted in real time according to the accurate SOT and SOH, and the SOC is estimated by the AEKF. Subsequently, the proposed method is validated under different dynamic conditions and different temperatures, the validation results show that the proposed method can achieve accurate SOC-SOTD-SOH co-estimation with an error of no more than 2.14%, 1.37°C, and 1.26%, respectively. Lastly, the investigation of the SOT and SOH influence on SOC estimation reveals the important significance of the co-estimation framework.