Acquisition of Lithium-ion battery’s (LIB) internal temperature is crucial to ensure the safety of the battery system for electric vehicle (EV), yet the immeasurable nature of it renders this goal as challenging. Approaches based on contact sensing and thermal models are widely investigated, but their realizability and effectiveness still beg for further validation. Methods based on electrochemical sensing are receiving more attention due to their attractive features. However, the largest gap lies in the access to the impedance information for on-vehicle application. Using results derived from the passive electrochemical impedance spectroscopy (EIS) with real driving conditions, this paper presents a thorough solution to obtain the internal temperature of LIB. With the accurate high-frequency part of passive EIS at hand, the internal temperature is estimated by employing the regression relationship between temperature and its corresponding EIS landscape. Independent of models and sensors, the proposed scheme uses the mere electrical measurements of LIB and achieves the internal temperature estimation with refreshing rate of 1Hz. This sensorless scheme can meet the real-time requirements of battery management so that precious time for safety countermeasures to act is saved. The proposal in this paper is expected to supplement the battery management techniques with critical inputs to secure the safer use of EV.
Keywords Lithium-ion battery, Internal temperature estimation, Spectral analysis, Passive EIS