Real-Time Sensorless Temperature Estimation of Lithium-Ion Batteries Based on Online Operando Impedance Acquisition

To guarantee safe, efficient, and reliable operations of lithium-ion battery (LIB) systems, it is indispensable to monitor their state of temperature. However, subject to limited onboard temperature sensors and the challenges in measuring battery internal temperature, the directly available temperature information in a battery system is extremely insufficient. To this end, developing sensorless temperature estimation techniques to obtain the temperature of each cell is important. This article proposes an operando impedance-based method to estimate the volume-averaged temperature of LIB in real time under dynamic operating conditions. A generalized rule for selecting optimal impedance parameters has been revealed for the first time through a comprehensive analysis of the electrochemical impedance spectroscopy from different batteries. This rule can greatly reduce the time and effort to select optimal impedance parameters of the target cell for temperature estimations. The selected impedance parameters are then measured intermittently during battery operations via active pulse current injection, which allows the impedance acquisition under both loading and resting conditions. An estimation framework based on long short-term memory recurrent neural network has been proposed by taking advantage of the measured operando impedance, time-series current, and voltage data to achieve real-time temperature estimation, which distinguishes this work from existing impedance-based methods. The proposed methodology has been experimentally validated against different batteries and operating conditions, with the root mean square error of the estimations within 0.46 °C for all cases.