Sensorless Temperature Monitoring of Lithium-Ion Batteries by Integrating Physics With Machine Learning

The large-scale application of lithium-ion batteries (LIBs) in electric vehicles (EVs) requires meticulous battery management to guarantee vehicular safety and performance. Temperatures play a significant role in the safety, performance, and lifetime of LIBs. Therefore, the state of temperature (SOT) of batteries should be monitored timely by the battery management system. Due to limited onboard temperature sensors in EVs, the SOT of most batteries must be estimated through other measured signals such as current and voltage. To this end, this article develops an accurate method to estimate the surface temperature of batteries by combining the physics-based thermal model with machine learning (ML). A lumped-mass thermal model is applied to provide prior knowledge of battery temperatures for ML. Temperature-related feature, such as internal resistance, is extracted in real time and fed into the ML framework as supplementary inputs to enhance the accuracy of the estimation. An ML model, which combines a convolutional neural network (CNN) with a long short-term memory (LSTM) neural network (NN), is sequentially integrated with the thermal model to learn the mismatch between the model outputs and the real temperature values. The proposed method has been verified against experimental results, with an accuracy improvement of 79.37% and 86.24% compared to conventional pure thermal model-based and pure data-driven approaches, respectively.