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

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