Application of Deep Neural Networks for Lithium-Ion Battery Surface Temperature Estimation Under Driving and Fast Charge Conditions

Abstract

The temperature of lithium-ion batteries (LIBs) is a critical factor that significantly impacts the performance of the battery. One of the essential roles of the battery management system (BMS) is to monitor and control the temperature of the cells in the battery pack. In this article, two deep neural network (DNN) modeling approaches are used to predict the surface temperature of LIBs. The first model type is based on a feedforward neural network (FNN) enhanced with external filters, while the second model is based on a recurrent neural network (RNN) with long short-term memory (LSTM). These models are trained and tested using experimental data from two batteries, one cylindrical cell, and one pouch cell at a range of driving, fast charging, and health conditions. The proposed models are shown to be capable of estimating temperature with less than 2 °C root-mean-square error (RMSE) for challenging low ambient temperature drive cycles and just 0.3 °C for 4 C rate fast charging conditions. In addition, a model which was trained to estimate the temperature of a new battery cell was found to still have a very low error of just 0.8 °C when tested on an aged cell. Both models are deployed to an NXP S32K344 microprocessor to measure their execution time and memory use. The FNN executes significantly faster on the microprocessor than the LSTM, 0.8 ms compared with 2.5 ms for models with around 3000 learnable parameters, and uses less random access memory (RAM), 0.4 kB compared with 1 kB.