A Recurrent Neural Networks Approach for Estimating the Core Temperature in Lithium-Ion Batteries

Abstract

— The safety and reliability of Lithium-ion batteries are increasingly critical, especially as more products on the market are powered by them. The core temperature of batteries is one of the important factors to consider when improving safety, longevity, and performance. To overcome the inability to practically obtain direct core temperature measurements, this paper proposes a neural network-based estimation method using a gated recurrent unit. This approach can estimate the core temperature to a high level of accuracy using commonly measured signals such as voltage, current, state of charge, ambient and surface temperatures. Experimental results demonstrate excellent estimation performances over cycling and between different batteries of the same type. The proposed method does not require a strenuous parameter tuning operation, model derivation and simplification, or a deep understanding of the electrochemical processes in the battery. It should be also highlighted that, compared to the other available options in literature, this technique has the advantage of easy implementation.