Core Temperature Estimation of Lithium-Ion Batteries Using Long Short-Term Memory (LSTM) Network and Kolmogorov–Arnold Network (KAN)

Health-conscious battery management systems (BMSs) that rely on surface temperature measurements are insufficient for managing automotive lithium-ion batteries (LIBs). Experimental studies have shown temperature differences of up to $10~^{\circ }$ C between surface and core of cylindrical LIBs. BMSs that consider only surface temperature overlook critical thermal information. The missing monitoring can delay detecting thermal events within the cell, accelerating battery degradation and increasing the risk of thermal runaway. This article introduces two deep learning algorithms to address this: Kolmogorov-Arnold network (KAN) and interconnected long short-term memory (LSTM) network. Both approaches estimate the core temperature of LIBs without requiring surface temperature feedback to the neural network. Experimental validation revealed a core temperature mean absolute error (MAE) of $0.55~^{\circ }$ C with a computational cost of 2.9–3.2 ms for KAN. The proposed interconnected LSTM reached a MAE of $0.80~^{\circ }$ C. The performance of the two core temperature estimation techniques was further evaluated under dynamic loading profile using urban dynamometer driving schedule (UDDS) drive cycle. The KAN method achieved a MAE of $0.325~^{\circ }$ C, demonstrating its adaptability to dynamic operating conditions. The two proposed methods, primarily KAN, are both adaptive and computationally efficient, making them suitable for integrating onboard BMS and cloud-enabled digital-twin-based BMS systems.