Learning-Based Faulty State Estimation Using SOH-Coupled Model Under Internal Thermal Faults in Lithium-Ion Batteries

Estimating the state of charge (SoC), state of health (SOH), and core temperature under internal faults will significantly improve the battery management system's (BMS's) autonomy and accuracy in range prediction. This article presents a neural network (NN)-based state estimation scheme that can estimate the SoC, core temperature, and SOH under internal faults in lithium-ion batteries (LIBs). First, we propose a model-based internal fault detection scheme by employing an SOH-coupled electro-thermal-aging (ETA) model of the LIB. Then, a nonlinear observer is used to estimate the proposed SOH-coupled model's healthy states for the residual generation. The fault diagnosis scheme compares the output voltage and surface temperature residuals against the designed adaptive threshold to detect thermal faults. The adaptive threshold effectively alleviates the false positives due to degradation and model uncertainties of the battery under no-fault conditions. Upon fault detection, we employ an additional NN-based observer in the second step to learn the faulty dynamics. A novel NN weight tuning algorithm is proposed using the measured voltage, surface temperature, and estimated healthy states. The convergence of the nonlinear and NN-based observer state estimation errors is proven using the Lyapunov theory. Finally, numerical simulation results are presented.