An adaptive threshold method for multi-faults diagnosis of lithium-ion batteries based on electro-thermal model

Abstract

Lithium-ion batteries have emerged as the primary power source for electrochemical energy storage, making battery safety a crucial concern. This study focuses on the detection of thermal faults in lithium-ion batteries, specifically heat generation faults, thermal parameter faults, and temperature sensor faults. To address the issue of low accuracy in temperature indirect estimation models, a framework that combines electrical and thermal models is proposed. This framework employs particle swarm optimization to identify optimal thermal model parameters using real-time open circuit voltage and terminal voltage data. Additionally, an unscented Kalman filter estimator is utilized to predict core temperature based on surface temperature measurements in real time. The results demonstrate that the proposed framework achieves rapid convergence of core and surface temperature biases within a range of 1 ℃ and 0.1 ℃, respectively. Furthermore, an adaptive threshold method is developed to diagnose faults by analyzing the change rates of the estimated temperatures. Experimental results reveal that the proposed methods exhibit quick response times for four different fault conditions, indicating their effectiveness and accuracy in multi-fault diagnosis.