Model-based thermal anomaly detection for lithium-ion batteries using multiple-model residual generation

The continuously increasing energy and power density of lithium-ion batteries will aggravate the safety and reliability concerns of advanced battery management systems (BMSs). To ensure the safety and reliability of lithium-ion batteries, the BMS must implement anomaly detection algorithms that are capable of capturing abnormal behaviors. Thermal anomalies are one of the most critical anomalies that can be potentially catastrophic. Motivated by this, a model-based strategy of anomaly detection of thermal parameters for lithium-ion-batteries is presented in this paper. The algorithm is based on a multiple-model adaptive estimation framework. Firstly, an equivalent-circuit-model-based electrothermal model is proposed to describe battery dynamic behaviors. Then, a combination of the recursive-least-square method and Kalman-filter is employed to generate residual signals for thermal anomaly detection. Furthermore, the probability of the signature anomaly is evaluated through the multiple-model adaptive estimation technique. Distinguished from existing threshold-based methods, the proposed method can determine particular anomalies according to the value of the generated conditional probability, without a manually determined threshold. Simulations are developed to simulate different faults and generate data for algorithm validation. The results show signature thermal anomaly can be detected accurately.