Adaptive fault detection for lithium-ion battery combining physical model-based observer and BiLSTMNN learning approach

Abstract

Lithium-ion batteries possess one of the best energy-weight ratios, while safety and reliability are critical issues for its continued operation. Due to extreme fast-charging, huge application sizes and variable usage environment, the risk of soft battery faults, including soft internal short circuit (SISC), is increased and may evolve into severe accidents. Therefore, accurate early detection of lithium-ion battery fault is imperative to guarantee the battery performance. Motivated by this fact, we proposed a real time fault detection framework for battery soft faults. Based on the Equivalent Circuit Model (ECM) and coupling thermal model, Extended Kalman Filter (EKF) observer is used for reliable monitoring of battery voltage and surface temperature. Inspired by uncertainty interference during the observation process, we explore an observed-based learning approach that combine physical model-based observer and Bidirectional Long Short-Term Memory neural networks (BiLSTMNN) to empower the observer with the ability to learn the uncertainties, which is efficient in distinguishing soft faults information and uncertainties from residual. Furthermore, the memory characteristics of BiLSTMNN are considered to improve the robustness of the detection system, including comprehensive training data and optimized input features. Finally, through the comparative analysis and the robustness evaluation experiments including various driving cycles and internal short circuits (ISC) faults test verity the good performance of the proposed method.