Multi-fault diagnosis for battery pack based on adaptive correlation sequence and sparse classification model

• A multi-fault diagnosis method for detecting, locating and evaluating battery faults is proposed. • The proposed diagnosis method based on adaptive correlation sequence and sparse classification model. • A probabilistic fault detection result is provided. • The proposed method is robust to noise, battery inconsistency, load dynamics, etc. • A fault platform is built to obtain real fault data to verify the reliability of the proposed method. Aiming for an efficient fault diagnosis scheme for battery pack, this paper develops a complete framework for the diagnosis of faults in battery packs. First, an interclass sensor topology is introduced to cover multi-fault abnormalities, and an adaptive correlation coefficient between adjacent sensors is used to encompass system information. Then, the discrete wavelet packet transform (DWPT) is utilized to process the correlation sequences. Thereby, a variety of characteristic indicators are attained to and the main components are extracted by Principal Component Analysis (PCA) as fault features. Afterwards, two sparse classification models are developed, based on the multiclass relevance vector machine, to distinguish fault type and evaluate fault degree respectively. A fault injection platform is established to physically trigger the faults of external short, internal short, thermal abuse and loose connection on a series-connected four-cell pack. Finally, experimental verifications suggest that the proposed method gives accurate and reliable judgements on different fault types, and evaluates the fault degree accurately.