Autoencoder-Enhanced Regularized Prototypical Network for New Energy Vehicle battery fault detection

Abstract

As the ownership of new energy vehicles (NEVs) is experiencing a sustained growth, the safety of NEVs has become increasingly prominent, with power battery faults emerging as the primary cause of fire accidents in NEVs. Successful detection of incipient faults can not only improve the safety and reliability but also provide optimal maintenance instructions for NEV batteries. Since NEVs operate under normal conditions most of the time, the occurrence of system faults leading to faulty samples is extremely rare, thus exhibiting the characteristics of a long-tail distribution. To resolve this problem, this paper proposes an Autoencoder-Enhanced Regularized Prototypical Network (ARPN) to overcome the limitations of class imbalance and uneven sample distribution in the data. The model consists of three key components. First, an autoencoder with an encoder and decoder structure is introduced to map the data to a low-dimensional latent space and back, effectively extracting key structural information from the original data and make full use of the limited number of faulty training samples. Secondly, a multi-layer regularized feature embedding strategy is designed to reduce the complexity of the embedding space, which makes the prototype vector get easier to be separated and distinguished and plays an important role in the proposed ARPN. Finally, a prototype-based classification method is adopted to achieve accurate classification by calculating the cosine similarity between the representation of query set samples in the embedding space and the prototype vectors of each class. Experiments on several battery datasets, obtained from the operational data of NEVs, demonstrate the effectiveness of the proposed method.