Diagnosing various failures of lithium-ion batteries using artificial neural network enhanced by likelihood mapping

Abstract

A simple but effective framework of diagnosing various failures of lithium-ion batteries (LIBs) equipped inside portable smart devices is proposed. In this framework, to detect failure event of batteries from partial charging curves obtained under adaptively varying charging scenarios, which are frequently the only dataset available from lithium ion battery packs equipped inside portable smart devices, partial charging data are mapped into characteristic statistical entity which we refer to as likelihood vector. Likelihood vectors are calculated by referring to probability distribution functions (PDFs) of voltage and current obtained from the experiments simulating various degradation/abuse conditions for LIBs. Compared to the brute-force training method using partial charging curves to train multi-layer perceptron (MLP) classifier models, training assisted by likelihood vectors leads to improvements in test set classification accuracy by 26 – 85% according to the size of neural networks. As a result, the optimized classification model achieves 99.8% precision for healthy data classification and 97.3% of average precision for abused data classification, reaching overall classification accuracy of 97.8%. Furthermore, by monitoring the failure index calculated from the cumulated list of detections made, it is experimentally demonstrated that the thermal runaway and resultant fatal explosion event of lithium pouch cell under operando dent test can be predicted before the event actually occurs.