RISK ASSESSMENT OF VEHICLE BATTERY SAFETY BASED ON ABNORMAL FEATURES AND NEURAL NETWORKS

In this study, we evaluate a proactive battery EV safety assessment method using abnormal feature detection and neural networks. Four sophisticated algorithms -Isolation Timberland, One-Class SVM, Autoenco der and also LSTM-were performed to assess their applicability in detecting anomalous battery behavior. The Isolation Woodland algorithm showed a balanced accuracy recall trade-off of the values 0.85 and 0.92 respectively One class SVM demonstrated highly sharp results with an accuracy and recall values of 0.78 and 0.8, respectively. The autoenco der, that used a large amount of learning and won with 0.92 accuracy score and an F1-score - 0.89 The LSTM structure, programmed for sequential information, indicated a great execution with a 0.94 review and the F1-score of 0. A comparative study has shown that these algorithms can provide alot flexibility in sending based on the clear requirements.