A Smart Battery Management System for Electric Vehicles Using Deep Learning-Based Sensor Fault Detection

Battery sensor data collection and transmission are essential for battery management systems (BMS). Since inaccurate brought on by faults, communication issues, or even cyber-attacks can impose serious harm BMS adversely impact the overall dependability of BMS-based applications, such as electric vehicles, it is critical to assess durability in BMS. Sensor necessary a perform every operation. Effective fault detection crucial sustainability security vehicle systems. This research suggests system data, especially lithium ion batteries, that allows deep learning-based classification faulty information. Initially, we collected preprocessing was carried out using z-score normalization. The features were extracted sparse principal component analysis (SPCA), enhanced marine predators algorithm (EMPA) used feature selection. BMS’s safety may be suggested incipient bat-optimized residual network (IB-DRN)-based false identification system. Simulations MATLAB (2021a), along with statistics, machine learning, learning toolbox, experimental research, show how well strategy performs. It shown superior traditional approaches.