Battery management systems (BMS) play a critical role in increasing vehicle range and prolonging battery life, contributing to the broader goal of making affordable and clean energy more accessible. BMS relies heavily on data from battery monitors, making it essential to evaluate the longevity and reliability of these links and data. However, sensor failures, communication issues, or even cyberattacks can lead to bad battery data being sent to the BMS, reducing the efficiency of electric vehicles and other applications utilizing BMS. One of the core functions of a BMS is to transmit data from sensors, and for electric vehicle battery systems to operate efficiently and safely over time, they must quickly identify faults. Deep learning techniques can be employed to detect and classify faulty sensor and communication data in batteries, especially in lithium-ion cells. To prepare the data, z-score normalization was applied, and features were identified using sparse principal component analysis (SPCA). The EMPA method was then utilized for feature selection. A novel approach leveraging the IB-DRN method is proposed to detect and categorize faulty battery data, enhancing the performance and reliability of the BMS. This advancement aligns with the aim of supporting affordable and clean energy initiatives by improving system efficiency. MATLAB (2021a) was used for model training, dataset preparation, and performance evaluation, demonstrating the effectiveness of the proposed method compared to alternative approaches.