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Dr. Khammampati R. Sree Jyothi is an Associate Professor in the Department of Electrical and Electronics Engineering at TKR College of Engineering and Technology, Hyderabad, India. Her academic background is in electrical and electronics engineering. Her contribution focuses on deep learning applications for electric vehicle technologies and battery management.
The swift embrace of electric vehicles (EVs) has heightened the necessity for advanced technologies that enhance battery safety, energy economy, driving range, charging capabilities, and operational dependability. Lithium-ion batteries are extensively utilised in electric vehicles due to their elevated energy density and advantageous performance traits. Advanced deep learning methodologies, such as CNNs, LSTM networks, Gated Recurrent Units (GRUs), Autoencoders, and Transformer architectures, facilitate the extraction of intricate patterns from battery sensor data and vehicle operational metrics.This chapter explores deep learning methodologies for electric vehicle and battery management, introduces a cohesive intelligent battery management system, and addresses data preparation, model creation, evaluation criteria, deployment factors, cybersecurity, and prospective research avenues. It underscores the necessity of incorporating verified physical models, battery safety thresholds, and fail-safe control systems into deep learning predictions prior to their implementation in actual vehicles.