Loading...
Vijesh Malappurath Paramadam is an Application Architect at Deepam, Westhill, Calicut, Kerala. His contribution focuses on Artificial Intelligence applications in electric vehicle systems and battery-related intelligent technologies.
The swift expansion of electric vehicles (EVs) has heightened the demand for advanced technology that can enhance vehicle efficiency, battery efficacy, safety, dependability, charging systems, and autonomous functionality. Artificial Intelligence (AI) is crucial in tackling these issues by facilitating data-informed forecasting, astute decision-making, instantaneous surveillance, and flexible regulation. This section elucidates the essential principles and primary uses of artificial intelligence in electric vehicle systems. It examines machine learning, deep learning, reinforcement learning, computer vision, and optimisation methods for battery management, state estimation, remaining useful life forecasting, energy consumption prediction, charging demand forecasting, intelligent charging, fault diagnosis, predictive maintenance, autonomous driving, and vehicle-to-grid integration. The section delves deeper into the function of AI within smart charging frameworks, interconnected electric vehicle ecosystems, digital replicas, edge artificial intelligence, and advanced transportation networks. An AI-integrated framework for electric vehicle systems is introduced to illustrate the interplay among vehicle sensors, battery management systems, cloud and edge platforms, AI models, and advanced control strategies. Ultimately, significant obstacles concerning data integrity, computational intricacy, cybersecurity, interpretability, real-time implementation, and model dependability are examined, alongside prospective study avenues for creating efficient, secure, sustainable, and autonomous electric mobility systems.
The battery is one of the most critical and expensive components of an electric vehicle (EV), and its performance directly influences vehicle range, reliability, safety, and operational cost. Continuous battery degradation caused by charging and discharging cycles, temperature variations, driving behavior, and operating conditions makes accurate battery health prediction essential for intelligent electric mobility. Machine learning provides a data-driven approach for analyzing complex battery behavior and predicting key health indicators such as State of Health (SOH), Remaining Useful Life (RUL), capacity degradation, internal resistance, and potential battery failures. This chapter presents the fundamental concepts, methodologies, and applications of machine learning for EV battery health prediction. It discusses battery degradation mechanisms, data acquisition, preprocessing, feature engineering, supervised and unsupervised machine learning, deep learning models, hybrid approaches, and battery health estimation techniques. The chapter also explores the integration of machine learning with Battery Management Systems, digital twins, edge computing, explainable AI, and federated learning. An integrated framework for real-time EV battery health prediction is presented, followed by key challenges and future research directions. The chapter demonstrates how intelligent data-driven approaches can support accurate, adaptive, and reliable battery monitoring for next-generation electric vehicle systems.