Deep Learning and Blockchain‐Enabled Predictive Maintenance in Electric Vehicles: A Comprehensive Review

ABSTRACT Globally, electric vehicles (EVs) are entirely revolutionizing conventional vehicles owing to the benefits of EVs, such as decarbonization, being environmentally friendly, and lower maintenance costs. The EVs' energy consumption is sensitive to environmental factors like wind speed, parasitic power, rolling resistance, and temperature, which can significantly affect the EVs' energy consumption range. Here, this comprehensive literature survey investigates the techniques used for managing the energy consumption of EVs while analyzing security utilizing Blockchain (BC) and deep learning (DL) algorithms. In the rapidly developing background of EVs, the incorporation of advanced technologies like DL and BC is transforming predictive maintenance (PM) strategies. This approach aims to optimize vehicle performance and minimize downtime by accurately predicting and addressing previous potential system failures. The amalgamation of DL and BC provides a robust approach for PM, enabling proactive maintenance strategies that reduce downtime and costs while improving overall vehicle performance. Thus, this review explains the importance of DL‐enabled PM in EVs, DL models for PM in EVs, the role of BC‐enabled PM in EVs, and the combination of DL models and BC for PM in EVs.