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This paper explores the pivotal role of data analysis and machine learning in advancing energy management strategies for New Energy Vehicles (NEVs) and Energy Storage Systems (ESS). Focused on the comprehensive journey from data collection and preprocessing to the application of dynamic programming, reinforcement learning, and genetic algorithms, our study underscores the transformational impact of these technologies on optimizing energy utilization and prolonging battery life. Initial stages involve meticulous data gathering and preprocessing to ensure the quality and usability of information derived from operational parameters. Subsequently, feature selection and engineering refine this data into meaningful insights, laying the groundwork for predictive modeling. These models forecast energy demands and system behavior, facilitating proactive maintenance and system efficiency improvements. We delve into optimization strategies, highlighting dynamic programming's role in decision-making, reinforcement learning's adaptability to environmental changes, and genetic algorithms' exploration of optimal charging/discharging strategies. These methodologies collectively contribute to sustainable energy practices and resource conservation, marking significant advancements in the field. The integration of machine learning not only enhances predictive maintenance and charging protocol optimization but also addresses challenges related to data scarcity, model generalizability, and interpretability. This paper provides a comprehensive analysis of current methodologies and future prospects, advocating for a multidisciplinary approach to further enrich the research landscape in energy management for NEVs and ESS.
Jiahong He (Fri,) studied this question.