Explainable AI for Electric Vehicle Charging Optimization: Challenges, Opportunities, and Future Directions: A Review

Abstract

The surge of electric vehicles (EVs) has heighted the demand for intelligent and efficient charging management systems that can manage energy demand, grid stability and user convenience. AI-powered charging optimization methods, such as machine learning and deep learning models, have proven to be powerful tools in predicting charging behavior, optimizing energy distribution, and integrating renewable energy sources. But the majority of AI systems are black boxes with a lack of transparency, interpretability, and trust, especially for vital energy use cases. Explainable Artificial Intelligence (XAI) tackles these challenges by offering transparent explanations to AI-driven decision-making, which enhances the reliability, accountability, and trust in EV charging systems. This review summarizes all the recent developments in EV charging optimization using XAI, including algorithms, architectures, applications, evaluation metrics, and implementation strategies. The paper also explores some of the challenges including computational complexity, scalability, privacy concerns, real-time adaptability, and standardization issues. Finally, existing research gaps are identified, future opportunities for human-centric and transparent charging systems are outlined and the real-world implications of implementing XAI-based solutions in next generation smart grids and sustainable transportation systems are stated.