Explainable Machine Learning for Predicting Electric-Vehicle (EV) Charging Demand in Renewable-Energy-Powered Grids: A Comprehensive Review

The rapid global adoption of electric vehicles (EVs) coupled with the increasing integration of renewable energy, presents new challenges in managing electricity demand, ensuring grid stability and achieving decarbonization targets. Accurate forecasting of EV charging demand has become vital for optimizing infrastructure deployment, dynamic pricing and renewable integration. This paper presents a comprehensive review of explainable machine leaning (ML) approaches for EV charging demand forecasting under renewable energy powered grids. The review categorizes methods into traditional statistical models ensemble-based techniques deep learning architectures and hybrid approaches. Emphasis is placed on explainable artificial intelligence (XAI) techniques particularly Shapley Additive Explanations (SHAP) which enhance interpretability by identifying the key features driving model predictions. Results from recent studies indicate that renewable energy penetration, electricity price and carbon intensity are the dominant factors influencing charging demand. The review highlights how explainable ML models can support policymakers in designing demand-response strategies grid operators in balancing supply and demand and consumers in making environmentally conscious charging decisions. Finally research challenges and future opportunities are discussed, including the need for causal interpretability, real-time adaptation and data privacy in federated learning frameworks.