Designing effective and compact state representations is a key challenge in applying Deep Reinforcement Learning (DRL) to combinatorial problems such as the Electric Vehicle Routing Problem (EVRP). Current approaches often include extensive handcrafted features to ensure constraint coverage, but this can lead to high-dimensional inputs that slow down learning, reduce generalization, and obscure policy behavior. In this work, we propose a novel methodology that leverages Explainable Artificial Intelligence (XAI), specifically SHapley Additive exPlanations (SHAP), to guide feature pruning in DRL-based EVRP agents. We begin by benchmarking commonly used features in state representations for EVRP and apply SHAP to evaluate their relative importance throughout training. Correlation analysis is used alongside SHAP scores to identify redundant or low-impact features. The pruned state representations are then retrained and compared against baseline models using the full feature set. Results show that agents trained on XAI-pruned features achieve comparable or improved performance in terms of average travel distance, model convergence, training stability, and loss, all while reducing computational cost and enhancing interpretability. This study demonstrates that explainability can go beyond post-hoc analysis to actively inform and optimize the design of DRL agents. While evaluated in the context of EVRP, the proposed methodology is generalizable to other structured decision-making tasks where DRL is applied. Our findings suggest that XAI-guided feature design is a promising direction for building more efficient, transparent, and adaptable reinforcement learning systems.