Energy efficiency and range optimization remain critical challenges to the widespread adoption of battery-electric vehicles (BEVs), driving demand for intelligent driver assistance systems that enhance operating range and mitigate driver range anxiety. The proposed adaptive eco-feedback and driver rating system leverages proximal policy optimization (PPO) reinforcement learning to support drivers in reducing energy consumption and extending the driving range of BEVs. It processes real-time driving data, such as velocity, acceleration, and powertrain state, and uses map data to anticipate traffic events, including but not limited to speed limit changes, curves, gradients, traffic signs, and traffic signals. This contextual awareness allows the system to continuously assess driving behavior and provide personalized, context-aware haptic and visual feedback alongside a dynamic driving behavior rating. A PPO agent learns optimal feedback strategies through continuous interaction and evaluates the impact of specific guidance actions, such as but not limited to ’release accelerator pedal’, ’brake gently’, ’recuperate’, or ’coast’, on immediate energy efficiency and long-term driver adaptation patterns. Feedback intensity and modality are dynamically tailored to individual driver profiles based on observed reaction patterns and adherence. This approach encourages drivers to prioritize energy efficiency, while aiming to minimize cognitive distraction and discomfort. The algorithm is implemented and validated using a driving simulator that replicates diverse and realistic conditions. Simulation studies across multiple driving scenarios, including congested urban, suburban, mountainous, and highway routes, demonstrate that the proposed PPO-based assistance system is able to achieve significant energy consumption reductions compared to unassisted driving and state-of-the-art feedback systems.