Meta-RL–Driven Energy and Link Aware Routing for Electric Vehicle Communication Networks

Routing in Electric Vehicle (EV) communication networks is inherently challenging due to rapid node mobility, heterogeneous residual energy levels, fluctuating wireless link quality, and non-stationary network topology. Conventional approaches such as geographic forwarding and energy-only routing address these dimensions in isolation, leading to suboptimal packet delivery and uneven node energy depletion. This paper presents a Meta-Reinforcement Learning (Meta-RL) based routing framework that jointly optimises four routing criteria simultaneously: residual energy, link quality, distance to sink, and energy harvesting rate. A Model-Agnostic Meta-Learning (MAML) policy is trained across diverse network configurations, enabling the router to adapt to new topologies without retraining. To extend node operational lifetime, three complementary energy harvesting mechanisms are incorporated: solar irradiance harvesting, ambient RF energy recovery, and regenerative braking. The proposed system is implemented in MATLAB with 50 mobile EV nodes over a 1000×1000 m simulation area. Experimental results demonstrate that the Meta-RL router achieves a Packet Delivery Ratio (PDR) of 97.4%, outperforming Energy-Aware routing (93.6%), Distance-Only routing (95.3%), and Deep RL (DQN) (95.3%), while sustaining all 50 nodes throughout the 1000-step simulation through energy harvesting. A key advantage of the MAML-based approach is immediate optimal performance from the first routing step, whereas DQN requires approximately 400 steps of exploration before converging to a comparable policy.