EcoLatent: A Physics Guided Latent Inner World Model for Vehicles and Robots

World models provide a basis for embodied decision-making, yet modeling thed surrounding environment alone leaves an essential question unanswered: how will the agent's own internal world evolve under its actions? We introduce EcoLATENT, a physics-guided latent inner world model for vehicles and robots, and instantiate it on vehicle longitudinal and powertrain dynamics. The model combines explicit physical state with a recurrent latent operating context inferred from causal sensor history. Physical equations and learned residual dynamics jointly advance the internal state under candidate actions, enabling imagined trajectories to support downstream decisions. We use vehicle energy management as a concrete case study, with energy represented as one component of the internal world. On 842 h of operational vehicle logs, an 85k-parameter model achieves 0.653 m/s speed RMSE and 1.61% cumulative-fuel normalized MAE over 60 s, matching a 272k-parameter GRU on speed while preserving a structural fuel-cut rule. A policy is optimized through the learned inner world; cross-model evaluation reveals that its estimated benefit depends strongly on the evaluation dynamics. These results demonstrate a compact inner-world representation and an imagination-based decision pipeline in vehicles, while motivating extensions to other internal subsystems and robotic embodiments.