A novel cabin–battery eco-cooling management scheme for extended range electric vehicle based on networking information fusion

Advances in vehicle networking have enabled long-term predictive eco-cooling control, crucial for balancing cabin comfort, battery lifetime, and energy efficiency. This study proposes an eco-cooling strategy for extended-range electric vehicles (EREVs) based on a hierarchical model predictive control (HMPC) framework that integrates traffic-flow and meteorological data. In the trajectory-planning layer, a steady-state air-conditioning (AC) model generates long-term temperature references, while the real-time layer employs a transient AC model to optimize compressor speed and valve operation, reconciling planning efficiency with control accuracy. Cooling allocation is achieved via a transient valve model and fuzzy logic, balancing cabin comfort, battery safety, and AC energy use. Long-term battery trajectory planning further suppresses temperature fluctuations and thermal stress, extending battery life. Uncertainties in solar irradiance and battery heat generation are also modeled, and the robustness of the proposed cabin–battery eco-cooling strategy is verified. Simulation results show that, relative to PID and MPC without battery-trajectory optimization, the method reduces battery-temperature tracking error to within 0.6 °C and limits overshoot by 16.44%–81.74%. AC energy consumption decreases by 1.21%–7.00%, COP rises to 2.647 (up to +5.05%), total cost falls by 1.23%–2.33%, fuel consumption by 0.36%–1.42%, and battery degradation is reduced by more than 10%. With resistance correction, AC energy use decreases by 2.07% and COP improves by 1.56%. Moreover, fusing solar-irradiance data from vehicle networking and onboard sensors achieves nearly the same economy as HMPC with real solar inputs, with only a 2.1% reduction while lowering computational burden.