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Future vehicles are expected to be able to exploit increasingly the connecteddriving environment for efficient, comfortable, and safe driving. Givenrelatively slow dynamics associated with the state of charge and temperatureresponse in electrified vehicles with large batteries, a longprediction/planning horizon is needed to achieve improved energy efficiencybenefits. In this paper, we develop a two-layer Model Predictive Control (MPC)strategy for battery thermal and energy management of electric vehicle (EV),aiming at improving fuel economy through real-time prediction and optimization.In the first layer, the long-term traffic flow information and an approximatemodel reflective of the relatively slow battery temperature dynamics areleveraged to minimize energy consumption required for battery cooling whilemaintaining the battery temperature within the desired operating range. In thesecond layer, the scheduled battery thermal and state of charge (SOC)trajectories planned to achieve long-term battery energy-optimal thermalbehavior are used as the reference over a short horizon to regulate the batterytemperature. Additionally, an intelligent online constraint handling (IOCH)algorithm is developed to compensate for the mismatch between the actual andpredicted driving conditions and reduce the chance for battery temperatureconstraint violation. The simulation results show that, depending on thedriving cycle, the proposed two-layer MPC is able to save 2.8-7.9% of thebattery energy compared to the traditional rule-based controller in connectedand automated vehicle (CAV) operation scenario. Moreover, as compared to asingle layer MPC with a long horizon, the two-layer structure of the proposedMPC solution reduces significantly the computing effort without compromisingthe performance.

Two-Layer Model Predictive Battery Thermal and Energy Management Optimization for Connected and Automated Electric Vehicles