摘要:
An integrated speed prediction framework based on historical traffic data mining and real-time V2I communications for CAVs. The present framework provides multi-horizon speed predictions with different fidelity over short and long horizons. The present multi-horizon speed prediction is integrated with an economic model predictive control (MPC) strategy for the battery thermal management (BTM) of connected and automated electric vehicles (EVs) as a case study. The simulation results over real-world urban driving cycles confirm the enhanced prediction performance of the present data mining strategy over long prediction horizons. Despite the uncertainty in long-range CAV speed predictions, the vehicle level simulation results show that 14% and 19% energy savings can be accumulated sequentially through eco-driving and BTM optimization (eco-cooling), respectively, when compared with normal-driving and conventional BTM strategy.
摘要:
An automatic modeling and screening method capable of exhaustively searching through all configurations with all possible clutch locations and operating modes for a hybrid vehicle. By combining this method with Power-weighted Efficiency Analysis for Rapid Sizing (PEARS), a near-optimal and computationally efficient energy management strategy, it is feasible to search through an extremely large design space of configuration, component sizing and control to identify optimal designs for hybrid power vehicles.