University of Michigan

Mohammad Reza Amini

Ilya V Kolmanovsky

Jing Sun

Hao Wang

Yiheng Feng

Zhen Yang

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.

Multi-range vehicle speed prediction using vehicle connectivity for enhanced energy efficiency of vehicles

An engine and cabin thermal management system for use with a vehicle having an engine, a cabin heating system configured to thermally heat a cabin of the vehicle, a coolant system operably coupled to the engine and to the cabin heating system to thermally manage a temperature of the engine and a temperature of the cabin. The coolant system having one or more coolant thermal storage units fluidly coupled with a radiator and heater core of the coolant system forming a coolant loop. The system further having a control system configured to monitor and maintain at least a predetermined coolant temperature at the cabin heating system even during a coolant temperature decrease at the engine stops.

Growing community of inventors

Ann Arbor, MI, United States of America

Mohammad Reza Amini