Energy management strategies in parallel Hybrid Electric Vehicles (HEVs) usually ignore effects of Internal Combustion Engine (ICE) dynamics and rely on static maps for required engine torque-fuel efficiency data. It is uncertain how neglecting these dynamics can affect fuel economy of a parallel HEV. This paper addresses this shortcoming by investigating effects of some major Spark Ignition (SI) engine dynamics and clutch dynamics on torque split management in a parallel HEV. The control strategy is implemented on a HEV model with an experimentally validated, dynamic ICE model. Simulation results show that the ICE and clutch dynamics can degrade performance of the HEV control strategy during the transient periods of the vehicle operation by 8.7% for city and highway driving conditions in a combined common North American drive cycle. This fuel penalty is often overlooked in conventional HEV energy management strategies. A Model Predictive Control (MPC) of torque split is developed by incorporating effects of the studied influencing dynamics. Results show that the integrated energy management strategy can improve the total energy consumption of HEV by more than 6% for combined Urban Dynamometer Driving Schedule (UDDS) and Highway Fuel Economy Driving Schedule (HWFET)drive cycles.
- Dynamic Systems and Control Division
Energy Management Control of a Hybrid Electric Vehicle by Incorporating Powertrain Dynamics
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Bidarvatan, M, & Shahbakhti, M. "Energy Management Control of a Hybrid Electric Vehicle by Incorporating Powertrain Dynamics." Proceedings of the ASME 2015 Dynamic Systems and Control Conference. Volume 1: Adaptive and Intelligent Systems Control; Advances in Control Design Methods; Advances in Non-Linear and Optimal Control; Advances in Robotics; Advances in Wind Energy Systems; Aerospace Applications; Aerospace Power Optimization; Assistive Robotics; Automotive 2: Hybrid Electric Vehicles; Automotive 3: Internal Combustion Engines; Automotive Engine Control; Battery Management; Bio Engineering Applications; Biomed and Neural Systems; Connected Vehicles; Control of Robotic Systems. Columbus, Ohio, USA. October 28–30, 2015. V001T10A003. ASME. https://doi.org/10.1115/DSCC2015-9702
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