MPC-based energy management of a parallel hybrid electric vehicle using terrain information

Mohamed Wahba, Sean Brennan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations


A parallel hybrid electric vehicle (HEV) combines the power produced by electric machines and a combustion engine to enable improved fuel economy. Optimization of the powersplit algorithm managing both torque sources can be readily achieved offline, but online implementation results often show great deviation from expected fuel economy due to traffic, hills, and similar effects that are not easily modeled. Of these external influences, the road grade for a travel route is potentially known a priori given a set destination choice from the driver. To examine whether grade information can improve the performance of a hybrid powertrain controller, we first formulate the vehicle model as a low-order dynamic model, recognizing that the primary dynamics of the energy system are slow. A model predictive control (MPC) strategy utilizing the terrain data is then developed to obtain a time-varying power split between the combustion engine and the electrical machine. Simulation results of the HEV model over multiple standard drive cycles, with different terrain profiles and different cost functions, are presented. Testing of the MPC performance compared to Argonne National Lab's powertrain simulation software Autonomie shows that the MPC strategy utilizing terrain data gives an improvement of up to 2.2% in fuel economy with respect to the same controller without terrain information, on the same route.

Original languageEnglish (US)
Title of host publicationAdaptive 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
Subtitle of host publicationHybrid 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
PublisherAmerican Society of Mechanical Engineers
ISBN (Electronic)9780791857243
StatePublished - Jan 1 2015
EventASME 2015 Dynamic Systems and Control Conference, DSCC 2015 - Columbus, United States
Duration: Oct 28 2015Oct 30 2015

Publication series

NameASME 2015 Dynamic Systems and Control Conference, DSCC 2015


OtherASME 2015 Dynamic Systems and Control Conference, DSCC 2015
Country/TerritoryUnited States

All Science Journal Classification (ASJC) codes

  • Industrial and Manufacturing Engineering
  • Mechanical Engineering
  • Control and Systems Engineering


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