Hybrid Modeling, Identification, and Predictive Control: An Application to Hybrid Electric Vehicle Energy Management

  • Authors:
  • G. Ripaccioli;A. Bemporad;F. Assadian;C. Dextreit;S. Cairano;I. V. Kolmanovsky

  • Affiliations:
  • Dept. of Information Engineering, University of Siena, Italy;Dept. of Information Engineering, University of Siena, Italy;Jaguar Land Rover Research,;Jaguar Land Rover Research,;Ford Motor Company, Dearborn, USA;Ford Motor Company, Dearborn, USA

  • Venue:
  • HSCC '09 Proceedings of the 12th International Conference on Hybrid Systems: Computation and Control
  • Year:
  • 2009

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Abstract

Rising fuel prices and tightening emission regulations have resulted in an increasing need for advanced powertrain systems and systematic model-based control approaches. Along these lines, this paper illustrates the use of hybrid modeling and model predictive control for a vehicle equipped with an advanced hybrid powertrain. Starting from an existing high fidelity nonlinear simulation model based on experimental data, the hybrid dynamical model is developed through the use of linear and piecewise affine identification methods. Based on the resulting hybrid dynamical model, a hybrid MPC controller is tuned and its effectiveness is demonstrated through closed-loop simulations with the high-fidelity nonlinear model.