Real-time powertrain optimization strategy for connected hybrid electrical vehicle

Mohd Azrin Mohd Zulkefli, Zongxuan Sun

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

2 Citations (Scopus)

Abstract

Connected Vehicle (CV) technology, which allows traffic information sharing, and Hybrid Electrical Vehicles (HEV) can be combined to improve vehicle fuel efficiency. However, transient traffic information in CV environment necessitates a fast HEV powertrain optimization for real-time implementation. Model Predictive Control (MPC) with Linearization is proposed, but the computational effort is still prohibitive. The Equivalent Consumption Minimization Strategy (ECMS) and Adaptive-ECMS are proposed to minimize computation time, but unable to guarantee charge-sustaining-operation (CS). Fast analytical result from Pontryagin's Minimum Principles (PMP) is possible but the input has to be unconstrained. Numerical solutions with Linear Programming (LP) are proposed, but over-simplifications of the cost and constraint functions limit the performance of such methods. In this paper, a nonlinear CS constraint is transformed into linear form with input variable change. With linear input and CS constraints, the problem is solved with Separable Programming by approximating the nonlinear cost with accurate linear piecewise functions which are convex. The piecewise-linear functions introduce new dimensionless variables which are solved as a large-dimension constrained linear problem with efficient LP solvers. Comparable fuel economy with Dynamic Programming (DP) is shown, with maximum fuel savings of 7% and 21.4% over PMP and Rule-Based (RB) optimizations. Simulations with different levels of vehicle speed prediction uncertainties to emulate CV settings are presented.

Original languageEnglish (US)
Title of host publicationMechatronics; Mechatronics and Controls in Advanced Manufacturing; Modeling and Control of Automotive Systems and Combustion Engines; Modeling and Validation; Motion and Vibration Control Applications; Multi-Agent and Networked Systems; Path Planning and Motion Control; Robot Manipulators; Sensors and Actuators; Tracking Control Systems; Uncertain Systems and Robustness; Unmanned, Ground and Surface Robotics; Vehicle Dynamic Controls; Vehicle Dynamics and Traffic Control
PublisherAmerican Society of Mechanical Engineers
ISBN (Electronic)9780791850701
DOIs
StatePublished - Jan 1 2016
EventASME 2016 Dynamic Systems and Control Conference, DSCC 2016 - Minneapolis, United States
Duration: Oct 12 2016Oct 14 2016

Publication series

NameASME 2016 Dynamic Systems and Control Conference, DSCC 2016
Volume2

Other

OtherASME 2016 Dynamic Systems and Control Conference, DSCC 2016
CountryUnited States
CityMinneapolis
Period10/12/1610/14/16

Fingerprint

Powertrains
Linear programming
Model predictive control
Fuel economy
Dynamic programming
Linearization
Costs

Cite this

Mohd Zulkefli, M. A., & Sun, Z. (2016). Real-time powertrain optimization strategy for connected hybrid electrical vehicle. In Mechatronics; Mechatronics and Controls in Advanced Manufacturing; Modeling and Control of Automotive Systems and Combustion Engines; Modeling and Validation; Motion and Vibration Control Applications; Multi-Agent and Networked Systems; Path Planning and Motion Control; Robot Manipulators; Sensors and Actuators; Tracking Control Systems; Uncertain Systems and Robustness; Unmanned, Ground and Surface Robotics; Vehicle Dynamic Controls; Vehicle Dynamics and Traffic Control (ASME 2016 Dynamic Systems and Control Conference, DSCC 2016; Vol. 2). American Society of Mechanical Engineers. https://doi.org/10.1115/DSCC2016-9727

Real-time powertrain optimization strategy for connected hybrid electrical vehicle. / Mohd Zulkefli, Mohd Azrin; Sun, Zongxuan.

Mechatronics; Mechatronics and Controls in Advanced Manufacturing; Modeling and Control of Automotive Systems and Combustion Engines; Modeling and Validation; Motion and Vibration Control Applications; Multi-Agent and Networked Systems; Path Planning and Motion Control; Robot Manipulators; Sensors and Actuators; Tracking Control Systems; Uncertain Systems and Robustness; Unmanned, Ground and Surface Robotics; Vehicle Dynamic Controls; Vehicle Dynamics and Traffic Control. American Society of Mechanical Engineers, 2016. (ASME 2016 Dynamic Systems and Control Conference, DSCC 2016; Vol. 2).

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

Mohd Zulkefli, MA & Sun, Z 2016, Real-time powertrain optimization strategy for connected hybrid electrical vehicle. in Mechatronics; Mechatronics and Controls in Advanced Manufacturing; Modeling and Control of Automotive Systems and Combustion Engines; Modeling and Validation; Motion and Vibration Control Applications; Multi-Agent and Networked Systems; Path Planning and Motion Control; Robot Manipulators; Sensors and Actuators; Tracking Control Systems; Uncertain Systems and Robustness; Unmanned, Ground and Surface Robotics; Vehicle Dynamic Controls; Vehicle Dynamics and Traffic Control. ASME 2016 Dynamic Systems and Control Conference, DSCC 2016, vol. 2, American Society of Mechanical Engineers, ASME 2016 Dynamic Systems and Control Conference, DSCC 2016, Minneapolis, United States, 10/12/16. https://doi.org/10.1115/DSCC2016-9727
Mohd Zulkefli MA, Sun Z. Real-time powertrain optimization strategy for connected hybrid electrical vehicle. In Mechatronics; Mechatronics and Controls in Advanced Manufacturing; Modeling and Control of Automotive Systems and Combustion Engines; Modeling and Validation; Motion and Vibration Control Applications; Multi-Agent and Networked Systems; Path Planning and Motion Control; Robot Manipulators; Sensors and Actuators; Tracking Control Systems; Uncertain Systems and Robustness; Unmanned, Ground and Surface Robotics; Vehicle Dynamic Controls; Vehicle Dynamics and Traffic Control. American Society of Mechanical Engineers. 2016. (ASME 2016 Dynamic Systems and Control Conference, DSCC 2016). https://doi.org/10.1115/DSCC2016-9727
Mohd Zulkefli, Mohd Azrin ; Sun, Zongxuan. / Real-time powertrain optimization strategy for connected hybrid electrical vehicle. Mechatronics; Mechatronics and Controls in Advanced Manufacturing; Modeling and Control of Automotive Systems and Combustion Engines; Modeling and Validation; Motion and Vibration Control Applications; Multi-Agent and Networked Systems; Path Planning and Motion Control; Robot Manipulators; Sensors and Actuators; Tracking Control Systems; Uncertain Systems and Robustness; Unmanned, Ground and Surface Robotics; Vehicle Dynamic Controls; Vehicle Dynamics and Traffic Control. American Society of Mechanical Engineers, 2016. (ASME 2016 Dynamic Systems and Control Conference, DSCC 2016).
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