Real-time optimal energy management strategy for a dual-mode power-split hybrid electric vehicle based on an explicit model predictive control algorithm

To improve fuel economy and reduce online computation time and microprocessor hardware resources, a real-time implementable energy management strategy for a dual-mode power-split hybrid electric vehicle (HEV) based on an explicit model predictive control (EMPC) method is proposed in this paper. The...

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Vydáno v:	Energy (Oxford) Ročník 172; s. 1161 - 1178
Hlavní autoři:	Li, Xunming, Han, Lijin, Liu, Hui, Wang, Weida, Xiang, Changle
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Oxford Elsevier Ltd 01.04.2019 Elsevier BV
Témata:	Algorithms Benchmarks Computer simulation Consumption Control algorithms Control systems Control theory Control-oriented model Controllers Dynamic coordination control Electric vehicles energy Energy efficiency Energy management energy use and consumption Explicit model predictive control algorithm Fuel consumption Fuel economy fuels Hardware Hybrid electric vehicles Internet Mathematical models Multiparameter quadratic programming Optimal control Optimization Power-split HEV Predictive control Quadratic programming Real time Real-time energy management strategy Resource management Strategy Time optimal control vehicles (equipment) Real-time energy management strategy Explicit model predictive control algorithm Dynamic coordination control Multiparameter quadratic programming Power-split HEV Control-oriented model
ISSN:	0360-5442, 1873-6785
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Abstract	To improve fuel economy and reduce online computation time and microprocessor hardware resources, a real-time implementable energy management strategy for a dual-mode power-split hybrid electric vehicle (HEV) based on an explicit model predictive control (EMPC) method is proposed in this paper. The proposed strategy includes an accurate control-oriented model and a dynamic process coordination control algorithm. The energy management optimal control problem is formulated as a multiparameter quadratic programming optimization problem, and the EMPC control laws are obtained by solving the multiparameter quadratic programming problem offline. The laws are then used online to realize real-time control. A traditional model predictive control (MPC)-based control strategy, DP-based control strategy and rule-based control strategy are considered benchmark strategies for verification of the proposed EMPC-based energy management strategy. The simulation results indicate the EMPC controller has far lower microprocessor hardware costs than the MPC controller but equivalent control performance. As the prediction horizon increases, fuel consumption remains nearly the same between the MPC-based control strategy and EMPC-based control strategy. The consumption time of the MPC-based control strategy increases significantly, while the consumption time of the EMPC-based control strategy is nearly unchanged. Compared with the benchmark algorithms, the elapsed time of the EMPC controller maximum reduced by 97.46%, and the fuel economy improved by 23.37%. •A novel control-oriented model reflects the dynamic characteristics of the powertrain.•An MPC-based strategy guarantees the feasibility and optimality of the controller.•A dynamic coordination control algorithm balances the relation of mode shift process.•A multiparameter quadratic programming algorithm obtains online control laws.•The elapsed time reduces 97.46% and fuel economy improves 23.37% by EMPC Controller.
AbstractList	To improve fuel economy and reduce online computation time and microprocessor hardware resources, a real-time implementable energy management strategy for a dual-mode power-split hybrid electric vehicle (HEV) based on an explicit model predictive control (EMPC) method is proposed in this paper. The proposed strategy includes an accurate control-oriented model and a dynamic process coordination control algorithm. The energy management optimal control problem is formulated as a multiparameter quadratic programming optimization problem, and the EMPC control laws are obtained by solving the multiparameter quadratic programming problem offline. The laws are then used online to realize real-time control. A traditional model predictive control (MPC)-based control strategy, DP-based control strategy and rule-based control strategy are considered benchmark strategies for verification of the proposed EMPC-based energy management strategy. The simulation results indicate the EMPC controller has far lower microprocessor hardware costs than the MPC controller but equivalent control performance. As the prediction horizon increases, fuel consumption remains nearly the same between the MPC-based control strategy and EMPC-based control strategy. The consumption time of the MPC-based control strategy increases significantly, while the consumption time of the EMPC-based control strategy is nearly unchanged. Compared with the benchmark algorithms, the elapsed time of the EMPC controller maximum reduced by 97.46%, and the fuel economy improved by 23.37%. To improve fuel economy and reduce online computation time and microprocessor hardware resources, a real-time implementable energy management strategy for a dual-mode power-split hybrid electric vehicle (HEV) based on an explicit model predictive control (EMPC) method is proposed in this paper. The proposed strategy includes an accurate control-oriented model and a dynamic process coordination control algorithm. The energy management optimal control problem is formulated as a multiparameter quadratic programming optimization problem, and the EMPC control laws are obtained by solving the multiparameter quadratic programming problem offline. The laws are then used online to realize real-time control. A traditional model predictive control (MPC)-based control strategy, DP-based control strategy and rule-based control strategy are considered benchmark strategies for verification of the proposed EMPC-based energy management strategy. The simulation results indicate the EMPC controller has far lower microprocessor hardware costs than the MPC controller but equivalent control performance. As the prediction horizon increases, fuel consumption remains nearly the same between the MPC-based control strategy and EMPC-based control strategy. The consumption time of the MPC-based control strategy increases significantly, while the consumption time of the EMPC-based control strategy is nearly unchanged. Compared with the benchmark algorithms, the elapsed time of the EMPC controller maximum reduced by 97.46%, and the fuel economy improved by 23.37%. •A novel control-oriented model reflects the dynamic characteristics of the powertrain.•An MPC-based strategy guarantees the feasibility and optimality of the controller.•A dynamic coordination control algorithm balances the relation of mode shift process.•A multiparameter quadratic programming algorithm obtains online control laws.•The elapsed time reduces 97.46% and fuel economy improves 23.37% by EMPC Controller.
Author	Xiang, Changle Liu, Hui Li, Xunming Wang, Weida Han, Lijin
Author_xml	– sequence: 1 givenname: Xunming orcidid: 0000-0003-2293-857X surname: Li fullname: Li, Xunming organization: School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China – sequence: 2 givenname: Lijin surname: Han fullname: Han, Lijin email: lj.han@163.com organization: School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China – sequence: 3 givenname: Hui surname: Liu fullname: Liu, Hui organization: School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China – sequence: 4 givenname: Weida surname: Wang fullname: Wang, Weida organization: School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China – sequence: 5 givenname: Changle surname: Xiang fullname: Xiang, Changle organization: School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
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Keywords	Real-time energy management strategy Explicit model predictive control algorithm Dynamic coordination control Multiparameter quadratic programming Power-split HEV Control-oriented model
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Snippet	To improve fuel economy and reduce online computation time and microprocessor hardware resources, a real-time implementable energy management strategy for a...
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SubjectTerms	Algorithms Benchmarks Computer simulation Consumption Control algorithms Control systems Control theory Control-oriented model Controllers Dynamic coordination control Electric vehicles energy Energy efficiency Energy management energy use and consumption Explicit model predictive control algorithm Fuel consumption Fuel economy fuels Hardware Hybrid electric vehicles Internet Mathematical models Multiparameter quadratic programming Optimal control Optimization Power-split HEV Predictive control Quadratic programming Real time Real-time energy management strategy Resource management Strategy Time optimal control vehicles (equipment)
Title	Real-time optimal energy management strategy for a dual-mode power-split hybrid electric vehicle based on an explicit model predictive control algorithm
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