A novel data-driven controller for plug-in hybrid electric vehicles with improved adaptabilities to driving environment

Instantaneous application optimality is one of the indispensable indicators to assess energy management performance of plug-in hybrid electric vehicles (PHEVs). The momentary optimality, nevertheless, cannot be flexibly reachable under various driving environments due to the partial unobservabilitie...

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Veröffentlicht in:Journal of cleaner production Jg. 334; S. 130250
Hauptverfasser: Liu, Yu, Zhang, Yuanjian, Yu, Hanzhengnan, Nie, Zhigen, Liu, Yonggang, Chen, Zheng
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.02.2022
Schlagworte:
Chaotic optimization
data collection
energy
issues and policy
Least square support vector machine
Plug-in hybrid electric vehicles
Quantum particle swarm optimization
Sequential quadratic programming
support vector machines
Quantum particle swarm optimization
Least square support vector machine
Chaotic optimization
Sequential quadratic programming
Plug-in hybrid electric vehicles
ISSN:0959-6526, 1879-1786
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Zusammenfassung:Instantaneous application optimality is one of the indispensable indicators to assess energy management performance of plug-in hybrid electric vehicles (PHEVs). The momentary optimality, nevertheless, cannot be flexibly reachable under various driving environments due to the partial unobservabilities in control algorithms. To cope with it, a novel data-driven controller for PHEVs is proposed in this paper to achieve the instantaneous optimality of energy management. The well-designed machine learning based controller translates the knowledge of global optimization to real-time controlling scheme with the consideration of adaptabilities to disperse driving conditions. To start with, the universal global optimal control policies for varying driving environment are generated offline based on the chaotic quantum particle swarm optimization with sequential quadratic programming (CQPSO-SQP). Then, the offline optimized global control policies are assembled to construct the dataset for training the least square support vector machine (LSSVM) based controller, which features the superior capability in instantly optimal policy making under different driving conditions. At last, the detailed assessment is performed in simulation test and hardware-in-loop (HIL) test to validate the promising role of CQPSO-PSO and LSSVM in designing the novel energy management controller, and the corresponding results highlight the preferable controlling performance of the proposed novel controller in practical applications. •Data-driven controller is devised with improved adaptability to driving environment.•Global optimal control policies are generated for various environment.•Improved meta-heuristic method is implemented in global control policy generation.•Data-driven method transfers offline optimal knowledge into instant control process.
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ISSN:0959-6526
1879-1786
DOI:10.1016/j.jclepro.2021.130250