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Adaptive Real-Time Energy Management Strategy for Plug-In Hybrid Electric Vehicle Based on Simplified-ECMS and a Novel Driving Pattern Recognition Method

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Mathematical Problems in Engineering

Hindawi Limited

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      Abstract

      This paper proposes an adaptive real-time energy management strategy for a parallel plug-in hybrid electric vehicle (PHEV). Three efforts have been made. First, a novel driving pattern recognition method based on statistical analysis has been proposed. This method classified driving cycles into three driving patterns: low speed cycle, middle speed cycle, and high speed cycle, and then carried statistical analysis on these three driving patterns to obtain rules; the types of real-time driving cycles can be identified according to these rules. Second, particle swarm optimization (PSO) algorithm is applied to optimize equivalent factor (EF) and then the EF MAPs, indexed vertically by battery’s State of Charge (SOC) and horizontally by driving distance, under the above three driving cycles, are obtained. Finally, an adaptive real-time energy management strategy based on Simplified-ECMS and the novel driving pattern recognition method has been proposed. Simulation on a test driving cycle is performed. The simulation results show that the adaptive energy management strategy can decrease fuel consumption of PHEV by 17.63% under the testing driving cycle, compared to CD-CS-based strategy. The calculation time of the proposed adaptive strategy is obviously shorter than the time of ECMS-based strategy and close to the time of CD-CS-based strategy, which is a real-time control strategy.

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      Classification and Review of Control Strategies for Plug-In Hybrid Electric Vehicles

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        Control Strategies for Hybrid Electric Vehicles: Evolution, Classification, Comparison, and Future Trends

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          Energy efficiency analysis of a series plug-in hybrid electric bus with different energy management strategies and battery sizes

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            Author and article information

            Affiliations
            [1 ]Jiangxi Province Key Laboratory of Precision Drive & Control, Nanchang Institute of Technology, Nanchang 330099, China
            [2 ]State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing 400044, China
            Journal
            Mathematical Problems in Engineering
            Mathematical Problems in Engineering
            Hindawi Limited
            1024-123X
            1563-5147
            October 14 2018
            October 14 2018
            : 2018
            : 1-12
            10.1155/2018/5816861
            © 2018

            http://creativecommons.org/licenses/by/4.0/

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