A predictive energy management strategy for multi-mode plug-in hybrid electric vehicles based on multi neural networks

Yitao Wu, Yuanjian Zhang, Guang Li, Jiangwei Shen, Zheng Chen and Yonggang Liu

Energy, 2020, vol. 208, issue C

Abstract: Online optimal energy management of plug-in hybrid electric vehicles has been continually investigated for better fuel economy. This paper proposed a predictive energy management strategy based on multi neural networks for a multi-mode plug-in hybrid electric vehicle. To attain it, firstly, the offline optimal results prepared for knowledge learning are derived by dynamic programming and Pontryagin’s minimum principle. Then, the mode recognition neural network is trained based on the optimal results of dynamic programming and the recurrent neural network is firstly exploited to realize online co-state estimation application. Consequently, the velocity prediction-based online model predictive control framework is established with the co-state correction and slacked constraints to solve the real-time optimal control sequence. A series of numerical simulation results validate that the optimal performance yielded from global optimal strategy can be exploited online to attain the satisfied cost reduction, compared with equivalent consumption minimum strategy, with the assistance of estimated real time co-state and slacked reference. In addition, the computation duration of proposed algorithm decreases by 23.40%, compared with conventional Pontryagin’s minimum principle-based model predictive control scheme, thereby proving its online application potential.

Keywords: Plug-in hybrid electric vehicles; Model predictive control; Dynamic programming; Neural network; Pontryagin’s minimum principle (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (19)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:208:y:2020:i:c:s0360544220314730

DOI: 10.1016/j.energy.2020.118366

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