Neural network energy management strategy with optimal input features for plug-in hybrid electric vehicles

Ziwang Lu, He Tian, Yiwen Sun, Runfeng Li and Guangyu Tian

Energy, 2023, vol. 285, issue C

Abstract: The neural network energy management strategy can be implemented online and effectively save the energy for the plug-in hybrid electric vehicles (PHEVs). However, the selection of input features and application to untrained driving cycles are two issues faced by the strategy. This paper proposes an energy management strategy with the optimal input features for the PHEV. The global optimal datasets are first obtained based on the dynamic programming (DP) algorithm. Then the random forest classification models are trained with different combinations of input features to select the input feature combination with the highest classification accuracy. A neural network with the selected input features is finally trained using the optimal datasets for online control. With the optimal input features, the strategy can be adopted to both trained driving cycles and untrained driving cycles. Results demonstrate that the proposed strategy can save 4.44% to 7.75% energy compared to the charge depleting and charge sustaining strategy on trained cycles and 3.80% to 7.70% on untrained cycles respectively. The efficiency of the proposed strategy is only less than 2.41% worse than the DP algorithm.

Keywords: Plug-in hybrid electric vehicle; Energy management; Dynamic programming; Input features; Neural network (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:285:y:2023:i:c:s0360544223027937

DOI: 10.1016/j.energy.2023.129399

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