Adaptability to various driving conditions (TCs) is one of the essential indicators to assess the optimality of power management strategies (PMSs) of plug-in hybrid electric vehicles (PHEVs). In this study, a novel optimal PMS with the improved adaptability to TCs is proposed for PHEVs to achieve the energy-efficient control in momentary scenarios by virtue of advanced internet of vehicles (IoVs), thus contributing to remarkable promotion in fuel economy of PHEV. Firstly, the optimal control rules in the novel PMS, corresponding to diverse driving conditions, are optimized offline by the chaotic particle swarm optimization with sequential quadratic programming (CPSO-SQP), which can effectively endow the global optimization knowledge into the rule inspired method. Then, an online TC identification (TCI) method is designed by cooperatively exploiting multi-dimensional Gaussian distribution (MGD) and random forest (RF), where the MGD based analysis on the macrocosmic state of traffic contributes to valuable inputs for the RF based TC classification, and additionally the super regression ability of RF further improves the identification accuracy. Finally, the numerical simulation validations showcase that the novel optimal PMS can reasonably and instantly manage the power flow within power sources of PHEV under different TCs, manifesting its anticipated preferable controlling performance.
Bibliographical noteFunding Information:
This work was supported in part by the National Natural Science Foundation of China (No. 61763021 and No. 51775063 ), in part by the National Key R&D Program of China (No. 2018YFB0104900 ), and in part by the EU-funded Marie Skłodowska-Curie Individual Fellowships under Grant 845102-HOEMEV-H2020-MSCA–IF–2018.
© 2021 Elsevier B.V.
Copyright 2021 Elsevier B.V., All rights reserved.
- Chaotic particle swarm optimization with sequential quadratic programming
- Multi-dimensional Gaussian distribution
- Plug-in hybrid electric vehicles
- Power management strategy
- Random forest
ASJC Scopus subject areas
- Renewable Energy, Sustainability and the Environment
- Energy Engineering and Power Technology
- Physical and Theoretical Chemistry
- Electrical and Electronic Engineering