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Towards a Smarter Energy Management System for Hybrid Vehicles: A Comprehensive Review of Control Strategies

1
State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China
2
General R&D Institute of China FAW, Changchun 130011, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(10), 2026; https://doi.org/10.3390/app9102026
Received: 30 March 2019 / Revised: 12 May 2019 / Accepted: 12 May 2019 / Published: 16 May 2019
(This article belongs to the Special Issue Smart Home and Energy Management Systems 2019)
PDF [4200 KB, uploaded 16 May 2019]

Abstract

This paper presents a comprehensive review of energy management control strategies utilized in hybrid electric vehicles (HEVs). These can be categorized as rule-based strategies and optimization-based strategies. Rule-based strategies, as the most basic strategy, are widely used due to their simplicity and practical application. The focus of rule-based strategies is to determine and optimize the optimal threshold for mode switching; however, they fall into a local optimal solutions. To have better performance in energy management, optimization-based strategies were developed. The categories of the existing optimization-based strategies are identified from the latest literature, and a brief study of each strategy is discussed, which consists of the main research ideas, the research focus, advantages, disadvantages and improvements to ameliorate optimality and real-time performance. Deterministic dynamic programming strategy is regarded as a benchmark. Based on neural network and the large data processing technology, data-driven strategies are put forward due to their approximate optimality and high computational efficiency. Finally, the comprehensive performance of each control strategy is analyzed with respect to five aspects. This paper not only provides a comprehensive analysis of energy management control strategies for HEVs, but also presents the emphasis in the future.
Keywords: energy management strategy; rule-based strategies; optimization-based strategies; data-driven strategies; hybrid electric vehicles energy management strategy; rule-based strategies; optimization-based strategies; data-driven strategies; hybrid electric vehicles
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Xu, N.; Kong, Y.; Chu, L.; Ju, H.; Yang, Z.; Xu, Z.; Xu, Z. Towards a Smarter Energy Management System for Hybrid Vehicles: A Comprehensive Review of Control Strategies. Appl. Sci. 2019, 9, 2026.

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