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

Xu, Nan; Kong, Yan; Liang, Chao; Ju, Hao; Yang, Zhihua; Xu, Zhe; Xu, Zhisheng

doi:10.3390/app9102026

Cited by 57 publications

(23 citation statements)

References 104 publications

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“…There are recent works that have analyzed the scientific literature related to EMSs for hybrid vehicles [8][9][10][11]. The common approach of these reviews is to classify the EMSs depending on the nature of the algorithm that the EMS is based on.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The same classification was carried out in the case of the work of Xu et al [8], where the EMSs were firstly categorized as rule-based strategies and optimization-based strategies. On the one hand, two strategies are categorized as rule-based strategies: deterministic rule-based (RB) and fuzzy logic-based (FLB).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Taking everything into account, a logical classification of HEV control strategies is rule-based and optimization-based strategies [8][9][10]. On the one hand, it can be concluded that rule-based strategies can be easily implemented in real controllers and their rules can be determined based on human intelligence, heuristics or mathematical models.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Analyzing the Improvements of Energy Management Systems for Hybrid Electric Vehicles Using a Systematic Literature Review: How Far Are These Controls from Rule-Based Controls Used in Commercial Vehicles?

et al. 2020

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The hybridization of vehicles is a viable step toward overcoming the challenge of the reduction of emissions related to road transport all over the world. To take advantage of the emission reduction potential of hybrid electric vehicles (HEVs), the appropriate design of their energy management systems (EMSs) to control the power flow between the engine and the battery is essential. This work presents a systematic literature review (SLR) of the more recent works that developed EMSs for HEVs. The review is carried out subject to the following idea: although the development of novel EMSs that seek the optimum performance of HEVs is booming, in the real world, HEVs continue to rely on well-known rule-based (RB) strategies. The contribution of this work is to present a quantitative comparison of the works selected. Since several studies do not provide results of their models against commercial RB strategies, it is proposed, as another contribution, to complete their results using simulations. From these results, it is concluded that the improvement of the analyzed EMSs ranges roughly between 5% and 10% with regard to commercial RB EMSs; in comparison to the optimum, the analyzed EMSs are nearer to the optimum than commercial RB EMSs.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Analyzing the Improvements of Energy Management Systems for Hybrid Electric Vehicles Using a Systematic Literature Review: How Far Are These Controls from Rule-Based Controls Used in Commercial Vehicles?

et al. 2020

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“…In this study, different approaches that can be integrated into EMSs are examined to enable energy management systems for real-time implementation. Authors in [51] have categorized the existing EMSs by considering data-based approaches such as machine learning-based EMSs. Overall, these aforementioned review articles indicate the need for a separate study that will enumerate the requirements of an iEMS.…”

Section: Novel Categorization Of Intelligent Emssmentioning

confidence: 99%

Intelligent Energy Management Systems for Electrified Vehicles: Current Status, Challenges, and Emerging Trends

Ostadian

Ramoul

Biswas

et al. 2020

IEEE Open J. Veh. Technol.

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By increasing the electrified vehicles trend, it is crucial to have a survey of what is done before and what is the open challenge for improving this industry. Powertrain electrification has heightened the need for an energy management strategy, which has been a continuing concern in the development of electrified vehicles. An energy management control unit manages power flow between different energy sources in an electrified powertrain that directly affects vehicle performance. There are inadequate numbers of studies on the most recent energy management strategies, which are compatible with real-world scenarios. Intelligent energy management strategies requirements are discussed in detail, and it is categorized to principle-based, data-driven, and composite-based methods. Besides, enabling technologies for implementing an energy management system with a comparison of different controller chips are described to give readers an experimental view. Future trends and existing challenges are presented, which generate fresh insight into energy management strategies. Index Terms-data-driven methods, electric vehicles, intelligent energy management strategy, reinforcement learning, powertrain architecture.

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“…2 (f), a two-stage process is imperative to realize the energy management in the proposed cooperative control strategy for cPHEVs. According to the shared traffic information, the future velocity profile in each segment will be predicted by the method that was proposed in our former work [42]. Based on the forecasted velocity profile, the reference battery SOC trajectory in each route segment will be globally calculated in the master level control unit installed in the MECU.…”

Section: A Two-step Cooperative Control Strategy For Cphevsmentioning

confidence: 99%

A novel strategy for power sources management in connected plug-in hybrid electric vehicles based on mobile edge computation framework

Zhang

Guo

Liu

et al. 2020

Journal of Power Sources

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This paper proposes a novel control framework and the corresponding strategy for power sources management in connected plug-in hybrid electric vehicles (cPHEVs). A mobile edge computation (MEC) based control framework is developed first, evolving the conventional on-board vehicle control unit (VCU) into the hierarchically asynchronous controller that is partly located in cloud. Elaborately contrastive analysis on the performance of processing capacity, communication frequency and communication delay manifests dramatic potential of the proposed framework in sustaining development of the cooperative control strategy for cPHEVs.On the basis of MEC based control framework, a specific cooperative strategy is constructed. The novel strategy accomplishes energy flow management between different power sources with incorporation of the active energy consumption plan and adaptive energy consumption management. The method to generate the reference battery state-of-charge (SOC) trajectories in energy consumption plan stage is emphatically investigated, fast outputting reference trajectories that are tightly close to results by global optimization methods. The estimation of distribution algorithm (EDA) is employed to output reference control policies under the specific terminal conditions assigned via the machine learning based method. Finally, simulation results highlight that the novel strategy attains superior performance in real-time application that is close to the offline global optimization solutions.

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

Cited by 57 publications

References 104 publications

Analyzing the Improvements of Energy Management Systems for Hybrid Electric Vehicles Using a Systematic Literature Review: How Far Are These Controls from Rule-Based Controls Used in Commercial Vehicles?

Analyzing the Improvements of Energy Management Systems for Hybrid Electric Vehicles Using a Systematic Literature Review: How Far Are These Controls from Rule-Based Controls Used in Commercial Vehicles?

Intelligent Energy Management Systems for Electrified Vehicles: Current Status, Challenges, and Emerging Trends

A novel strategy for power sources management in connected plug-in hybrid electric vehicles based on mobile edge computation framework

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