A survey on key techniques and development perspectives of equivalent consumption minimisation strategy for hybrid electric vehicles

Chen, Zheng; Liu, Yunpan; Ye, M.; Zhang, Y.; Li, G.

doi:10.1016/j.rser.2021.111607

Cited by 52 publications

(17 citation statements)

References 133 publications

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“…Hannan from Universiti Tenaga Nasional, Malaysia, developed the following recent manuscripts on EMSs for EV applications: "Techno-Economic Analysis and Environmental Impact of Electric Buses" [188], "Fuzzy Based Charging-Discharging Controller for Lithium-ion Battery" [189], and "Energy Storage Integrated Microgrid Performance Enhancement" [190]. Yonggang Liu from Chongqing University, China, has a primary research interest in Plug-in Hybrid Vehicles, Powertrains, and Energy Management [191][192][193]. "A survey on key techniques and development perspectives of equivalent consumption minimization strategy for hybrid electric vehicles" [193] and "Prediction of vehicle driving conditions with the incorporation of stochastic forecasting and machine learning and a case study in energy management of plug-in hybrid electric vehicles" [187] are the manuscripts recently produced by Yonggang Liu.…”

Section: Document Authorship Analysismentioning

confidence: 99%

“…Yonggang Liu from Chongqing University, China, has a primary research interest in Plug-in Hybrid Vehicles, Powertrains, and Energy Management [191][192][193]. "A survey on key techniques and development perspectives of equivalent consumption minimization strategy for hybrid electric vehicles" [193] and "Prediction of vehicle driving conditions with the incorporation of stochastic forecasting and machine learning and a case study in energy management of plug-in hybrid electric vehicles" [187] are the manuscripts recently produced by Yonggang Liu.…”

Section: Document Authorship Analysismentioning

confidence: 99%

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Optimized Energy Management Schemes for Electric Vehicle Applications: A Bibliometric Analysis towards Future Trends

et al. 2021

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Concerns over growing greenhouse gas (GHG) emissions and fuel prices have prompted researchers to look into alternative energy sources, notably in the transportation sector, accounting for more than 70% of carbon emissions. An increasing amount of research on electric vehicles (EVs) and their energy management schemes (EMSs) has been undertaken extensively in recent years to address these concerns. This article aims to offer a bibliometric analysis and investigation of optimized EMSs for EV applications. Hundreds (100) of the most relevant and highly influential manuscripts on EMSs for EV applications are explored and examined utilizing the Scopus database under predetermined parameters to identify the most impacting articles in this specific field of research. This bibliometric analysis provides a survey on EMSs related to EV applications focusing on the different battery storages, models, algorithms, frameworks, optimizations, converters, controllers, and power transmission systems. According to the findings, more articles were published in 2020, with a total of 22, as compared to other years. The authors with the highest number of manuscripts come from four nations, including China, the United States, France, and the United Kingdom, and five research institutions, with these nations and institutions accounting for the publication of 72 papers. According to the comprehensive review, the current technologies are more or less capable of performing effectively; nevertheless, dependability and intelligent systems are still lacking. Therefore, this study highlights the existing difficulties and challenges related to EMSs for EV applications and some brief ideas, discussions, and potential suggestions for future research. This bibliometric research could be helpful to EV engineers and to automobile industries in terms of the development of cost-effective, longer-lasting, hydrogen-compatible electrical interfaces and well-performing EMSs for sustainable EV operations.

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Section: Document Authorship Analysismentioning

confidence: 99%

Section: Document Authorship Analysismentioning

confidence: 99%

Optimized Energy Management Schemes for Electric Vehicle Applications: A Bibliometric Analysis towards Future Trends

et al. 2021

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“…The PMP-based EMS and its derived ECMS have demonstrated great potential in real-time practice, due to their instantaneous optimality and simplified control variable. 39,40 Its crucial problem is to online determine the costate of PMP (or the equivalent factor of ECMS) to guarantee the optimality and robustness of EMS as much as possible. 41 In previous investigations, the optimal costate is generally assumed to be constant when neglecting the impact of the varying battery state-ofcharge (SOC) on voltage and internal resistance, but the globally optimal constant costate can only be obtained with the future driving condition is provided.…”

Section: Introductionmentioning

confidence: 99%

“…It should be not only uncomplicated but also have strong robustness. The PMP‐based EMS and its derived ECMS have demonstrated great potential in real‐time practice, due to their instantaneous optimality and simplified control variable 39,40 . Its crucial problem is to online determine the costate of PMP (or the equivalent factor of ECMS) to guarantee the optimality and robustness of EMS as much as possible 41 .…”

Section: Introductionmentioning

confidence: 99%

A modified model‐free‐adaptive‐control‐based real‐time energy management strategy for plug‐in hybrid electric vehicle

Liu

Guo

et al. 2022

Energy Science & Engineering

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To further improve the energy-saving potential and robustness of the energy management strategy (EMS) for plug-in hybrid electric vehicles (PHEVs) in a real-time application, this paper proposes a modified model-free-adaptivecontrol-based (MFAC-based) EMS to overcome the disadvantages in our previous MFAC-based EMS. First, the influence of external disturbance on MFAC-based EMS is discussed, and the results show that both the vehicle velocity and load change have a significant impact on its performance. Second, a modified MFAC-based real-time EMS is designed based on history driving data obtained from a repeated route in which a state-of-charge (SOC)constraint-based reference SOC planning method is firstly proposed to simultaneously consider the vehicle velocity and changing load. Then, global SOC constraints are incorporated in Pontryagin's minimum principle (PMP) to enhance the adaptive capability of the proposed method. Finally, the optimal solution of PMP (i.e., optimal constant) is deployed as a benchmark, and the performance of the modified MFAC-based EMS (namely MFAC-II and MFAC-III) is in contrast to the previous one (MFAC for short) under various real-world driving cycles. The results demonstrate that the MFAC-III has a remarkable improvement in both economic performance and robustness.

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“…Currently, the automotive industry has adopted the energy structure transformation and will gradually replace the internal combustion engine (ICE) with multiple energy sources. The existing industrial base, hybrid electric vehicles (HEV), and electric vehicles (EV) are one of the best solutions to this problem at this stage [1][2][3]. Among them, HEV refers to vehicles with two or more power sources, such as the most widely used vehicles driven by a mixture of internal combustion engine and electric motor, and the type and capacity of the battery in this system will directly affect the intensity of the mixture and the space available for optimization.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Energy Management Strategy Based on Driving Conditions Using a Feature Fusion Extreme Learning Machine

Qiang

Pan

et al. 2022

Energies

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To address the problem that a single energy management strategy cannot adapt to complex driving conditions, in this paper, a real-time energy management strategy for different driving conditions is proposed to improve fuel economy. First, in order to improve the accuracy and stability of the driving condition identifier, a feature fusion extreme learning machine (FFELM) is used for identification. Secondly, equivalent consumption minimization strategy (ECMS) offline optimization is conducted for different types of driving cycles, and the effect of driving cycle type and driving distance on the energy management strategy under the optimization result is analyzed. A real-time energy management strategy combining driving cycle type, driving distance, and optimal power allocation factor is proposed. To demonstrate the effectiveness of the proposed strategy, combined driving cycles were used for testing. The simulation results show that the proposed strategy can improve the equivalent fuel consumption by 10.21% compared to the conventional strategy CD-CS. The equivalent fuel economy can be improved by 2.5% compared to the single ECMS strategy with the less computational burden. Thus, it is demonstrated that the proposed strategy can be effectively adapted to different driving conditions and shows better real-time and economic performance.

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A survey on key techniques and development perspectives of equivalent consumption minimisation strategy for hybrid electric vehicles

Cited by 52 publications

References 133 publications

Optimized Energy Management Schemes for Electric Vehicle Applications: A Bibliometric Analysis towards Future Trends

Optimized Energy Management Schemes for Electric Vehicle Applications: A Bibliometric Analysis towards Future Trends

A modified model‐free‐adaptive‐control‐based real‐time energy management strategy for plug‐in hybrid electric vehicle

Real-Time Energy Management Strategy Based on Driving Conditions Using a Feature Fusion Extreme Learning Machine

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