An adaptive ECMS with driving style recognition for energy optimization of parallel hybrid electric buses

Tian, Xiang; Cai, Yingfeng; Sun, Xiaodong; Zhen, Zaili; Xu, Yiqiang

doi:10.1016/j.energy.2019.116151

Cited by 114 publications

(52 citation statements)

References 39 publications

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“… The impact of regenerative breaking as a future technological advancement which can decrease city route electricity consumption was briefly highlighted in the third case study. Recently published works [29]- [32] have investigated the use of intelligent control algorithms to enhance the driving strategies, also with the objective of decreasing losses due to frequent breaking in urban settings.…”

Section: Discussion and Recommendations For Future Workmentioning

confidence: 99%

Optimal Design of Electric Bus Transport Systems With Minimal Total Ownership Cost

et al. 2020

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In this work, a generalized mathematical formulation is proposed to model a generic public transport system, and a mixed-integer linear programming (MILP) optimization is used to determine the optimal design of the system in terms of charging infrastructure deployment (with on-route and off-route charging), battery sizing, and charging schedules for each route in the network. Three case studies are used to validate the proposed model while demonstrating its universal applicability. First, the design of three individual routes with different characteristics is demonstrated. Then, a large-scale generic transport system with 180 routes, consisting of urban and suburban routes with varying characteristics is considered and the optimal design is obtained. Afterwards, the use of the proposed model for a long-term transport system planning problem is demonstrated by adapting the system to a 2030 scenario based on forecasted technological advancements. The proposed formulation is shown to be highly versatile in modeling a wide variety of components in an electric bus (EB) transport system and in achieving an optimal design with minimal TOC. INDEX TERMS Electric Buses, Mixed-Integer Linear Programming, Charging Infrastructure NOMENCLATURE Acronyms AML Algebraic Modeling System CC City Center DC Depot Charger DER Distributed Energy Resource EB Electric Bus ESS Energy Storage System EV Electric Vehicle FC Flash Charger FLC Fuzzy Logic Controller GA Genetic Algorithm HF High Frequency LD Long Distance LF Low Frequency LV Low Voltage MD Medium Distance MILP Mixed-Integer Linear Programming MIP Mixed-Integer Programming MPC Model Predictive Control MV Medium Voltage NLP Non-Linear Programming SD Short Distance SoC State-of-Charge SU Binary variable indicating presence of on-route charger type h, at stop i, in route r Annual ownership cost of on-route charger type h. , , Energy charged at stop i, during trip j, in route r. , , Energy cost per unit at stop i during trip j, in route r.

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Section: Discussion and Recommendations For Future Workmentioning

confidence: 99%

Optimal Design of Electric Bus Transport Systems With Minimal Total Ownership Cost

et al. 2020

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“…In [19], the chaining neural network method was used to predict the future velocity profile, and an ECMS based on energy prediction using the velocity profile was suggested. In [20], driving style recognition was used for the energy management of hybrid electric buses. The driving style level from conservative to aggressive was classified using the K-nearest neighbors method, which was subsequently used to adjust the equivalent factor for the A-ECMS while considering the variation of the battery SOC.…”

Section: B Literature Review : Dp and Machine Learning Based Approachesmentioning

confidence: 99%

Reinforcement Learning Based on Equivalent Consumption Minimization Strategy for Optimal Control of Hybrid Electric Vehicles

Lee

Cha

2021

IEEE Access

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“…Driving pattern recognition algorithms such as fuzzy and machine learning methods can be used to improve an A-ECMS performance. Authors in [116] have used k nearest neighbor (KNN) to classify different driving styles. A driving simulator is used for gathering the driver's driving style in order to feed the KNN module.…”

Section: Composite 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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An adaptive ECMS with driving style recognition for energy optimization of parallel hybrid electric buses

Cited by 114 publications

References 39 publications

Optimal Design of Electric Bus Transport Systems With Minimal Total Ownership Cost

Optimal Design of Electric Bus Transport Systems With Minimal Total Ownership Cost

Reinforcement Learning Based on Equivalent Consumption Minimization Strategy for Optimal Control of Hybrid Electric Vehicles

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

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