Impacts of wireless charging lanes on travel time and energy consumption in a two-lane road system

He, Jia; Yang, Hai; Huang, Hai-Jun; Tang, Tie-Qiao

doi:10.1016/j.physa.2018.02.074

Cited by 28 publications

(6 citation statements)

References 43 publications

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“…Nevertheless, additional challenges arise coupled with inherent advantages of green mobility. Notably, the range and charging are enormous practical problems for EVs: its range is shorter and the charging time is longer than traditional internal combustion engine vehicles which restricts the popularity of EVs (He et al , 2018). Nevertheless, on the bright side, the research studies on eco-driving of EVs have brought a great promise to solve this problem (Hardman et al , 2018; He and Wu, 2018; Afshar et al , 2021; Tran et al , 2021), especially under the condition of connected vehicles (Vahidi and Sciarretta, 2018; Olovsson et al , 2022; Shi et al , 2021).…”

Section: Introductionmentioning

confidence: 99%

An enhanced eco-driving strategy based on reinforcement learning for connected electric vehicles: cooperative velocity and lane-changing control

Ding

et al. 2022

JICV

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Purpose This study aims to propose an enhanced eco-driving strategy based on reinforcement learning (RL) to alleviate the mileage anxiety of electric vehicles (EVs) in the connected environment. Design/methodology/approach In this paper, an enhanced eco-driving control strategy based on an advanced RL algorithm in hybrid action space (EEDC-HRL) is proposed for connected EVs. The EEDC-HRL simultaneously controls longitudinal velocity and lateral lane-changing maneuvers to achieve more potential eco-driving. Moreover, this study redesigns an all-purpose and efficient-training reward function with the aim to achieve energy-saving on the premise of ensuring other driving performance. Findings To illustrate the performance for the EEDC-HRL, the controlled EV was trained and tested in various traffic flow states. The experimental results demonstrate that the proposed technique can effectively improve energy efficiency, without sacrificing travel efficiency, comfort, safety and lane-changing performance in different traffic flow states. Originality/value In light of the aforementioned discussion, the contributions of this paper are two-fold. An enhanced eco-driving strategy based an advanced RL algorithm in hybrid action space (EEDC-HRL) is proposed to jointly optimize longitudinal velocity and lateral lane-changing for connected EVs. A full-scale reward function consisting of multiple sub-rewards with a safety control constraint is redesigned to achieve eco-driving while ensuring other driving performance.

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Section: Introductionmentioning

confidence: 99%

An enhanced eco-driving strategy based on reinforcement learning for connected electric vehicles: cooperative velocity and lane-changing control

Ding

et al. 2022

JICV

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“…Therefore, some researches focused on the influence of wireless charging lanes on traffic system. He et al [22,23] investigated the impacts of the dynamic charging lane on EV's motion behavior, and the results show that the each EV could run slowly on the charging lane with causing low traffic efficiency. To improve the traffic efficiency, He et al [24] established a model to optimize the location of the charging lane by considering their adverse effects on road capacity.…”

Section: Introductionmentioning

confidence: 99%

Differentiated Speed Planning for Connected and Automated Electric Vehicles at Signalized Intersections considering Dynamic Wireless Power Transfer

Yang

Han

Fang

et al. 2022

Journal of Advanced Transportation

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Deploying the dynamic wireless power transfer (DWPT) equipment at intersections can improve the transportation efficiency and decrease the energy consumption. It can easily turn the disadvantages of waiting time for red phase into the advantages of charging benefits for electric vehicles (EVs). This study develops a multiobjective speed planning model with differentiated charging strategy to optimize the EV’s trajectory at signalized intersections with DWPT. To mitigate the negative impact of DWPT, the proposed model is divided into charging priority driving (C-eco-driving) mode and time priority driving (T-eco-driving) mode for connected and automated electric vehicles (CAEVs) to apply different scenarios. Meanwhile, a widely applied car-following model, i.e., the Intelligent Driver Model (IDM), has been calibrated with the ground-truth dataset to meet the intersection characteristic for EVs in mixed traffic. The efficiency and robustness of two eco-driving modes were validated in single-vehicle and mixed traffic based on MATLAB simulation. For single vehicle, the electricity benefits of the C-eco-driving mode and T-eco-driving mode increase about 0.0867 kWh and 0.0532 kWh, respectively. T-eco-driving mode reduces 2.5 s of travel time. For mixed traffic, the C-eco-driving mode provides more charging benefits about 51~73% than no-control strategy. In contrast, the T-eco-driving mode provides significant time benefits of 2.6 s and slight electricity benefits of 12~30% than no-control strategy. The increment of market penetration rate and wireless charging length can significantly improve the charging benefits. When charging length has the same value, the closer the DWPT facilities to the intersection, the more obvious the charging benefits.

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“…The solenoid winding coil to the central column of the magnetic core increased the coupling coefficient of the two coils and reduced the size of the coupling mechanism, so that the charging efficiency applied to ZTE new energy vehicles was higher than 90%. [17] uses the method of parameter solving to calculate the overall change trend of the space magnetic field and circuit parameters of singleturn coils with different radii and proposed five groups of parallel-wound disk-shaped coil structures. The transmitting coil was excited by a voltage source.…”

Section: Introductionmentioning

confidence: 99%

Design of High‐Power High‐Efficiency Wireless Charging Coils for EVs with MnZn Ferrite Bricks

et al. 2021

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In wireless power transmission systems, the inductance, equivalent resistance, and quality factors of the coils are the main factors that influence the system’s transmission efficiency. When designing high-power wireless charging coils for electric vehicles (EVs), ferrite bricks that increase magnetic flux can be selected to increase the self-inductance of the coils, improving the wireless transmission distance and transmission efficiency. In this paper, the effects of the ferrite bricks, the size of the coils, the charging distance, and many other factors in real applications have been extensively studied. After theoretical analysis and simulation, the wireless transmission system has been fabricated and measured. The measured and simulated results are in good agreement. High-power high-efficiency wireless power transmission has been achieved for EVs compared with many other previous literatures.

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Impacts of wireless charging lanes on travel time and energy consumption in a two-lane road system

Cited by 28 publications

References 43 publications

An enhanced eco-driving strategy based on reinforcement learning for connected electric vehicles: cooperative velocity and lane-changing control

An enhanced eco-driving strategy based on reinforcement learning for connected electric vehicles: cooperative velocity and lane-changing control

Differentiated Speed Planning for Connected and Automated Electric Vehicles at Signalized Intersections considering Dynamic Wireless Power Transfer

Design of High‐Power High‐Efficiency Wireless Charging Coils for EVs with MnZn Ferrite Bricks

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