Abstract
A direct vehicle-to-vehicle (V2V) charging scheme supplies flexible and fast energy exchange way for electric vehicles (EVs) without the supports of charging stations. Main technical challenges in cooperative V2V charging may include the efficient charging navigation structure designs with low communication loads and computational complexities, the decision-making intelligence for the selection of stopping locations to operate V2V charging services, and the optimal matching issue between charging EVs and discharging EVs. In this paper, to solve the above problems, we propose an intelligent V2V charging navigation strategy for a large number of mobile EVs. Specifically, by means of a hybrid vehicular ad-hoc networks (VANETs) based communication paradigm, we first study a mobile edge computing (MEC) based semi-centralized charging navigation framework to ensure the reliable communication and efficient charging coordination. Then, based on the derived charging models, we propose an effective local charging navigation scheme to adaptively select the optimal traveling route and appropriate stopping locations for mobile EVs via the designed Q-learning based algorithm. After that, an efficient global charging navigation mechanism is proposed to complete the best charging-discharging EV pair matching based on the constructed weighted bipartite graph. A series of simulation results and theoretical analyses are presented to demonstrate the feasibility and effectiveness of the proposed V2V charging navigation strategy.
Highlights
With the significantly increasing concerns on the issues of environmental conservations and intelligent transportation systems (ITS), electric vehicles (EVs) have attracted more and more attentions from both industry and academia due to the advantages of zero emissions, low noises, efficient energy conversions and so on [1], [2]
To choose the optimal moving path towards stopping location for subsequent V2V charging services, we propose a Q-learning based optimal method for mobile EVs with charging/discharging demands to sufficiently cope with rapid traffic changes in urban scenarios, by means of predicted traveling time and corresponding energy consumption in each road segment with time series
According to the aforementioned descriptions, busy situation estimation of stopping locations and local charging navigation decisions of mobile EVs are implemented in a distributed manner, while global charging navigation decisions are given by navigation control center (NCC) in a centralized way, as a result, our proposed mobile edge computing (MEC)-based charging navigation framework is managed in a semi-centralized pattern
Summary
With the significantly increasing concerns on the issues of environmental conservations and intelligent transportation systems (ITS), electric vehicles (EVs) have attracted more and more attentions from both industry and academia due to the advantages of zero emissions, low noises, efficient energy conversions and so on [1], [2]. In order to solve the aforementioned challenges, we propose an intelligent direct V2V charging navigation strategy for a large number of mobile EVs. In particular, to avoid heavy computation loads in traditional centralized manner and achieve the reliable charging information transmission among the navigation control center, distributed stopping locations and moving EVs, we establish a semi-centralized charging navigation framework and design the corresponding management protocol on the basis of flexible VANET-based communication pattern and available mobile edge computing (MEC). To choose the optimal moving path towards stopping location for subsequent V2V charging services, we propose a Q-learning based optimal method for mobile EVs with charging/discharging demands to sufficiently cope with rapid traffic changes in urban scenarios, by means of predicted traveling time and corresponding energy consumption in each road segment with time series.
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