Drivingon GWB: energy‐efficiency‐driven route optimisation for EVs

Wang, Hui; Cai, Xuelian; Zhang, Yao; Liu, Anqi; Huang, Pengfei; Li, Changle

doi:10.1049/iet-its.2019.0223

Cited by 4 publications

(2 citation statements)

References 30 publications

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“…As soon as the AEV moves, it gradually discharges the battery. Therefore, the consumption of charge [43] of AEV k on road segment (i, j) can be expressed as…”

Section: Charge Consumptionmentioning

confidence: 99%

Dynamic Path-Planning and Charging Optimization for Autonomous Electric Vehicles in Transportation Networks

Tang

Zhang

et al. 2023

Applied Sciences

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With the growing popularity of autonomous electric vehicles (AEVs), optimizing their path-planning and charging strategy has become a critical research area. However, the dynamic nature of transport networks presents a significant challenge when ensuring their efficient operation. The use of vehicle-to-everything (V2X) communication in vehicular ad hoc networks (VANETs) has been proposed to tackle this challenge. However, establishing efficient communication and optimizing dynamic paths with charging selection remain complex problems. In this paper, we propose a joint push–pull communication mode to obtain real-time traffic conditions and charging infrastructure information (i.e., charging stations and energy segments). We also analyze the selection of relay vehicles in multi-hop communication routing, considering factors such as link stability, vehicle distance, and reputation values. Furthermore, we formulate a dynamic optimization problem based on real-time information to minimize travel and charging costs. Our proposed algorithm enables AEVs to obtain charging services from charging stations and conduct dynamic wireless charging via energy segments. We present a dynamic real-time A* algorithm to solve the path-optimization problem and a dynamic real-time charging selection algorithm based on dynamic path optimization when the state of charge is lower than the charging threshold. Extensive simulations demonstrate that the proposed joint push-pull communication mode can provide vehicles the up-to-date information and the developed optimization algorithms effectively reduce travel and charging costs.

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“…As soon as the AEV moves, it gradually discharges the battery. Therefore, the consumption of charge [43] of AEV k on road segment (i, j) can be expressed as…”

Section: Charge Consumptionmentioning

confidence: 99%

Dynamic Path-Planning and Charging Optimization for Autonomous Electric Vehicles in Transportation Networks

Tang

Zhang

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…For the energy saving control method, there are two main types of energy management (EM) strategies and control methods [12,13] developed in the literature: one is the single-vehicle energy management strategies and the study focuses on routing optimization [14] and energy-saving control [15][16][17]. The other is the same issue for the entire vehicle platooning.…”

Section: Introductionmentioning

confidence: 99%

Energy‐efficiency optimization and control for electric vehicle platooning with regenerating braking

Li,

Wang

2023

IET Intelligent Trans Sys

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It is a critical problem to improve energy efficiency for electric vehicle platooning systems. Moreover, different from internal combustion engine vehicles, the electric engine has higher efficiency, and further regenerating braking is widely used to recycle part of the energy in the electric vehicle when it is braking. What is more, if vehicles take a formation to drive, they can save more energy. Combining all the favorable factors, this paper presents a two‐layer energy‐efficiency optimization strategy for electric vehicle platooning. The upper layer presents an optimization method to find the optimal velocities and distances between vehicles under different road conditions during the cruise status of the electric vehicle platooning. Due to the nonconvex cost function and considering regenerative braking, the optimization problem is addressed by the dynamic programming method combined with the successive convex approximation method. Further, the lower layer presents a real‐time Model Predictive Control (MPC) strategy, and it directly introduces the battery pack state of charge consumption as the input, which not only finishes the control mission but also consumes minimal energy. Finally, simulation results are provided to verify the effectiveness and advantages of the proposed methods.

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Optimal road grade design based on stochastic speed trajectories for minimising transportation energy consumption

Liu

Feng

2021

IET Intelligent Trans Sys

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Reducing energy consumption, regardless of fuel or electricity, of ground vehicles is a paramount pursuit in academia and industry. Current research concentrates exclusively on the improvement of the vehicle, but accepts the road conditions as external influences. The innovation of this article is to consider the road conditions, particularly the grade angle, as design variables. It is assumed that the stochastic speed trajectories of all vehicles on the road can be modelled by a Markov chain. The expected value of the average energy consumptions of all vehicles running on the road is defined as the objective function. The optimisation problem is solved by dynamic programming with Markov model. Evaluations have been made on both simulated and measured speed trajectories. For the simulated speed trajectories, the optimal road grade profile designed by the method saves up to 22% energy compared with a flat road. For the measured speed trajectories, the optimal road grade profile saves up to 2.7% transportation energy compared with the actual road profile. Application of this method on building road could lead to considerable energy saving.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Drivingon GWB: energy‐efficiency‐driven route optimisation for EVs

Cited by 4 publications

References 30 publications

Dynamic Path-Planning and Charging Optimization for Autonomous Electric Vehicles in Transportation Networks

Dynamic Path-Planning and Charging Optimization for Autonomous Electric Vehicles in Transportation Networks

Energy‐efficiency optimization and control for electric vehicle platooning with regenerating braking

Optimal road grade design based on stochastic speed trajectories for minimising transportation energy consumption

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