Optimization of future charging infrastructure for commercial electric vehicles using a multi-objective genetic algorithm and real travel data

Zeng, Li; Krallmann, Timo; Fiege, Andrea; Stess, Marek; Graen, Timo; Nolting, Michael

doi:10.1007/s12530-019-09295-4

Cited by 15 publications

(8 citation statements)

References 16 publications

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“…Other studies have investigated the localization problem of charging stations that are accessible for different commercial vehicles, such as [71], [109]. In these studies, multiday travel data collected from different commercial electric vehicles was pre-processed, and stop points were clustered to define candidate sites for charging stations.…”

Section: A Optimal Location Of Charging Infrastructurementioning

confidence: 99%

Charging Infrastructure for Commercial Electric Vehicles: Challenges and Future Works

et al. 2021

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The journey towards transportation electrification started with small electric vehicles (i.e., electric cars), which have enjoyed an increasing level of global interest in recent years. Electrification of commercial vehicles (e.g., trucks) seems to be a natural progression of this journey, and many commercial vehicle manufacturers have shifted their focus on medium-and heavy-duty vehicle electrification over the last few years. In this paper, we present a comprehensive review and analysis of the existing works presented in the literature on commercial vehicle charging. The paper starts with a brief discussion on the significance of commercial vehicle electrification, especially heavy-and medium-duty vehicles. The paper then reviews two major charging strategies for commercial vehicles, namely the return-to-base model and the on route charging model. Research challenges related to the return-to-base model are then analysed in detail. Next, different methods to charge commercial vehicles on route during their driving cycles are summarized. The paper then analyzes the challenging issues related to charging commercial vehicles at public charging stations. Future works relevant to these challenges are highlighted. Finally, the possibility of accommodating vehicle to grid technology for commercial vehicles is discussed.INDEX TERMS Commercial electric vehicles, electric trucks, return-to-base model, smart charging system, charging infrastructure.

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Section: A Optimal Location Of Charging Infrastructurementioning

confidence: 99%

Charging Infrastructure for Commercial Electric Vehicles: Challenges and Future Works

et al. 2021

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“…However, challenges such as the intermittency of sunlight and the size and weight of PV panels need to be addressed in the design and implementation of the system. Efficient energy management and storage solutions [20] are crucial to ensure continuous operation, even in unfavorable weather conditions.…”

Section: Thdmentioning

confidence: 99%

Optimized PV-based induction motor drive for electric vehicle system with bidirectional converter: modeling and analysis

Anand,

Sharma,

Yadav

et al. 2024

Preprint

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An electric vehicle (EV) system is a transportation solution that relies on electric propulsion rather than traditional internal combustion engines. Induction motors are well-suited for EVs, offering high torque at low speeds, ideal for city driving. To enhance the sustainability of the EV system, photovoltaic (PV) panels are integrated to directly power the induction motor using renewable solar energy. The PV panels' optimal sizing and placement are achieved through the modified sandpiper optimization (MSO) algorithm, maximizing their efficiency. Additionally, an improved competitive swarm optimization (ICSO) algorithm is employed to optimize power interfaces in the PV-powered EV system, addressing issues with traditional maximum power point tracking (MPPT) techniques. To facilitate bidirectional power flow and mitigate voltage unbalance, a bidirectional DC-DC converter is implemented. Through extensive simulation scenarios, the proposed induction motor-driven EV system is thoroughly validated and compared to state-of-the-art EV systems utilizing brushless DC (BLDC) motors. The comparative analysis assesses system efficiency, power output, torque characteristics, and overall performance, providing valuable insights into the suitability and advantages of using induction motors in EV systems. This research contributes to advancing sustainable and efficient EV technologies, offering a greener mode of transportation for a more environmentally friendly future.

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“…Although the previous studies considered an entire charging infrastructure solution, these studies did not integrate the existing charging stations. Zeng et al [23], as well as Krallmann et al [11] extended the existing public infrastructure by using a genetic algorithm. Liu et al [14] also examined the incremental case by using a greedy algorithm.…”

Section: Related Workmentioning

confidence: 99%

“…As the problem of charging station placement is NP-hard, approximation algorithms are adopted. Authors of previous studies employed different greedy algorithms [8], [14], [19], genetic algorithms [23], [21], and Bayesian Optimisation [3] that either tend to cluster the nodes or neglect the cost-effectiveness. We can avoid this by combining different greedy strategies to a more refined policy by adopting reinforcement learning.…”

Section: Related Workmentioning

confidence: 99%

Reinforcement Learning-based Placement of Charging Stations in Urban Road Networks

von Wahl,

Tempelmeier,

Sao

et al. 2022

Preprint

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The transition from conventional mobility to electromobility largely depends on charging infrastructure availability and optimal placement. This paper examines the optimal placement of charging stations in urban areas. We maximise the charging infrastructure supply over the area and minimise waiting, travel, and charging times while setting budget constraints. Moreover, we include the possibility of charging vehicles at home to obtain a more refined estimation of the actual charging demand throughout the urban area. We formulate the Placement of Charging Stations problem as a non-linear integer optimisation problem that seeks the optimal positions for charging stations and the optimal number of charging piles of different charging types. We design a novel Deep Reinforcement Learning approach to solve the charging station placement problem (PCRL). Extensive experiments on real-world datasets show how the PCRL reduces the waiting and travel time while increasing the benefit of the charging plan compared to five baselines. Compared to the existing infrastructure, we can reduce the waiting time by up to 97% and increase the benefit up to 497%. CCS CONCEPTS• Computing methodologies → Reinforcement learning; • Mathematics of computing → Combinatorial optimization.

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Optimization of future charging infrastructure for commercial electric vehicles using a multi-objective genetic algorithm and real travel data

Cited by 15 publications

References 16 publications

Charging Infrastructure for Commercial Electric Vehicles: Challenges and Future Works

Charging Infrastructure for Commercial Electric Vehicles: Challenges and Future Works

Optimized PV-based induction motor drive for electric vehicle system with bidirectional converter: modeling and analysis

Reinforcement Learning-based Placement of Charging Stations in Urban Road Networks

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