The depot and charging facility location problem for electrifying urban bus services

Hsu, Yu Ting; Yan, Shangyao; Huang, Po Chin

doi:10.1016/j.trd.2021.103053

Cited by 29 publications

(11 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Note that, this equation is acceptable under the consideration of the short charging time and suitable to the conventional transit operation, where the dwelling time at bus stops is often less than 30s to pick up or drop off passengers. In some special scenarios, the operator intends to take a few minutes to charge the BEBs, in which the interaction between battery capacity, fleet size and schedules become more important [33], [34], [35]. For example, the travel duration for a trip increases as the charging time increases, which further result in the increase in the fleet size, under the specific bus schedule and battery capacity.…”

Section: B Minimum Beb Fleet Size Required For Each Routementioning

confidence: 99%

Lifecycle Cost Optimization for Electric Bus Systems With Different Charging Methods: Collaborative Optimization of Infrastructure Procurement and Fleet Scheduling

Wang

Sai

et al. 2023

IEEE Trans. Intell. Transport. Syst.

View full text Add to dashboard Cite

Battery electric buses (BEBs) have been regarded as effective options for sustainable mobility while their promotion is highly affected by the total cost associated with their entire life cycle from the perspective of urban transit agencies. In this research, we develop a collaborative optimization model for the lifecycle cost of BEB system, considering both overnight and opportunity charging methods. This model aims to jointly optimize the initial capital cost and use-phase operating cost by synchronously planning the infrastructure procurement and fleet scheduling. In particular, several practical factors, such as charging pattern effect, battery downsizing benefits, and timeof-use dynamic electricity price, are considered to improve the applicability of the model. A hybrid heuristic based on the tabu search and immune genetic algorithm is customized to effectively solve the model that is reformulated as the bi-level optimization problem. A numerical case study is presented to demonstrate the model and solution method. The results indicate that the proposed optimization model can help to reduce the lifecycle cost by 7.77% and 6.64% for overnight and opportunity charging systems, respectively, compared to the conventional management strategy. Additionally, a series of simulations for sensitivity analysis are conducted to further evaluate the key parameters and compare their respective life cycle performance. The policy implications for BEB promotion are also discussed.

show abstract

Section: B Minimum Beb Fleet Size Required For Each Routementioning

confidence: 99%

Lifecycle Cost Optimization for Electric Bus Systems With Different Charging Methods: Collaborative Optimization of Infrastructure Procurement and Fleet Scheduling

Wang

Sai

et al. 2023

IEEE Trans. Intell. Transport. Syst.

View full text Add to dashboard Cite

show abstract

“…However, since every bus has a different mileage for every unit of fuel, minimizing the dead kilometer cost (DKC) is a more appropriate objective function. This has been addressed in a few studies (Forbes et al 1994;Kepaptsoglou et al 2010;Kontou et al 2014;Hsu et al 2021) mainly in countries other than India. Hence, the main objective of this study is to optimize the cost of city bus operation by minimizing the dead kilometer cost (DKC) considering the fuel cost per kilometer.…”

Section: Problem Descriptionmentioning

confidence: 99%

“…The hybrid GA proved immensely superior in terms of the computational time and the objective function value. Hsu et al (2021) proposed an augmented (0-1) ILP model to address the problem of the depot, charging, and maintenance-related facilities' location for the comprehensive deployment of a mixed-bus system. The objective of their study was to minimize the total cost, which included the cost of the deadhead mileage, the cost of establishing depots, maintenance, and charging stations, cost of renting external buses minus the salvage value obtained after selling off the land of existing depots.…”

Section: Literature Reviewmentioning

confidence: 99%

Efficient Heuristic Methods for Minimizing the Cost of City Bus Operations of Indian Urban Road Transportation Organizations

Mathirajan¹

2022

Proceedings of the International Conference on Industrial Engineering and Operations Management

View full text Add to dashboard Cite

The statistics on financial performances of Indian-Urban Road Transportation Organizations (I-URTOs) indicate that almost every I-URTO is facing huge losses in operating city buses. Considering monetary loss and increased fuel cost, there is a need to address decision problem(s) to minimize the cost of city bus operations (CCBO) without increasing bus fares. These buses traverse many non-revenue generating distances. One such distance is the 'dead kilometers' (DK) which is the summation of the distance traversed from the depot to starting point of first trip and ending point of last trip to the depot without carrying passengers. A report by the Central Institute of Road Transport (CIRT) revealed more than 27 crores DK being incurred in 2016-2017. An efficient allocation of city buses to depots (ACBD) not only reduces DK but also reduces CCBO. The problem of minimizing DK is addressed widely. However, since every bus has a different mileage per unit of fuel, minimizing DK cost (DKC) is more appropriate, which has not been considered in India. Hence, this study considers this issue. Since ACBD can be viewed as a special type of transportation problem, 8 best initial basic feasible solution (IBFS) methods reported in the literature are considered to reduce CCBO. From the performance evaluation, the best-performing method is identified. As this is an ongoing Ph.D. dissertation work, many future research activities like developing and/or including a greater number of IBFS methods, considering depot operating costs/cost due to CO2 /etc. are in the pipeline as immediate future research issues.

show abstract

“…Global carbon emissions, especially from the transportation sector, have been a worldwide concern for a while now ( 1 ). To mitigate this, major economies are planning to move toward sustainable and eco-friendly transportation.…”

mentioning

confidence: 99%

“…While all these studies considered complete electrification of BEB fleets, few considered provision for partial electrification by providing measures for fleet replacement in the network ( 3 , 13 ) and proposed the bus fleet replacement problem for cost-effectively switching toward electrification. Along similar lines, in a recent work Hsu et al ( 1 ) developed an optimization model to systematically locate depots for charging facilities, the optimal number of electric buses, and the associated routes that should be serviced by them in an existing diesel-bus system.…”

mentioning

confidence: 99%

Design of Battery Electric Bus System Considering Waiting Time Limitations

Gairola

Nezamuddin

2022

Transportation Research Record: Journal of the Transportation R

View full text Add to dashboard Cite

Battery electric bus (BEB) is getting increasing consideration from transit agencies as a sustainable public transportation alternative. Although BEBs limited driving range and longer charging duration cause concern for bus operators, fast-charging technology is a potential remedy for extending the daily driving range of BEBs while simultaneously reducing the charging duration and battery size. However, increased queue length at the terminals for fast charging can also cause a problem in adhering to BEB schedules. In this context, a comprehensive analysis is required to plan and design the BEB system considering waiting time limitations, and finding the right trade-off between battery sizes and charging infrastructure. In this study, we developed a base optimization model, as well as its stochastic version, accounting for varying energy demand scenarios under different operating conditions. Our models determined the effects of waiting time limitations over the BEB battery sizes and charger design variables (location, size, and capacity) while minimizing the total cost of the BEB system. The sample average across all the scenarios is used to estimate the stochastic optimization model objective, which is then solved using the Lagrangian relaxation method. We implemented our model over a public bus subnetwork proposed for electrification in the city of New Delhi, India. The results suggest that considering waiting time limitations at a fast-charging station can affect the system design and will help bus operators abide by the service level agreements covering trip delay, frequency, daily mileage, and so forth, made with the transit agency.

show abstract

The depot and charging facility location problem for electrifying urban bus services

Cited by 29 publications

References 32 publications

Lifecycle Cost Optimization for Electric Bus Systems With Different Charging Methods: Collaborative Optimization of Infrastructure Procurement and Fleet Scheduling

Lifecycle Cost Optimization for Electric Bus Systems With Different Charging Methods: Collaborative Optimization of Infrastructure Procurement and Fleet Scheduling

Efficient Heuristic Methods for Minimizing the Cost of City Bus Operations of Indian Urban Road Transportation Organizations

Design of Battery Electric Bus System Considering Waiting Time Limitations

Contact Info

Product

Resources

About