Optimal charging strategy for large-scale electric buses considering resource constraints

Liu, Kai; Gao, Hong; Liang, Zhe; Zhao, Meng; Li, Cheng

doi:10.1016/j.trd.2021.103009

Cited by 75 publications

(24 citation statements)

References 39 publications

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“…The main obstacles to the widespread adoption of EVs include their prolonged charging times, limited driving range, inadequate charging infrastructure, and hefty investment prices. [4][5][6] EV charging operations, whether at home or at a public charging station, necessitate the construction of EV suitable charging platforms and infrastructure. Furthermore, increasing EVs penetration in the distribution network requires a massive financial investment in smart grid technologies.…”

Section: Introductionmentioning

confidence: 99%

“…Despite their apparent benefits, the adoption of EVs is still substantially lower than gasoline‐based vehicles. The main obstacles to the widespread adoption of EVs include their prolonged charging times, limited driving range, inadequate charging infrastructure, and hefty investment prices 4‐6 . EV charging operations, whether at home or at a public charging station, necessitate the construction of EV suitable charging platforms and infrastructure.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of electric vehicle charging duration time using ensemble machine learning algorithm and Shapley additive explanations

Ullah

Liu

Yamamoto

et al. 2022

Intl J of Energy Research

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Summary Electric vehicles (EVs) are the most important components of smart transportation systems. Limited driving range, prolonged charging times, and inadequate charging infrastructure are the key barriers to EV adoption. To address the problem of prolonged charging time, the simple approach of developing a new charging station to enhance the charging capacity may not work due to the limitation of physical space and strain on power grids. Prediction of precise EV charging time can assist the drivers in effective planning of their trips to alleviate range anxiety during trips. Therefore, this study employed four different ensemble machine learning (EML) algorithms: random forest, extreme gradient boosting (XGBoost), categorical boosting, and light gradient boosting machine, for predicting EVs' charging time. The prediction experiments were based on 2 years of real‐world charging event data from 500 EVs in Japan's private and commercial vehicles. The study emphasized predicting charging time for different charging modes, that is, normal and fast charging operations. The results indicate that EML models performed well under various scenarios, with the XGBoost model having the highest accuracy. Moreover, we also employ the newly developed Shapley additive explanation (SHAP) approach to tackle the non‐interpretability issues of the ML algorithm by interpreting the XGBoost model outputs. The obtained SHAP value plots demonstrated the nonlinear relationship between explanatory variables and EV charging time.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction of electric vehicle charging duration time using ensemble machine learning algorithm and Shapley additive explanations

Ullah

Liu

Yamamoto

et al. 2022

Intl J of Energy Research

Self Cite

View full text Add to dashboard Cite

show abstract

“…The transportation network in Davis, CA, was selected to testify the proposed charging framework. The solution methods for the above charging schedule optimization models are relatively diverse, including heuristic algorithms (such as genetic algorithms) (Rogge et al , 2018), branch-and-bound methods (Zhang et al , 2021b) and column generation algorithms (Liu et al , 2021).…”

Section: Introductionmentioning

confidence: 99%

“…Rogge et al (2018) proposed a mixed-integer nonlinear programming model with the consideration of energy consumption, driving range limitation and required charging time to optimize the charging schedule. Moreover, some other special conditions, such as the combination of bus mobile charging and fixed charging (Zhang et al , 2021a), heterogeneous charging modes (Zhang et al , 2022) and charging resource constraints (Liu et al , 2021), were considered to obtain the charging schedule for BEBs. Researchers also applied real bus systems to verify the proposed charging schedule optimization models.…”

Section: Introductionmentioning

confidence: 99%

Battery electric buses charging schedule optimization considering time-of-use electricity price

Yan

Zhang

et al. 2022

JICV

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Purpose This paper aims to optimize the charging schedule for battery electric buses (BEBs) to minimize the charging cost considering the time-of-use electricity price. Design/methodology/approach The BEBs charging schedule optimization problem is formulated as a mixed-integer linear programming model. The objective is to minimize the total charging cost of the BEB fleet. The charge decision of each BEB at the end of each trip is to be determined. Two types of constraints are adopted to ensure that the charging schedule meets the operational requirements of the BEB fleet and that the number of charging piles can meet the demand of the charging schedule. Findings This paper conducts numerical cases to validate the effect of the proposed model based on the actual timetable and charging data of a bus line. The results show that the total charge cost with the optimized charging schedule is 15.56% lower than the actual total charge cost under given conditions. The results also suggest that increasing the number of charging piles can reduce the charging cost to some extent, which can provide a reference for planning the number of charging piles. Originality/value Considering time-of-use electricity price in the BEBs charging schedule will not only reduce the operation cost of electric transit but also make the best use of electricity resources.

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“…The aforementioned papers mainly use more conventional mathematical programming methods to show the benefits of optimally scheduling the charging process of BEBs. However, these methods can have long computation times which make them appropriate for day-ahead scheduling, but less suitable for solving real-time charging scheduling problems [19,20]. This is important to enable smart charging in real-world applications such as bus depots and is currently missing in the scientific literature.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Charging Scheduling and Optimization of Electric Buses in a Depot

Verbrugge

Rauf²,

Rasool

et al. 2022

Energies

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To improve the air quality in urban areas, diesel buses are getting replaced by battery electric buses (BEBs). This conversion introduces several challenges, such as the proper control of the charging process and a reduction in the operational costs, which can be addressed by introducing smart charging concepts for BEB fleets. Therefore, this paper proposes a real-time scheduling and optimization (RTSO) algorithm for the charging of multiple BEBs in a depot. The algorithm assigns a variable charging current to the different time slots the charging process of each BEB is divided to provide an optimal charging schedule that minimizes the charging cost, while satisfying the power limitations of the distribution network and maintaining the operation schedule of the BEBs. A genetic algorithm is used to solve the formulated cost function in real time. Several charging scenarios are tested in simulation, which show that a reduction in the charging cost up to 10% can be obtained under a dynamic electricity price scheme. Furthermore, the RTSO is implemented in a high-level charging management system, a new feature required to enable smart charging in practice, to test the developed algorithm with existing charging infrastructure. The experimental validation of the RTSO algorithm has proven the proper operation of the entire system.

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Optimal charging strategy for large-scale electric buses considering resource constraints

Cited by 75 publications

References 39 publications

Prediction of electric vehicle charging duration time using ensemble machine learning algorithm and Shapley additive explanations

Prediction of electric vehicle charging duration time using ensemble machine learning algorithm and Shapley additive explanations

Battery electric buses charging schedule optimization considering time-of-use electricity price

Real-Time Charging Scheduling and Optimization of Electric Buses in a Depot

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