Trip energy consumption estimation for electric buses

Ji, Jinhua; Bie, Yiming; Zeng, Ziling; Wang, Lınhong

doi:10.1016/j.commtr.2022.100069

Cited by 79 publications

(22 citation statements)

References 41 publications

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“…Parameters used in numerical instances for the methodology assessment are presented in this section. The capacity of BEBs is set to 200 kWh, the energy consumption rate is 0.37 kWh/km (Ji et al., 2022), the hourly operating cost of a BEB is $33/h (Li et al., 2018), and the capital cost of purchasing a BEB per year is $42,619 (Hensher et al., 2022). The network of numerical instances consisting of 40 candidate locations for charging stations (16 at bus terminals and 24 not at bus terminals) and 50 bus routes (25 lines with double arrows) is shown in Figure 5.…”

Section: Computational Experimentsmentioning

confidence: 99%

Optimal location planning of electric bus charging stations with integrated photovoltaic and energy storage system

Liu

Zhang

et al. 2022

Computer aided Civil Eng

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This study presents a novel bus charging station planning problem considering integrated photovoltaic (PV) and energy storage systems (PESS) to smooth the carbon-neutral transition of transportation. This paper illustrates a twostage stochastic programming model capturing the uncertainty of PV power outputs and designs a step-wise solution approach in which a conventional charging station location problem is solved in the first step and an improved L-shaped algorithm is developed in the second step to determine charging station locations with PESS. The numerical experiments indicate that the step-wise solution approach can provide high-quality solutions within acceptable computational time. A case study is performed using a real-world transit network in Beijing, China, with 34 bus routes and 15 candidate bus charging stations. Compared with the benchmark model, both recharging cost and carbon emission are reduced considerably. This paper provides novel insights into the development of integrated transportation and renewable energy systems.

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Section: Computational Experimentsmentioning

confidence: 99%

Optimal location planning of electric bus charging stations with integrated photovoltaic and energy storage system

Liu

Zhang

et al. 2022

Computer aided Civil Eng

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“…In reality, it is very difficult to estimate the required power, because it depends on both stationary parameters (mass, heat capacity, partly the difference between the required changeover temperature and the temperature of the surrounding environment) and dynamic ones -the rate of environmental temperature change, changes in climatic conditions depending on the climatic dynamic impacting factors -wind strength, humidity, atmospheric pressure [10,11]. In the study [12], the study of power changes depending on various environmental factors was carried out. The research was conducted in one of Scandinavian country, where the climatic conditions are close to the conditions in Latvia.…”

Section: Development Of the Heating Element Power Control Conceptmentioning

confidence: 99%

Indirect measurements in the intelligent heating control system for the turnout using adaptive systems

Muhitovs¹,

Mezitis²,

Strautmanis³

et al. 2022

J. meas. eng.

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The article discusses the possibility of applying the results of regression analysis [1], fuzzy logic modelling and adaptive systems as such for use in heating control circuits of railway turnouts in order to reduce energy consumption, reduce current surges during on/off switching, and extend the life of heating elements. Based on various researches [2-4] some new solutions are proposed to fulfil shortages of current solutions. As a proposed solution to the described problem, an experimental point electric heating adaptive control system is proposed and described. Experiment of controlling point electric heating system using pulse-width modulation driven by values of regression analysis and fuzzy logic elements. Adaptive methods allowed to dismiss contact temperature sensors, which is associated with a decrease in the reliability of the system, but this fact is prevented by the use of such control methods that can indirectly determine the required rail heating temperature. Conclusions show that the approach of introducing adaptive methods like pulse-width modulation allows to control point electric heating in more efficient way – using program code, control system will adjust heating by adjusting time intervals when the heating is turned on and off.

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“…The aforementioned approaches are feasible for many applications and are supported by additional work that estimates route times and energy demands [25], although the current methods do not scale with the number of buses while bounding the optimality of their solution.…”

Section: Introductionmentioning

confidence: 99%

A Scalable Approach to Minimize Charging Costs for Electric Bus Fleets

Mortensen,

Gunther

2024

WEVJ

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Incorporating battery electric buses into bus fleets faces three primary challenges: a BEB’s extended refuel time, the cost of charging, both by the consumer and the power provider, and large compute demands for planning methods. When BEBs charge, the additional demands on the grid may exceed hardware limitations, so power providers divide a consumer’s energy needs into separate meters even though doing so is expensive for both power providers and consumers. Prior work has developed a number of strategies for computing charge schedules for bus fleets; however, prior work has not worked to reduce costs by aggregating meters. Additionally, because many works use mixed integer linear programs, their compute needs make planning for commercial-sized bus fleets intractable. This work presents a multi-program approach to computing charge plans for electric bus fleets. The proposed method solves a series of subproblems where the solution to the charge problem becomes more refined with each problem, moving closer to the optimal schedule. The results demonstrate how runtimes are reduced by using intermediate subproblems to refine the bus charge solution so that the proposed method can be applied to large bus fleets of 100+ buses. Not only will we demonstrate that runtimes scale linearly with the number of buses but we will also show how the proposed method scales to large bus fleets of over 100 buses while managing the monthly cost of energy.

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Trip energy consumption estimation for electric buses

Cited by 79 publications

References 41 publications

Optimal location planning of electric bus charging stations with integrated photovoltaic and energy storage system

Optimal location planning of electric bus charging stations with integrated photovoltaic and energy storage system

Indirect measurements in the intelligent heating control system for the turnout using adaptive systems

A Scalable Approach to Minimize Charging Costs for Electric Bus Fleets

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