Multi-Objective Optimisation of the Management of Electric Bus Fleet Charging

Houbbadi, Adnane; Trigui, Rochdi; Pélissier, Serge; Bouton, Tanguy; Redondo-Iglesias, Eduardo

doi:10.1109/vppc.2017.8331015

Cited by 14 publications

(14 citation statements)

References 9 publications

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“…The present paper differs from the referred ones [13][14][15][16][17][18][19][20][21][22][23][24][25] as it investigates more precisely the battery electro-thermal and aging behavior in order to minimize the battery aging cost. This work differs also from our previous results [26,27] that dealt with the reduction of the charging cost applied to a multi-objective small-scale EBs fleet and a mono-objective large-scale EBs fleet. The present study includes a large-scale EBs fleet with a possible extension of the proposed algorithm to hundreds of buses while using a mono-objective nonlinear programming optimization to minimize battery aging and requiring relatively short calculation time.…”

Section: Introductioncontrasting

confidence: 57%

“…As mentioned in Section 5.1, the optimal charging power Table 2 presents the CPU (Central Processing Unit) time for the charging optimization of different EBs fleet sizes over a period of 1 day. The NLP programming was compared with non-dominated sorting genetic algorithm (NSGA-II) used in a previous work [26]. From a bus fleet larger than 10 EBs and more, NSGA-II does not converge.…”

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confidence: 99%

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Optimal Scheduling to Manage an Electric Bus Fleet Overnight Charging

et al. 2019

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Electro-mobility is increasing significantly in the urban public transport and continues to face important challenges. Electric bus fleets require high performance and extended longevity of lithium-ion battery at highly variable temperature and in different operating conditions. On the other hand, bus operators are more concerned about reducing operation and maintenance costs, which affects the battery aging cost and represents a significant economic parameter for the deployment of electric bus fleets. This paper introduces a methodological approach to manage overnight charging of an electric bus fleet. This approach identifies an optimal charging strategy that minimizes the battery aging cost (the cost of replacing the battery spread over the battery lifetime). The optimization constraints are related to the bus operating conditions, the electric vehicle supply equipment, and the power grid. The optimization evaluates the fitness function through the coupled modeling of electro-thermal and aging properties of lithium-ion batteries. Simulation results indicate a significant reduction in the battery capacity loss over 10 years of operation for the optimal charging strategy compared to three typical charging strategies.

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Section: Introductioncontrasting

confidence: 57%

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confidence: 99%

Optimal Scheduling to Manage an Electric Bus Fleet Overnight Charging

et al. 2019

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“…The proposed algorithm has the objective to minimize the operational costs as well as to determine the amount of electrical buses necessary to replace hybrid buses. Houbbadi et al focus on multiobjective evolutionary algorithms for management of electric bus fleet charging [20]. The authors propose an optimal charging schedule with the goal to reduce the charging costs and battery ageing.…”

Section: Introductionmentioning

confidence: 99%

Charging Schedule for Load Peak Minimization on Large-Scale Electric Bus Depots

2019

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The city of Hamburg has decided to electrify its bus fleets. The two public transportation companies in this city expect to operate up to 1500 buses by 2030. In order to accomplish this ambitious goal, both companies need to build an appropriate charging infrastructure. They have both decided to implement the centralized depot charging concept. Buses can therefore charge only at the depot and do not have the possibility for opportunity charging at intermediate stations. The load profile of such a bus depot is highly dependent on the charging schedule of buses. Without an intelligent scheduling system, the buses charge on demand as soon as they arrive to the depot. This can lead to an unevenly distributed load profile with high load peaks, which is problematic for the local grid as well as for the equipment dimensioning at the depot. Charging scheduling on large-scale bus depots is a relatively new and poorly researched topic. This paper addresses the issue and proposes two algorithms for charging scheduling on large-scale bus depots with the goal to minimize the peak load. The schedules created with the proposed algorithms were both tested and validated in the Bus Depot Simulator, a cosimulation platform used for bus depot simulations.

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“…In this work, we investigate more precisely the battery electro-thermal and aging behavior of a large-scale EBs fleet while handling a multi-objective optimization problem. In our previous work [12], NSGA-II method has been tested with success for sloving multiobjective problem. The main drawback was the processing time and the possibility of considering only a weak number of buses.…”

Section: Introductionmentioning

confidence: 99%

“…The optimization takes place during the overnight charging. We present and discuss our results in Section V. The results of the optimization problem will be compared to the previous NSGA-II algorithm taken as a reference for a small number of buses [12]. Finally, Section VI draws some conclusions and introduces future work.…”

Section: Introductionmentioning

confidence: 99%

Optimal Charging Strategy to Minimize Electricity Cost and Prolong Battery Life of Electric Bus Fleet

Houbbadi

Redondo-Iglesias

Trigui

et al. 2019

2019 IEEE Vehicle Power and Propulsion Conference (VPPC)

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Smart charging is becoming an important and indispensable asset for electric bus fleet to become economically competitive. This paper proposes an innovative approach for setting overnight charging schedules of electric bus fleet. This approach uses nonlinear programming in order to minimize both the electricity cost and the battery aging. The optimization is constrained by the operating buses conditions, the electric vehicle supply equipment, and the power grid. A comparison between the nonlinear programming results and non-dominated sorted genetic algorithm (NSGA-II) will show the difference and complementarities of both approaches and proposes a number of trade-off optimal solutions.

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Multi-Objective Optimisation of the Management of Electric Bus Fleet Charging

Cited by 14 publications

References 9 publications

Optimal Scheduling to Manage an Electric Bus Fleet Overnight Charging

Optimal Scheduling to Manage an Electric Bus Fleet Overnight Charging

Charging Schedule for Load Peak Minimization on Large-Scale Electric Bus Depots

Optimal Charging Strategy to Minimize Electricity Cost and Prolong Battery Life of Electric Bus Fleet

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