Planning of Fast-Charging Stations for a Battery Electric Bus System under Energy Consumption Uncertainty

Battery-powered electric buses are gaining popularity as an energy-efficient and emission-free alternative for bus fleets. However, battery electric buses continue to struggle with concerns related to their limited driving range and time-consuming recharging processes. Fast-charging technology, which utilizes dwelling time at bus stops or terminals to recharge buses in operation employing high power, can raise battery electric buses to the same level of capability as their diesel counterparts in terms of driving range and operating time. To develop an economical and effective battery electric bus system using fast-charging technology, fast-charging stations must be strategically deployed. Moreover, due to the instability of traffic conditions and travel demands, the energy consumption uncertainty of buses should also be considered. This study addresses the planning problem of fast-charging stations that is inherent in a battery electric bus system in light of the energy consumption uncertainty of buses. A robust optimization model that represents a mixed integer linear program is developed with the objective of minimizing the total implementation cost. The model is then demonstrated using a real-world bus system. The performances of deterministic solutions and robust solutions are compared under a worst-case scenario. The results demonstrate that the proposed robust model can provide an optimal plan for a fast-charging battery electric bus system that is robust against the energy consumption uncertainty of buses. The trade-off between system cost and system robustness is also addressed.

[1]  Arkadi Nemirovski,et al.  Robust solutions of uncertain linear programs , 1999, Oper. Res. Lett..

[2]  Robert Prohaska,et al.  Foothill Transit Battery Electric Bus Demonstration Results , 2016 .

[3]  A. Schroeder,et al.  The economics of fast charging infrastructure for electric vehicles , 2012 .

[4]  Azwirman Gusrialdi,et al.  Numerical analysis of electric bus fast charging strategies for demand charge reduction , 2016 .

[5]  Chi Xie,et al.  Robust Optimization Model for a Dynamic Network Design Problem Under Demand Uncertainty , 2011 .

[6]  Stephen Potter,et al.  Developing a viable electric bus service: The Milton Keynes demonstration project , 2014 .

[7]  Jean-Philippe Vial,et al.  Robust Optimization , 2021, ICORES.

[8]  Ziqi Song,et al.  Optimal Deployment of Dynamic Wireless Charging Facilities for an Electric Bus System , 2017 .

[9]  Board on Energy,et al.  Transitions to Alternative Vehicles and Fuels , 2013 .

[10]  Ziqi Song,et al.  Robust planning of dynamic wireless charging infrastructure for battery electric buses , 2017 .

[11]  François Glineur,et al.  Topics in Convex Optimization: Interior-Point Methods, Conic Duality and Approximations , 2001 .

[12]  Tao Yao,et al.  Robust optimization for emergency logistics planning: Risk mitigation in humanitarian relief supply chains , 2011 .

[13]  D. Goehlich,et al.  Electrification of a city bus network—An optimization model for cost-effective placing of charging infrastructure and battery sizing of fast-charging electric bus systems , 2017 .

[14]  Jing-Quan Li,et al.  Battery-electric transit bus developments and operations: A review , 2016 .

[15]  Chung-Cheng Lu,et al.  Robust Multi-period Fleet Allocation Models for Bike-Sharing Systems , 2016 .

[16]  Michel Bierlaire,et al.  Planning of feeding station installment for electric urban public mass-transportation system , 2013 .

[17]  A. Ben-Tal,et al.  Adjustable robust solutions of uncertain linear programs , 2004, Math. Program..

[18]  Tingting Mu,et al.  Context-Aware and Energy-Driven Route Optimization for Fully Electric Vehicles via Crowdsourcing , 2013, IEEE Transactions on Intelligent Transportation Systems.