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Wireless charger deployment for an electric bus network: A multi-objective life cycle optimization

Bi, Zicheng, Keoleian, Gregory A., Ersal, Tulga
Applied energy 2018 v.225 pp. 1090-1101
batteries, bus transportation, case studies, economic investment, energy, greenhouse gas emissions, greenhouse gases, infrastructure, life cycle assessment, life cycle costing, models, prices, Michigan
Deploying large-scale wireless charging infrastructure at bus stops to charge electric transit buses when loading and unloading passengers requires significant capital investment and brings environmental and energy burdens due to charger production and deployment. Optimal siting of wireless charging bus stops is key to reducing these burdens and enhancing the sustainability performance of a wireless charging bus fleet. This paper presents a novel multi-objective optimization model framework based on life cycle assessment (LCA) for siting wireless chargers in a multi-route electric bus system. Compared to previous studies, this multi-objective optimization framework evaluates not only the minimization of system-level costs, but also newly incorporates the objectives of minimizing life cycle greenhouse gas (GHG) emissions and energy consumption during the entire lifetime of a wireless charging bus system. The LCA-based optimization framework is more comprehensive than previous studies in that it encompasses not only the burdens associated with wireless charging infrastructure deployment, but also the benefits of electric bus battery downsizing and use-phase vehicle energy consumption reduction due to vehicle lightweighting, which are directly related to charger siting. The impact of charger siting at bus stops with different route utility and bus dwell time on battery life is also considered. To demonstrate the model application, the route information of the University of Michigan bus routes is used as a case study. Results from the baseline scenario show that the optimal siting strategies can help reduce life cycle costs, GHG, and energy by up to 13%, 8%, and 8%, respectively, compared to extreme cases of “no charger at any bus stop” and “chargers at every stop”. Further sensitivity analyses indicate that the optimization results are sensitive to the initial battery unit price ($/kWh), charging power rate (kW), charging infrastructure costs, and battery life estimation methods.