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.

Abstract-The paper introduces a methodical approach which can be used to identify the optimum charging strategy for a fleet of electrical-powered buses. The methodical approach allows minimizing the energy consumption, the peak load demand and ageing of the batteries. This method uses optimisation algorithms to search for optimal plans taking into account technical constraints.

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.

The use of electric buses (EBs) is expected to increase significantly in the coming years. Uncontrolled charging of EBs can affect not only the power grid (grid instability, harmonic pollution) but also the operating cost. This paper introduces an optimal charging strategy based on charging schedule planning and modulation of charging power for a fleet of electrical-powered buses. The optimal charging strategy allows minimising the charging cost as well as the load power variations using quadratic programming. The proposed quadratic programming can significantly reduce the computation time and simultaneously handle a large bus fleet. First results indicate a significant reduction in customer energy bills while avoiding potential penalties due to peak loads.

The use of electric buses (EBs) is expected to increase significantly in the coming years. Uncontrolled charging of EBs can affect not only the power grid (grid instability, harmonic pollution) but also the operating cost. This paper introduces an optimal charging strategy based on charging schedule planning and modulation of charging power for a fleet of electrical-powered buses. The optimal charging strategy allows minimising the charging cost as well as the load power variations using quadratic programming. The proposed quadratic programming can significantly reduce the computation time and simultaneously handle a large bus fleet. First results indicate a significant reduction in customer energy bills while avoiding potential penalties due to peak loads.