Abstract
The lifetime of traction battery systems is an essential feature of the economy of battery electric urban bus fleets. This paper presents a model for the analysis and prediction of the lifetime of urban electric bus batteries. The parameterization of the model is based on laboratory measurements. The empirical ageing model is an integral part of a three-stage battery model, which in turn is an important component of the methodology for the overall system design, evaluation and optimisation of battery electric urban bus fleets. In an equidistant closed simulation loop, the electrical and thermal loads of the traction battery are determined, which are then used in the ageing model to calculate the SOH (state of health) of the battery. The closed simulation loop also considers the effects of a constantly changing SOH on the driving dynamics of the vehicles. The model for lifetime analysis and prognosis is presented in the paper, placed in the context of the overall system design and demonstrated by means of a practice-oriented example. The results show that the optimal system design depends, among other things, on whether an ageing simulation was used. Taking battery aging into account, system costs in the example presented can be reduced by up to 17 %.
Highlights
Driven by the ongoing discussion about clean air in German cities, many municipal transport companies are pushing ahead with the conversion of their bus fleets from diesel buses to electric buses
In a few projects was the number of vehicles procured sufficient to operate an entire line with electric buses
In this paper a methodology was presented which enables the dimensioning of energy storage and charging infrastructure of electric bus lines
Summary
Driven by the ongoing discussion about clean air in German cities, many municipal transport companies are pushing ahead with the conversion of their bus fleets from diesel buses to electric buses. The battery current resulting from the power requirement and the State of Charge (SOC) are calculated. Both values are transferred to the thermal model. All possible combinations of the variation parameters within the defined boundaries are calculated In this example, the step size of the charging power is 10 kW. All technically possible configurations within the defined boundaries are determined At this point, the evaluation criteria is the daily SOC curve, at which the lower limit SOCmin must not be violated. The system costs over the vehicle life cycle can be reduced by 17 % due to the extended lifetime
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