Efficient GPS Route Matching Method for Battery Electric Bus Fleets

Abstract

<div class="section abstract"><div class="htmlview paragraph">A challenge of public transportation GPS data is the frequent utilization of monitoring systems with low sampling rates, primarily driven by the high costs associated with cellular data transmission of large datasets. Altitude data is often imprecise or not recorded at all in regions without large elevation changes. The low data quality limits the use of the data for further detailed investigations like a realistic energy consumption forecast for assessing the electrical grid load resulting from charging the vehicle fleet. Modern research often reconstructs speed data only, or uses additional GPS loggers, which is associated with increased costs in the vehicle fleet. The importance of precise and high-quality altitude data and specialized expertise in mountainous regions are frequently overlooked. This paper introduces an efficient new route matching method to reconstruct speed and respective road slope data of a GPS signal sampled at low frequency for a public transportation electric bus. To that end, an algorithm is presented which merges speed data with the corresponding altitude information. It accomplishes this by optimizing time segments of the resampled speed signal based on the distance traveled on the road, which is extracted via map matching. The optimized data feeds a verified longitudinal dynamics model of a battery electric bus and evaluates the energy consumption and battery SOC for different operating conditions. The consumption is compared to the energy consumption evaluated by a simulation model using high-frequency sampled real route data collected by a dedicated GPS data logger installed in a battery electric bus to verify the algorithm. The proposed method reconstructs and approximates the driven route (speed and slope) with high resolution. Therefore, it enables model-based predictions for the bus fleet for different operating conditions e.g. ambient temperature, battery age or loading. The method facilitates the optimization of fleet operations, focusing on battery sizing, charging management and energy grid conservation. In subsequent works, the toolchain is integrated into an ecosystem supporting bus and energy grid operators.</div></div>