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Energy Consumption Estimation for Electric Vehicles Using Routing API Data
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DOI：10.1007/978-3-031-25049-1_3.png)
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Electric vehicle (EV) range anxiety influences electric vehicles' low penetration into the transportation system. There have been several developments in range estimation for electric vehicles. However, the studies that focus on determining the remaining range based on real-time publicly available data remain low. Most of the current methods employed consider limited data collection and do not consider the most substantial factors that directly impact energy consumption. This paper introduces a velocity model based on route information for the range estimation of electric vehicles. It uses publicly available data sets from several map service APIs and incorporates them into the range estimation algorithm. Three map service APIs were used to collect the data over an extended period. Then we analysed this data to extract the most representative data to generate the velocity profiles. The paper uses MATLAB code and Python libraries to process the representative data and apply the velocity model. Moreover, we have integrated it into an electric vehicle model, including the battery, to estimate the power demand for each trip and the remaining driving range. We observed that producing realistic driving cycles using public data is possible; furthermore, it simulates the driving patterns and satisfies the constraints of the vehicle.
Keyword:
Electric vehicles
Driving cycles
Range estimation
SOC estimation
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