State-of-charge Stream Processing and Modeling for Electric Vehicle-Based Trips

Abstract

This paper first describes how to process SoC (State of Charge) streams made up of temporal and spatial stamps, altitude, SoC reading, velocity, and the like. Collected by a probe vehicle on the same road in Jeju city, Republic of Korea, multiple times, each stream has its own initial SoC and speed. The driving distance is calculated by adding up the line segment connecting two consecutive sensor points. For each stream, we plot 1) the absolute SoC change to figure out the SoC dynamics, 2) the consumed SoC change to find the similarity between different streams, and 3) the normalized curves to build a common SoC change model. Next, the artificial neural network is exploited to trace a common pattern out of them, aiming at providing an essential basis for navigation applications supporting electric vehicles. The analysis result reveals that the model can find reasonable representative values out of different streams for each subsection, even with a simple model having just one input variable denoting the distance from the start point and one output variable denoting the SoC reading. Our model keeps the trace error less than 5 % for the duration in which the vehicle speed is the same for each stream.