Measurement Correction for Electric Vehicles Based on Compressed Sensing

Abstract

Deviations between system current measurements and real values in the power train of Electric Vehicles (EVs) can cause severe problems. Among others, these are restricted performance and cruising range. In this work, we propose a fleetbased framework to correct such deviations. We assume that the real value is the mean of all identically constructed EVs' measurements for the same input. Under this assumption, we decide for each vehicle whether it displays hardware errors with the help of a binary classifier. Depending on the classification, if no hardware errors are detected, we recover the parameters of an assumed measurement error model via Linear Regression. Otherwise, we combine the regression with a convex optimization problem and sparsity constraints. We achieve an overall recovery rate of up to 90 %, allowing the full automation of the measurement correction procedure with no need to add more sensors, or computational units on-board of the EV.

A deployment model of EV charging piles and its impact on EV promotion

“…Between 2013 and 2019, the average EV model range increased from 136 mi to 241 mi [15]. Over the past decade, battery and other EV technologies have dramatically advanced; for example, the cost of lithium battery price dropped 80%, the capacity of each battery cell doubled, and the performance of the powertrain advanced [16], [17].…”