Bidirectional Charging in V2G Systems: An In-Cell Variation Analysis of Vehicle Batteries

Abstract

Vehicle-to-grid (V2G) technology enables bidirectional charging of electric vehicle (EV) and facilitates power grid ancillary services. However, battery pack in EV may develop in-cell dynamic variations over time. This is due to the structural complexity and electrochemical operations in the battery pack. These variations may arise in V2G systems due to: first, additional charging and discharging cycles to power grid; second, external shocks; and third, long exposures to high temperatures. A particular source of these variations is due to faulty sensors. Therefore, it can be argued that the battery packs in EV are highly reliant on the monitoring of these in-cell variations and their impact of propagation with each involved component. In this article, a prediction-based scheme to monitor the health of variation induced sensors is proposed. First, a propagation model is developed to predict the in-cell variations of a battery pack by calculating the covariance using a median-based expectation. Second, a hypothesis model is developed to detect and isolate each variation. This is obtained by deriving a conditional probability-based density function for the measurements. The proposed monitoring framework is evaluated using experimental measurements collected from Li-ion battery pack in EVs. The in-cell variation profiles have been verified using D-SAT Chroma 8000ATS hardware platform. The performance results of the proposed scheme show accurate analysis of these emerged variations.