Fast Open Circuit Voltage Estimation of Lithium-Ion Batteries Using a Relaxation Model and Genetic Algorithm

Abstract

Battery Open Circuit Voltage (OCV) is of fundamental characteristic for enabling battery modeling and states estimation. However, the traditional OCV measurement method takes a very long time to make the battery reaches its equilibrium, which is rather inconvenient and cannot be performed online for battery energy storage application. Motived by this, this paper proposes an effective method for fast OCV estimation in the relaxation process. In this work, a novel relaxation model is designed for capturing the voltage response of a battery during relaxation time and the Genetic Algorithm (GA) is further applied for optimizing the model parameters and acquiring accurate OCV estimation results. Experimental results confirm the validity of the proposed method under different State of Charges (SOCs), current rates, ambient temperatures, and aging conditions. The results suggest that the proposed method can accurately and quickly estimate battery OCV, which only takes 10 minutes of measurement data (more than 2 hours for the traditional method) and the maximum estimation error is limited to merely 1.8 mV. INDEX TERMSLithium-ion battery, open circuit voltage, relaxation model.

High-Precision State of Charge Estimation of Urban-Road-Condition Lithium-Ion Batteries Based on Optimized High-Order Term Compensation-Adaptive Extended Kalman Filtering

“…38 The algorithm's parameter discrimination estimation capability does not overly depend on the current data as the model parameters change in real time. 39,40 The least squares method is a simple and effective fitting algorithm that can give more accurate parameter fitting results for nonlinear systems, calculated as in Eq. 8.…”