State of charge

About: State of charge is a research topic. Over the lifetime, 12013 publications have been published within this topic receiving 201419 citations. The topic is also known as: SoC & SOC.

System Identification and Estimation Framework for Pivotal Automotive Battery Management System Characteristics

Abstract: The battery management system (BMS) is an integral part of an automobile. It protects the battery from damage, predicts battery life, and maintains the battery in an operational condition. The BMS performs these tasks by integrating one or more of the functions, such as protecting the cell, thermal management, controlling the charge-discharge, determining the state of charge (SOC), state of health (SOH), and remaining useful life (RUL) of the battery, cell balancing, data acquisition, communication with on-board and off-board modules, as well as monitoring and storing historical data. In this paper, we propose a BMS that estimates the critical characteristics of the battery (such as SOC, SOH, and RUL) using a data-driven approach. Our estimation procedure is based on a modified Randles circuit model consisting of resistors, a capacitor, the Warburg impedance for electrochemical impedance spectroscopy test data, and a lumped parameter model for hybrid pulse power characterization test data. The resistors in a Randles circuit model usually characterize the self-discharge and internal resistance of the battery, the capacitor generally represents the charge stored in the battery, and the Warburg impedance represents the diffusion phenomenon. The Randles circuit parameters are estimated using a frequency-selective nonlinear least squares estimation technique, while the lumped parameter model parameters are estimated by the prediction error minimization method. We investigate the use of support vector machines (SVMs) to predict the capacity fade and power fade, which characterize the SOH of a battery, as well as estimate the SOC of the battery. An alternate procedure for estimating the power fade and energy fade from low-current Hybrid Pulse Power characterization (L-HPPC) test data using the lumped parameter battery model has been proposed. Predictions of RUL of the battery are obtained by support vector regression of the power fade and capacity fade estimates. Survival function estimates for reliability analysis of the battery are obtained using a hidden Markov model (HMM) trained using time-dependent estimates of capacity fade and power fade as observations. The proposed framework provides a systematic way for estimating relevant battery characteristics with a high-degree of accuracy.

Lithium Polymer Battery State-of-Charge Estimation Based on Adaptive Unscented Kalman Filter and Support Vector Machine

Abstract: An accurate algorithm for lithium polymer battery state-of-charge (SOC) estimation is proposed based on adaptive unscented Kalman filters (AUKF) and least-square support vector machines (LSSVM). A novel approach using the moving window method is applied, with AUKF and LSSVM to accurately establish the battery model with limited initial training samples. The effectiveness of the moving window modeling method is validated by both simulations and lithium polymer battery experimental results. The measurement equation of the proposed AUKF method is established by the LSSVM battery model and AUKF has the advantage of adaptively adjusting noise covariance during the estimation process. In addition, the developed LSSVM model is continuously updated online with new samples during the battery operation, in order to minimize the influence of the changes in battery internal characteristics on modeling accuracy and estimation results after a period of operation. Finally, a comparison of accuracy and performance between the AUKF and UKF is made. Simulation and experiment results indicate that the proposed algorithm is capable of predicting lithium battery SOC with a limited number of initial training samples.