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

• Published in: IEEE transactions on human-machine systems 2011-11, Vol.41 (6), p.869-884
• Main Authors: Pattipati, B., Sankavaram, C., Pattipati, K.
• Format: Article
• Language: English
• Subjects: Applied sciences; Automotive components; Battery; Battery charge measurement; Battery management system (BMS); Battery management systems; Charge; Circuits; Computer science; control theory; systems; Data processing. List processing. Character string processing; Direct energy conversion and energy accumulation; Electric batteries; Electrical engineering. Electrical power engineering; Electrical power engineering; Electrochemical conversion: primary and secondary batteries, fuel cells; Estimates; Exact sciences and technology; hidden Markov model (HMM); Hidden Markov models; Hybrid electric vehicles; Management systems; Mathematical models; Mechanical engineering. Machine design; Memory and file management (including protection and security); Memory organisation. Data processing; Remaining life assessment; remaining useful life (RUL); Software; state of charge (SOC); state of health (SOH); Studies; Support vector machines; support vector machines (SVMs)
• ISSN: 1094-6977; 2168-2291; 1558-2442; 2168-2305
• DOI: 10.1109/TSMCC.2010.2089979

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.