State of charge determination in a lead-acid battery: combined EMF estimation and Ah-balance approach

• Published in: 2004 IEEE 35th Annual Power Electronics Specialists Conference (IEEE Cat. No.04CH37551) Vol. 3; pp. 1908 - 1914 Vol.3
• Main Authors: Zhu, C.B., Coleman, M., Hurley, W.G.
• Format: Conference Proceeding
• Language: English
• Published: Piscataway NJ IEEE 2004
• Subjects: Applied sciences; Batteries; Chemical processes; Chemical technology; Degradation; Design. Technologies. Operation analysis. Testing; Direct energy conversion and energy accumulation; Displays; Electrical engineering. Electrical power engineering; Electrical power engineering; Electrochemical conversion: primary and secondary batteries, fuel cells; Electronics; Exact sciences and technology; Integrated circuits; Performance evaluation; Prediction algorithms; Semiconductor electronics. Microelectronics. Optoelectronics. Solid state devices; State estimation; Testing; Voltage control; Lead acid batteries; Secondary cell; System on a chip; Algorithm; Discharge charge cycle; Overvoltage; Least squares method; Integrated circuit; Equivalent circuit; Laboratory test; Chemical composition; State of charge; Curve fitting
• ISBN: 9780780383999; 0780383990
• ISSN: 0275-9306
• DOI: 10.1109/PESC.2004.1355409

State of charge (SOC) determination is an increasingly important issue in battery technology. In addition to the immediate display of the remaining battery capacity to the user, precise knowledge of SOC exerts additional control over the charging/discharging process which in turn reduces the risk of overvoltage and gassing, which degrade the chemical composition of the electrolyte and plates. This paper describes a new approach to SOC determination for the lead-acid battery by combining Ah-balance with an EMF estimation algorithm, which predicts the battery's EMF value while it is under load. The EMF estimation algorithm is based on an equivalent-circuit representation of the battery, with the parameters determined from a pulse test performed on the battery and a curve-fitting algorithm by means of least-square regression. The whole battery cycle is classified into seven states where the SOC is estimated with the Ah-balance method and the proposed EMF based algorithm. Laboratory tests and results are described in detail in the paper.