Prediction of Battery Behavior Subject to High-Rate Partial State of Charge

• Published in: IEEE transactions on vehicular technology Vol. 58; no. 2; pp. 588 - 595
• Main Authors: van Bree, P.J., Veltman, A., Hendrix, W.H.A., van den Bosch, P.P.J.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.02.2009; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Applied sciences; Battery; Battery charge measurement; Battery model; Charge; charge acceptance; Cities and towns; Current measurement; Design. Technologies. Operation analysis. Testing; Direct energy conversion and energy accumulation; Drives; Electric batteries; Electric potential; Electric variables measurement; Electric vehicles; Electrical engineering. Electrical power engineering; Electrical power engineering; Electrochemical conversion: primary and secondary batteries, fuel cells; Electronics; Exact sciences and technology; Ground, air and sea transportation, marine construction; high-rate partial state-of-charge (HRPSOC); hybrid electric vehicles (HEVs); Integrated circuits; lead-acid battery; Mathematical models; On-line systems; Optimization; Predictive models; Pulse measurements; Road transportation and traffic; Semiconductor electronics. Microelectronics. Optoelectronics. Solid state devices; state of function (SOF); Vehicle dynamics; Voltage; Lead-acid battery; hybrid electric vehicles (HEVs); battery model; charge acceptance; state-of-function; high rate partial state-of-charge; Lead acid batteries; Hybrid electric powered vehicles; Hybrid vehicle; Optimization method; Electrical method; On-line systems; Secondary cell; System on a chip; Electric vehicle; Urban area; state of function (SOF); Battery model; Accuracy; high-rate partial state-of-charge (HRPSOC); Non linear model; Integrated circuit; State of charge; lead-acid battery
• ISSN: 0018-9545; 1939-9359
• DOI: 10.1109/TVT.2008.928005

An online optimization procedure provides the parameters of a nonlinear battery model by taking into account a few minutes of measured current-voltage data. Within a defined range in terms of charge current, state of charge (SOC), and duration of charge and discharge events, the model is able to capture the relevant battery dynamics and predict the behavior for the next few minutes. From the battery behavior during specific events, the state of the battery can be revealed, which is defined as the state of function. Validation, which is carried out on measured current-voltage profiles, shows the accuracy of prediction during the high-rate partial SOC operation. Even with the data measured during a city drive within a microhybrid electrical vehicle, the method is able to predict the voltage level during high-rate discharge pulses (cranking).cranking