An analytical model for predicting the remaining battery capacity of lithium-ion batteries

• Published in: IEEE transactions on very large scale integration (VLSI) systems Vol. 14; no. 5; pp. 441 - 451
• Main Authors: Peng Rong, Pedram, M.
• Format: Journal Article
• Language: English
• Published: Piscataway, NJ IEEE 01.05.2006; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accelerated rate capacity; Aging; Analytical models; Applied sciences; Battery; Battery management systems; Computer simulation; cycle aging and dynamic voltage scaling; Degradation; Dynamic voltage scaling; Dynamics; Electric batteries; Electrical engineering. Electrical power engineering; Electronic equipment and fabrication. Passive components, printed wiring boards, connectics; Electronics; Energy management; Exact sciences and technology; Exact solutions; Frequency; Lithium-ion batteries; Mathematical analysis; Mathematical models; Power electronics, power supplies; Predictive models; remaining battery capacity; Temperature; Voltage measurement; Performance evaluation; cycle aging and dynamic voltage scaling; Voltage current curve; Thermal ageing; Voltage regulation; Thermal cycle; On-line systems; Secondary cell; Power electronics; Algorithm; Dynamic conditions; Low voltage; Accelerated rate capacity; Power consumption; Electronic component; remaining battery capacity; Analytical method; temperature; Portable equipment; Damaging
• ISSN: 1063-8210; 1557-9999
• DOI: 10.1109/TVLSI.2006.876094

Predicting the residual energy of the battery source that powers a portable electronic device is imperative in designing and applying an effective dynamic power management policy for the device. This paper starts up by showing that a 30% error in predicting the battery capacity of a lithium-ion battery can result in up to 20% performance degradation for a dynamic voltage and frequency scaling algorithm. Next, this paper presents a closed form analytical expression for predicting the remaining capacity of a lithium-ion battery. The proposed high-level model, which relies on online current and voltage measurements, correctly accounts for the temperature and cycle aging effects. The accuracy of the high-level model is validated by comparing it with DUALFOIL simulation results, demonstrating a maximum of 5% error between simulated and predicted data.