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A Development of Battery Aging Prediction Model Based on Actual Vehicle Driving Pattern
ISSN: 0148-7191, e-ISSN: 2688-3627
Published April 14, 2020 by SAE International in United States
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Premature failure in lead-acid batteries used in starting, lighting, and ignition applications has led to warranty issues which can be resolved by predicting the contributing factors of battery aging and evaluating different design alternatives. Battery degradation in real vehicles is accelerated by dark currents from an integrated dashboard camera which are drawn while the ignition is turned off, high ambient temperatures, a shortage of the battery charge rate, and the intermittent occurrence of bad starts during idle-stop-and-go operation. Existing battery durability verification requires a long period of more than 4 months using experimental deep discharge testing and does not reflect the various actual vehicle driving conditions of the customer. In order to improve this, the present work aims to develop a battery aging prediction model that reflects the various operating conditions of actual vehicle driving patterns. A battery aging model that was developed by the National Renewable Energy Laboratory has been has been adapted for use with lead-acid battery chemistries and coupled with a 3D TAITherm battery thermal/electric simulation to predict capacity fade and internal resistance growth over time. The lifetime model was fit to experimental data, including laboratory bench tests for calendar fade, cycling fade behavior taken from literature, employee vehicle battery replacement data from the Hyundai-Kia Motors Corporation, and a validation bench test using a real-world drive cycle. Model error was within 4% for capacity and 2% for resistance. Trade-off simulations showed that the hypothesized solution of increasing battery capacity was not an economical means of meeting warranty requirements, but that effective thermal management and setting electrical load limits was. Other proposed solutions include using a separate battery for the dashboard camera, using a lighter and more compact battery chemistry such as Lithium-Ion, and using intelligent alternator control to re-charge the battery when the state-of-charge gets too low.
CitationLIM, Y. and Edel, Z., "A Development of Battery Aging Prediction Model Based on Actual Vehicle Driving Pattern," SAE Technical Paper 2020-01-1059, 2020, https://doi.org/10.4271/2020-01-1059.
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