A Development of Battery Aging Prediction Model Based on Actual Vechile Driving Pattern

The battery of the real vehicle accelerates the battery aging due to the dark current of the black box, the high ambient temperature, the shortage of the battery charge rate and the customer complaints such as intermittent occurrence of ISG intermittently entering the vehicle and bad start. In addition, the existing battery durability verification requires a long period of more than 4 months through the deep discharge and it has not secured the advance verification technology through battery durability verification and battery aging simulation that reflect the various actual vehicle driving conditions of the customer. In order to improve this, it is aimed to develop a battery aging prediction model that reflects various operating conditions of actual vehicle driving pattern based customer. The NREL battery lifetime model has been adapted for AGM lead-acid batteries using experimental test data. Battery stress statistics are created with a battery thermal/electric simulation and then applied to the aging model to predict resistance growth and capacity fade over the expected life of the battery. The thermal/electric simulations are performed incrementally, with progressively modified resistance and capacity values from the aging model, so that over time the increase in resistance and decrease in capacity affect the battery temperature and state-of-charge cycling. The coupled lifetime model was validated using a laboratory bench test, in addition to a series of measurements from 80ah AGM & 68ah FLD starting batteries that were taken from vehicles and tested at different service intervals. The coupled lifetime model predicts relative capacity within 4% of measurements and relative internal resistance within 2% of measurements. These error rates correspond to an end-of-life prediction accuracy of 1 month after 3 years' service life. Results indicate that the most effective means of extending service life include more effective thermal management and using Intelligent Alternator Control (IAC) to achieve longer charge times.