State-of-Health Estimation for LiFePO₄Battery System on Real-World Electric Vehicles Considering Aging Stage

Comprehensive and accurate estimation methods of the state of health (SOH) of battery systems play a significant role in online monitoring for the safe and reliable operation of electric vehicles (EVs). Most existing estimation techniques are established on top of data from well-controlled experimental environments and tend to focus on a single health feature, thus not practical for real-world EVs' SOH monitoring throughout the life cycle. To address this problem, based on real-world EV operation data, a novel SOH estimation model is presented for LiFePO4 battery systems in EVs with consideration of degradation mechanisms. The ohmic resistance and the peak value of the incremental capacity (IC) curve are extracted from a large number of electric buses as health features. The influence of temperature on the ohmic resistance is eliminated by exponential fitting, which transforms ohmic resistance into the relative change rate of ohmic resistance. With joint considerations of the two health features and the mechanism of battery aging, three stages of SOH degradation can be quantitively described. The test results show that the proposed model is able to track SOH degradation from 0 to over 300 000 km and reflect the SOH of the battery more comprehensively in the early and late life cycles of the battery compared with single-health-feature methods.