In this session, see how we developed a high accuracy onboard battery state of health estimation method based on the differential voltage (DVA) and incremental capacity analysis (ICA) for electric vehicles. Using cycling data from lithium-ion battery cells at various temperatures, we extracted the charging cycles and calculated the DVA and ICA curves, which are then filtered with an IIR-filter to reduce noise. Multiple features (i.e., peaks or valleys of the curves) are extracted and analyzed, and the most promising features are selected for further steps. The selected features are brought into correlation with the capacity fade, and a linear regression model is calculated between the selected features at various temperatures. With these linear models, a 2D Look-Up Table (LUT) is created by interpolating the values between the linear models. For the onboard implementation, we developed a Simulink® model which realized the calculation and filtering of the ICA- and DVA-curve. Also, we implemented a feature detection algorithm that detects and verifies the selected features, which are forwarded to the 2D LUT to calculate the current SOH. We tested and converted this model to AUTOSAR-compliant code and will validate it on Gotion’s in-house developed BMS.
