Parameter Identification of Lithium-Ion Batteries Using Operational Data Without Prior Knowledge of the SOC-OCV Relationship

We address the problem of estimating model parameters of a battery without prior knowledge of the battery's specifications, using only operational data. Many existing studies on parameter estimation for batteries use prior knowledge of the battery's specifications, such as typical values of model parameters and the nonlinear relationship between the state-of-charge (SOC) and open-circuit voltage (OCV). However, there are cases where battery's specifications are completely unknown, but battery modeling is still needed, such as in model-based design and condition monitoring of battery-powered products. To achieve that, we propose a parameter identification technique using the Variable Projection-based Output Error Method for the battery state-space model. Our proposed method estimates the parameters of a polynomial OCV function with respect to cumulative current, as well as the parameters of an equivalent circuit model (ECM), solely from operational data. Moreover, with the aid of Variable Projection, the number of actual parameters to be estimated is significantly reduced, enabling automatic parameter estimation. The performance of the proposed method is validated using both simulated and operational data obtained from a test drive of an electric vehicle (EV).