A Local Polynomial Approach to Nonparametric Estimation of the Best Linear Approximation of Lithium-Ion Battery From Multiple Datasets
For battery engineers, it provides a practical way to handle fragmented experimental data, but the improvement is incremental over existing nonparametric methods.
The paper proposes a local polynomial method to estimate the best linear approximation of lithium-ion battery impedance from multiple datasets with varying nonlinear distortions, enabling accurate modeling without requiring a single long experiment.
Battery short-term electrical impedance behavior varies between linear, linear time-varying, or nonlinear at different operating conditions. Data-based electrical impedance modeling techniques often model the battery as a linear time-invariant system at all operating conditions. In addition, these techniques require extensive and time consuming experimentation. Often due to sensor failures during experiments, constraints in data acquisition hardware, varying operating conditions, and the slow dynamics of the battery, it is not always possible to acquire data in a single experiment. Hence, multiple experiments must be performed. In this letter, a local polynomial approach is proposed to estimate nonparametrically the best linear approximation of the electrical impedance affected by varying levels of nonlinear distortion, from a series of input current and output voltage data subrecords of arbitrary length.