Internal Parameter Estimation of Lithium-Ion Battery Using AC Ripple With DC Offset Wave in Low and High Frequencies

Abstract

xEV batteries are not easy to accurately estimate and measure aging parameters for reuse due to continuous charging and discharging. In addition, existing AC-IR measurement methods require high-performance micro-controller units (MCUs) to handle complex operations for parametric estimation. As a cost-effective way to solve this problem, we propose AC-IR estimation techniques and implementation circuits to estimate internal battery parameters using AC ripple with DC offset wave in low and high frequencies. It estimates electrolyte resistance ( $R_{i}$ ) by using band-pass filter (BPF) to extract AC components of a particular frequency voltage and current and by entering very high frequencies that have the effect of shorting the equivalent capacitance by the electrical double layer ( $C_{d}$ ) of the Randls model. The charge transfer resistance ( $R_{d}$ ) and electrical double layer capacitance ( $C_{d}$ ) are estimated by Discrete Fourier transform (DFT), detecting effective and reactive current by inserting low frequencies. To realize the proposed approach, we propose analog power measurements and peak detection circuits, and a circuit configuration of converter supplying AC ripple with DC offset wave to estimate internal parameters at the same time to charge battery. To verify the feasibility and high-performance of the proposed AC-IR estimation method, we carry out PSIM simulations and experiments, and compare results with commercial battery parameter measuring instrument. As a result, we found that the error rate of the proposed method is lower than that of the commercial instrument as $R_{i}$ is 0.24% and $R_{d}$ is 1.18%, respectively.

Keywords

• Alternating current internal resistance (AC-IR)
• band-pass filter (BPF)
• direct current internal resistance (DC-IR)
• discrete fourier transform (DFT)
• end of life (EOL)
• Randles model