Battery parameter identification is a key challenge for battery management systems, as parameterizing lithium-ion batteries is resource-intensive. Electrical circuit models (ECMs) provide an alternative, but their parameters change with physical conditions and battery age, necessitating regular parameter identification. This paper presents two modular algorithms to improve data quality and enable fast, robust parameter identification. First, the dequantizer algorithm restores the time series generating the noisy, quantized data using the inverse normal distribution function. Then, the Linear Continuous-Time Regression (LCTR) algorithm extracts exponential parameters from first-order or overdamped second-order systems, deducing ECM parameters and guaranteeing optimality with respect to RMSE. The parameters have low sensitivity to measurement noise since they are continuous-time. Sensitivity analyses confirm the algorithms’ suitability for battery management across various Gaussian measurement noise, accuracy, time constants and state-of-charge (SoC), using evaluation metrics like root-mean-square-error (RMSE) (<2 mV), relative time constant errors, and steady-state error. If the coarseness of rounding is not extreme, the steady-state is restored within a fraction of a millivolt. While a slight overestimation in the lower time constants occurs for overdamped systems, the algorithms outperform the conventional benchmark for first-order systems. Their robustness is further validated in real-life applications, highlighting their potential to enhance commercial battery management systems.

Pulse-width Modulated (PWM) charging of lithiumion battery cells offers high-resolution control of voltage and current by modulating cell voltage as the fraction of the nominal value. This study (1) reviews the literature of PWM affecting the performance and lifespan of lithium-ion cells; (2) conducts electrochemical impedance spectroscopy (EIS) on LFP chemistry for assessing SoC and current dependencies; (3) experiments large-current effects on the fast dynamics (≥1Hz), and the correlation of fast and slow dynamics. The Nyquist plot’s semicircles relate to the intercalation at the solid-electrolyte interphase (SEI), where the cutoff of electrochemical dynamics occurs too. These dynamics are excited by 1-100Hz, with frequent electrochemical reactions accelerating degradation. Impedance increases above 10kHz, and RMS current – thus duty cycle – increases losses and temperature. Our cell’s semicircle has the lowest frequency of 1Hz, which is independent of state-ofcharge (SoC) and current, while current increases impedance and SoC reduces resistance. Time-series experiments demonstrate that the fast-dynamics equivalent resistance is independent of current and is reduced by frequency. The fundamental PWM frequency has no impact on slow dynamics, enabling simulation and investigation of fast and slow dynamics independently. We believe that this work can inspire more holistic and standardized battery characterization methods.