Design and calibration of a semi-empirical model for capturing dominant aging mechanisms of a PbA battery

Directional DC Charge-Transfer Resistance on an Electrode–Electrolyte Interface in an AC Nyquist Curve on Lead-Acid Battery

“…Under positive half-wave sinusoidal current injection, periodic voltage responses of charging impedances on electrode-electrolyte interfaces are excited; According to average switch modeling principles, an equivalent DC positive response 〈v Cha 〉 is on electrode-electrolyte interfaces, as Equation 7. Similarly, an equivalent DC negative response 〈v Dis 〉 is on electrode-electrolyte interfaces, as Equation (8). Then equivalent DC response V DC on electrode-electrolyte interfaces is the sum of 〈v Cha 〉 and 〈v Dis 〉, as Equation (9).…”

“…Elements in a circuit, fitting for EIS, are simplified from governing equations [7,8] in lead-acid battery and have clear physical/electrochemical meanings; so, they are the first principle and basis of classic threshold-based BMS design. Charge-transfer resistance and double-layer capacitance are related to non-cohesion of active mass of electrodes; contacting resistances represent the existence of stratification and corrosion of electrodes; nonlinear diffusion impedances characterize sulfating of electrodes [4,9].…”

“…Charge-transfer resistance and double-layer capacitance are related to non-cohesion of active mass of electrodes; contacting resistances represent the existence of stratification and corrosion of electrodes; nonlinear diffusion impedances characterize sulfating of electrodes [4,9]. Multi-scale SoC/SoH judgment based on EIS is an extension of threshold-based management [10,11]; circuits elements are nonlinearly influenced by various factors, e.g., SoC, charging or discharging, superimposed DC current [12]; other form signals, such as short step response, can take place of sinusoidal excitation to demonstrate dominant aging mechanisms on battery capacity [8]. The leading edge of BMS to forecast and optimize batteries' SoC/SoH is usually based on time domain simulation [13,14]; therefore, extracting directional DC charge-transfer resistances from an AC Nyquist curve is significant in time domain research on BMS.…”

“…The physical and electrochemical meanings of contacting resistor (R C ), stray inductor (L S ) and electrolyte bulk capacitor (C bulk ) in BHLC ACequ are relatively clear [3]. Individual elements in BHLC ACequ rely on the first principle [17], simplified from governing equations [7,8], but the elements' combination does not consider the first principle. Linear impedance on positive (or negative) electrode-electrolyte interfaces is generally modeled by a charge-transfer resistor (R ct ) and a double-layer capacitor (C dl ) [3,18].…”

Deep-learning based spatio-temporal generative model on assessing state-of-health for Li-ion batteries with partially-cycled profiles

“…The semi-empirical model was used to describe physical and electrochemical phenomena, such as loss of lithium inventory and loss of active material in electrodes, indicating capacity degradation in Li-ion batteries for SOH assessment. 7–9 Moreover, a combination of a particle filter and the semi-empirical model showed successful SOH prediction. 9–11 Based on the development of computing ability and ease of data access, data-driven methods, such as machine learning and deep learning, were proposed for SOH estimation.…”