Improved splice‐electrochemical circuit polarization modeling and optimized dynamic functional multi‐innovation least square parameter identification for lithium‐ion batteries

Abstract

The internal nonlinearity of the lithium-ion battery makes its mathematical modeling a big challenge. In this paper, a novel lithium-ion battery spliceelectrochemical circuit polarization (S-ECP) model is proposed, which integrates the strengths of various lithium-ion battery models and refines the ohm and polarization characteristics of the electrochemical Nernst model and the differences in charge-discharge internal resistance. Moreover, by applying the one-sided limit to the discrete system, a multi-innovation least squares algorithm optimized based on the dynamic function (F-MILS) introduced to constrain the original innovation weight is put forward, which tackles the problem of large algorithm errors caused by huge changes in the system input. In order to evaluate the regulating ability of weight constraint factors, the relevant parameter values in the dynamic function are discussed as independent variables. Furthermore, parameters of the S-ECP model are identified online by HPPC experimental data combined with the multi-innovation least squares (MILS) algorithm ameliorated by the dynamic function, and the convergence speed of parameters in the identification process is analyzed. Finally, an urban dynamometer driving schedule experiment is carried out on the lithium-ion battery under more complex working conditions. It is revealed that the accuracy of F-MILS is 0.5% higher than that of unoptimized MILS, further confirming the accuracy of the S-ECP model and the superiority of the F-MILS algorithm. Highlights• A novel lithium-ion battery splice-electrochemical circuit polarization (S-ECP) model is proposed, which integrates the strengths of various lithiumion battery models and refines the ohm and polarization characteristics of

Battery hysteresis compensation modeling and state‐of‐charge estimation adaptive to time‐varying ambient temperature conditions

“…In the current commercial BMS, many models have been developed to provide applications for battery-integrated systems, such as lumped electrical characteristic models, 6 one-order resistor-capacitor (RC) networks, 7 fractional-order models, 8 and spliceelectrochemical circuit polarization models. 9 Their model accuracy is higher in the battery type where the hysteretic effect is not obvious and in an environment where the external temperature does not change much. However, these battery modeling methods have certain limitations in the characterization of the open circuit voltage hysteresis effect, and ignore the impact of ambient temperature on the battery modeling.…”

Nonlinear Dynamic Model for Parameter Estimation of Li-Ion Batteries Using Supply–Demand Algorithm

“…Model-based techniques are extensively studied, particularly for a SOC assessment [17]. The analogous circuit-based online extraction of the SOC provides great robustness and accuracy [18]. The acceptable temperature region for LIBs is normally −20 • C~60 • C. Both low and high temperatures that are outside of this region, however, will lead to a degradation of performance and irreversible damage, such as lithium plating and thermal runaway.…”

A novel collaborative multiscale weighting factor‐adaptive Kalman filtering method for the time‐varying whole‐life‐cycle state of charge estimation of lithium‐ion batteries

“…54 The maximum available capacity is combined with the least-squares algorithm based on dual capacity estimation. 55 The highfidelity capacity degradation model is constructed to reflect battery internal activity, which is combined with chemical kinetics to identify model parameters, making the model adaptive to the battery aging process.…”

“…Significant results are achieved for the model‐based SOC estimation with the whole‐life‐cycle fading trajectory estimation that can meet the urgent requirements effectively for the reliable battery system application 54 . The maximum available capacity is combined with the least‐squares algorithm based on dual capacity estimation 55 . The high‐fidelity capacity degradation model is constructed to reflect battery internal activity, which is combined with chemical kinetics to identify model parameters, making the model adaptive to the battery aging process.…”