No abstract

No 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

No abstract

No abstract

No abstract

In lithium-ion batteries, the accuracy of estimation of the state of charge is a core parameter which will determine the power control accuracy and management reliability of the energy storage systems. When using unscented Kalman filtering to estimate the charge of lithium-ion batteries, if the pulse current change rate is too high, the tracking effects of algorithms will not be optimal, with high estimation errors. In this study, the unscented Kalman filtering algorithm is improved to solve the above problems and boost the Kalman gain with dynamic function modules, so as to improve system stability. The closed-circuit voltage of the system is predicted with two non-linear transformations, so as to improve the accuracy of the system. Meanwhile, an adaptive algorithm is developed to predict and correct the system noises and observation noises, thus enhancing the robustness of the system. Experiments show that the maximum estimation error of the second-order Circuit Model is controlled to less than 0.20V. Under various simulation conditions and interference factors, the estimation error of the unscented Kalman filtering is as high as 2%, but that of the improved Kalman filtering algorithm are kept well under 1.00%, with the errors reduced by 0.80%, therefore laying a sound foundation for the follow-up research on the battery management system.

Temperature and cell hysteretic effects are two major factors that influence the reliability and safety in long-term management of battery-integrated systems.In this paper, a hysteresis-compensated electrical characteristic model is established to track the terminal voltage of batteries with the uncertain hysteretic effect of the open-circuit voltage. Then, an autoregressive exogenous model with multi-feature coupling is employed for the identification of the parameters to make them adaptive to the uncertainties of the temperature and hysteretic effects. After that, a novel method for state-of-charge (SOC) estimation based on an adaptive moving window-square root unscented Kalman filter is constructed to avoid the filtering divergence problem caused by the negative error covariance matrix. Multiple constraints, such as Coulombic efficiency, varying ambient temperatures, and hysteresis voltage, are considered for the SOC estimation. Experimental results show that the root-mean-square error for SOC calculation can be limited to 0.0211 when the temperature varied up to 40 C and the root-mean-square error of the voltage measurement noise up to 61.9 mV. The proposed method provides an effective way for batteryintegrated management of electric vehicles. K E Y W O R D Sadaptive moving window-square root unscented Kalman filter, adaptive noise matching, hysteresis-compensated modeling, lithium-ion battery, state-of-charge