Abstract
Accurate estimation of the state of charge (SOC) is an important criterion to prevent the batteries from being over-charged or over-discharged, and this assures an electric vehicle’s safety and reliability. To investigate the effect of different operating conditions on the SOC estimation results, a dual-polarization model (DPM) and a fractional-order model (FOM) are established in this study, taking into account the prediction accuracy and structural complexity of a battery model. Based on these two battery equivalent circuit models (ECMs), a hybrid Kalman filter (HKF) algorithm is adopted to estimate the SOC of the battery; the algorithm comprehensively utilizes the ampere-hour (Ah) integration method, the Kalman filter (KF) algorithm, and the extended Kalman filter (EKF) algorithm. The SOC estimation results of the DPM and FOM, under the dynamic stress test (DST), federal urban driving schedule (FUDS), and hybrid pulse power characteristic (HPPC) cycle conditions, are compared and analyzed through six sets of experiments. Simulation results show that the SOC estimation accuracy of both the models is high and that the errors are within the range of ±0.06. Under any operating conditions, the SOC estimation error, based on the FOM, is always lower than the SOC estimation error of the DPM, but the adaptability of the FOM is not as high as that of the DPM.
Highlights
Lithium-ion power batteries are widely used in electric vehicles (EVs), owing to their advantages of high energy density, low self-discharge rate, long cycle life, and no memory effect [1]
We studied the influence of different operating conditions on state of charge (SOC) estimation based on different battery models
It is reflected in other functions, including charging and discharging control, balance management, safety management, and fault diagnosis cannot be achieved without the high-precision SOC estimation
Summary
Lithium-ion power batteries are widely used in electric vehicles (EVs), owing to their advantages of high energy density, low self-discharge rate, long cycle life, and no memory effect [1]. Wang et al [10] established a nonlinear black-box battery model, and the verification under federal urban driving schedule (FUDS) operating conditions showed that the relative error of voltage was within 3.8%. The authors in [23] critically reviewed the existing SOC estimation methods in the past five years and introduced the basic principles and main disadvantages of various methods Among these methods, the KF is an optimized autoregressive data filtering algorithm [24], which utilizes the principle of minimum mean square error to achieve an optimal state estimation for a complex dynamic system. Energies 2020, 13, x FOR PEER REVIEW estimation results of the DPM and the FOM under DST, FUDS, and hybrid pulse power characteristic cycling conditions are analyzed by comparing sets power of experiments. DST, FUDS, and hybridsix pulse characteristic (HPPC) cycling conditions are analyzed by comparing six sets of experiments
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