Abstract
To improve the use of lithium-ion batteries in electric vehicle (EV) applications, evaluations and comparisons of different equivalent circuit models are presented in this paper. Based on an analysis of the traditional lithium-ion battery equivalent circuit models such as the Rint, RC, Thevenin and PNGV models, an improved Thevenin model, named dual polarization (DP) model, is put forward by adding an extra RC to simulate the electrochemical polarization and concentration polarization separately. The model parameters are identified with a genetic algorithm, which is used to find the optimal time constant of the model, and the experimental data from a Hybrid Pulse Power Characterization (HPPC) test on a LiMn2O4 battery module. Evaluations on the five models are carried out from the point of view of the dynamic performance and the state of charge (SoC) estimation. The dynamic performances of the five models are obtained by conducting the Dynamic Stress Test (DST) and the accuracy of SoC estimation with the Robust Extended Kalman Filter (REKF) approach is determined by performing a Federal Urban Driving Schedules (FUDS) experiment. By comparison, the DP model has the best dynamic performance and provides the most accurate SoC estimation. Finally, sensitivity of the different SoC initial values is investigated based on the accuracy of SoC estimation with the REKF approach based on the DP model. It is clear that the errors resulting from the SoC initial value are significantly reduced and the true SoC is convergent within an acceptable error.
Highlights
Since the battery is a nonlinear system, the models usually used in electric vehicles (EVs) can be divided into three kinds: the simplified electrochemical model was proposed based on the electrochemical theory [7,8,9], and could fully describe the characteristics of the power battery by using mathematics to describe the inner action of the battery
Comparisons between the model-based simulation data and the experimental data are carried out to evaluate the validity of the foregoing models, which provides a foundation for the model-based state of charge (SoC) estimation
The dual polarization (DP) model the composed of three parts: (1) Open-circuit voltage Uoc; (2) Internal resistances such as the ohmic resistance Ro and the polarization resistances, which include Rpa to represent the effective resistance characterizing electrochemical polarization and Rpc to represent the effective resistance characterizing concentration polarization; (3) the effective capacitances like Cpa and Cpc, which are used to characterize the transient response during transfer of power to/from the battery and to describe the electrochemical polarization and the concentration polarization separately
Summary
With the increased research in the fields of hybrid electric vehicle dynamic simulation, energy distribution and power control strategy, as well as the estimation of batteries’ state of charge (SoC) and state of health (SoH) [1,2,3,4,5,6], nowadays improving the accuracy of the charging and discharging model of power batteries, especially lithium-ion batteries, is a significant objective. In order to overcome the drawbacks of the mathematical models, the neural network model was put forward, which took the weights of neurons into account instead of the state variables [10,11,12,13,14]. The accuracy of this model could reach 3% under certain conditions. Based on the dynamic characteristics and working principles of the battery, the equivalent circuit model was developed by using resistors, capacitors and voltage sources to form a circuit network [15,16,17]. The sensitivity of the different SoC initial values on the presented model-based SoC estimation is discussed
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