A nonlinear equivalent circuit model for lithium ion cells

Hariharan, Krishnan S.; Kumar, Vinay

doi:10.1016/j.jpowsour.2012.08.090

Cited by 72 publications

(31 citation statements)

References 23 publications

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“…Then, the data of the open circuit voltage were collected; › the battery was discharged for 2 min at a constant current density of 4.6 mA/cm 2 , and the stack voltage data were collected; fi the battery was idled for 5 min, and the stack voltage data were collected; fl steps › and fi were repeated until the battery reaches the cut-off voltage; (2) batteries 1 and 2 were then connected in series, thereby repeating step (1) at a constant current density of 4.6 mA/cm 2 , and the stack voltage data were collected; (3) batteries 1 and 2 were then connected in parallel, thereby repeating step (1) at a constant of 9.2 mA/cm 2 current density, and the stack voltage data were collected. In addition, the basic parameters of ZNB are summarized in Table I.…”

Section: Experimental Test and Parameter Identificationmentioning

confidence: 99%

Series-parallel grouping modeling simulation and experimental analysis of zinc-nickel single flow batteries

Yao

Liu

Cheng

et al. 2018

Journal of Renewable and Sustainable Energy

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This study investigates a battery system with a zinc-nickel single flow battery (ZNB) stack by a series-parallel-connected system based on a two-order Thévenin equivalent circuit model for single-cell stack and parameter identification of the experimental model. Two single-cell stacks are simulated with three kinds of series-parallel battery pack equivalent circuit models, and compared with the experimental data. The results show that the simplified equivalent circuit model can accurately characterize the voltage of discharge for the series-parallel battery pack. The relative errors of the series/parallel battery pack are 0.8% and 0.71%. Furthermore, the simulation and experimental test data of the large-scale series-parallel system are compared, thereby indicating that the simplified equivalent circuit model of the series/parallel battery based on the two-order Thévenin model can provide a high-precision prediction for the external characteristics of a large-scale ZNB energy storage system.

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Section: Experimental Test and Parameter Identificationmentioning

confidence: 99%

Series-parallel grouping modeling simulation and experimental analysis of zinc-nickel single flow batteries

Yao

Liu

Cheng

et al. 2018

Journal of Renewable and Sustainable Energy

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“…In this paper, we will use the following definition: the SoC of a battery is the ratio of the full capacity to the present maximum available capacity. According to the SoC definition [27] it can be represented by Equation (1):…”

Section: Battery Modelingmentioning

confidence: 99%

A Comparative Study Based on the Least Square Parameter Identification Method for State of Charge Estimation of a LiFePO4 Battery Pack Using Three Model-Based Algorithms for Electric Vehicles

Zahid

2016

Energies

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Battery energy storage management for electric vehicles (EV) and hybrid EV is the most critical and enabling technology since the dawn of electric vehicle commercialization. A battery system is a complex electrochemical phenomenon whose performance degrades with age and the existence of varying material design. Moreover, it is very tedious and computationally very complex to monitor and control the internal state of a battery's electrochemical systems. For Thevenin battery model we established a state-space model which had the advantage of simplicity and could be easily implemented and then applied the least square method to identify the battery model parameters. However, accurate state of charge (SoC) estimation of a battery, which depends not only on the battery model but also on highly accurate and efficient algorithms, is considered one of the most vital and critical issue for the energy management and power distribution control of EV. In this paper three different estimation methods, i.e., extended Kalman filter (EKF), particle filter (PF) and unscented Kalman Filter (UKF), are presented to estimate the SoC of LiFePO 4 batteries for an electric vehicle. Battery's experimental data, current and voltage, are analyzed to identify the Thevenin equivalent model parameters. Using different open circuit voltages the SoC is estimated and compared with respect to the estimation accuracy and initialization error recovery. The experimental results showed that these online SoC estimation methods in combination with different open circuit voltage-state of charge (OCV-SoC) curves can effectively limit the error, thus guaranteeing the accuracy and robustness.

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“…In [49], the capacitances of the RC networks are firstly obtained from the initial voltage values. Both of the above two methods have an assumption that the capacitors of the RC networks have already converged to the steady state at the end of the pulse-discharging period.…”

Section: Improved Fitting Functionmentioning

confidence: 99%

“…In [37,[39][40][41], two initial voltages across the RC networks are predetermined as IR short and IR long respectively, from which the resistances of the RC networks can be derived under the knowledge of the current value. In [49], the capacitances of the RC networks are firstly obtained from the initial voltage values. Both of the above two methods have an assumption that the capacitors of the RC networks have already converged to the steady state at the end of the pulse-discharging period.…”

Section: Improved Fitting Functionmentioning

confidence: 99%

Improved Battery Parameter Estimation Method Considering Operating Scenarios for HEV/EV Applications

et al. 2016

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This paper presents an improved battery parameter estimation method based on typical operating scenarios in hybrid electric vehicles and pure electric vehicles. Compared with the conventional estimation methods, the proposed method takes both the constant-current charging and the dynamic driving scenarios into account, and two separate sets of model parameters are estimated through different parts of the pulse-rest test. The model parameters for the constant-charging scenario are estimated from the data in the pulse-charging periods, while the model parameters for the dynamic driving scenario are estimated from the data in the rest periods, and the length of the fitted dataset is determined by the spectrum analysis of the load current. In addition, the unsaturated phenomenon caused by the long-term resistor-capacitor (RC) network is analyzed, and the initial voltage expressions of the RC networks in the fitting functions are improved to ensure a higher model fidelity. Simulation and experiment results validated the feasibility of the developed estimation method.

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A nonlinear equivalent circuit model for lithium ion cells

Cited by 72 publications

References 23 publications

Series-parallel grouping modeling simulation and experimental analysis of zinc-nickel single flow batteries

Series-parallel grouping modeling simulation and experimental analysis of zinc-nickel single flow batteries

A Comparative Study Based on the Least Square Parameter Identification Method for State of Charge Estimation of a LiFePO4 Battery Pack Using Three Model-Based Algorithms for Electric Vehicles

Improved Battery Parameter Estimation Method Considering Operating Scenarios for HEV/EV Applications

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