Time-Domain Parameter Extraction Method for Thévenin-Equivalent Circuit Battery Models

Hentunen, Ari; Lehmuspelto, Teemu; Suomela, Jukka

doi:10.1109/tec.2014.2318205

Cited by 188 publications

(111 citation statements)

References 23 publications

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“…The capacitances C 1 and C 2 were calculated based on the time constant of RC parallel networks, which is given in Equation (8). Furthermore, the time constant of the two RC parallel networks was determined according to the methodology presented in [37]:…”

Section: Parameter Identificationmentioning

confidence: 99%

Influence of Battery Parametric Uncertainties on the State-of-Charge Estimation of Lithium Titanate Oxide-Based Batteries

et al. 2018

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State of charge (SOC) is one of the most important parameters in battery management systems, as it indicates the available battery capacity at every moment. There are numerous battery model-based methods used for SOC estimation, the accuracy of which depends on the accuracy of the model considered to describe the battery dynamics. The SOC estimation method proposed in this paper is based on an Extended Kalman Filter (EKF) and nonlinear battery model which was parameterized using extended laboratory tests performed on several 13 Ah lithium titanate oxide (LTO)-based lithium-ion batteries. The developed SOC estimation algorithm was successfully verified for a step discharge profile at five different temperatures and considering various initial SOC initialization values, showing a maximum SOC estimation error of 1.16% and a maximum voltage estimation error of 44 mV. Furthermore, by carrying out a sensitivity analysis it was showed that the SOC and voltage estimation error are only slightly dependent on the variation of the battery model parameters with the SOC.

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Section: Parameter Identificationmentioning

confidence: 99%

Influence of Battery Parametric Uncertainties on the State-of-Charge Estimation of Lithium Titanate Oxide-Based Batteries

et al. 2018

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“…While for the system-level simulations of automotive applications, the time periods of interest are typically in the range of seconds to hours or days in special cases [43,45]. Hence, the long-term aging effect is usually ignored in the parameter estimation process and handled separately in most cases [39,46].…”

Section: Parameter Extraction Test Designmentioning

confidence: 99%

“…In Equation (14), since the values of R i and R i+m are nearly of the same order of magnitude [39,43,46], the value of R i /R i+m can be neglected when compared to the value of τ 2 i+m /τ 2 i ; thus, ∆t can be simplified as:…”

Section: Determination Of the Length Of The Fitted Experimental Datasetmentioning

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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“…While simulating the voltage behavior, the proposed battery simulator applies different modes in different SOC regions. In the past research, the measured data of the internal resistance of a Li-ion battery cell show that the internal resistance in the flat region that is normally 20% to 80% SOC [19,28,29] is less affected by the current rate effect, so ECMs usually model the internal resistance in this SOC region as a constant resistance. However, when the battery SOC is close to the empty or full state, the SOC effect and current rate effect cannot be ignored.…”

Section: Proposed Battery Simulatormentioning

confidence: 99%

A Lithium-Ion Battery Simulator Based on a Diffusion and Switching Overpotential Hybrid Model for Dynamic Discharging Behavior and Runtime Predictions

et al. 2016

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Abstract:A new battery simulator based on a hybrid model is proposed in this paper for dynamic discharging behavior and runtime predictions in existing electronic simulation environments, e.g., PSIM, so it can help power circuit designers to develop and optimize their battery-powered electronic systems. The hybrid battery model combines a diffusion model and a switching overpotential model, which automatically switches overpotential resistance mode or overpotential voltage mode to accurately describe the voltage difference between battery electro-motive force (EMF) and terminal voltage. Therefore, this simulator can simply run in an electronic simulation software with less computational efforts and estimate battery performances by further considering nonlinear capacity effects. A linear extrapolation technique is adopted for extracting model parameters from constant current discharging tests, so the EMF hysteresis problem is avoided. For model validation, experiments and simulations in MATLAB and PSIM environments are conducted with six different profiles, including constant loads, an interrupted load, increasing and decreasing loads and a varying load. The results confirm the usefulness and accuracy of the proposed simulator. The behavior and runtime prediction errors can be as low as 3.1% and 1.2%, respectively.

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Time-Domain Parameter Extraction Method for Thévenin-Equivalent Circuit Battery Models

Cited by 188 publications

References 23 publications

Influence of Battery Parametric Uncertainties on the State-of-Charge Estimation of Lithium Titanate Oxide-Based Batteries

Influence of Battery Parametric Uncertainties on the State-of-Charge Estimation of Lithium Titanate Oxide-Based Batteries

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

A Lithium-Ion Battery Simulator Based on a Diffusion and Switching Overpotential Hybrid Model for Dynamic Discharging Behavior and Runtime Predictions

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