Various battery models for various simulation studies and applications

G., S.M. Mousavi; Nikdel, M.

doi:10.1016/j.rser.2014.01.048

Cited by 243 publications

(102 citation statements)

References 34 publications

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“…Secondly, the state-space equations can be easily derived according to the model. A commonly used model that well meets the above two requirements is the equivalent circuit model (ECM) with lumped parameters [46]. The most common ECM is comprised of resistor and parallel resistor-capacitor (RC) network(s) connected in series [47].…”

Section: Battery Equivalent Circuit Modelmentioning

confidence: 99%

State of Charge Estimation of Lithium-Ion Batteries Using an Adaptive Cubature Kalman Filter

et al. 2015

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Accurate state of charge (SOC) estimation is of great significance for a lithium-ion battery to ensure its safe operation and to prevent it from over-charging or over-discharging. However, it is difficult to get an accurate value of SOC since it is an inner sate of a battery cell, which cannot be directly measured. This paper presents an Adaptive Cubature Kalman filter (ACKF)-based SOC estimation algorithm for lithium-ion batteries in electric vehicles. Firstly, the lithium-ion battery is modeled using the second-order resistor-capacitor (RC) equivalent circuit and parameters of the battery model are determined by the forgetting factor least-squares method. Then, the Adaptive Cubature Kalman filter for battery SOC estimation is introduced and the estimated process is presented. Finally, two typical driving cycles, including the Dynamic Stress Test (DST) and New European Driving Cycle (NEDC) are applied to evaluate the performance of the proposed method by comparing with the traditional extended Kalman filter (EKF) and cubature Kalman filter (CKF) algorithms. Experimental results show that the ACKF algorithm has better performance in terms of SOC estimation accuracy, convergence to different initial SOC errors and robustness against voltage measurement noise as compared with the traditional EKF and CKF algorithms. OPEN ACCESSEnergies 2015, 8 5917

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Section: Battery Equivalent Circuit Modelmentioning

confidence: 99%

State of Charge Estimation of Lithium-Ion Batteries Using an Adaptive Cubature Kalman Filter

et al. 2015

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“…5 se observa la conexión serie de dos conjuntos PV-convertidor formando un arreglo PV, el cual está acoplado a un bus de CC. En aplicaciones comerciales, el convertidor CC/CA regula el nivel de voltaje del bus, el cual se puede representar por su equivalente de Thevenin [19], [20]. Los parámetros de ese equivalente son el voltaje y la resistencia del bus, y , respectivamente.…”

Section: Modelo Matemático De Dos Módulos En Serieunclassified

Modelo matemático de sistemas fotovoltaicos para búsqueda distribuida del punto de máxima potencia

Henao‐Bravo

Márquez-Viloria

Villegas-Ceballos

et al. 2016

TecnoL.

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Este articulo presenta un modelo para sistemas de generación fotovoltaica orientado a evaluar el desempeño de algoritmos distribuidos de búsqueda del punto de máxima potencia. El modelo considera la conexión en serie de n conjuntos módulo-convertidor que pueden ser dimensionados dependiendo de la aplicación en particular. Dicho modelo está compuesto por un conjunto de ecuaciones que pueden ser implementadas en cualquier software de cálculo matemático o en un lenguaje de programación que soluciones este tipo de ecuaciones. El modelo incluye el comportamiento estático del módulo fotovoltaico a partir de una ecuación que estima su relación voltaje-corriente, además, incluye el modelo del convertidor elevador de potencia con pérdidas en el inductor, esto con el fin de aproximar el modelo general a una solución realística de la aplicación. El modelo final es validado mediante simulaciones y comparado con una implementación del mismo en un software de simulación electrónica tradicional.

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“…This is achieved by accurately obtaining the main parameters of the cells i.e., the impedance, capacity and open circuit voltage, and by deriving a suitable model for the microprocessors of the battery management system (BMS) that are designed to monitor and perform an optimal operation within a predefined safe-operation-area (SoA) and power limits [4]. The modeling of the Li-ions is achieved with one of the following methods [5,6]:…”

Section: Introductionmentioning

confidence: 99%

Comparative Study on Parameter Identification Methods for Dual-Polarization Lithium-Ion Equivalent Circuit Model

et al. 2019

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Md.Sazzad.Hosen@vub.be (M.S.H.); Mohsen.Akbarzadeh.Sokkeh@vub.be (M.A.S.); Shovon.Goutam@vub.be (S.G); Joris.Jaguemont@vub.be (J.J.); Maitane.Berecibar@vub.be (M.B.); Joeri.van.mierlo@vub.be (J.V.M.) 2 Flanders Make,Abstract: A lithium-ion battery cell's electrochemical performance can be obtained through a series of standardized experiments, and the optimal operation and monitoring is performed when a model of the Li-ions is generated and adopted. With discrete-time parameter identification processes, the electrical circuit models (ECM) of the cells are derived. Over their wide range, the dual-polarization (DP) ECM is proposed to characterize two prismatic cells with different anode electrodes. In most of the studies on battery modeling, attention is paid to the accuracy comparison of the various ECMs, usually for a certain Li-ion, whereas the parameter identification methods of the ECMs are rarely compared. Hence in this work, three different approaches are performed for a certain temperature throughout the whole SoC range of the cells for two different load profiles, suitable for light-and heavy-duty electromotive applications. Analytical equations, least-square-based methods, and heuristic algorithms used for model parameterization are compared in terms of voltage accuracy, robustness, and computational time. The influence of the ECMs' parameter variation on the voltage root mean square error (RMSE) is assessed as well with impedance spectroscopy in terms of Ohmic, internal, and total resistance comparisons. Li-ion cells are thoroughly electrically characterized and the following conclusions are drawn: (1) All methods are suitable for the modeling, giving a good agreement with the experimental data with less than 3% max voltage relative error and 30 mV RMSE in most cases. (2) Particle swarm optimization (PSO) method is the best trade-off in terms of computational time, accuracy, and robustness. (3) Genetic algorithm (GA) lack of computational time compared to PSO and LS (4) The internal resistance behavior, investigated for the PSO, showed a positive correlation to the voltage error, depending on the chemistry and loading profile.

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Various battery models for various simulation studies and applications

Cited by 243 publications

References 34 publications

State of Charge Estimation of Lithium-Ion Batteries Using an Adaptive Cubature Kalman Filter

State of Charge Estimation of Lithium-Ion Batteries Using an Adaptive Cubature Kalman Filter

Modelo matemático de sistemas fotovoltaicos para búsqueda distribuida del punto de máxima potencia

Comparative Study on Parameter Identification Methods for Dual-Polarization Lithium-Ion Equivalent Circuit Model

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