A LiFePO4 battery discharge simulator for EV applications — Part 1: Determining the optimal circuit based battery model

2015 ◽  
Author(s):  
Bogdan-Adrian Enache ◽  
Magdalena Emilia Alexandru ◽  
Luminita Mirela Constantinescu
Keyword(s):  
Battery Model ◽  
Battery Discharge

A Battery Dynamic Model for the Power System Simulation

2013 ◽  
Vol 805-806 ◽  
pp. 458-463
Author(s):  
Xiao Dong Li ◽  
Zhong Xing Dong ◽  
Wei Zong ◽  
Zong Qi Liu
Keyword(s):  
Voltage Source ◽  
Power System Simulation ◽  
Battery Model ◽  
Grid Simulation ◽  
Constant Resistance ◽  
Discharge Curve ◽  
In Series ◽  
Algebraic Loop ◽  
Battery Discharge ◽  
Loop Problem

This paper analyzes the battery dynamic characteristics as well as some existing battery models, then presents a universal battery model applicable to micro-grid simulation. The model is composed of a controlled voltage source in series with a constant resistance .The voltage of the controlled voltage source is a one-to-one correspondence with the state of charge (SOC) of the battery, which can effectively avoid the algebraic loop problem. The parameters of the model can be easily extracted from the battery discharge curve. The simulation results shows that the biggest advantage of this model is that the initial SOC of the battery can be set accordingly, which allows the battery to be charged or discharged from any SOC conveniently.

scholarly journals Comparison of Advanced Charge Strategies for Modular Cascaded Battery Chargers

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3361
Author(s):  
Nicolas T. D. Fernandes ◽  
Anderson Rocha ◽  
Danilo Brandao ◽  
Braz C. Filho
Keyword(s):  
Control Strategies ◽  
Measure Data ◽  
Capacity Fade ◽  
Battery Lifetime ◽  
Control Techniques ◽  
Battery Model ◽  
Battery Chargers ◽  
Battery Energy Storage Systems ◽  
Battery Energy ◽  
Photovoltaic Plant

Although the literature extensively covers the development of battery chargers control strategies, a comparison of these strategies remains a literary gap. The inherent conditions (i.e., State of Health and State of Charge) of each unit in the Battery Energy Storage Systems directly influence the charger control techniques for extending battery lifetime, which makes modular battery chargers an appealing topology for this analysis. This work groups charger control strategies presented in the literature into two: Adapted SoC strategies, directly linked to the field of overstress management, and SoH strategies, which are directly linked to the field of wear-out management. The methodology for comparing the control strategies encompasses battery lifetime, charger, and photovoltaic plant models. Three distinct cases were simulated using real measure data from a solar power plant and a battery model provided by MathWorks®. The results evidence that the Capacity Fade and Energy Throughput strongly depend on the strategy. The controller action evidences the previous statement, as the strategies have different goals that are related to each field. Furthermore, this work analyses the effect of the estimation process in the action of the controller.

scholarly journals Parameter Identification and State-of-Charge Estimation for Lithium-Ion Batteries Using Separated Time Scales and Extended Kalman Filter

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1054
Author(s):  
Kuo Yang ◽  
Yugui Tang ◽  
Zhen Zhang
Keyword(s):  
Kalman Filter ◽  
Parameter Identification ◽  
Open Circuit Voltage ◽  
Lithium Ion ◽  
Open Circuit ◽  
State Of Charge ◽  
Recursive Least Squares ◽  
Model Parameters ◽  
Soc Estimation ◽  
Battery Model

With the development of new energy vehicle technology, battery management systems used to monitor the state of the battery have been widely researched. The accuracy of the battery status assessment to a great extent depends on the accuracy of the battery model parameters. This paper proposes an improved method for parameter identification and state-of-charge (SOC) estimation for lithium-ion batteries. Using a two-order equivalent circuit model, the battery model is divided into two parts based on fast dynamics and slow dynamics. The recursive least squares method is used to identify parameters of the battery, and then the SOC and the open-circuit voltage of the model is estimated with the extended Kalman filter. The two-module voltages are calculated using estimated open circuit voltage and initial parameters, and model parameters are constantly updated during iteration. The proposed method can be used to estimate the parameters and the SOC in real time, which does not need to know the state of SOC and the value of open circuit voltage in advance. The method is tested using data from dynamic stress tests, the root means squared error of the accuracy of the prediction model is about 0.01 V, and the average SOC estimation error is 0.0139. Results indicate that the method has higher accuracy in offline parameter identification and online state estimation than traditional recursive least squares methods.

Measuring battery discharge characteristics for accurate UAV endurance estimation

2020 ◽  
Vol 124 (1277) ◽  
pp. 1099-1113
Author(s):  
L. Mariga ◽  
I. Silva Tiburcio ◽  
C.A. Martins ◽  
A.N. Almeida Prado ◽  
C. Nascimento
Keyword(s):  
Low Cost ◽  
Estimation Error ◽  
Flight Time ◽  
Time Estimates ◽  
Discharge Rates ◽  
Nonlinear Optimisation ◽  
Time Calculation ◽  
Discharge Profile ◽  
Battery Discharge ◽  
Battery Discharge Characteristics

ABSTRACTThe increasing use of unmanned aerial vehicles in areas such as rescue, mapping, and transportation have made it necessary to study more accurate techniques for calculating flight time estimates. Such calculations require knowing the battery discharge profile. Simplified flight time calculation methods provide data with uncertainties as they are based solely on manufacturer datasheet information. This study presents a setup to measure the battery discharge curve using a LabVIEW interface with a low-cost acquisition system. The acquired data passes through a nonlinear optimisation algorithm to find the battery coefficients, which enables the more precise estimation of its range and endurance. The great advantage of this model is that it makes it possible to predict how the battery will discharge at different rates using just one experimental curve. The methodology was applied to three different batteries and the model was validated with different discharge rates in a controlled environment, which resulted in endurance lower than 3.0% for most conditions and voltage estimation error lower than 3.0% in operational voltage. The work also presented a methodology for estimating cruise time based on the current used during each flight stage.

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