Electrical model to predict current–voltage behaviours of lithium ferro phosphate batteries using a transient response correction method

Low, Wen Yao; Aziz, Maniruzzaman A.; Idris, Nik Rumzi Nik; Saidur, R.

doi:10.1016/j.jpowsour.2012.07.140

Cited by 22 publications

(4 citation statements)

References 16 publications

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“…In fact, the OCV is nearly constant within the battery SOC operating range for non-plug-in types of hybrid vehicles regardless of the battery type (Kim et al, 2011;Roscher and Sauer, 2011;Thele et al, 2008;Low et al, 2013). In our case study presented in section 3, the amount…”

Section: Comparison Of the Ecms And The Pmp-based Optimal Control Strmentioning

confidence: 76%

Numerical comparison of ECMS and PMP-based optimal control strategy in hybrid vehicles

Zheng

et al. 2014

Int.J Automot. Technol.

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During the last decade, the equivalent consumption minimization strategy (ECMS) and the Pontryagin's Minimum Principle (PMP)-based optimal control strategy have been developed for power management of hybrid vehicles, and it has been noticed that there are some similarities between the two strategies. Establishing their exact relationship and distinguishing their fundamental differences have become necessary for further development in the field of power management strategy. The two strategies are numerically compared and their relationship is established in this research. The two strategies are applied to a fuel cell hybrid vehicle (FCHV) in a computer simulation environment and the simulation results of the two strategies are also compared. It is concluded that the numerical comparison result depends on the opencircuit-voltage (OCV) of the battery model. As a result, the ECMS and the PMP-based optimal control strategy can numerically have the same solutions for non-plug-in hybrid vehicles by adjusting two parameters. Differences between the two strategies are also discussed and the superiority of the PMP-based optimal control strategy is emphasized.

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Section: Comparison Of the Ecms And The Pmp-based Optimal Control Strmentioning

confidence: 76%

Numerical comparison of ECMS and PMP-based optimal control strategy in hybrid vehicles

Zheng

et al. 2014

Int.J Automot. Technol.

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“…Consequently, it is essential either to know the event history during the first life cycle or to have the capability to perform rapid tests on the state of health on a single cell level. For the latter topic, there is a whole range of research work addressing, for example, impedance spectroscopy [15], voltage jump responses [16], ultrasonic layer analysis [17] or surface scanning [18]. On the other hand, there is a clear potential for application in permanent single-cell monitoring, for example, by means of electrical [19] or optical surface strain sensors [20] to specifically detect aging effects.…”

Section: Potential For Utilization Of Second-life Lithium Ion Battmentioning

confidence: 99%

Blueprint and Implementation of Rural Stand-Alone Power Grids with Second-Life Lithium Ion Vehicle Traction Battery Systems for Resilient Energy Supply of Tropical or Remote Regions

et al. 2019

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Developed societies with advanced economic performance are undoubtedly coupled with the availability of electrical energy. Whilst industrialized nations already started to decrease associated carbon emissions in many business sectors, e.g., by substituting combustion engines with battery-powered vehicles, less developed countries still lack broad coverage of reliable electricity supply, particularly in rural regions. Progressive electrification leads to a need for storage capacity and thus to increasing availability of advanced battery systems. To achieve a high degree of sustainability, re-used batteries from the electromobility sector are appropriate, as they do not consume further primary resources and still have sufficient residual capacity for stationary electrical storage applications. In this article, a blueprint for the electrification of a remote region by utilizing second-life lithium ion traction batteries for an integrated energy system in a stand-alone grid is presented and the implementation by the example case of a Tanzanian island in Lake Victoria is demonstrated. First, economic potentials and expected trends in the disposability of second-life lithium ion batteries and their foreseeable costs are outlined. Subsequently, key decision variables are identified to evaluate logistic aspects and the feasibility of the implementation of an off-grid electrical system in remote areas for economically and geographically unfavorable environments. The practical realization is pictured in detail with a focus on technical performance and safety specificities associated with second-life applications. Therefore, a new type of battery management system is introduced, which meets the special requirements of climate compatibility, low maintenance, enhanced cell balancing capability and cell configuration flexibility, and combined with a fiber-optical sensor system, provides reliable status monitoring of the battery. By carrying out on-site measurements, the overall system efficiency is evaluated along with a sustainability analysis. Finally, the socioeconomic and humanitarian impact for the people on the island is debated.

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“…In this type of modeling, all the dynamic characteristics of the battery can be calculated which are the non-linear parameters such as open circuit voltage, current, temperature, number of pulses, capacity-dependent storage time [1]. In order to estimate the current-voltage behavior of the batteries, there are simulation models using the MATLAB curve fitting toolbox by combining the electrical circuit model with the RC circuit [2]. The battery equivalent circuit model can be realized in MATLAB / Simulink environment so that the space state equations of the battery can be obtained by using the differential equations which calculate the dynamic parameters of the battery [3].…”

Section: Introductionmentioning

confidence: 99%

Estimation of li-ion battery state of charge using adaptive neural fuzzy inference system (ANFIS)

Karabacak

Özkan

Sarıtaş

2020

International Journal of Energy Applications and Technologies

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Thanks to their electrochemical structure, batteries are the elements that can store electrical energy and spend on a load when the electrical energy they store is needed. Today, with the widespread use of electrically powered mobile devices, rechargeable batteries have become widespread and battery technologies have developed. With the idea that the latest technology systems and electric vehicles will become widespread in the future, the studies on batteries are increasing day by day. In this study, charge state estimation of Li-ion battery cell used to provide power in many applications was realized by using adaptive neural fuzzy inference system (ANFIS). A Li-ion battery was discharged using variable electrical loads with a battery discharge circuit modeled on MATLAB Simulink and current, voltage, temperature and current power parameters of the battery were selected as input variables. Battery parameters and charge status data obtained from discharge tests using different electrical loads on MATLAB Simulink were used as training and test parameters of neural network. Using the MATLAB ANFIS toolbox, the system was trained with 80% of the battery parameters obtained in the battery discharge experiments and with 20% as testing data, the success performance was interpreted by applying the adaptive neural fuzzy inference system.

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Electrical model to predict current–voltage behaviours of lithium ferro phosphate batteries using a transient response correction method

Cited by 22 publications

References 16 publications

Numerical comparison of ECMS and PMP-based optimal control strategy in hybrid vehicles

Numerical comparison of ECMS and PMP-based optimal control strategy in hybrid vehicles

Blueprint and Implementation of Rural Stand-Alone Power Grids with Second-Life Lithium Ion Vehicle Traction Battery Systems for Resilient Energy Supply of Tropical or Remote Regions

Estimation of li-ion battery state of charge using adaptive neural fuzzy inference system (ANFIS)

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