Modeling and SOC estimation of lithium iron phosphate battery considering capacity loss

Li, Junhui; Gao, Fengjie; Yan, Gangfeng; Zhang, Tianyang; Li, Jianlin

doi:10.1186/s41601-018-0078-0

Cited by 20 publications

(9 citation statements)

References 4 publications

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“…The equivalent circuit models, which are composed of simple circuit elements, whose accuracy can be improved by increasing the number of resistors and capacitances, have received much attention from academia [24]. This kind of model has been widely employed, built by a circuit network with a voltage source, capacitances, and resistances to simulate the external transient and characteristics of the batteries [25]. Compared with the other complex circuits, the first-order resistor and capacitance (RC) equivalent circuit model have the advantages of simple configuration, high accuracy, and easy parameterization.…”

Section: The Equivalent Circuit Modelmentioning

confidence: 99%

An Improved State of Charge and State of Power Estimation Method Based on Genetic Particle Filter for Lithium-ion Batteries

Liu

Zheng

et al. 2020

Energies

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In this paper, an improved method for estimating the state of charge (SOC) of lithium-ion batteries is proposed, which is developed from the particle filter (PF). An improved genetic particle filter (GPF), owing to the advantages of the PF and genetic algorithm, is proposed to overcome the disadvantage of the traditional particle filter: lacking the diversity of particles. Firstly, the relationship between SOC and open-circuit voltage (OCV) is identified on the low-current OCV test. Secondly, a first-order resistor and capacitance (RC) model is established, then, the least-squares algorithm is used to identify the model parameters via the incremental current test. Thirdly, GPF and the improved GPF (IGPF) are proposed to solve the problems of the PF. The method based on the IGPF is proposed to estimate the state of power (SOP). Finally, IGPF, GPF, and PF are employed to estimate the SOC on the federal urban driving schedule (FUDS). The results show that compared with traditional PF, the errors of the IGPF are 20% lower, and compared with GPF, the maximum error of the IGPF has declined 1.6% SOC. The SOC that is estimated by the IGPF is applied to estimate the SOP for battery, considering the restrictions from the peak SOC, the voltage, and the instruction manual. The result shows that the method based on the IGPF can successfully estimate SOP.

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Section: The Equivalent Circuit Modelmentioning

confidence: 99%

An Improved State of Charge and State of Power Estimation Method Based on Genetic Particle Filter for Lithium-ion Batteries

Liu

Zheng

et al. 2020

Energies

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“…The structure of the single power cell model can be divided into two major parts: The open circuit voltage characteristics and the internal resistance characteristics of a cell [34][35][36]. The relationship between the open circuit voltage and the state of charge (SOC) of a cell can be expressed by Gregory L. Plett's "composite model" [37].…”

Section: Single Power Cell Modelmentioning

confidence: 99%

Electric Vehicle Battery Simulation System for Mobile Field Test of Off-Board Charger

et al. 2019

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An electric vehicle power battery simulation system simulating different power battery packs for the field test of the off-board charger is designed, which can be used to test the performance of an off-board charger. Specifically, the improved power battery model is combined with the improved lightweight charging load and the online estimation of the state of charge as well as the electromotive force of the battery model are used to adjust charging load parameters in real time to simulate the charging response. An acceleration coefficient is introduced into the traditional battery model to improve test efficiency, and the type, specification, temperature and voltage parameters of the battery can be set online according to the test requirements. An improved charging load scheme is proposed, in which a DC converter cascaded power battery pack of the mobile test vehicle is used to form a lightweight charging load with the mode of constant voltage, constant current, constant power and constant resistance and the ability to be adjusted continuously within the rated range. As a result, the size and weight of the charging load are reduced and the autonomous test of the off-board charger is realized. The performances of the proposed battery simulation system are validated through the various experimental results.

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“…Table 3 shows the difference between the SOFM algorithm and the traditional empirical formula. The traditional empirical formula determined the classification of units according to the size of C p , which can be calculated by the Equation 10: 2,7,11,17,18,19,21,22,23,24,31,33,34,38,45,47,48,57,58 Third class 3,4,7,10,11,12,14,19,21,22,26,27,28,29,30,31,35,42,43,46,48,50,51,53,54 Third class 5,6,8,9,15,16,…”

Section: Turbines With Energy Storagementioning

confidence: 99%

“…Moreover, the energy storage capacity of a WPP with an ESS is proposed in [24], which configure the storage power station based on maximum power fluctuation of initial restoration system at WPP access point. Additionally, the related literatures have studied the key technologies of the charging control strategy of transformer [25], the charge and discharge characteristic of an ESS [26], the cooperative frequency control [27,28], the optimal configuration of energy storage [29], the reactive voltage control [30,31], the optimal allocation of output transmission capacity and energy storage capacity of WPP [32,33], and the specific battery modeling technology [34].…”

Section: Introductionmentioning

confidence: 99%

Method for the Energy Storage Configuration of Wind Power Plants with Energy Storage Systems used for Black-Start

Zhang

et al. 2018

Energies

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With the increasing participation of wind generation in the power system, a wind power plant (WPP) with an energy storage system (ESS) has become one of the options available for a black-start power source. In this article, a method for the energy storage configuration used for black-start is proposed. First, the energy storage capacity for starting a single turbine was determined. Then, a hierarchical planning model was established. This model did not consider the starting efficiency of the WPP, but it did consider the layout of the energy storage in the WPP and the balance of the terminal voltage when starting the WPP. Finally, the feasibility and value of the proposed method and model were verified in a 49.5 MW WPP with an ESS (a power rating of 2.24 MW and energy capacity of about 1.68 MWh). The result suggests that configuring an ESS with a small capacity on the side of the turbine can achieve black-start for a WPP with an ESS as the power source.

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Modeling and SOC estimation of lithium iron phosphate battery considering capacity loss

Cited by 20 publications

References 4 publications

An Improved State of Charge and State of Power Estimation Method Based on Genetic Particle Filter for Lithium-ion Batteries

An Improved State of Charge and State of Power Estimation Method Based on Genetic Particle Filter for Lithium-ion Batteries

Electric Vehicle Battery Simulation System for Mobile Field Test of Off-Board Charger

Method for the Energy Storage Configuration of Wind Power Plants with Energy Storage Systems used for Black-Start

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