Precise prediction of open circuit voltage of lithium ion batteries in a short time period

Zhou, Ming-Ying; Zhang, Jian-Bang; Ko, Chi-Jyun; Chen, Kuo-Ching

doi:10.1016/j.jpowsour.2022.232295

Cited by 20 publications

(10 citation statements)

References 39 publications

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“…Table 2 presents the parameters used for modeling the resonant structure on Altium designer and Simulink. For the load we adopt an equivalent RC model of the lithium battery [44][45][46] where Figure 17 shows the equivalent electrical diagram of the model. Table 3 presents the estimated values of the model parameters, all depending on the current and the charging voltage, where the estimated battery capacity is according to Equation (22).…”

Section: Impedance Analysis Of Class-e Invertermentioning

confidence: 99%

Photovoltaic Class‐E Inverter for Resonance Wireless Power Transfer for Electric Vehicle Charging Applications with Optimization Algorithms

et al. 2023

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A wireless power transfer (WPT) station supplied by an array of solar panels is presented, where solar energy comes from an array of panels with 120 V voltage and 3 A current. It is subjected to maximum power point tracking algorithm optimization on a boost converter, where the duty cycle of the converter is adjusted with an interval chosen between [0.2, 0.8], which is used to iteratively compare between the input and output signals in order to have a maximum power at the output. After regulating the panel array signal through the boost converter, the signal is transferred to an additional middle stage whose role is to raise the frequency until it reaches the resonance frequency of the WPT charger. The device responsible for this task is an inverter based on a class‐E inverter architecture and a GaN transistor technology. The inverter is controlled by a DSP card in a hard‐switch mode and delivers the output signal with a frequency of 10 Mhz, which is the resonance frequency of spiral coils of wireless chargers. Herein, the signal nature can be seen at the terminal of a fixed load with signal optimization through the MPPT algorithm and the HF inverter control mode.

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Section: Impedance Analysis Of Class-e Invertermentioning

confidence: 99%

Photovoltaic Class‐E Inverter for Resonance Wireless Power Transfer for Electric Vehicle Charging Applications with Optimization Algorithms

et al. 2023

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“…The process of identifying model parameters is designed and implemented using a transfer function in the frequency domain. Based on the discussion and analysis of the aforementioned analysis and the Laplace transform and Kirchhoff 's law, the frequency domain representation of the terminal voltage is described by (6).…”

Section: High-order Ecmmentioning

confidence: 99%

“…The six-order polynomial function for the curve fitting is shown in (17). 6 + P 2 * (SOC ) 5 + P 3 * (SOC ) 4 + P 4 * (SOC ) 3 + P 5 * (SOC ) 2 + P 6 * (SOC ) 1 + P 7 (17) After conducting numerous experiments to compare the effect of fitting performance, it was got that the sixth-order polynomial provides robust fitting results and was deemed appropriate for the processor. Consequently, the functional relation within SOC-OCV is obtained by fitting the six-order polynomial.…”

Section: Exploration Of Open Circuit Voltagementioning

confidence: 99%

“…The coefficient obtained by MATLAB using the least-squares method from P 1 to 7 (with 95% confidence bounds) is shown in (18). 6 − 61.22 * (SOC ) 5 + 68.12 * (SOC ) 4 − 32.07 * (SOC ) 3 + 5.234 * (SOC ) 2 + 0.6794 * (SOC ) 1 + 3.36 (18) Equation ( 18) establishes a mathematical relation between SOC-OCV, where f(SOC) represents the OCV. The SOC-OCV relationship acts as a foundation for the revised mathematical support of the parameters, which measures the OCV.…”

Section: Exploration Of Open Circuit Voltagementioning

confidence: 99%

“…In order to reduce global warming, EVs have been recognized as a practical and environmentally friendly substitute [1,2]. Lithium-ion batteries are considered a top choice for the EVs because of their numerous benefits, such as the huge capacity of energy storage, long life span, low maintenance cost, and easiness in transfer [3][4][5][6]. The lithium-ion battery has been hugely popular for over a century due to the critical need for a reliable source of power for portable and non-portable electronic equipment, such as smartphones and watches, smart grids, submarines, and space vehicles [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

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Adaptive state of charge estimation for lithium‐ion batteries using feedback‐based extended Kalman filter

Monirul

Pan

Ruby

et al. 2023

IET Control Theory & Appl

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The battery management system (BMS) is a crucial component of electric vehicles (EVs) owing to its sustainable operation. To ensure optimal performance of the BMS, state of charge (SOC) of the equipped battery is required to be effectively and accurately estimated. In this paper, the authors consider high‐order equivalent circuit model (ECM) to capture the dynamic characteristics of lithium‐ion batteries, which are connected in series with internal resistance by 2‐RC networks. The parameters of the RC network are determined by mathematically solving the working conditions of the two states. Moreover, the parameters of the battery can be derived by hybrid pulse power characterization (HPPC) tests. Then, based on the open‐circuit voltage, the proposed feedback‐based extended Kalman filtering (FEKF) algorithm is established. The parameters from the simulation have shown that the highest error is 0.0306 V, the optimal knowledge of which can improve the SOC estimation approach remarkably and can provide a reference value. Afterwards, the non‐linear predicting and corrective techniques are applied to the experiment in the extended calculation process. The original error is reduced by the FEKF algorithm, where the maximum and average errors are 0.0298 and 0.0240 V, respectively. Consequently, the established high‐order ECM utilizing the FEKF algorithm may provide SOC estimation with an error of 1.5% or less, resulting in superb performance from the lithium‐ion battery pack.

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A comprehensive review of state-of-charge and state-of-health estimation for lithium-ion battery energy storage systems

Tao,

Wang,

Cao

et al. 2024

Ionics

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Precise prediction of open circuit voltage of lithium ion batteries in a short time period

Cited by 20 publications

References 39 publications

Photovoltaic Class‐E Inverter for Resonance Wireless Power Transfer for Electric Vehicle Charging Applications with Optimization Algorithms

Photovoltaic Class‐E Inverter for Resonance Wireless Power Transfer for Electric Vehicle Charging Applications with Optimization Algorithms

Adaptive state of charge estimation for lithium‐ion batteries using feedback‐based extended Kalman filter

A comprehensive review of state-of-charge and state-of-health estimation for lithium-ion battery energy storage systems

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