A new state-of-charge estimation method for electric vehicle lithium-ion batteries based on multiple input parameter fitting model

Liu, Xintian; He, Ye; Zheng, Xinxin; Zhang, Jiangfeng; Zeng, Guojian

doi:10.1002/er.3705

Cited by 33 publications

(29 citation statements)

References 32 publications

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“…Lithium-ion batteries have been widely used in mobile phones, digital cameras, laptops, other portable consumer electronics, and renewable energy storage [5,6]. With the continuous development and improvement of science and technology, the application range of lithium-ion batteries has further widened to the electric power, aerospace, military, and other critical fields [7,8]. Lithium-ion batteries have become core components of energy supply for many critical devices or systems, and are often critical to the reliability and functionality of the overall system [3,8].…”

Section: Introductionmentioning

confidence: 99%

Comparative Research on RC Equivalent Circuit Models for Lithium-Ion Batteries of Electric Vehicles

et al. 2017

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Equivalent circuit models are a hot research topic in the field of lithium-ion batteries for electric vehicles, and scholars have proposed a variety of equivalent circuit models, from simple to complex. On one hand, a simple model cannot simulate the dynamic characteristics of batteries; on the other hand, it is difficult to apply a complex model to a real-time system. At present, there are few systematic comparative studies on equivalent circuit models of lithium-ion batteries. The representative first-order resistor-capacitor (RC) model and second-order RC model commonly used in the literature are studied comparatively in this paper. Firstly, the parameters of the two models are identified experimentally; secondly, the simulation model is built in Matlab/Simulink environment, and finally the output precision of these two models is verified by the actual data. The results show that in the constant current condition, the maximum error of the first-order RC model is 1.65% and the maximum error for the second-order RC model is 1.22%. In urban dynamometer driving schedule (UDDS) condition, the maximum error of the first-order RC model is 1.88%, and for the second-order RC model the maximum error is 1.69%. This is of great instructional significance to the application in practical battery management systems for the equivalent circuit model of lithium-ion batteries of electric vehicles.

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Section: Introductionmentioning

confidence: 99%

Comparative Research on RC Equivalent Circuit Models for Lithium-Ion Batteries of Electric Vehicles

et al. 2017

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“…These models are used for various purposes such as the estimation of state of charge, [15][16][17][18][19] state of health, 18,20,21 and capacity. These mathematical models are broadly classified into 3 categories (1) empirical/data-based, (2) equivalent circuit, and (3) physics-based.…”

Section: Discussionmentioning

confidence: 99%

Fast computational framework for optimal life management of lithium ion batteries

et al. 2018

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Summary The determination of optimal charging profiles over cycle life of a lithium ion battery is a challenging problem that is extremely important for commercial applications. It is a difficult problem to solve owing to the complex degradation processes occurring inside the battery. Further, modeling of a realistic battery operation, let alone the degradation mechanisms, results in computationally expensive mathematical models. In the present study, a framework is developed towards addressing this problem by (1) developing a method to formulate extremely fast and accurate algebraic models that capture essential features such as charging time and aging characteristics described by battery models and (2) utilizing these algebraic models in an optimization framework involving genetic algorithms for determining the optimal charging profiles over the cycle life of the battery. The utility of the present framework in determining the optimal charging solutions is illustrated with various real‐life usage scenarios such as fast charging and extension of cycle life. The proposed solution can be utilized onboard for generating the optimal charging profiles over cycle life of the battery.

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“…The common SOC estimation methods include current integration method, Kalman filtering algorithm, and neural network algorithm . The current integration method is simple and is the most widely used SOC estimation method.…”

Section: Introductionmentioning

confidence: 99%

Equalization of series connected lithium‐ion batteries based on back propagation neural network and fuzzy logic control

Wang

Qin²,

Zhao

et al. 2020

Int J Energy Res

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Summary In this article, a nondissipative equalization scheme is proposed to reduce the inconsistency of series connected lithium‐ion batteries. An improved Buck‐Boost equalization circuit is designed, in which the series connected batteries can form a circular energy loop, equalization speed is improved, and modularization is facilitated. This article use voltage and state of charge (SOC) together as equalization variables according to the characteristics of open‐circuit voltage (OCV)‐SOC curve of lithium‐ion battery. The second‐order RC equivalent circuit model and back propagation neural network are used to estimate the SOC of lithium‐ion battery. Fuzzy logic control (FLC) is used to adjust the equalization current dynamically to reduce equalization time and improve efficiency. Simulation results show that the traditional Buck‐Boost equalization circuit and the improved Buck‐Boost equalization circuit are compared, and the equalization time of the latter is reduced by 34%. Compared with mean‐difference algorithm, the equalization time of FLC is decreased by 49% and the energy efficiency is improved by 4.88% under static, charging and discharging conditions. In addition, the proposed equalization scheme reduces the maximum SOC deviation to 0.39%, effectively reducing the inconsistency of batteries.

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A new state-of-charge estimation method for electric vehicle lithium-ion batteries based on multiple input parameter fitting model

Cited by 33 publications

References 32 publications

Comparative Research on RC Equivalent Circuit Models for Lithium-Ion Batteries of Electric Vehicles

Comparative Research on RC Equivalent Circuit Models for Lithium-Ion Batteries of Electric Vehicles

Fast computational framework for optimal life management of lithium ion batteries

Equalization of series connected lithium‐ion batteries based on back propagation neural network and fuzzy logic control

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