State-of-charge inconsistency estimation of lithium-ion battery pack using mean-difference model and extended Kalman filter

Zheng, Yuejiu; Gao, Wenkai; Ouyang, Minggao; Lu, Languang; Zhou, Long; Han, Xiao

doi:10.1016/j.jpowsour.2018.02.058

Cited by 208 publications

(78 citation statements)

References 22 publications

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“…Time-domain models are obtained from the analysis of the battery voltage evolution during charge-discharge tests by means of the procedure called current interruption test. In order to improve the model accuracy, some authors [15,16] use online parameter identification methods to predict battery dynamical behavior as a function of time. These models are relatively easy to obtain, but their validity is usually limited to specific load regimes [17].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Modeling Procedure of Li-Ion Battery Modules for Reproducing Wide Frequency Applications in Electric Systems

Castaño-Solís¹,

Somoza²,

Fraile-Ardanuy³

et al. 2020

Research Trends and Challenges in Smart Grids

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In this chapter, a hybrid modeling procedure of Li-ion battery modules is presented. From experimental results, the parameters of an electrical circuit have been determined by means of time-and frequency-domain tests. In this way, the dynamic behavior of the battery-pack is modeled. The tests have been performed at the whole battery-pack, instead of a single-cell approach, in order to consider the packaging effects of multicell devices. The real performance of the battery-pack under dynamic applications associated with distribution grids has been simulated using a hardware-in-the-loop (HIL) experimental setup. According to simulation results, the hybrid model follows the battery-pack response with high accuracy.

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Section: Introductionmentioning

confidence: 99%

Hybrid Modeling Procedure of Li-Ion Battery Modules for Reproducing Wide Frequency Applications in Electric Systems

Castaño-Solís¹,

Somoza²,

Fraile-Ardanuy³

et al. 2020

Research Trends and Challenges in Smart Grids

View full text Add to dashboard Cite

show abstract

“…The core factor SOC in the Battery Management System (BMS) is quite important to the battery-based energy storage and supply systems in various working conditions [5]. With respect to the cellto-cell battery difference, the technology design of the associated BMS equipment becomes more and more challenging [6], which should be investigated under the limited computational resource requirement conditions [7].…”

Section: Introductionmentioning

confidence: 99%

A comprehensive working state monitoring method for power battery packs considering state of balance and aging correction

Wang

Fernandez

Zou

et al. 2019

Energy

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A comprehensive working state monitoring method is proposed to protect the power lithium-ion battery packs, implying accurate estimation effect but using minimal time demand of self-learning treatment. A novel state of charge estimation model is conducted by using the improved unscented Kalman filtering method, in which the state of balance and aging process correction is considered, guaranteeing the powered battery supply reliability effectively. In order to realize the equilibrium state evaluation among the internal battery cells, the numerical description and evaluation is putting forward, in which the improved variation coefficient is introduced into the iterative calculation process. The intermittent measurement and real-time calibration calculation process is applied to characterize the capacity change of the battery pack towards the cycling maintenance number, according to which the aging process impact correction can be investigated. This approach is different to the traditional methods by considering the multi-input parameters with real-time correction, in which every calculation step is investigated to realize the working state estimation by using the synthesis algorithm. The state of charge estimation error is 1.83%, providing the technical support for the reliable power supply application of the lithium-ion battery packs.

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“…3 The effective implementation was established with the charge replacement, in which the extended Kalman filtering algorithm was introduced and realized. 9 The incremental capacity and differential voltage were analyzed for the state-of-charge estimation of the lithium-ion batteries, 10 and a new multi-time-scale filter was proposed to obtain the state of energy and the state of power values. 5 A systematic and reliable state-of-charge estimation method was proposed for the lithium-ion battery pack by using the comprehensive state evaluation algorithm.…”

Section: Introductionmentioning

confidence: 99%

A novel weight coefficient calculation method for the real‐time state monitoring of the lithium‐ion battery packs under the complex current variation working conditions

Wang

Fernandez

Xie

et al. 2019

Energy Science & Engineering

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A novel real-time state monitoring method is proposed to realize the real-time energy management of the lithium-ion battery packs, which is conducted in the iterative computational calculation process by introducing an improved weighting factorunscented Kalman filtering algorithm. The accurate state monitoring treatment is investigated by applying a new iterate calculation thought, in which the improved weight coefficient parameter is constructed and its numerical stability is improved.Meanwhile, the recursive calculation is derived by using the real-time measured factors, according to which the state-of-charge estimation is realized accurately. Aiming to adapt the complex current variation working conditions, the nonlinear treatment is introduced to construct the mathematical unscented transforming function. As can be known from the experimental results, the state-of-charge estimation accuracy is 98.34% under the complex current charge-discharge working conditions. Meanwhile, the effective closed-circuit voltage trackage is also investigated accurately and its tracking error is within 3.51% in the complex working conditions, which provides a good security guarantee for the reliable energy supply of the lithium-ion battery packs. K E Y W O R D Scomplex current variation, Kalman filter, lithium-ion battery, state monitoring, unscented transform, weight coefficient | 3039 WANG et Al.

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State-of-charge inconsistency estimation of lithium-ion battery pack using mean-difference model and extended Kalman filter

Cited by 208 publications

References 22 publications

Hybrid Modeling Procedure of Li-Ion Battery Modules for Reproducing Wide Frequency Applications in Electric Systems

Hybrid Modeling Procedure of Li-Ion Battery Modules for Reproducing Wide Frequency Applications in Electric Systems

A comprehensive working state monitoring method for power battery packs considering state of balance and aging correction

A novel weight coefficient calculation method for the real‐time state monitoring of the lithium‐ion battery packs under the complex current variation working conditions

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