Cell state-of-charge inconsistency estimation for LiFePO4 battery pack in hybrid electric vehicles using mean-difference model

Zheng, Yuejiu; Ouyang, Minggao; Lu, Languang; Li, Jianqiu; Han, Xiao; Xu, Liangfei; Hai, Ma; Dollmeyer, Thomas A.; Freyermuth, Vincent

doi:10.1016/j.apenergy.2013.05.048

Cited by 176 publications

(75 citation statements)

References 26 publications

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“…But how to determine the tunable correction gain was not discussed in this paper. Zheng et al [28] introduced a cell state-of-charge inconsistency estimation for LiFePO 4 battery pack using mean-difference model, and SOC differences is determined with estimated OCV differences using SOC-difference/OCV-difference curve. However, to implement this method, hundreds of total least squares methods are needed to obtain the OCV differences when applying it to the battery pack in EVs, which makes its application unrealistic due to the high computational effort.…”

Section: Introductionmentioning

confidence: 99%

A multi time-scale state-of-charge and state-of-health estimation framework using nonlinear predictive filter for lithium-ion battery pack with passive balance control

Hua

Cordoba-Arenas

Warner

et al. 2015

Journal of Power Sources

147

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Section: Introductionmentioning

confidence: 99%

A multi time-scale state-of-charge and state-of-health estimation framework using nonlinear predictive filter for lithium-ion battery pack with passive balance control

Hua

Cordoba-Arenas

Warner

et al. 2015

Journal of Power Sources

147

View full text Add to dashboard Cite

“…In this case, the battery is often partially charged and reference points that provide a SoC calibration are seldom available. In addition, the estimation of the SoC of batteries pack, usually used in real-life contexts, introduces more complexity due to the heterogeneity of the elementary battery constituting the pack [77]. The display rate of the SoC is also an important factor in real-time applications.…”

Section: Discussionmentioning

confidence: 99%

From a novel classification of the battery state of charge estimators toward a conception of an ideal one

Kalawoun

Biletska

Suard

et al. 2015

Journal of Power Sources

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. From a novel classification of the battery state of charge estimators toward a conception of an ideal one. Journal of Power Sources, Elsevier, 2015, 279, pp.694 -706. 10 b s t r a c tAn efficient estimation of the State of Charge (SoC) of an electrical battery in a real-time context is essential for the development of an intelligent management of the battery energy. The main performance limitations of a SoC estimator originate in limited Battery Management System hardware resources as well as in the battery behavior cross-dependence on the battery chemistry and its cycling conditions. This paper presents a review of methods and models used for SoC estimation and discusses their concept, adaptability and performances in real-time applications. It introduces a novel classification of SoC estimation methods to facilitate the identification of aspects to be improved to create an ideal SoC model. An ideal model is defined as the model that provides a reliable SoC for any battery type and cycling condition, online. The benefits of the machine learning methods in providing an online adaptive SoC estimator are thoroughly detailed. Remaining challenges are specified, through which the characteristics of an ideal model can emerge.

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“…Therefore, increasing battery pack power and charge/discharge levels is needed in battery packs and can be obtained with advanced estimation methods for state of charge (SOC) [23][24][25][26][27][28][29][30][31][32][33][34]. Prolonging the lifetime of battery packs with improved state of health (SOH) algorithms [35][36][37][38][39][40][41] can also reduce costs because the total capacity of battery cells and packs should be minimized to reduce vehicle weight.…”

Section: Introductionmentioning

confidence: 99%

Characterization and modeling of the thermal mechanics of lithium-ion battery cells

Epureanu

2016

Applied Energy

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h i g h l i g h t sThermal swelling shape is different than Li-ion intercalation swelling shape. Nonuniform temperature and gap creation leads to a convex shape at free conditions. Important parameters of thermal mechanics are estimated through experiments. A coupled thermal-structural analysis accurately predicts thermal swelling shape. Nonuniform temperature still plays a critical role at pack conditions. Keywords:Lithium-ion battery Battery swelling shape Thermal expansion The coefficient of thermal expansion The coupled thermal-structural analysis a b s t r a c tThe thermal mechanics of Lithium-ion (Li-ion) batteries is explored with a focus on thermal swelling. Experiments show for the first time that the swelling shape of prismatic battery cells due to temperature variations is significantly different from that due to Li-ion intercalation in unconstrained conditions. In contrast to uniform and orthotropic Li-ion intercalation swelling in a direction perpendicular to electrodes, the nonuniform temperature distribution in the jellyroll and the gaps/voids between electrodes result in distinguishable different swelling shapes. A unique coupled thermal-structural analysis with a simple, but efficient 3-D finite numerical model is proposed to investigate the impact of temperature variations on the thermal behaviors of battery cells. Anisotropic heat conduction and temperature dependency of the coefficient of thermal expansion are taken into account and found to have an impact on temperature distribution and thermal expansion. Experimental validation of the proposed model clearly demonstrates that the coupled thermal-structural analysis with the proposed model can predict accurately the thermal swelling at unconstrained conditions. The solution at pack (constrained) conditions shows that the nonuniform temperature distribution of the jellyroll still plays a critical role for the thermal swelling shape, although the gaps/voids do not occur because of the constraints from spacers in the pack, suggesting that the estimation of core temperature is important. Such an accurate model, able to estimate cell thermal behavior, is beneficial to the design of Li-ion batteries, development of stress or strain sensors, optimum sensor placement, and thermal management of not only single battery cells but also battery packs.

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Cell state-of-charge inconsistency estimation for LiFePO4 battery pack in hybrid electric vehicles using mean-difference model

Cited by 176 publications

References 26 publications

A multi time-scale state-of-charge and state-of-health estimation framework using nonlinear predictive filter for lithium-ion battery pack with passive balance control

A multi time-scale state-of-charge and state-of-health estimation framework using nonlinear predictive filter for lithium-ion battery pack with passive balance control

From a novel classification of the battery state of charge estimators toward a conception of an ideal one

Characterization and modeling of the thermal mechanics of lithium-ion battery cells

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