SOC Oriented Electrochemical-Thermal Coupled Modeling for Lithium-Ion Battery

Ma, Yan; Li, Xin; Li, Guangyuan; Hu, Yunfeng; Bai, Qingwen

doi:10.1109/access.2019.2949357

Cited by 19 publications

(5 citation statements)

References 10 publications

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“…Heat transfer considers the transfer and distribution of heat within the battery, while heat generation considers the heat generated by chemical reactions within the battery. 80 The thermal model can accurately estimate and predict the temperature distribution and thermal characteristics of LIBs, which is helpful to improve the performance and safety of batteries. 81 It can also help optimize the design and control strategies of battery systems and improve battery life and performance.…”

Section: Analysis Of Various Modeling Methods Of Libsmentioning

confidence: 99%

Review—Optimized Particle Filtering Strategies for High-Accuracy State of Charge Estimation of LIBs

Wang¹,

Jia²,

Takyi‐Aninakwa³

et al. 2023

J. Electrochem. Soc.

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Lithium-ion batteries (LIBs) are used as energy storage systems due to their high efficiency. State of charge (SOC) estimation is one of the key functions of the battery management system (BMS). Accurate SOC estimation helps to determine the driving range and effective energy management of electric vehicles (EVs). However, due to complex electrochemical reactions and nonlinear battery characteristics, accurate SOC estimation is challenging. Therefore, this review examines the existing methods for estimating the SOC of LIBs and analyzes their respective advantages and disadvantages. Subsequently, a systematic and comprehensive analysis of the methods for constructing LIB models is conducted from various aspects such as applicability and accuracy. Finally, the advantages of particle filtering (PF) over the Kalman filter (KF) series algorithm for estimating SOC are summarized, and various improved PF algorithms for estimating the SOC of LIBs are compared and discussed. Additionally, this review provides corresponding suggestions for researchers in the battery field.

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Section: Analysis Of Various Modeling Methods Of Libsmentioning

confidence: 99%

Review—Optimized Particle Filtering Strategies for High-Accuracy State of Charge Estimation of LIBs

Wang¹,

Jia²,

Takyi‐Aninakwa³

et al. 2023

J. Electrochem. Soc.

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“…Arrhenius equation governs the most significant temperaturedependent parameters including diffusion coefficient and electrolyte ionic conductivity. So, the diffusion coefficient under the discharge process can be described as [14]…”

Section: Thermal Dependent Parametersmentioning

confidence: 99%

“…Resistance and OCV also follow Arrhenius equation. And because the effect of temperature to them is relatively minor, R (T ) and V oc (T ) can be simplified as linear equation [14]…”

Section: Thermal Dependent Parametersmentioning

confidence: 99%

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A Simplified Extension of Physics-Based Single Particle Model for Dynamic Discharge Current

Chen

Wang

et al. 2019

IEEE Access

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The prediction of the battery temperature and terminal voltage under dynamic load condition is crucial for a satellite battery management system. Restricted by parameter measurability and computing resources, equivalent circuit model has been commonly used in battery management system. But this model cannot satisfy the necessary performance under dynamic load for usual work of satellites. On account of this problem, a combined temperature single particle model is developed for 18650 cells in this paper. The proposed model consists of two sub-models, an electrochemical model and a thermal model, which are coupled together in an iterative manner through physicochemical temperature dependent parameters. The electrochemical sub-model mainly simplifies the calculation of lithium-ion concentration in electrode, while an expression for battery temperature distribution is employed in the thermal sub-model. In addition, genetic algorithm is adopted to estimate model parameters by exciting the battery under different operation conditions. This proposed model can provide accurate predictions of terminal voltage and surface temperature at various operating conditions and the proper simplification of mathematical structure making it ideal for real-time battery management system application. Finally, the model is validated against both constant and dynamic load conditions.INDEX TERMS Battery management systems, mathematical model, satellites, thermal analysis.

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“…Ma et al developed an advanced model to couple the electrochemical-thermal effect for the state of charge estimation of lithium-ion batteries. The results showed that the model could effectively evaluate the state of charge [8]. Zhou, Li and Xie proposed a systematic fastmodelling approach to numerically investigate the thermal effect's impact on battery performances [9].…”

Section: Introductionmentioning

confidence: 99%

A novel thermal management system for battery packs in hybrid electrical vehicles utilising waste heat recovery

Liu

Wen

Yuen

et al. 2022

International Journal of Heat and Mass Transfer

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SOC Oriented Electrochemical-Thermal Coupled Modeling for Lithium-Ion Battery

Cited by 19 publications

References 10 publications

Review—Optimized Particle Filtering Strategies for High-Accuracy State of Charge Estimation of LIBs

Review—Optimized Particle Filtering Strategies for High-Accuracy State of Charge Estimation of LIBs

A Simplified Extension of Physics-Based Single Particle Model for Dynamic Discharge Current

A novel thermal management system for battery packs in hybrid electrical vehicles utilising waste heat recovery

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