A lumped thermal model of lithium-ion battery cells considering radiative heat transfer

Allafi, Walid; Zhang, Cheng; Uddin, Kotub; Dinh, Truong Quang; Ormeno, Pedro Ascencio; Li, Kang; Marco, James

doi:10.1016/j.applthermaleng.2018.07.105

Cited by 53 publications

(21 citation statements)

References 35 publications

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“…The heat generation models reported in the literature can be broadly classified from two different aspects; based on modelling strategy and based on the source of heat generation. Heat generation models based on modelling strategy can be classified into three groups, physics-based electro-chemical model [17][18][19][20], equivalent circuit models (ECM) [21][22][23], black-box models [24][25][26]. Whereas, based on the source of heat generation these models can be grouped as a concentrated model, distributed model [27] and heterogeneous model [21,28].…”

Section: Classification Of Temperature Estimation Strategiesmentioning

confidence: 99%

A Comprehensive Review of Lithium-ion Cell Temperature Estimation Techniques Applicable to Health-Conscious Fast Charging and Smart Battery Management Systems

Samanta¹,

Williamson²

2021

Preprint

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Highly nonlinear characteristics of lithium-ion batteries (LIBs) are significantly influenced by the external and internal temperature of the LIB cell. Moreover, cell temperature beyond the manufacturer’s specified safe operating limit could lead to thermal runaway and even fire hazards and safety concerns to operating personnel. Therefore, accurate information of cell internal and surface temperature of LIB is highly crucial for effective thermal management and proper operation of a battery management system (BMS). Accurate temperature information is also essential to BMS for the accurate estimation of various important states of LIB such as state of charge, state of health and so on. High capacity LIB pack, used in electric vehicles and grid-tied stationary energy storage system essentially consists of thousands of individual LIB cells. Therefore, installing a physical sensor at each cell especially at the cell core is not practically feasible from the solution cost, space and weight point of view. A solution is to develop a suitable estimation strategy which led scholars to propose different temperature estimation schemes aiming to establish a balance among accuracy, adaptability, modelling complexity and computational cost. This article presented an exhaustive review of these estimation strategies covering recent developments, current issues, major challenges, and future research recommendations. The prime intention is to provide a detailed guideline to the researchers and industries towards developing a highly accurate, intelligent, adaptive, easy to implement and compute efficient online temperature estimation strategy applicable to health-conscious fast charging and smart onboard BMS.

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Section: Classification Of Temperature Estimation Strategiesmentioning

confidence: 99%

A Comprehensive Review of Lithium-ion Cell Temperature Estimation Techniques Applicable to Health-Conscious Fast Charging and Smart Battery Management Systems

Samanta¹,

Williamson²

2021

Preprint

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“…Broadly, heat generation models reported in the literature can be classified into three groups, Electrochemical model [23][24][25][26], data-driven empirical models [27][28][29] and equivalent circuit models (ECM) [30][31][32]. Few other researchers also grouped the heat generation model from the perspective of heat concentration.…”

Section: Introductionmentioning

confidence: 99%

Smart Core and Surface Temperature Estimation Techniques for Health-conscious Lithium-ion Battery Management Systems: A Model-to-Model Comparison

Surya¹,

Samanta²,

Williamson³

2021

Preprint

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Estimation of core and surface temperature is one of the crucial functionalities of the lithium-ion Battery Management System (BMS) towards providing effective thermal management, fault detection and operational safety. While, it is impractical to measure core temperature using physical sensors, implementing a complex estimation strategy in on-board low-cost BMS is challenging due to high computational cost and the cost of implementation. Typically, a temperature estimation scheme consists of a heat generation model and a heat transfer model. Several researchers have already proposed ranges of thermal models having different levels of accuracy and complexity. Broadly, there are first-order and second-order heat capacitor-resistor-based thermal models of lithium-ion batteries (LIBs) for core and surface temperature estimation. This paper deals with a detailed comparative study between these two models using extensive laboratory test data and simulation study to access suitability in online prediction and onboard BMS. The aim is to guide whether it’s worth investing towards developing a second-order model instead of a first-order model with respect to prediction accuracy considering modelling complexity, experiments required and the computational cost. Both the thermal models along with the parameter estimation scheme are modelled and simulated using MATLAB/Simulink environment. Models are validated using laboratory test data of a cylindrical 18650 LIB cell. Further, a Kalman Filter with appropriate process and measurement noise levels are used to estimate the core temperature in terms of measured surface and ambient temperatures. Results from the first-order model and second-order models are analyzed for comparison purposes.

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“…Heat generation models can be roughly classified into three categories: physics-based electrochemical models [1,2,9,17], black-box empirical models [10,19,20] and equivalent circuit models (ECM) [8,11,12]. The black-box empirical model is usually based on experimentally measured rates of heat generation and relies on empirical equations or other fitting strategies to acquire the heat generation under different working conditions.…”

Section: Graphic Abstract 1 Introductionmentioning

confidence: 99%

“…FEA based approaches can usually reach a very high accuracy, but the heavy computational cost greatly limits its real-time application. Alternative solutions to model the temperature profile of LIBs are the lumped multi-node [9,10,12,13,25,26] or heat capacitor-resistance models [8] based on the analogy between thermal and electrical phenomena [14]. Usually, a lumped multi-node model assumes all parameters to be uniform and irrelevant to the location of the nodes [11].…”

Section: Graphic Abstract 1 Introductionmentioning

confidence: 99%

A computational multi-node electro-thermal model for large prismatic lithium-ion batteries

Pan

Hua

Zhou

et al. 2020

Journal of Power Sources

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A lumped thermal model of lithium-ion battery cells considering radiative heat transfer

Cited by 53 publications

References 35 publications

A Comprehensive Review of Lithium-ion Cell Temperature Estimation Techniques Applicable to Health-Conscious Fast Charging and Smart Battery Management Systems

A Comprehensive Review of Lithium-ion Cell Temperature Estimation Techniques Applicable to Health-Conscious Fast Charging and Smart Battery Management Systems

Smart Core and Surface Temperature Estimation Techniques for Health-conscious Lithium-ion Battery Management Systems: A Model-to-Model Comparison

A computational multi-node electro-thermal model for large prismatic lithium-ion batteries

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