Thermofluidic modeling and temperature monitoring of Li-ion battery energy storage system

Tao, Fengbo; Zhang, Weijiang; Guo, Dongliang; Cao, Wenbin; Sun, Lei; Jiang, Fangming

doi:10.1016/j.applthermaleng.2020.116026

Cited by 25 publications

(11 citation statements)

References 47 publications

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“…In the embedded system application of lithium-ion battery, the value of Tss is easier to measure safely than Tin. Therefore, this paper selects Tss as the output vector of the battery thermal circuit system to obtain the observation equation under the continuous time state, as shown in Equation (6).…”

Section: Model-based Time Domain Analysismentioning

confidence: 99%

“…Therefore, the adaptive working condition prediction method of the external heat transfer coefficient of the battery is extremely important for the high-accuracy thermal characteristic modeling and the construction of an efficient thermal management system. The complex internal structure and changeable application environment of lithium-ion batteries make it easy to produce uneven internal temperature distribution and higher internal temperature during use, which increases the difficulty of estimating the internal temperature of the battery [6,7]. For the thermal characteristics modeling of power lithium-ion batteries, different modeling methods often have differences in reliability and accuracy [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

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A novel lumped thermal characteristic modeling strategy for the online adaptive temperature and parameter co-estimation of vehicle lithium-ion batteries

Shi

Wang

et al. 2022

Journal of Energy Storage

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Section: Model-based Time Domain Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A novel lumped thermal characteristic modeling strategy for the online adaptive temperature and parameter co-estimation of vehicle lithium-ion batteries

Shi

Wang

et al. 2022

Journal of Energy Storage

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“…LIBs have complicated internal structures and variable application environments, which makes it challenging to determine the interior temperature of the battery. 34,35 Several modeling techniques frequently differ in their dependability and accuracy when simulating the thermal properties of power LIBs. 36,37 Nowadays, methods for measuring the interior temperature of batteries that are often utilized including offline internal temperature prediction techniques and experimental internal temperature measurement techniques.…”

Section: Introductionmentioning

confidence: 99%

“…Many studies focus on the thermal model to determine the temperature distribution of the battery. LIBs have complicated internal structures and variable application environments, which makes it challenging to determine the interior temperature of the battery 34,35 . Several modeling techniques frequently differ in their dependability and accuracy when simulating the thermal properties of power LIBs 36,37 .…”

Section: Introductionmentioning

confidence: 99%

Performance evaluation of lithium battery pack based on MATLAB simulation with lumped parameter thermal model

Zeng

Pan

2023

Energy Science & Engineering

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The capacity of lithium batteries varies under different temperature conditions. However, many studies still neglect the influence of temperature on battery capacity. Besides, some studies only considered the combination of the heat and electricity of the battery monomer and failed to study the performance of the battery pack in groups. This paper investigates a new method for dynamic situations that takes the influence of temperature on battery capacity into consideration. After acquiring the parameter through experiment, we can substitute the performance parameters into the thermoelectric coupling model and use MATLAB to realize the iterative calculation of thermoelectric coupling. By combining the grouping mode with the model, we can simulate the thermodynamic and electrical properties of the battery cell in the pack. The novelty is evident because the paper focused on the battery pack instead of a single cell and the lumped parameter thermal model is adopted. Besides, the method proposed in this paper considered the influence of temperature on battery capacity. The program of calculation process can be solidified on the vehicle system. After the simulation, we can get the available capacity of the battery pack at different initial temperatures, with different grouping modes and different inconsistency of parameters. The results predicted by MATLAB model are compared with the results of MATLAB‐FLUENT thermoelectric coupling simulation. The result reflects that the prediction by the MATLAB model about the available capacity, temperature, and residual electricity is of good precision.

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“…In particular, lithiumion battery (LIB) energy storage system has developed and matured by leaps and bounds. This also brings about the increasing safety requirements (Ashkboos et al 2021;Killer et al 2020;Tao et al 2020). However, with the increasing capacity and battery density of ESS, a simple air-conditioning system can no longer meet the high heat flux of a lithium-ion battery container storage system (Kalogiannis et al 2022;Schimpe et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Research on a Novel Concept of Self-Forming Air Cooling Battery Rack

Jü¹,

Zhang²,

Yang³

et al. 2022

Front. Heat Mass Transf.

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Lithium-ion batteries used for energy storage systems will release amount of heat during operation. It will cause serious consequences of thermal runaway if not dissipate in time. In this study, a self-forming air-cooled battery rack of the energy storage system is established based on the normal battery rack for energy storage and the shape of the energy storage battery itself. The frames of the battery rack acts as air ducts, which greatly reduce the system complexity. In this paper, the heat generation model is established based on the experiment, and the four battery rack forms are studied by CFD simulation. The flow uniformity of the two-level shunt structure, the maximum temperature of the battery, the temperature difference and the overall pressure drop of the battery rack are analyzed. It was found that the self-forming battery rack in the form of Case Ⅳ has the highest flow rate and temperature uniformity due to the tapered air ducts. Case Ⅳ can also resist flow non-uniformity when increase the flow rate. The maximum temperature can be maintained at 33.8 o C at 0.5 C under the 46 g/s flowrate. Where the maximum bulk temperature difference between batteries and inside a single battery is less than 3.8 o C and 1 o C, respectively. The pressure drop is only 4.8 Pa. What's more, Case Ⅳ achieves optimal cooling performance at a 92 g/s flow rate, under which the maximum bulk temperature is 31.8 o C with the pressure drop of 19.5 Pa.

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Thermofluidic modeling and temperature monitoring of Li-ion battery energy storage system

Cited by 25 publications

References 47 publications

A novel lumped thermal characteristic modeling strategy for the online adaptive temperature and parameter co-estimation of vehicle lithium-ion batteries

A novel lumped thermal characteristic modeling strategy for the online adaptive temperature and parameter co-estimation of vehicle lithium-ion batteries

Performance evaluation of lithium battery pack based on MATLAB simulation with lumped parameter thermal model

Research on a Novel Concept of Self-Forming Air Cooling Battery Rack

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