Study on a Battery Thermal Management System Based on a Thermoelectric Effect

Zhang, Chuanwei; Xu, Ke-Jun; Li, Lin-Yang; Yang, Manzhi; Gao, Huaibin; Chen, Shang-Rui

doi:10.3390/en11020279

Cited by 35 publications

(10 citation statements)

References 16 publications

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“…A battery thermal management system was investigated based on a thermoelectric effect [11]. In addition, an uncomplicated heat generation model for a cooling model and single battery cell were developed.…”

Section: Introductionmentioning

confidence: 99%

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An Experimental Analysis of Entropic Coefficient of a Lithium Titanate Oxide Battery

2019

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In order to understand the thermal behaviour of a lithium-ion battery, the heat generation within the cell should be determined. The entropic heat coefficient is necessary to determine for the heat generation calculation. The entropic heat coefficient is one of the most important factors, which affects the magnitude of the reversible heat. The purpose of this research is to analyze and investigate the effect of different parameters on the entropic coefficient of lithium titanate oxide batteries. In this research, a lithium ion pouch cell was examined in both charging and discharging situations. The state of charge levels range was considered from 10% to 90%, and vice versa, in 10% increments. The temperature levels vary from 5 °C to 55 °C and the voltage levels vary from 1.5 V to 2.8 V. The effect of different parameters such as initial temperature, state of charge, thermal cycle, time duration for thermal cycles, and procedure prior to the thermal cycle on the entropic coefficient of lithium titanate oxide batteries were investigated. It was concluded that there is a strong influence of the battery cell state of charge on the entropic heat coefficient compared with other parameters.

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Section: Introductionmentioning

confidence: 99%

“…In addition, an uncomplicated heat generation model for a cooling model and single battery cell were developed. It was concluded that the thermoelectric cooling combined with liquid cooling structure demonstrated better performance [11].…”

Section: Introductionmentioning

confidence: 99%

An Experimental Analysis of Entropic Coefficient of a Lithium Titanate Oxide Battery

2019

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“…This paper selected a TAFEL-LAE895 100Ah ternary lithium ion power battery for study, and its main parameters are shown in Table 1 below: Calculation method was used to estimate the heat generated by the ternary lithium battery. At present, the commonly adopted calculation method is that supposing the internal material of the lithium ion battery generates heat uniformly [25,26]. The heat generation mechanism of the lithium ion battery was analyzed from two aspects, internal resistance heat generation and entropy increase reaction heat generation.…”

Section: Battery Calorie Calculationmentioning

confidence: 99%

“…In order to solve the problem that CPCM has limited thermal management capabilities, this paper introduces semiconductor refrigeration sheets, also known as TEC, using the Peltier effect of semiconductor material to carry heat from the cold terminal to the hot terminal [25,26]. A semiconductor refrigeration sheet is combined with the CPCM through heat pipe, and CPCM is cooled by its refrigeration function, such as is shown in Figure 11.…”

Section: Thermal Management Experiments With Combined Semiconductor Rementioning

confidence: 99%

Study of Thermal Management System Using Composite Phase Change Materials and Thermoelectric Cooling Sheet for Power Battery Pack

et al. 2019

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Scientific and reasonable battery thermal management systems contribute to improve the performance of a power battery, prolong its life of service, and improve its safety. Based on TAFEL-LAE895 type 100Ah ternary lithium ion power battery, this paper is conducted on charging and discharging experiments at different rates to study the rise of temperature and the uniformity of the battery. Paraffin can be used to reduce the surface temperature of the battery, while expanded graphite (EG) is added to improve the thermal conductivity and viscosity of the composite phase change material (CPCM), and to reduce the fluidity after melting. With the increase of graphite content, the heat storage capacity of phase change material (PCM) decreases, which affects the thermal management effect directly. Therefore, this paper combines heat pipe and semiconductor refrigeration technology to transform heat from the inner CPCM to the thermoelectric cooling sheet for heat dissipation. The results show that the surface temperature of the battery can be kept within a reasonable range when discharging at high rate. The temperature uniformity of the battery is improved and the energy of the battery is saved.

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“…The Seebeck effect results in an induced voltage in response to the temperature difference applied across the two ends of a thermocouple, where the induced voltage is directly proportional to the temperature difference [4,5]. The Peltier effect is the reverse of the Seebeck effect, and it results in cooling or heating at the two junctions of a thermocouple when an electric current passes through it [6]. The Peltier heat is directly proportional to the electric current, but its sign (cooling or heating) depends upon the direction of the electric current.…”

Section: Introductionmentioning

confidence: 99%

Combinatory Finite Element and Artificial Neural Network Model for Predicting Performance of Thermoelectric Generator

2018

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Thermoelectric generators (TEGs) are rapidly becoming the mainstream technology for converting thermal energy into electrical energy. The rise in the continuous deployment of TEGs is related to advancements in materials, figure of merit, and methods for module manufacturing. However, rapid optimization techniques for TEGs have not kept pace with these advancements, which presents a challenge regarding tailoring the device architecture for varying operating conditions. Here, we address this challenge by providing artificial neural network (ANN) models that can predict TEG performance on demand. Out of the several ANN models considered for TEGs, the most efficient one consists of two hidden layers with six neurons in each layer. The model predicted TEG power with an accuracy of ±0.1 W, and TEG efficiency with an accuracy of ±0.2%. The trained ANN model required only 26.4 ms per data point for predicting TEG performance against the 6.0 minutes needed for the traditional numerical simulations.Energies 2018, 11, 2216 2 of 17 in the Thomson heat, which is proportional to the electric current and absorbed or released based on the direction of the current [4].Continuous efforts have been made toward developing high performance n-type and p-type thermoelectric (TE) materials [1,[7][8][9]. The performance of TE materials is measured in terms of a dimensionless metric called the figure of merit, which is denoted as ZT. It combines three key material properties: thermal conductivity (κ), electrical resistivity (ρ), and the Seebeck coefficient (S), along with the absolute temperature (T), and it is given as ZT = S 2 κρ T [10,11]. The magnitude of ZT for most of the commercial thermoelectric materials is close to unity; however, recent studies have reported higher values for some of the state-of-the-art TE materials, such as a quantum-dot superlattice with ZT~3.5 at 575 K [12], a superlattice structure with ZT~2.4 at 300 K and ZT~2.9 at 400 K [13], and lead antimony silver telluride with ZT~2.2 at 800 K [14]. The thermoelectric modules built using nanostructured materials have been reported to exhibit thermal-to-electrical energy conversion efficiency up to 10% at a temperature difference of 500 K [15].The performance of TEG modules depends not only on the ZT of the TE material, but also on the geometric dimensions of the thermocouples and operating conditions such as the temperature difference and electrical load [16,17]. Several researchers in the past have attempted to optimize p-n leg geometry comprising of length, a cross-sectional area, and the number of thermocouples [18][19][20][21]. A few studies have attempted to provide optimal shape factor parameters such as the area ratio for p-n legs (Ap/An), the aspect ratio (leg length/leg area), and the slenderness ratio ((Ap/Lp)/(An/Ln), where Ap and An denote base area and Lp and Ln denote length of p-and n-type legs) [22,23]. However, these parameter changes depend upon the TE material used for fabricating TEG legs, the materials used in assembling the TEG modules, and oper...

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Study on a Battery Thermal Management System Based on a Thermoelectric Effect

Cited by 35 publications

References 16 publications

An Experimental Analysis of Entropic Coefficient of a Lithium Titanate Oxide Battery

An Experimental Analysis of Entropic Coefficient of a Lithium Titanate Oxide Battery

Study of Thermal Management System Using Composite Phase Change Materials and Thermoelectric Cooling Sheet for Power Battery Pack

Combinatory Finite Element and Artificial Neural Network Model for Predicting Performance of Thermoelectric Generator

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