scholarly journals Erratum: “Effects of Nonuniform Temperature Distribution on Degradation of Lithium-Ion Batteries” [ASME J. Electrochem. Energy. Conv. Stor., 2020, 17(2), p. 021101; DOI: 10.1115/1.4045205]

2021 ◽  
Vol 18 (4) ◽  
Author(s):  
Gabriel M. Cavalheiro ◽  
Takuto Iriyama ◽  
George J. Nelson ◽  
Shan Huang ◽  
Guangsheng Zhang
Keyword(s):  
Temperature Distribution ◽  
Lithium Ion Batteries ◽  
Lithium Ion ◽  
Nonuniform Temperature ◽  
Nonuniform Temperature Distribution

Effects of Nonuniform Temperature Distribution on Degradation of Lithium-Ion Batteries

2019 ◽  
Vol 17 (2) ◽  
Author(s):  
Gabriel M. Cavalheiro ◽  
Takuto Iriyama ◽  
George J. Nelson ◽  
Shan Huang ◽  
Guangsheng Zhang
Keyword(s):  
Temperature Distribution ◽  
Current Distribution ◽  
Lithium Ion ◽  
Cell Resistance ◽  
Nonuniform Temperature ◽  
Capacity Retention ◽  
Nonuniform Temperature Distribution ◽  
Accelerated Degradation ◽  
Li Ion ◽  
Pattern Of Variation

Abstract The effects of nonuniform temperature distribution on the degradation of lithium-ion (Li-ion) batteries are investigated in this study. A Li-ion battery stack consisting of five 3 Ah pouch cells connected in parallel was tested for 2215 cycles and compared with a single baseline cell. The behaviors of temperature distribution, degradation, and current distribution of the stack were characterized and discussed. Results supported the hypothesis that nonuniform temperature distribution causes nonuniform and accelerated degradation. All cells in the stack experienced higher temperature rise and degraded faster than the baseline cell. In particular, capacity retention of the middle cell in the stack decreased to 50.7% after 2215 cycles, while the baseline cell capacity retention was still 87.8%. The resistance of cells in the stack experienced nonuniform but similar pattern of variation with cycling. The resistances remained stable in early cycles, then experienced a rapid increase, and then became stable again. The middle cell resistance increased abruptly in the last 20 cycles before failure. Current distribution behaviors of the stack also changed significantly during cycling, which was consistent with cell resistance behaviors. The middle cell experienced much higher C rate than average, suggesting that its accelerated degradation can be attributed to the synergized effects of higher local temperature and higher local current.

Direct and Indirect Methods for Calculating Thermal Emission From Layered Structures With Nonuniform Temperatures

2011 ◽  
Vol 133 (7) ◽  
Author(s):  
L. P. Wang ◽  
S. Basu ◽  
Z. M. Zhang
Keyword(s):  
Temperature Distribution ◽  
Thermal Emission ◽  
Local Density ◽  
Direct Method ◽  
Direct Methods ◽  
Layered Structures ◽  
Nonuniform Temperature ◽  
Indirect Methods ◽  
Nonuniform Temperature Distribution ◽  
Direct And Indirect Methods

The determination of emissivity of layered structures is critical in many applications, such as radiation thermometry, microelectronics, radiative cooling, and energy harvesting. Two different approaches, i.e., the “indirect” and “direct” methods, are commonly used for computing the emissivity of an object. For an opaque surface at a uniform temperature, the indirect method involves calculating the spectral directional-hemispherical reflectance to deduce the spectral directional emissivity based on Kirchhoff’s law. On the other hand, a few studies have used a combination of Maxwell’s equations with the fluctuation-dissipation theorem to directly calculate the emissivity. The present study aims at unifying the direct and indirect methods for calculating the far-field thermal emission from layered structures with a nonuniform temperature distribution. Formulations for both methods are given to illustrate the equivalence between the indirect and the direct methods. Thermal emission from an asymmetric Fabry–Pérot resonance cavity with a nonuniform temperature distribution is taken as an example to show how to predict the intensity, emissivity, and the brightness temperature. The local density of states, however, can only be calculated using the direct method.

scholarly journals Optimal Control of Centralized Thermoelectric Generation System under Nonuniform Temperature Distribution Using Barnacles Mating Optimization Algorithm

Electronics ◽  
2021 ◽  
Vol 10 (22) ◽  
pp. 2839
Author(s):  
Mirza Imran Tariq ◽  
Majad Mansoor ◽  
Adeel Feroz Mirza ◽  
Nouman Mujeeb Khan ◽  
Muhammad Hamza Zafar ◽  
...  
Keyword(s):  
Temperature Distribution ◽  
Fossil Fuels ◽  
Low Cost ◽  
Cuckoo Search ◽  
Energy Harvest ◽  
Global Maximum ◽  
Renewable Energy Resources ◽  
Generation System ◽  
Nonuniform Temperature ◽  
Nonuniform Temperature Distribution

The need for renewable energy resources is ever-increasing due to the concern for environmental issues associated with fossil fuels. Low-cost high-power-density manufacturing techniques for the thermoelectric generators (TEG) have added to the technoeconomic feasibility of the TEG systems as an effective power generation system in heat recovery, cooling, electricity, and engine-efficiency applications. The environment-dependent factors such as the nonuniform distribution of heat, damage to the heat-transfer coating between sinks and sources, and mechanical faults create nonuniform current generation and impedance mismatch causing power loss. As a solution to this nonlinear multisolution problem, an improved MPPT control is presented, which utilizes the improvised barnacle mating optimization (BMO). The case studies are formulated to gauge the performance of the proposed BMP MPPT control under nonuniform temperature distribution. The results are compared to the grey wolf optimization (GWO), particle swarm optimization (PSO), and cuckoo search (CS) algorithm. Faster global maximum power point tracking (GMPP) within 381 ms, higher power tracking efficiency of up to 99.93%, and least oscillation ≈0.8 W are achieved by the proposed BMO with the highest energy harvest on average. The statistical analysis further solidifies the better performance of the proposed controller with the least root mean square error (RMSE), RE, and highest SR.

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