A novel battery thermal management system using nano-enhanced phase change materials

Jilte, Ravindra; Afzal, Asif; Panchal, Satyam

doi:10.1016/j.energy.2020.119564

Cited by 278 publications

(57 citation statements)

References 48 publications

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“…The work of Lin et al [9] includes an overview of the latest work in electric mobility, but there is still room to improve battery management algorithms, despite the enormous efforts made. Regarding possible improvements in the battery management system, thermal management is also important, as shown by the study by Jilte et al [10]. Possibly one of the positive messages of this report is the importance of collecting as much experimental data as possible to implement incremental improvements in the models.…”

Section: Introductionmentioning

confidence: 89%

“…The authors studied different optimization algorithms to match a parameter setgenerated spectrum to the experimental spectrum. This issue was explained in Equation (10), and the corresponding frequencies are shown in Table 1. The first-step differential evolution algorithm options are included in Table 2, and the second-step Nelder-Mead Simplex algorithm options are in Table 3.…”

Section: Identification Process Setupmentioning

confidence: 99%

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Automatic Identification Algorithm of Equivalent Electrochemical Circuit Based on Electroscopic Impedance Data for a Lead Acid Battery

Olarte

Ilarduya²,

Zulueta

et al. 2021

Electronics

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Obtaining tools to analyze and predict the performance of batteries is a non-trivial challenge because it involves non-destructive evaluation procedures. At the research level, the development of sensors to allow cell-level monitoring is an innovative path, and electrochemical impedance spectrometry (EIS) has been identified as one of the most promising tools, as is the generation of advanced multivariable models that integrate environmental and internal-battery information. In this article, we describe an algorithm that automatically identifies a battery-equivalent electrochemical model based on electroscopic impedance data. This algorithm allows in operando monitoring of variations in the equivalent circuit parameters that will be used to further estimate variations in the state of health (SoH) and state of charge (SoC) of the battery based on a correlation with experimental aging data corresponding to states of failure or degradation. In the current work, the authors propose a two-step parameter identification algorithm. The first consists of a rough differential evolution algorithm-based identification. The second is based on the Nelder–Mead Simplex search method, which gives a fine parameter estimation. These algorithm results were compared with those of the commercially available Z-view, an equivalent circuit tool estimation that requires expert human input.

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Section: Introductionmentioning

confidence: 89%

Section: Identification Process Setupmentioning

confidence: 99%

Automatic Identification Algorithm of Equivalent Electrochemical Circuit Based on Electroscopic Impedance Data for a Lead Acid Battery

Olarte

Ilarduya²,

Zulueta

et al. 2021

Electronics

View full text Add to dashboard Cite

show abstract

“…The results indicated that the hybrid BTMS endows the module with outstanding secondary heat dissipation capability. Jilte et al [27] compared two different configurations of nanoparticle-enhanced PCM with external natural air cooling for battery cooling. Their results confirmed that the transradial arrangement showed better performance even at 40 C condition.…”

Section: Doi: 101002/ente202100060mentioning

confidence: 99%

Optimization Investigation on Air Phase Change Material Based Battery Thermal Management System

et al. 2021

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To ensure the normal operation of a battery pack, a battery thermal management system (BTMS) is required to control the temperature of batteries. Herein, the method using an artificial neural network (ANN) combined with a genetic algorithm (GA) is proposed to optimize the thermal performance of air phase change material (PCM) cooling based BTMS. The ANN is applied to describe the relationship between BTMS parameters (inlet air velocity, inlet air temperature, PCM thickness, battery unit spacing, and discharge rate) and battery pack thermal characteristics. The results show that the PCM thickness and battery unit spacing have little effect on the battery temperature. Then, the optimal parameter combinations of BTMS are solved by GA with the goal of minimizing the maximum temperature. The maximum relative error between simulation and prediction is 0.484 °C, which is only 1.3835% of the simulated value. The optimal parameter combinations help to slow down the temperature rise of the battery pack and delay the phase transition of PCM. The results indicate that the developed model can accurately describe the relationship between the BTMS parameters and battery temperature, which provides a time‐saving and efficient method for the optimal design of BTMS.

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“…It was said that indirect liquid cooling systems were better than direct liquid cooling systems. Jilte et al 28 proposed an interesting shape of the battery module using blade bilayer particle-cured phase transformation materials. They used the 3C discharge rate for two different configurations and stated that the configuration in which the batteries were cooled individually had a better performance.…”

Section: Introductionmentioning

confidence: 99%

Computational modeling of a lithium‐ion battery thermal management system with Al ₂ O ₃ ‐based nanofluids

2021

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Lithium-ion batteries are seen as the primary energy storage tools for hybrid electric aircraft. However, the high temperature that occurs in battery modules can lead to a reduction in the capacity of the batteries and may even lead to serious safety issues such as fire. Therefore, it is essential to design an effective and reliable thermal management system to maintain battery temperatures under acceptable thresholds. In this study, a battery management system was designed using Al 2 O 3 nanofluid in different configurations, incorporating two different cooling models based on longitudinal and transverse flow directions, where the battery module consisted of 20 prismatic lithium-ion batteries. The two models were compared in terms of discharge rates, volume fraction values of nanofluid, and inlet velocities, and temperatures of the coolant. Overall, it was evident that the battery module was within the safe temperature range when cooled with the nanofluid, but not air.Novelty Statement: Lithium-ion batteries are seen as primary energy storage tools for hybrid electric aircraft. Thermal analysis of the batteries was performed in three dimensions by taking the bus bars into consideration. Nano fluid cooling was used as an effective way to keep battery temperatures within acceptable ranges.

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A novel battery thermal management system using nano-enhanced phase change materials

Cited by 278 publications

References 48 publications

Automatic Identification Algorithm of Equivalent Electrochemical Circuit Based on Electroscopic Impedance Data for a Lead Acid Battery

Automatic Identification Algorithm of Equivalent Electrochemical Circuit Based on Electroscopic Impedance Data for a Lead Acid Battery

Optimization Investigation on Air Phase Change Material Based Battery Thermal Management System

Computational modeling of a lithium‐ion battery thermal management system with Al ₂ O ₃ ‐based nanofluids

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A novel battery thermal management system using nano-enhanced phase change materials

Cited by 278 publications

References 48 publications

Automatic Identification Algorithm of Equivalent Electrochemical Circuit Based on Electroscopic Impedance Data for a Lead Acid Battery

Automatic Identification Algorithm of Equivalent Electrochemical Circuit Based on Electroscopic Impedance Data for a Lead Acid Battery

Optimization Investigation on Air Phase Change Material Based Battery Thermal Management System

Computational modeling of a lithium‐ion battery thermal management system with Al 2 O 3 ‐based nanofluids

Contact Info

Product

Resources

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Computational modeling of a lithium‐ion battery thermal management system with Al ₂ O ₃ ‐based nanofluids