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    Battery thermal management of a novel helical channeled cylindrical Li-ion battery with nanofluid and hybrid nanoparticle-enhanced phase change material
    (Pergamon-Elsevier Science Ltd, 2023) Jilte, Ravindra; Afzal, Asif; Agbulut, Uemit; Shaik, Saboor; Khan, Sher Afghan; Linul, Emanoil; Asif, Mohammad
    Electric vehicles (EVs) have emerged as a viable alternative to Internal Combustion (IC) engine-powered vehicles, and efforts have been directed toward developing EVs that are more reliable and safer to operate. The safe working of EVs necessitates the use of an efficient battery cooling system. In this paper, cooling of cylindrical type Li-ion battery embedded with helical coolant channels is proposed. The effects of nanoparticles on removing heat from the battery cooling system have been investigated for four different nanoparticle concentrations: 0, 2, 5, and 10% of Al2O3 in the base fluid. Two cases of base fluids are considered: phase change material kept in a concentric container surrounding battery volume and coolant water circulated through liquid channels attached to the outer walls of the PCM (phase change material) cylindrical container. This study presented the three configurations (i) base case PCM-WLC: battery cooling system with a cylindrical enclosure filled with RT-42 phase change material. (ii) base case nePCM-WLC: battery cooling system filled with nano-enhanced phase change material. (iii) nePCM-LC: battery cooling system with helical liquid channels and filled with nanoenhanced PCM. The nanofluid was circulated through the liquid passages connected to the PCM container. Results showed using the helical channels, the nePCM-LC arrangement efficiently removes accumulated heat from the phase change material and provides better battery cooling than straight rectangular channel-based BTMS (battery thermal management system).
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    Melting numerical simulation of hydrated salt phase change material in thermal management of cylindrical battery cells using enthalpy-porosity model
    (Elsevier, 2023) Afzal, Asif; Jilte, Ravindra; Samee, Mohammed; Agbulut, Umit; Shaik, Saboor; Park, Sung Goon; Alwetaishi, Mamdooh
    Battery thermal management using different cooling techniques is rapidly growing. Understanding the proper cooling and melting process when phase change materials (PCM) are used is of prime importance in this area. Hence, a transient thermal-fluid and melting process of hydrated salt PCM enclosed in a battery module with six cylindrical cells is numerically investigated to understand the melting process of the PCM. Four structural models S1, S2, S3, and S4 are constructed for the present numerical simulation. The battery cell wall is kept at a constant temperature of 35celcius, while the rectangular enclosure walls are assumed to be insulated. A finite volume scheme -based CFD (computational fluid dynamics) software is used to simulate the melting process of hydrated salt PCM. In order to capture the phase change phenomenon from solid to liquid, an enthalpy-porosity equation is solved. The temporal temperature distribution, liquid fraction, velocity and enthalpy are analyzed. The results obtained by the numerical computation suggest that the battery cell arrangement used in S1 and S2 model at the initial time step gives better space for temperature distribution and liquid fraction up to the time step of 420 s, while S3 and S4 model after a time interval of 420 s provide better scope for temperature distribution and complete melting of hydrated PCM.
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    A study on a milk chiller latent storage system with phase change material encapsulated spherical balls
    (Elsevier Ltd, 2023) Jilte, Ravindra; Afzal, Asif; Ağbulut, Ümit; Alahmadi, Ahmad Aziz; Alwetaishi, Mamdooh; Alzaed, Ali Nasser
    For dispersed or remotely located families, the collection of raw milk takes place less frequently or transportation to the nearest center is not feasible. It requires chilling of collected milk from udder temperature (?35 °C) to storage temperature (?4 °C) and maintaining it throughout thus it demands running chilling at the discrete locations. In this study, a novel design of a milk chiller for coolness storage of 12/24 h based on phase change material is presented. System performance has been demonstrated following the prevailing practice of milk collection and loading/unloading of milk. By switching off the refrigeration after a certain interval, the coolness storage was demonstrated to meet the chilling conditions even during the non-availability of power. The study proposes an integrated portable mobile milk chilling system that can move between solar PV plants and the nearest electric grid during non-sunny days. The proposed milk chiller latent storage system (MC-LSS) contains three major components: a helical coil for refrigerant circulation during charging of the system, spherical capsules for encapsulating phase change materials and interspaced occupied brine solution for storing coolness and circulating throughout PCM-filled capsules. MC-LSS is tested under two cases: FLS-12(first loading of milk chiller and storage for 12 h) and SLS-12 (second loading of milk and storage for 12 h). The temperature history of the Milk chiller latent storage system for the FLS-12 h case is qualitatively analyzed which shows an appreciable reduction in milk temperature around 10–15 °C within the first 20 min and in another ?40 min of further cooling, milk temperature attains the desired storage temperature (4–5 °C). © 2023 Elsevier Ltd
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    Use of modern algorithms for multi-parameter optimization and intelligent modelling of sustainable battery performance
    (Elsevier, 2023) Afzal, Asif; Buradi, Abdulrajak; Jilte, Ravindra; Sundara, Vikram; Shaik, Saboor; Agbulut, Umit; Alwetaishi, Mamdooh
    The focus of this computational work is to predict and optimize the battery thermal performance indicators for its sustainable operation using different meta-heuristic optimization algorithms and machine learning models. The contribution of this work is two-fold, first, the heat removal ability from battery indicated by average Nusselt number (Nuavg) and hotspots (MaxT) to avoid battery thermal runaway are optimized as single objective optimization (SOO) and as multi-level objective optimization (MOO) problem. Second, intelligent algorithms: Gradient boosting (GB) algorithm and Gaussian process regressor (GPR) algorithm are used for modelling of Nuavg and MaxT. For SOO, Multi-verse optimization (MVO) and Grey wolf optimization (GWO) algorithms are used for individual battery performance indicators. Similarly, the enhanced version of MVO and GWO for MOO (MMVO and MGWO) algorithms is customized. Each algorithm is operated for five cycles and 100 iterations in each cycle of execution. In GB algorithm the effect of different loss functions and in GPR algorithm the effect of parameter alpha (alpha) is analyzed. SOO gives highest fitness of Nuavg and lowest hotspots occurrence from both the algorithms with same converged positions of operating parameters. MMVO and MGWO relatively provide lower Nuavg with MaxT in the same range of SOO. The MOO provides different set of particle positions compared to SOO. MGWO algorithm has outperformed in providing the best non-dominated solution. The GB and GPR algorithm are good enough for the forecasting of battery thermal parameters. GPR is even accurate, however the range of alpha is important during training and testing. The best Nuavg obtained from SOO using MVO algorithm is around 82.06 while MaxT is 0.34. The same from GWO algorithm is 82.05 and 0.33 respectively. MGWO algorithm in MOO provides Nuavg and MaxT around 75.57 and 0.34 while MMWO provides 66.76 and 0.33 respectively. GPR algorithm gives accuracy as close as 98 % for MaxT while it gives 94 % accuracy for Nuavg. On the other hand GB algorithm gives 99 % and 97.5 % accuracy for MaxT and Nuavg respectively.