Imran Mokashi

Bio: Imran Mokashi is an academic researcher from Bearys Institute of Technology. The author has contributed to research in topics: Nanofluid & Battery pack. The author has an hindex of 1, co-authored 1 publications receiving 15 citations.

Optimization and analysis of maximum temperature in a battery pack affected by low to high Prandtl number coolants using response surface methodology and particle swarm optimization algorithm

Abstract: Thermal analysis of heat-generating battery pack cooled by several coolants is analyzed numerically. The coolant used are gases, oils, thermal oils, nanofluids, and liquid metals to find the best c...

A Review on Battery Modelling Techniques

Abstract: The growing demand for electrical energy and the impact of global warming leads to a paradigm shift in the power sector. This has led to the increased usage of renewable energy sources. Due to the intermittent nature of the renewable sources of energy, devices capable of storing electrical energy are required to increase its reliability. The most common means of storing electrical energy is battery systems. Battery usage is increasing in the modern days, since all mobile systems such as electric vehicles, smart phones, laptops, etc., rely on the energy stored within the device to operate. The increased penetration rate of the battery system requires accurate modelling of charging profiles to optimise performance. This paper presents an extensive study of various battery models such as electrochemical models, mathematical models, circuit-oriented models and combined models for different types of batteries. It also discusses the advantages and drawbacks of these types of modelling. With AI emerging and accelerating all over the world, there is a scope for researchers to explore its application in multiple fields. Hence, this work discusses the application of several machine learning and meta heuristic algorithms for battery management systems. This work details the charging and discharging characteristics using the black box and grey box techniques for modelling the lithium-ion battery. The approaches, advantages and disadvantages of black box and grey box type battery modelling are analysed. In addition, analysis has been carried out for extracting parameters of a lithium-ion battery model using evolutionary algorithms.

Back propagation modeling of shear stress and viscosity of aqueous Ionic-MXene nanofluids

Abstract: Back-propagation modeling of viscosity and shear stress of Ionic-MXene nanofluid is carried out in this work. The data for Ionic-MXene nanofluid of 0.05, 0.1, and 0.2 mass concentration (mass%) are collected from the experimental analysis. Shear stress and viscosity as a function of shear rate and mass% of MXene nanoparticles is used as input. Additionally, viscosity as a function of temperature and % of MXene nanoparticles is collected separately. Based on the possible combinations, five back-propagation algorithms are developed. In each algorithm, five models depending upon the number of neurons in the hidden layer are used. The training and testing of all the models in each algorithm are performed. Statistical analysis of the network output is done to evaluate the accuracy of models by finding the losses in terms of mean squared error (MAE), root-mean-squared error, mean absolute error, (MAE), and error deviation. Model 1 is found to have lower accuracy than the remaining models as the number of neurons in its hidden layer is only one. The performance evaluation metrices of the back-propagation model show that the error involved is acceptable. The training and testing of the algorithms are satisfactory as the network output is found to be in comfortably good agreement with the desired experimental output.

Modelling and Computational Experiment to Obtain Optimized Neural Network for Battery Thermal Management Data

Abstract: The focus of this work is to computationally obtain an optimized neural network (NN) model to predict battery average Nusselt number (Nuavg) data using four activations functions. The battery Nuavg is highly nonlinear as reported in the literature, which depends mainly on flow velocity, coolant type, heat generation, thermal conductivity, battery length to width ratio, and space between the parallel battery packs. Nuavg is modeled at first using only one hidden layer in the network (NN1). The neurons in NN1 are experimented from 1 to 10 with activation functions: Sigmoidal, Gaussian, Tanh, and Linear functions to get the optimized NN1. Similarly, deep NN (NND) was also analyzed with neurons and activations functions to find an optimized number of hidden layers to predict the Nuavg. RSME (root mean square error) and R-Squared (R2) is accessed to conclude the optimized NN model. From this computational experiment, it is found that NN1 and NND both accurately predict the battery data. Six neurons in the hidden layer for NN1 give the best predictions. Sigmoidal and Gaussian functions have provided the best results for the NN1 model. In NND, the optimized model is obtained at different hidden layers and neurons for each activation function. The Sigmoidal and Gaussian functions outperformed the Tanh and Linear functions in an NN1 model. The linear function, on the other hand, was unable to forecast the battery data adequately. The Gaussian and Linear functions outperformed the other two NN-operated functions in the NND model. Overall, the deep NN (NND) model predicted better than the single-layered NN (NN1) model for each activation function.