What is he best known for?
The fields of study he is best known for:
- Mechanical engineering
- Composite material
- Artificial intelligence
Akhil Garg mainly focuses on Genetic programming, Artificial neural network, Battery pack, Electric vehicle and Mechanical engineering.
His Genetic programming research incorporates themes from Computational intelligence, Machining, Mathematical optimization, Water content and Rapid prototyping.
The Artificial neural network study combines topics in areas such as Empirical modelling, Stepwise regression and Support vector machine.
His Battery pack research is multidisciplinary, incorporating perspectives in Lithium-ion battery and Anode.
His work deals with themes such as Process engineering, Operating temperature, Compressed hydrogen and Power density, which intersect with Mechanical engineering.
The various areas that Akhil Garg examines in his Battery study include Automotive engineering and Air cooling.
His most cited work include:
- Process characterisation of 3D-printed FDM components using improved evolutionary computational approach (78 citations)
- State-of-the-art in empirical modelling of rapid prototyping processes (73 citations)
- Metallurgical and mechanical methods for recycling of lithium-ion battery pack for electric vehicles (71 citations)
What are the main themes of his work throughout his whole career to date?
Akhil Garg focuses on Genetic programming, Artificial neural network, Automotive engineering, Battery and Mechanical engineering.
His Genetic programming study incorporates themes from Mathematical optimization and Model selection.
His Artificial neural network study integrates concerns from other disciplines, such as Empirical modelling, Simulation, Support vector machine, Rapid prototyping and Regression analysis.
His study in Battery focuses on Battery pack in particular.
His Battery pack study combines topics from a wide range of disciplines, such as Nuclear engineering, Electric vehicle and Lithium-ion battery.
His Machine learning research incorporates themes from Process and Fuzzy logic.
He most often published in these fields:
- Genetic programming (27.27%)
- Artificial neural network (15.45%)
- Automotive engineering (12.73%)
What were the highlights of his more recent work (between 2019-2021)?
- Battery (12.27%)
- Automotive engineering (12.73%)
- Lithium-ion battery (7.27%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Battery, Automotive engineering, Lithium-ion battery, Thermal management of electronic devices and systems and Computer cooling.
His Battery research is multidisciplinary, incorporating elements of Artificial neural network, Electrolyte, Embedded system and Big data.
He performs integrative study on Artificial neural network and Volume.
The Automotive engineering study combines topics in areas such as Battery pack, Electric vehicle and Optimal design.
His work carried out in the field of Battery pack brings together such families of science as Robotics, Residual energy, Artificial intelligence and Battery recycling.
His studies deal with areas such as Energy consumption, Fast charging and Nuclear engineering as well as Computer cooling.
Between 2019 and 2021, his most popular works were:
- A probability and integrated learning based classification algorithm for high-level human emotion recognition problems (26 citations)
- Electrochemical Performance Enhancement of Sodium-Ion Batteries Fabricated With NaNi1/3Mn1/3Co1/3O2 Cathodes Using Support Vector Regression-Simplex Algorithm Approach (16 citations)
- Electrochemical performance investigation of LiFePO4/C0.15-x (x=0.05, 0.1, 0.15 CNTs) electrodes at various calcination temperatures: Experimental and Intelligent Modelling approach (15 citations)
In his most recent research, the most cited papers focused on:
- Mechanical engineering
- Composite material
- Artificial intelligence
His primary scientific interests are in Battery pack, Battery, Computer cooling, Lithium-ion battery and Automotive engineering.
His Battery pack research includes themes of Internal resistance, Reliability engineering and Battery recycling.
The Computer cooling study which covers Battery thermal management that intersects with Scheduling and Energy consumption.
His Lithium-ion battery study deals with Nuclear engineering intersecting with Fluid dynamics, Air cooling, Water cooling and Computational fluid dynamics.
While working in this field, Akhil Garg studies both Automotive engineering and Standard deviation.
His Thermal management of electronic devices and systems study is associated with Mechanical engineering.
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