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.

The present investigation uses a blend of Nigella sativa biodiesel, diesel, n-butanol, and graphene oxide nanoparticles to enhance the performance, combustion and symmetric characteristics and to reduce the emissions from the diesel engine of a modified common rail direct injection (CRDI). A symmetric toroidal-type combustion chamber and a six-hole solenoid fuel injector were used in the current investigation. The research aimed to study the effect of two fuel additives, n-butanol and synthesized asymmetric graphene oxide nanoparticles, in improving the fuel properties of Nigella sativa biodiesel (NSME25). The concentration of n-butanol (10%) was kept constant, and asymmetric graphene oxide nano-additive and sodium dodecyl benzene sulphonate (SDBS) surfactant were added to n-butanol and NSME25 in the form of nanofluid in varying proportions. The nanofluids were prepared using a probe sonication process to prevent nanoparticles from agglomerating in the base fluid. The process was repeated for biodiesel, n-butanol and nanofluid, and four different stable and symmetric nanofuel mixtures were prepared by varying the graphene oxide (30, 60, 90 and 120 ppm). The nanofuel blend NSME25B10GO90 displayed an enhancement in the brake thermal efficiency (BTE) and a reduction in brake-specific fuel consumption (BSFC) at maximum load due to high catalytic activity and the enhanced microexplosion phenomenon developed by graphene oxide nanoparticles. The heat release rate (HRR), in-cylinder temperature increased, while exhaust gas temperature (EGT) decreased. Smoke, hydrocarbon (HC), carbon monoxide (CO₂) and carbon monoxide (CO) emissions also fell, in a trade-off with marginally increased NO_x, for all nanofuel blends, compared with Nigella sativa biodiesel. The results obtained indicates that 90 ppm of graphene oxide nanoparticles and 10% n-butanol in Nigella sativa biodiesel are comparable with diesel fuel.