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.

In this study, engine performance on thermal factors for different biodiesels has been studied and compared with diesel fuel. Biodiesels were produced from Pongamia pinnata (PP), Calophyllum inophyllum (CI), waste cooking oil (WCO), and acid oil. Depending on their free fatty acid content, they were subjected to the transesterification process to produce biodiesel. The main characterizations of density, calorific range, cloud, pour, flash and fire point followed by the viscosity of obtained biodiesels were conducted and compared with mineral diesel. The characterization results presented benefits near to standard diesel fuel. Then the proposed diesel engine was analyzed using four blends of higher concentrations of B50, B65, B80, and B100 to better substitute fuel for mineral diesel. For each blend, different biodiesels were compared, and the relative best performance of the biodiesel is concluded. This diesel engine was tested in terms of BSFC (brake-specific fuel consumption), BTE (brake thermal efficiency), and EGT (exhaust gas temperature) calculated with the obtained results. The B50 blend of acid oil provided the highest BTE compared to other biodiesels at all loads while B50 blend of WCO provided the lowest BSFC compared to other biodiesels, and B50 blends of all biodiesels provided a minimum % of the increase in EGT compared to diesel.