Energy storage systems (ESSs) are critically important for the future of electric vehicles. Despite this, the safety and management of ESSs require improvement. Battery management systems (BMSs) are vital components in ESS systems for Lithium-ion batteries (LIBs). One parameter that is included in the BMS is the state-of-charge (SoC) of the battery. SoC has become an active research area in recent years for battery electric vehicle (BEV) LIBs, yet there are some challenges: the LIB configuration is nonlinear, making it hard to model correctly; it is difficult to assess internal environments of a LIB (and this can be different in laboratory conditions compared to real-world conditions); and these discrepancies can lead to raising the instability of the LIB. Therefore, further advancement is required in order to have higher accuracy in SoC estimation in BEV LIBs. SoC estimation is a key BMS feature, and precise modeling and state estimation will improve stable operation. This review discusses current methods use in BEV LIB SoC modelling and estimation. The review culminates in a brief discussion of challenges in BEV LIB SoC prediction analysis.

The increasing use of electric vehicle batteries in the world has a significant impact on both society and the environment. Thus, there is a need for the availability of transparent information on resource allocation. Battery manufacturing process details in this regard are not available in academia or the public. The available energy data on manufacturing has a high variation. Furthermore, different process steps have different energy and material demands. A process model can benchmark the energy usage, provide detailed process data, and compare various cell productions which in turn can be used in life-cycle assessment studies to reduce the variation and provide directions for improvements. Therefore, a cell manufacturing model is developed for the calculation of energy and material demands for different battery types, plant capacities, and process steps. The model consists of the main process steps, machines, intermediate products and building service units. Furthermore, the results are validated using literature values. For a case study of a 2 GWh plant that produces prismatic NMC333 cells, the total energy requirement on a theoretical and optimal basis is suggested to be 44.6Whinproduction/Whcellcapacity. This energy consumption in producing batteries is dominated by electrode drying, and dry room. Energy usage for a variety of cell types for a similar plant capacity shows that the standard deviation in the results is low (47.23±13.03Wh/Wh).

Lithium-ion batteries (LiBs) are widely used as energy storage systems (ESSs). The biggest challenge they face is retaining intrinsic health under all conditions, and understanding internal thermal behaviour is crucial to this. The key concern is the potentially large temperature differences at high charge/discharge rates. Excess heat created during charge/discharge will accelerate irreversible aging, eventually leading to failure. As a consequence, it is important to keep battery states within their safe operating range, which is determined by voltage, temperature, and current windows. Due to the chemically aggressive and electrically noisy environment, internal temperature measurement is difficult. As a result, non-invasive sensors must be physically stable, electromagnetic interference-resistant, and chemically inert. These characteristics are provided by fibre Bragg grating (FBG) sensors, which are also multiplexable. This review article discusses the thermal problems that arise during LiB use, as well as their significance in terms of LiB durability and protection. FBG-based sensors are described as a technology, with emphasis on their importance for direct temperature measurement within the LiB cell.

The temperature of the lithium-ion battery is a crucial measurement during usage for better operation, safety and health of the battery. In-situ monitoring of the internal temperature of the cells is an important input for temperature control of battery management systems and various other related measurements of the battery, such as state-of-charge and state-of-health. Currently, most commercial battery management systems rely on the surface temperature measurements of the cell. However, the internal temperature is comparatively higher than the surface temperature due to heat generation within the cell and lower heat rejection compared to the surface; therefore, accurate internal temperature monitoring methods are essential to improve our knowledge of battery safety and health. This paper reviews the most recent studies of various online internal temperature monitoring techniques under two main themes of hard sensors and soft sensors. The hard sensors include sensors that need to be inserted into the cell and other methods that use contact-less measuring techniques to infer the internal temperature. The soft sensors include estimators/observers that use surface measurements and various models to estimate the internal temperature. More focus is given to the soft sensors due to the lack of an existing, in-depth review of these. These methods are analyzed in detail with their accuracy, implementation, measurement frequency, and the common challenges and benefits are discussed. Further, possible future trends in internal temperature sensing are also discussed.

Abstract:The use of Alternanthera sessilis, which is commonly known as Mukunuwenna in Sri Lanka as a source of chlorophyll was examined. The extraction of chlorophyll was carried out using buffered 80 % (v/v) aqueous acetone. The optimum operating conditions such as solvent to A. sessilis ratio, extraction temperature and extraction time were found to be 5 mL/g, 50 °C and 45 minutes, respectively. The yield of chlorophyll a and chlorophyll b under these optimum operating conditions were 659 and 261 µg/g of A. sessilis, respectively. Mechanical grinding of A. sessilis gave a higher yield as compared to blanching and drying. Refrigeration at 15 °C was found to be ideal for storing of fresh A. sessilis up to 3 days without a considerable loss of chlorophyll content. Chlorophyll extraction could be modelled successfully using basic mass transfer equations up to 30 °C. It failed above this temperature due to the degradation effect. Kinetic study on the degradation of chlorophyll extracted from A. sessilis confirmed first order reaction model and the effect of temperature on the rate constant was also adequately modelled by the Arrhenius equation.