As electric vehicles become promising alternatives for sustainable and cleaner energy emissions in transportation, the modeling and simulation of electric vehicles has attracted increasing attention from researchers. This paper presents a simulation model of a full electric vehicle on the Matlab-Simulink platform to examine power flow during motoring and regeneration. The drive train components consist of a motor, a battery, a motor controller and a battery controller; modeled according to their mathematical equations. All simulation results are plotted and discussed. The torque and speed conditions during motoring and regeneration were used to determine the energy flow, and performance of the drive. This study forms the foundation for further research and development.

Energy management system is an area of emerging interest in a full electric vehicle research. With the increasing moves to a more sustainable vehicle, there is a need to extend the battery range that simultaneously satisfying the conflicting demand between battery capacity and vehicle weight or volume. This paper presents a research conducted in the Universiti Putra Malaysia, focusing on the energy management strategy of a battery-powered electric vehicle. Three vehicle driving modes; sport, comfort, and eco have been individually modelled. Each mode is capable to dominate different driving environments; highway, suburban, and urban. In European driving cycle simulation test, comfort and eco modes have shown large extension in driving range with the maximum of 7.33% and 19.70% respectively. However the speeds have been confined by certain specific limits. The proposed of integrated multimode driving using fuzzy logic has enabled an adaptive driving by automatically select the driving parameters based on the speed conditions. The results have proven its ability in reducing the energy consumption as much as 32.25%, and increasing the driving range of 4.21% without downgrading the speed performance.Keywords-battery electric vehicle; energy management; fuzzy logic controller; multi-mode driving.

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As part of the implementation of a rainwater harvesting system as an alternative water source supply for nonpotable use, therefore the characteristic of chemical compounds was significantly explored. The Department of Chemistry, Malaysia, gave the data set for three years (2017-2019). Some chemometric techniques, including PCA, were performed to identify the dimensionality of the rainwater data, hence establishing the rainfall index's purity to determine the quality of rainwater in the study area. Discriminant analysis managed to differentiate each rain gauge station. Cluster analysis was then applied to perform smaller group of rain gauge stations. The result demonstrates that sea salt, secondary aerosols, trace metals, crustal origin, and organic acid dominated the dimensionality of rainwater data with a total variance of 53.38% and indicated that the PRI was significantly diversified into good purity of rainfall index (GPRI), (Labuan and Danum Valley), moderate purity of rainfall index (MPRI), (Kuching and Tawau) and bad purity of rainfall index (BPRI), (Kota Kinabalu and Bintulu). From the study, it can be stipulated that the chemical composition of rainwater in the study area was attributable to the local activities.