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The rapid alteration to land cover, combined with climate change, results in the variation of the land surface temperature (LST). This LST variation is mainly affected by the spatiotemporal changes of land cover classes, their geospatial characteristics, and spectral indices. Melbourne has been the subject of previous studies of land cover change but often over short time periods without considering the trade-offs between land use/land cover (LULC) and mean daytimes summer season LST over a more extended period. To fill this gap, this research aims to investigate the role of LULC change on mean annual daytime LST in the hot summers of 2001 and 2018 in Melbourne. To achieve the study’s aim, LULC and LST maps were generated based on the cost-effective cloud-based geospatial analysis platform Google Earth Engine (GEE). Furthermore, the geospatial and geo-statistical relationship between LULC, LST, and spectral indices of LULC, including the Normalised Difference Built-up Index (NDBI) and the Normalised Difference Vegetation Index (NDVI), were identified. The findings showed that the mean daytime LST increased by 5.1 °C from 2001 to 2018. The minimum and maximum LST values were recorded for the vegetation and the built-up area classes for 2001 and 2018. Additionally, the mean daytime LST for vegetation and the built-up area classes increased by 5.5 °C and 5.9 °C from 2001 to 2018, respectively. Furthermore, both elevation and NDVI were revealed as the most influencing factors in the LULC classification process. Considering the R2 values between LULC and LST and their NDVI values in 2018, grass (0.48), forest (0.27), and shrubs (0.21) had the highest values. In addition, urban areas (0.64), bare land (0.62), and cropland (0.61) LULC types showed the highest R2 values between LST regarding their NDBI values. This study highlights why urban planners and policymakers must understand the impacts of LULC change on LST. Appropriate policy measures can be proposed based on the findings to control Melbourne’s future development.

Hybrid energy storage system (HESS) has emerged as the solution to achieve the desired performance of an electric vehicle (EV) by combining the appropriate features of different technologies. In recent years, lithium-ion battery (LIB) and a supercapacitor (SC)-based HESS (LIB-SC HESS) is gaining popularity owing to its prominent features. However, the implementation of optimal-sized HESS for EV applications is a challenging task due to the complex behavior of LIB and SC under different driving behaviors. Besides, the power electronics (PE) converter configurations and system-level optimizations, include component sizing (CS) and power-energy management strategy (PEMS), are essential for developing efficient HESS. Therefore, this paper reviews existing LIB-SC HESS, different possible combinations of CS and PEMS, generalized algorithm formulation, and algorithms used for both CS and PEMS. The current issues of LIB-SC HESS regarding the performance in EV applications, PE converters, and optimization algorithms are also analyzed. In addition, future recommendations for the development of efficient LIB-SC HESS are provided to inspire researchers for further studies. Highlights• Lithium-ion battery (LIB) and supercapacitor (SC)-based hybrid energy storage system (LIB-SC HESS) suitable for EV applications is analyzed comprehensively.

The transition from conventional to electric transportation has become inevitable in recent years owing to the significant impact of electric vehicles (EVs) on energy sustainability, reduction of global warming and carbon emission reduction. Despite the rapidly growing global adoption of EVs in today’s electrical and transportation networks, energy storage in EVs, particularly in regards to bulky size and charging process, still remains a major bottleneck. As a result, wireless charging of EVs via inductively coupled power transfer (ICPT) through coupled coils is becoming a promising solution. However, the efficiency of charging EV batteries via wireless charging is hugely affected by misalignment between the primary and secondary coils. This paper presents an in-depth analysis of various key factors affecting the efficiency of EV battery charging. Finite element analysis (FEA) using Ansys Maxwell® is performed on commonly used coil designs such as circular and rectangular coils under various misalignment conditions. In addition, various reactive power compensation topologies applied in ICPT are investigated and the behavior of each topology is observed in simulation. It is revealed that circular structures with S–S compensation topology show more robustness in misalignment conditions and maintain the desired efficiency for a wider range of displacement. A critical analysis of coil designs, compensation techniques and the combination of both factors is accomplished and conclusions are presented.