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Electric vehicle technologies have seen rapid development in recent years. However, Reliability, Availability, and Maintainability (RAM) related concerns still have restricted large-scale commercial utilization of these vehicles. This paper presents an approach to carry out a quantitative RAM analysis of a plug-in electric vehicle. A mathematical model is developed in the Markov Framework incorporating the reliability characteristics of all significant electrical components of the vehicle system, namely battery, motor, drive, controllers, charging unit, and energy management unit. The study shows that the vehicle’s survivability can be increased by improving its components’ restoration rates. The paper also investigates the role of a charging station on the availability of the vehicle. It illustrates how the grid power supply’s reliability influences the operational effectiveness of a plug-in electric vehicle. The concepts that are presented in the article can support further study on the reliability design and maintenance of a plug-in electric vehicle.

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Solar electric vehicle (SEV) is an emerging transport utility, which primarily uses solar energy for its propulsion. It is regarded as an ideal solution for clean and sustainable transportation. This article presents an approach to investigate the reliability and availability of a SEV facilitated with a standby plug-in option. A composite reliability model is developed in Markov framework incorporating the stochastic failure and repair characteristics of all critical electrical systems of the vehicle, namely, power supply system, energy storage system, motor-drive system, controllers, and energy management system. A new probabilistic index is proposed to determine the availability of power generated by the photovoltaic modules mounted on the SEV. This index can be treated as a useful parameter for designing area-specific reliable SEV models. The reliability improvement due to the standby plug-in facility is also examined. The study quantitatively justifies that a SEV with a plug-in facility is much more reliable and effective than a regular plug-in EV. The article also illustrates some novel concepts which can support further research on new reliability design and maintenance of a SEV.List of Symbols and Abbreviations: G, Solar irradiation; G std , Standard solar irradiation; G ref , Reference solar irradiation; T max , Maximum temperature; T min , Minimum temperature; α, Temp. coefficient of PV arrays; P max , PVG capacity; P mup , minimum useful power; r sw , reliability of the changeover switch; R PVG , Reliability of the PVG; A PVG , Availability of the PVG; λ PVS , FR of the PVS; μ PVS , RR of the PVS; λ PVG , FR of the PVG; N i , No. of SEV at i th CS; U i , interruption time per year at i th CS; λ c , FR of the converter system; μ c , RR of the converter system; R CS , Reliability of the CS; A CS , Availability of the CS; λ CS , EFR of the CS; R PSS , Reliability of the power supply system; r SW , Reliability of the change-over switch; Q, truncated matrix; λ 1 , discharging rate of battery; μ 1 , charging rate of battery; λ B , EFR of the battery; μ B , ERR of the battery; A B , Availability of the battery; λ CC , EFR of the charge controller; μ CC , ERR of charge controller; A CC , Availability of charge controller; A ESS , Availability of the ESS; λ ESS , EFR of the ESS; μ ESS ,

The global trends toward increasing clean and sustainable power generation have brought significant changes and developments in electrical distribution networks. The existing passive networks are being restructured to facilitate renewable distributed generations to ensure high degree of reliability of power supply to customers. A distribution system reliability assessment (DSRA) is crucial to analyze the risk and cost associated with power interruptions and take necessary preventive and corrective actions. This paper presents an approach to DSRA and carries out a comprehensive reliability assessment of an active distribution network (ADN) connected with solar, wind, and tidal energy sources. A multi-state reliability model is developed, combining the Markov and well-being frameworks to determine the reliability of a network energized by multiple renewable energy sources (RESs). The load point reliability and worth-oriented indices, namely SAIDI, SAIFI, CAIDI, AENS, EENS, ECOST and IEAR, are evaluated to analyze the impacts of the three RESs on the reliability of the ADN. The paper also proposes two new metrics to estimate the benefits of adding RESs from the reliability worth and cost perspectives. The analyses have been carried out on the Roy Billinton Test System. The results show that the maximum reliability worth and cost benefits can be extracted from a combined operation of multiple RESs. A combination of solar-wind-tidal energy sources, therefore, can be an effective solution for countries having long coastlines to fulfill not only the sustainable energy goals but also to improve the grid's reliability and reduce the monetary losses incurred due to customer interruptions.