Optimizing SEV Performance: Solutions for Urban Shading Issues

By Muhammad OsamaReviewed by Laura ThomsonAug 13 2024

In a recent study published in the Ain Shams Engineering Journal, researchers thoroughly explored the challenges and opportunities of solar-powered electric vehicles (SEVs) in urban environments. They aimed to investigate the impact of urban shading on SEV performance and propose innovative solutions to mitigate these effects.

Background

The global shift toward sustainable energy has accelerated the development and adoption of electric vehicles (EVs). EVs offer a promising alternative to gasoline-powered vehicles, with lower carbon dioxide emissions, improved energy efficiency, and reduced noise. They can also serve as backup power sources during grid failures. Photovoltaic (PV) technology advancements have led to SEVs, which use solar energy as their primary power source, contributing to zero emissions and reduced transportation costs.

A key challenge for SEVs is their limited driving range, particularly in shaded urban areas.

SEVs typically use solar modules connected to a direct current/direct current (DC/DC) power converter controlled by a maximum power point tracking (MPPT) algorithm to optimize power generation. Shading complicates MPPT, causing multiple peaks in the power-voltage curve. Various MPPT techniques have been proposed, differing in complexity, speed, cost, and convergence. The incremental conductance (InC) and perturb and observe (P&O) techniques are widely used commercially for their simplicity.

About the Research

This paper addresses the challenges of shading on SEV performance by developing an adaptive InC MPPT algorithm and a decentralized energy management strategy. The authors used simulation and real-time hardware-in-the-loop (HIL) testing to evaluate these solutions. They analyzed how urban shading affects power production in SEV PV panels, identifying key factors such as shading sources (clouds or urban obstacles), vehicle speed, solar altitude and azimuth, and direction of movement. Furthermore, MATLAB SimScape software was used to simulate responses to various shading scenarios.

The focus shifted to the PV DC/DC power converter, which connects the PV panels to the DC bus. The researchers proposed an enhanced adaptive MPPT technique to optimize power extraction from each solar module and regulate the DC bus voltage. This algorithm adjusts the switching frequency and control time constant to ensure fast transitions and minimize power losses during sudden irradiation changes.

A decentralized energy management strategy was proposed to improve SEV efficiency, incorporating functions such as frequency separation, reduction, prediction, and shedding. The frequency separation function optimizes frequency bandwidth for each storage component (battery and supercapacitor), extending their lifespan.

The reduction function uses fuzzy logic to determine the percentage of secondary systems to be shed and the operational mode of the PV system (MPPT or PV curtailment) based on solar irradiation, battery power, and load demand.

The prediction function uses a neural network model to prioritize the deactivation of secondary systems based on route profiles and real-time weather information. Finally, the shedding function deactivates relays based on power requirements and battery state of charge.

Research Findings

The study demonstrated that the adaptive InC MPPT algorithm effectively tracked the maximum power point of the PV panel under various shading conditions, reducing power losses from shading by up to 30%. The decentralized energy management strategy optimized power flow between the PV panel, battery, and supercapacitor, significantly reducing energy losses and extending the lifespan of the storage components.

The adaptive MPPT algorithm addressed the limitations of traditional techniques, particularly in managing shading from urban obstacles. It dynamically adjusted the switching frequency for quicker responses and reduced the control time constant during sudden irradiation changes, improving the system's response to rapid shading variations.

The energy management strategy's functions include frequency separation, prediction, reduction, and shedding, as well as optimized power distribution and extended lifespan of energy storage components. The load-shedding mechanism prioritized essential systems based on real-time weather conditions and power availability.

Applications

The proposed solutions significantly impact the development and integration of SEVs in urban transportation systems. The adaptive InC MPPT algorithm and decentralized energy management strategy can enhance SEV performance in urban settings, reducing energy losses and extending the lifespan of energy storage components. The study's results can guide the development of smart charging systems and urban planning strategies that address urban shading effects on SEV performance. By tackling these challenges, the solutions support the widespread adoption of SEVs, contributing to a more sustainable and environmentally friendly transportation ecosystem.

Conclusion

In summary, the novel approach effectively mitigated the effects of urban shading on SEV performance, facilitating the development and integration of SEVs into urban transportation systems. Adaptive control strategies and energy management systems will be essential as the world shifts towards sustainable transportation.

Future work should refine the MPPT algorithm and energy management strategy, possibly incorporating advanced technologies like artificial intelligence and machine learning. Studying various urban environments and driving conditions will also provide valuable insights for optimizing these solutions.

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