Simulation delivers smarter cities

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24 Jun, 2021

Simulation technology is key to mapping and maintaining the future of our new urban and transport realities.

Together with Ansys, a developer of simulation software, we are creating a series of content about the technologies that make sustainable transportation possible.

Electric vehicles, hybrids, and hydrogen cars are greener alternatives to traditional vehicles powered by fossil fuels, but they still have an environmental impact. In this article, we take a closer look at the way smart cities can use simulation to ensure a seamless transition to connected and autonomous transportation systems. 

This article was based on a whitepaper by Tarun Tejpal, the Global Director for Transportation and Mobility at Ansys, with significant editing and additions by Wevolver and journalist Mark Smith.

Making smart cities smarter

Harnessing the Internet of Things (IoT) with the rapid connectivity of 5G (and beyond), cities are evolving to become smarter, cleaner, more efficient, and ideally more pleasurable to live in. But ensuring that emerging technologies such as alternative fuel cars work within the current envelope and limit of our cities presents immense challenges to architects, engineers, urban planners, and automakers.

These challenges can be met by using simulation software in the design and testing of various city features, from the number and placement of EV charging stations to the placement of 5G towers for maximum effect and aesthetic.

Simulating, testing, and iterating smart city solutions before implementation is key to a ‘smarter’ smart city future.

Transportation solutions fit for the future.

The rapid development of new modes of transport such as electric vehicles (EVs) and autonomous vehicles (AVs) has been one of the defining technology trends of the decade. In the US alone, the number of EVs on US roads is projected to reach 18.7 million by 2030, up from one million in 2018[1]. But for these types of vehicles to truly have any chance of replacing internal combustion engine (ICE) vehicles, the cities they traverse will have to evolve alongside them. Developing smart cities which can accommodate future transport models is arguably the greatest civic design task in a century.

But what are the key features of a city that can accommodate electric and autonomous vehicles at a large scale?

Charging infrastructure

All battery-electric cars need to be charged in some manner. Dominant EV maker Tesla created their own-branded charging stations that are now prevalent across the US. But for other carmakers and for mass transit operators, there needs to be a more public solution. Easy to access, fast and reliable charging is key to making electric cars as convenient as internal combustion engine vehicles, which can be filled up with gas in under 15 minutes.

A recent study from the University of California Davis[2] drilled this home by revealing that one in five electric car owners switched back to a conventional fuel car due to the hassle of needing EV charging stations. However, charging infrastructure is not just about increasing the number of physical charging stations; it is also a complex negotiation between the city, energy providers, and carmakers to balance the responsibilities of cost and maintenance.

Simulation can be used to tackle these challenges. Modeling of EV charging fleets allows an understanding of EV charging capacity and demand response potential of EVs in the power systems. A modeling approach based on the charger occupancy data of local charging sites could enable cities to simulate load profiles and to find how many chargers are necessary to suffice the approximate demand of EV charging from the traffic characteristics, such as arrival time duration of charging, and maximum charging power.[3] 

Additionally, to better understand the potential impact of demand response, a simulation modeling approach enables authorities to compare charging profiles while adjusting the maximum power consumption of chargers. Modeling also provides insight into the understanding of the potential impact of demand response in relation to overall grid load. Equipped with this knowledge, city planners can implement policy that puts charging infrastructure in the right place, at the right rollout pace, and with a thorough understanding of the impact of charging on overall city resources.

Map of a city with a red dotted line circle outside of a blue dotted line circle. Illustration of a simulated charging cluster. The cross indicates the weighted center, the boxes contain the number of sessions at the charging station, red dash indicates cluster region based on maximum distance between the center and charging point (CP), and the blue circle indicates the minimum walking distance in the simulation.
Image credit:Vermeulen.

It is likely that cities and their surroundings will need to invest in other means of EV charging aside from the typical point-to-car solution to meet future demands. Researchers at Cornell University[4] have been developing a wireless EV charging system that is built into roads – meaning cars can charge while they drive. This could have a massive impact on the transport of goods between urban areas if small trucks or cars can extend their range through on-road charging.

Modeling of alternative-fuel methods for the transportation of goods is essential for cities to understand the cost-benefits analysis of overhauling roads and highways for emerging technologies such as in-road charging. Researchers are using various simulation modeling approaches to determine possible options for decarbonized long-haul transport.[5]

Connectivity

Cars have been increasingly electrified for several decades. Connectivity within the vehicle is being solved by emerging standards such as single pair ethernet, the connection from the car to its environment is met by the recently released 5G. All new cars, but especially autonomous vehicles, can be enhanced by connectivity to their surroundings, also known as ‘vehicle to everything communication’ or V2XC. For V2XC to reach its potential it is necessary to engage with information that originates from the most diverse data sources. This dependence on exterior data can create multiple potential security issues, however these can be at least partially addressed by reliable and flexible planning between function and architecture. Using simulation[6] in the early phases of development can increase reliability and reduce 5G development costs by avoiding additional design cycles and predicting and mitigating potential security or reliability issues in action.

But as future vehicles become more and more complex, so too will the demands on connectivity infrastructure. 5G is being hailed as the key to unlock the potential of autonomous vehicles by enabling fast connectivity to everything around them. However, adequate modeling of radio wave propagation in urban environments becomes an urgent problem for this to be true. Simulation of 5G system coverage in urban conditions ensures the maximum coverage area in the selected place. Simulation can also be used as part of a digital twin used to monitor and improve 5G connectivity when changes to the urban environment occur and instigate beamforming or other tactics.  Antenna design is a key part of this successful connectivity, from the smallest RFID antennas that enable access to cars and buildings to large 5G antenna designs that have massive connectivity implications. Simulation again plays a role in not only designing antennas, to begin with but to imagine their interactivity before networks are even established. 

Vehicle ownership and use

Autonomous Vehicles Fleets (AVF) that can drive themselves can drop off and pick up commuters, then head towards parking structures outside the city are likely to become mainstream. These services will sit between private car ownership and public transport (PT). In some cities, AVFs have the potential to serve first-mile connections to PT stations and provide efficient and affordable shared mobility in low-density suburban areas that are typically inefficient to serve by conventional fixed-route PT services. A simulation model such as the research of Chen Yu would examine the interaction between travelers (demand) and operators (supply). The demand could estimate the volume for different modes based on modal attributes. The supply will use a simulation platform capable of incorporating critical operational decisions on factors including fleet sizes, vehicle capacities, sharing policies, fare schemes, and hailing strategies such as in-advance and on-demand requests. Using feedback between demand and supply enables interactions between the decisions of the service operator and those of the travelers to model the choices of both parties and therefore systematically optimize service design variables to determine the best outcome in accordance with AV and PT stakeholder goals.[7] 

People and space

All of the technology previously mentioned is ultimately being developed to aid and assist humans. Cities, in effect, are useless without their citizens. The trend for urban living has been on the rise now for several decades, and by 2050, it is projected that 68 percent of the world's population will live in urban areas – up from just over 50 percent in 2016.[8] Simulation has long been used to predict and understand population trends and will continue to be employed to help policymakers manage resources and provide essential services. This is particularly important when thinking about mass migration possibilities due to climate catastrophes and other events. Simulation can enable the modeling of hypothetical situations and assist cities in emergency planning.

While we have talked a lot about how people will move inside future smart cities, its important to think about the emergence of new possibilities for recreation and living. Physical changes in the city will likely come from the repurposing of space that results from advances such as the autonomous vehicle fleets that may eliminate the need for parking in the city center, making this space available for new uses. Designers will be tasked with thinking through how these spaces can be used, perhaps for additional green spaces, areas for micro-mobility, or even additional building space. 

Black and white photograph of a city with a green overlay of cities and buildings. A green smart city design from UN Studio. 

Digital Twins

As already demonstrated, the city is no longer just an inanimate collection of concrete and asphalt – it is a complete data ecosystem. Building these cities will go far beyond how they look on paper. If the city is not designed correctly, its transport system may not even function - the smart city will no longer be ‘smart.’ However, cities are also evolving ecosystems, and no design can anticipate every possible technology advance, weather conditions, or population changes. 

City Digital twins are a digital replica of a city or urban area that can be monitored in simulation in real-time to anticipate change, predict maintenance, and streamline processes. Digital twins are common in advanced manufacturing setups to expect issues before a machine breaks down. Similarly, many smart buildings have a digital twin that enables building management to monitor air temperature and quality and adjust HVAC systems remotely. 

A digital twin of a city can do the same on a much larger scale. Digital twins can monitor and improve transport systems, track demand for PT and AVF, adjust schedules and availability, and play a significant role in city cybersecurity and resource protection. Digital Twins can also create a new level of transparency around public services. In theory, some layers of the digital twin of a city can be viewed by its inhabitants to enable citizens to hold government and private entities to account for the condition of infrastructure and the delivery of services.

Digital model of the Golden Gate Bridge on a virtual mesh background.Modeling of a digital twin of the Golden Gate Bridge. Image Credit: Ansys

Summary

Smart cities will emerge from the existing fabric of our urban clusters as technology delivers new ways to move and communicate. Simulation in both the planning and operation of these new cities enables smooth transition, well-tested scenarios, responsive maintenance, and anticipatory security.

For an interactive experience about the possibilities of mission engineering, visit the on-demand Smart City experience from Ansys.

About the sponsor: Ansys

Ansys provides engineering simulation software used to predict how product designs will behave in real-world environments. Founded in 1970, Ansys employs more than 4,400 professionals, many of whom are expert M.S. and Ph.D.-level engineers in finite element analysis, computational fluid dynamics, electronics, semiconductors, embedded software, and design optimization.

References:

1. How to become an EV city - Cities Today - Connecting the world's urban leaders [Internet]. Cities Today - Connecting the world's urban leaders. 2021. Available from: https://cities-today.com/industry/how-to-become-an-ev-city/ 

2. Hardman S, Tal G. Understanding discontinuance among California’s electric vehicle owners. Nature Energy. 2021;6(5):538-545. 

3. Uimonen, Semen & Lehtonen, Matti. (2020). Simulation of Electric Vehicle Charging Stations Load Profiles in Office Buildings Based on Occupancy Data. Energies. 13. 5700. 10.3390/en13215700. 

4. Regensburger B, Sinha S, Kumar A, Maji S, Afridi K. High-Performance Multi-MHz Capacitive Wireless Power Transfer System for EV Charging Utilizing Interleaved-Foil Coupled Inductors. IEEE Journal of Emerging and Selected Topics in Power Electronics. 2020;:1-1. 

5. Vermeulen I, Helmus JR, Lees M, van den Hoed R. Simulation of Future Electric Vehicle Charging Behavior—Effects of Transition from PHEV to FEV. World Electric Vehicle Journal [Internet] 2019;10(2):42. Available from: http://dx.doi.org/10.3390/wevj10020042 

6. E. Zhang and N. Masoud, "V2XSim: A V2X Simulator for Connected and Automated Vehicle Environment Simulation," 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), 2020, pp. 1-6, doi: 10.1109/ITSC45102.2020.9294660.

7. Chen, Yu. (2018). Simulation-based design of integrated public transit and shared autonomous mobility-on-demand systems. 

8. 6. Brockerhoff M, Nations U. World Urbanization Prospects: The 1996 Revision. Population and Development Review. 1998;24(4):883. 

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