Electric vehicles (EV) are often considered a promising technology to decrease external costs of road transport. Therefore, main external cost components are estimated for EV and internal combustion engine vehicles (ICEV). These include costs of accidents, air pollu-tion, climate change, noise, and congestion. All components are estimated over the product lifetime and, where appropriate, differentiated according to fuel type, vehicle size as well as emission location and time. The advantage of this differentiation is, however, compen-sated by high uncertainties of most cost estimates. Overall, the external costs of EV and ICEV do not differ significantly. Only for climate change, local air pollutants in congested inner-cities, and noise some advantageous effects can be observed for EV. The advantages depend strongly on the national electricity power plant portfolio and potentially also on the charging strategy. Controlled charging might allow for higher emission reductions than uncontrolled charging of EV.

External costs of electric vehicles

Effects of ambient temperature and trip characteristics on the energy consumption of an electric vehicle

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Innovation ecosystems evolve and adapt to crises, but what are the factors that stimulate ecosystem growth in spite of dire circumstances? We study the arduous path forward of the electric vehicle (EV) ecosystem and analyse in depth those factors that influence ecosystem growth in general and during the pandemic in particular. For the EV ecosystem, growth implies outcompeting the less sustainable internal combustion engine (ICE) vehicles, thus achieving a transition towards sustainable transportation. New mobility patterns provide a strategic opportunity for such a shift to green mobility and for EV ecosystem growth. For innovation ecosystems in general, we suggest that a crisis can serve as an opportunity for new innovations to break through by disrupting prior behavioural patterns. For the EV ecosystem in particular, it remains to be seen if the ecosystem will be able to capitalize on the opportunity provided by the unfortunate disruption generated by the pandemic.

https://www.mdpi.com/2071-1050/13/3/1319/pdf

The Risk of Dissolution of Sustainable Innovation Ecosystems in Times of Crisis: The Electric Vehicle during the COVID-19 Pandemic

Electrification of powertrain systems offers numerous advantages in the global trend in vehicular applications. A wide range of energy sources and zero-emission propulsion in the tank to wheel significantly add to electric vehicles’ (EV) attractiveness. This paper presents analyses of the energy balance between micro-photovoltaic (PV) installation and small electric vehicle in real conditions. It is based on monitoring PV panel’s energy production and car electricity consumption. The methodology included energy data from real household PV installation (the most common renewable energy source in Poland), electric vehicle energy consumption during real driving conditions, and drivetrain operating parameters, all collected over a period of one year by indirect measuring. A correlation between energy produced by the micro-PV installation and small electric car energy consumption was described. In the Winter, small electric car energy consumption amounted to 14.9 kWh per 100 km and was 14% greater than summer, based on test requirements of real driving conditions. The 4.48 kW PV installation located in Poznan produced 4101 kWh energy in 258 days. The calculation indicated 1406 kWh energy was available for EV charging after household electricity consumption subtraction. The zero-emission daily distance analysis was done by the simplified method.

https://www.mdpi.com/1996-1073/14/4/1085/pdf

Experimental Analysis of Residential Photovoltaic (PV) and Electric Vehicle (EV) Systems in Terms of Annual Energy Utilization

Internal and external circumstances affect a vehicles’ fuel consumption. Various studies reviewed internal combustion engine vehicles (ICEVs) energy consumption. While Battery Electric Vehicles (BEVs) are expected to replace ICEVs, more research is necessary to understand their energy performance. To fill this research gap, a microscopic traffic model and an energy prediction model were combined to estimate the effects of driving styles, weather, traffic and infrastructure on the energy consumption of BEVs. By using a VISSIM model, various scenarios were tested. This resulted in a qualitative insight into the energy consumption and travel time, which was validated by performing 30 driving tests and by using dynamometer data. The results indicate that built environment variables have a large effect on the energy consumption of BEVs. An increasing traffic intensity has been found to not always increase the energy consumption. Moreover, the energy consumption can be decreased by choosing a more energy-efficient route.

Influence of driving style, infrastructure, weather and traffic on electric vehicle performance

https://pure.tue.nl/ws/files/207368433/1_s2.0_S0926580522003272_main.pdf

BIM-enabled semantic web for automated safety checks in subway construction

The need to integrate siloed data in the built environment led to a gaining interest in semantic web technologies in the Architecture, Engineering and Construction (AEC) sector. Especially for smart home developments, the integration of information about the building, users and Internet of Things (IoT) devices could be valuable. The Resource Description Framework (RDF) is the standard model for the semantic web, however, labeled property graphs (LPG) also proved to be effective in linking data. This research used the Open Smart Home Dataset and a dataset representing a kitchen to compare the two graph models both qualitatively and quantitatively. The comparison shows that native labeled property graphs are less complex and outperform the atomic RDF in complex graph traversals. However, RDF shows qualitative advantages for multi-domain and multi-stakeholder environments, such as the use of ontologies and HTTP URIs, making it a more stable interoperability format.

Linked Data for Smart Homes: Comparing RDF and Labeled Property Graphs

Indoor environmental quality (IEQ) affects occupants’ satisfaction, health, productivity, comfort, and well-being. IoT developments enable better monitoring of IEQ parameters; however, integrating the various types of heterogeneous data from both the IoT and BIM domains is cumbersome and capital intensive, and therefore, limits the potential of smart buildings. Semantic web technologies can reduce heterogeneity issues, which is necessary to facilitate complex IEQ models. An ontology integrating data related to a building’s topology and its static and dynamic properties is still lacking. The outline of this research is twofold. First, a systematic literature review was conducted to find state-of-the-art semantic web technologies related to building topology, static properties, and dynamic properties from the IoT and BIM domains. By graphically reviewing various ontologies, their valuable patterns, commonalities, and best practices were revealed. Secondly, those results were used to develop a new ontology that integrates topological building information with static and dynamic properties. This Building Performance Ontology (BOP) provides a generic upper-level description of properties and two lower-level ontologies representing observations and actuation. The ontology results in intuitive queries and is both horizontally and vertically extensible. Multiple levels of detail are introduced to ensure practical applicability and efficient patterns based on the data modeler’s needs. BOP opens up a new range of research opportunities in the IEQ domain.

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Semantic Web Technologies for Indoor Environmental Quality: A Review and Ontology Design

Occupant feedback enables building managers to improve occupants’ health, comfort, and satisfaction. However, acquiring continuous occupant feedback and integrating this feedback with other building information is challenging. This paper presents a scalable method to acquire continuous occupant feedback and directly integrate this with other building information. Semantic web technologies were applied to solve data interoperability issues. The Occupant Feedback Ontology was developed to describe feedback semantically. Next to this, a smartwatch app – Mintal – was developed to acquire continuous feedback on indoor environmental quality. The app gathers location, medical information, and answers on short micro surveys. Mintal applied the Occupant Feedback Ontology to directly integrate the feedback with linked building data. A case study was performed to evaluate this method. A semantic digital twin was created by integrating linked building data, sensor data, and occupant feedback. Results from SPARQL queries gave more insight into an occupant’s perceived comfort levels in the Open Flat. The case study shows how integrating feedback with building information allows for more occupant-centric decision support tools. The approach presented in this paper can be used in a wide range of use cases, both within and without the architecture, building, and construction domain.

/pdf/creating-occupant-centered-digital-twins-using-the-occupant-208l6l58.pdf

Alex J.A. Donkers

Papers

Influence of driving style, infrastructure, weather and traffic on electric vehicle performance

BIM-enabled semantic web for automated safety checks in subway construction

Linked Data for Smart Homes: Comparing RDF and Labeled Property Graphs

Semantic Web Technologies for Indoor Environmental Quality: A Review and Ontology Design

Creating occupant-centered digital twins using the Occupant Feedback Ontology implemented in a smartwatch app