The ever-increasing electrification of society has been a cause of utility grid issues in many regions around the world. With the increased adoption of electric vehicles (EVs) in the Netherlands, many new charge points (CPs) are required. A common installation practice of CPs is to group multiple CPs together on a single grid connection, the so-called charging hub. To further ensure EVs are adequately charged, various control strategies can be employed, or a stationary battery can be connected to this network. A pilot project in Amsterdam was used as a case study to validate the Python model developed in this study using the measured data. This paper presents an optimisation of the battery energy storage capacity and the grid connection capacity for such a P&R-based charging hub with various load profiles and various battery system costs. A variety of battery control strategies were simulated using both the optimal system sizing and the case study sizing. A recommendation for a control strategy is proposed.
The transition from diesel-driven urban freight transport towards more electric urban freight transport turns out to be challenging in practice. A major concern for transport operators is how to find a reliable charging strategy for a larger electric vehicle fleet that provides flexibility based on different daily mission profiles within that fleet, while also minimizing costs. This contribution assesses the trade-off between a large battery pack and opportunity charging with regard to costs and operational constraints. Based on a case study with 39 electric freight vehicles that have been used by a parcel delivery company and a courier company in daily operations for over a year, various scenarios have been analyzed by means of a TCO analysis. Although a large battery allows for more flexibility in planning, opportunity charging can provide a feasible alternative, especially in the case of varying mission profiles. Additional personnel costs during opportunity charging can be avoided as much as possible by a well-integrated charging strategy, which can be realized by a reservation system that minimizes the risk of occupied charging stations and a dense network of charging stations.
MULTIFILE
To reach the European Green Deal by 2050, the target for the road transport sector is set at 30% less CO2 emissions by 2030. Given the fact that heavy-duty commercial vehicles throughout Europe are driven nowadays almost exclusively on fossil fuels it is obvious that transition towards reduced emission targets needs to happen seamlessly by hybridization of the existing fleet, with a continuously increasing share of Zero Emission vehicle units. At present, trailing units such as semitrailers do not possess any form of powertrain, being a missed opportunity. By introduction of electrically driven axles into these units the fuel consumption as well as amount of emissions may be reduced substantially while part of the propulsion forces is being supplied on emission-free basis. Furthermore, the electrification of trailing units enables partial recuperation of kinetic energy while braking. Nevertheless, a number of challenges still exist preventing swift integration of these vehicles to daily operation. One of the dominating ones is the intelligent control of the e-axle so it delivers right amount of propulsion/braking power at the right time without receiving detailed information from the towing vehicle (such as e.g. driver control, engine speed, engine torque, or brake pressure, …etc.). This is required mainly to ensure interoperability of e-Trailers in the fleets, which is a must in the logistics nowadays. Therefore the main mission of CHANGE is to generate a chain of knowledge in developing and implementing data driven AI-based applications enabling SMEs of the Dutch trailer industry to contribute to seamless energetic transition towards zero emission road freight transport. In specific, CHANGE will employ e-Trailers (trailers with electrically driven axle(s) enabling energy recuperation) connected to conventional hauling units as well as trailers for high volume and extreme payload as focal platforms (demonstrators) for deployment of these applications.
298 woorden: In the upcoming years the whole concept of mobility will radically change. Decentralization of energy generation, urbanization, digitalization of processes, electrification of vehicles and shared mobility are only some trends which have a strong influence on future mobility. Furthermore, due to the shift towards renewable energy production, the public and the private sector are required to develop new infrastructures, new policies as well as new business models. There are countless opportunities for innovative business models emerging. Companies in this field – such as charging solution provider, project management or consulting companies that are part of this project, Heliox and Over Morgen respectively – are challenged with countless possibilities and increasing complexity. How to overcome this problem? Academic research proposes a promising approach, namely the use of business model patterns for business model innovation. In short, these business model patterns are descriptions of proven practical solutions to common business model challenges. An example for a general pattern would be the business model pattern “Consumables”. It describes how to lock in a customer into an ecosystem by using a subsidized basic product and complement it with overpriced consumables. This pattern works really well and has been used by many companies (e.g. Senseo, HP, or Gillette). To support the business model innovation process of Heliox and Over Morgen as well as companies in the electric mobility space in general, we propose to systematically consolidate and develop business model patterns for the electric mobility sector and to create a database. Electric mobility patterns could not only foster creativity in the business model innovation process but also enhance collaboration in teams. By having a classified list of business model pattern for electric mobility, practitioners are equipped which a heuristic tool to create, extend and revise business models for the future.
Residential electricity distribution grid capacityis based on the typical peak load of a house and the loadsimultaneity factor. Historically, these values have remainedpredictable, but this is expected to change due to increasingelectric heating using heat pumps and rooftop solar panelelectricity generation. It is currently unclear how this increasein electrification will impact household peak load and loadsimultaneity, and hence the required grid capacity of residentialelectricity distribution grids. To gain better insight, transformerand household load measurements were taken in an all-electricneighborhood over a period of three years. These measurementswere analyzed to determine how heat pumps and solar panelswill alter peak load and load simultaneity and hence gridcapacity design parameters. Moreover, the potential for smartgrids to reduce peak loads and load simultaneity, and hencereduce required grid capacities, was examined.
