Simulation environment for the transition to e-vehicles

The electrification of vehicle fleets is an important step towards sustainability for small and medium-sized companies and businesses. Real estate developers, freight forwarders or fleet managers who are planning or have already made the transition to electric vehicles can make their fleet more efficient and economical through intelligent charging management. However, there is often uncertainty as to whether the current mobility needs can be met when switching to electric vehicles and charging the fleet on company premises, given the limited capacity of the grid.

The experts in the "Research Data" unit at Fraunhofer IFAM use a specifically developed simulation environment to determine the most cost-effective solutions for operating electric fleets on site for individual scenarios of the users listed above.

The grid connection capacity as a limitation of the (re)charging capacity

The installation of a charging infrastructure for electric vehicles can have significant technical and economic effects on a building's energy system. It is therefore crucial to analyze individual factors such as the electricity load profile of the existing building, among many other aspects, and to reconcile the existing mobility requirements, such as route planning, with the future use of the charging stations. Another important factor is the capacity of the grid connection. This determines how many vehicles can be charged at the same time and at what speed the charging process takes place.

Insufficient grid connection capacity can lead to an increase in the power price. This is because the power supply may not be able to meet demand, especially during peak times. To ensure mobility, a grid connection extension can be applied for in such cases. However, this usually involves considerable costs and waiting times, as the construction of new infrastructure is required.

One possible alternative is to invest in photovoltaics (PV) and electrochemical buffer storage (battery). The use of surplus electricity produced at times of low demand opens up the possibility of efficient use in situations where there is a high demand for charging power, for example at night. This can help to reduce dependence on the electricity grid and cut energy costs.

Before investing in such systems, it is important to carefully weigh up the costs and benefits. Site-specific parameters such as the economically optimal size of the storage unit or PV system and the orientation of the modules should be considered.

The complex issues involved in this techno-economic potential analysis require the use of solution-oriented simulation software that is tailored to the specific needs and circumstances of the company.

Solver-based optimization tool can also simulate large vehicle fleets and complex energy systems

Solver-based systems use algorithms to determine the optimal charging and deployment strategy for e-vehicle fleets. Factors such as charging infrastructure, vehicle availability, route planning and energy costs are taken into account. The aim is to plan battery charging in such a way that it is sufficient for the planned mobility while minimizing operating costs. Solver-based optimizations are able to solve significantly more complex models than is the case with classic rule-based methods. This means that even large vehicle fleets and complex energy systems can be simulated.

Charging requirements depend on various factors, including mobility requirements, the local energy system and the individual load profiles of consumers. With the help of prediction models, these factors can be derived from past experience. This makes it possible to simulate location-specific charging requirements and determine a recommended course of action for future charging management.

Data generation and analysis at Fraunhofer IFAM

The "Data Generation and Analysis" working group is part of the "Research Data" unit, which is headed by Dr.-Ing. Stefan Lösch. As part of an interdisciplinary team of scientific staff from the departments of electrical engineering, physics, data science and (industrial) engineering, the techno-economic potential analysis for the integration of e-vehicles (cars, trucks) into energy networks and systems and the economically optimal dimensioning of building energy systems is carried out here.

We calculate the economic efficiency of photovoltaics, storage and e-mobility for electrical energy and power requirements using our own simulation environment. Our calculations are manufacturer- and technology-independent and are based on scientifically sound analyses and extensive expertise. We are also researching deep learning algorithms for use in this area.

The load profiles obtained from the simulation can be implemented on a small scale in our in-house laboratory and tested using hardware. The system offers the option of running load profiles via sources and sinks. An integrated PV generator completes the system. Other topics such as bidirectional charging have already been physically researched under real conditions as part of project work. In this context, data in the form of charging profiles of electric vehicles was generated in field tests and output via the charging station interface.