Energy storage systems and intelligent charging infrastructures are critical components addressing the challenges arising with the growth of renewables and the rising energy demand. Hybrid energy storage systems, in particular, are promising, as they combine two or more types of energy storage technologies with complementary characteristics to enhance the overall performance. Managing electric vehicle charging enables the demand to align with fluctuating generation, while storage systems can enhance energy flexibility and reliability. In the case of bidirectional charging, EVs can even function as mobile, flexible storage systems that can be integrated into the grid. This paper introduces a novel testing environment that integrates unidirectional and bidirectional charging infrastructures into an existing hybrid energy storage system. It describes the test environment in technical detail, explains the functionality, and outlines its usefulness in practical applications. The test system not only supports grid integration but also expands the degrees of freedom for testing, enabling flexible and realistic experimental setups. This environment facilitates comprehensive investigations into EV behavior, charging strategies, control algorithms, and user interactions. It provides a platform for exploring the possibilities, limitations, and optimal use cases for smart charging and hybrid storage systems in practice.

High-speed electric drive units promise improved power density and, theoretically, driving range of battery electric vehicles. An essential step of the development process is extensive testing of the drive unit on a test rig. In particular, at a high rotational speed level, experimental testing can be challenging. This paper describes a test rig for investigating the overall function of a high-speed drive unit and the transmission’s efficiency and dynamics. The high-speed drive unit developed in the Speed4E research project was the reference drive unit. The test rig is based on the concept of electrical power circulation. Thus, the test rig can be used universally for different drive unit designs and operating modes. A reaction torque measurement unit was developed to enable measurements at high rotational speeds. Simultaneously, this unit allows robust measurements at low costs. The expected measurement uncertainties of torque, rotational speed, transmission efficiency, and power losses were calculated using the Monte Carlo method. The results demonstrate that the developed torque measurement unit combines precise torque measurement with a robust design and low costs, making it competitive with state-of-the-art solutions for torque measurement at high speeds.

This paper demonstrates the potential application of reinforcement learning approaches to generate optimized charging strategies for bidirectional electric vehicles (EVs). Simulative modeling of charging infrastructure and electric vehicles is used in combination with a Proximal Policy Optimization algorithm to train an agent to control the charging parameters. While ensuring the sufficient charging of the EV, our approach aims to decrease power peaks within a production environment, by taking the energy demand of the complete industrial plant into account. Apart from an uncontrolled charging strategy, two agents are benchmarked against each other: a degradation-aware agent that accounts for battery degradation during training and one that does not. The results indicate that both RL agents can significantly reduce power peaks in all investigated situations, while uncontrolled charging frequently leads to higher peaks. Furthermore, by taking battery degradation into account during training, the trained agent can even decrease battery degradation compared to uncontrolled charging, while still maintaining peak shaving performance.