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Browse AllThe rapid evolution of electric vehicles (EVs) necessitates advanced electronic control units (ECUs) for enhanced safety, monitoring, and performance. This study introduces an innovative ECU system designed with a modular architecture, incorporating real-time monitoring, cloud connectivity, and crash sensing. The methodology includes cost-effective design strategies, integrating STM32 controllers, CAN bus systems, and widely available sensors for motor RPM and temperature monitoring. Key findings demonstrate that the proposed ECU system improves data reliability, enhances vehicle safety through crash response systems, and enables predictive maintenance via cloud connectivity. This scalable and affordable ECU is adaptable to a broad range of EV models.
Problem definition: Battery-electric commercial vehicles in particular have large battery capacities with several hundred kilowatt hours, some of which do not have enough energy for an entire working day, which is why they need to be recharged if necessary. High charging power with correspondingly high charging currents is required to recharge the electrical energy storage in an acceptable time. Due to the electrical losses, waste heat is generated, which places a thermal load on the charging components. In particular, the CCS charging inlet is subject to high thermal loads and, for safety reasons, must not exceed the maximum temperature of 90°C according to DIN EN IEC 62196-1. Depending on the ambient temperature, the charging inlet in the charging path often represents a thermally limiting component, as the charging current must be reduced before the maximum temperature is reached. Solution: Three general solution approaches are used to investigate how the CCS charging inlet can be
The automotive industry faces the challenge of developing vehicles that meet current customer needs while being future-proof. Surveys conducted for this study show that customers are concerned about the financial risks of essential components such as energy storage systems, mainly due to aging and performance degradation, which significantly affect vehicle lifespans. Based on vehicle developer surveys, a clear need for action was identified. Given the rapid technological advancements in electrified drive systems, there is a need for innovative approaches that can easily adapt to changing requirements. Therefore, this paper presents a strategy combining foresight-based planning of system upgrades with product architecture design to create adaptable and sustainable vehicles through modularity. First, dynamic subsystem characteristics are identified to establish future energy storage technology requirements. Subsequently, future energy storage system technologies are examined to determine
Wind Tunnels are complex and cost-intensive test facilities. Thus, increasing the test efficiency is an important aspect. At the same time, active aerodynamic elements gain importance for the efficiency of modern cars. For homologation, such active aero-components pose an extra level of test complexity as their control strategies, the relevant drive cycles and their aerodynamics in different positions must be considered for homologation-relevant data. Often, active components have to be manually adjusted between test runs, which is a time-consuming process because the vehicle is not integrated into the test automation. Even if so, designing a test sequence stepping through the individual settings for each component of a vehicle is a tedious task in the test session. Thus, a sophisticated integration of the wind tunnel control system with a test management system, supporting the full homologation process is one aspect of a solution. The other is the integration of the vehicle’s active
The road network is a critical component of modern urban mobility systems, with signalized traffic intersections playing a pivotal role. Traditionally, traffic light phase timings and durations at intersections are designed by transportation engineers using historical traffic data. Some modern intersections employ trigger-based mechanisms to improve traffic flow; however, these systems often lack global awareness of traffic conditions across multiple intersections within a network. With the increasing availability of traffic data and advancements in machine learning, traffic light systems can be enhanced by modeling them as agents operating in an environment. This paper proposes a Reinforcement Learning (RL) based approach for multi-agent traffic light systems within a simulation environment. The simulation is calibrated using real-world traffic data, enabling RL agents to learn effective control strategies based on realistic scenarios. A key advantage of using a calibrated simulation