Load Balancing towards ECU Integration

There has been an exponential increase in the number of electronic components embedded in vehicles. Development processes, techniques and tools have changed to accommodate that evaluation. A wide range of electronic functions such as navigation, adaptive control, infotainment, traffic information, safety system etc are implemented in today’s vehicles. Many of the new functions are not stand alone and hence they need to exchange information, sometimes with stringent time constraints for time critical functions such as engine management, collision warning systems etc. The complexity of the embedded architecture in a vehicle is continually increasing. Today up to 2500 signals are exchanged through up to 70 Electronic Control Units (ECUs) using 5 different buses. This paper introduces the load balancing approach across ECUs supplied by various Tier1 suppliers

Load Balancing in Multi ECU Configuration

Electronic Control Units (ECUs) are widely used to improve the comfort and reliability of vehicles. It has become the fundamental building block of any automotive subsystem and is interfaced with electro mechanics counterpart. To meet the system wide requirements, these ECUs are interconnected using the communication infrastructure. Although the communication infrastructure in terms of, predominantly, the CAN based vehicle network took its birth to enable ECUs to work in a coordinated manner in order to support system wide requirements, during the past decade, this infrastructure was also viewed as a potential means to incorporate extensibility in terms of addition of newer ECUs which are built for implementing additional requirements. With this paradigm, the number of ECUs started growing in a steep manner, uncontrolled and as a result, today, it is not hard to see a high segment automotive housing ECUs as large as 75-80. Hence, load balancing mechanisms are needed to ease ECU integration and for efficient utilization of CPU power in ECUs. In this paper, we explain the mathematical approach for load balancing across ECUs on the basis of CPU utilization

Load balancing issues in automotives

Electronic Control Units (ECUs) are widely used to improve the comfort and reliability of vehicles. It has become the fundamental building block of any automotive subsystem and is interfaced with electro mechanics counterpart. To meet the system wide requirements, these ECUs are interconnected using the communication infrastructure. Although the communication infrastructure in terms of, predominantly, the CAN based vehicle network took its birth to enable ECUs to work in a coordinated manner in order to support system wide requirements, during the past decade, this infrastructure was also viewed as a potential means to incorporate extensibility in terms of addition of newer ECUs which are built for implementing additional requirements. With this paradigm, the number of ECUs started growing in a steep manner, uncontrolled and as a result, today, it is not hard to see a high segment automotive housing ECUs as large as 75–80. Hence, load balancing mechanisms are needed to ease ECU integration and for efficient utilization of CPU power in ECUs. In this paper, we explain the concept of load balancing on the basis of CPU utilization across ECUs

Potential of demand response for chlor-alkali electrolysis processes

Chlor-alkali electrolysis indicates significant demand response potential, accounting for over 2% of Germany’s total elec-tricity demand. To fully analyze this potential, digital models or digital twins are necessary. In this study, we use the IRPopt modeling framework to develop a digital model of an electrolysis process and examine the cost-optimal load shifting application in the day-ahead spot and balancing reserve market for various price scenarios (2019, 2030, 2040). We also investigate the associated CO2 emissions. Combined optimization at both markets results in greater and more robust cost savings of 16.1% but cannibalizes the savings that are possible through optimization separately at each market. In future scenarios, the shares of savings from spot and reserve market could potentially reverse. CO2 savings between 2.5% and 9.2% appear only through optimization at the spot market and could even turn negative if optimized solely at the reserve market

Fog Computing: A Taxonomy, Survey and Future Directions

In recent years, the number of Internet of Things (IoT) devices/sensors has increased to a great extent. To support the computational demand of real-time latency-sensitive applications of largely geo-distributed IoT devices/sensors, a new computing paradigm named "Fog computing" has been introduced. Generally, Fog computing resides closer to the IoT devices/sensors and extends the Cloud-based computing, storage and networking facilities. In this chapter, we comprehensively analyse the challenges in Fogs acting as an intermediate layer between IoT devices/ sensors and Cloud datacentres and review the current developments in this field. We present a taxonomy of Fog computing according to the identified challenges and its key features.We also map the existing works to the taxonomy in order to identify current research gaps in the area of Fog computing. Moreover, based on the observations, we propose future directions for research

Energy storage systems and power conversion electronics for e-transportation and smart grid

The special issue “Energy Storage Systems and Power Conversion Electronics for E-Transportation and Smart Grid” on MDPI Energies presents 20 accepted papers, with authors from North and South America, Asia, Europe and Africa, related to the emerging trends in energy storage and power conversion electronic circuits and systems, with a specific focus on transportation electrification and on the evolution of the electric grid to a smart grid. An extensive exploitation of renewable energy sources is foreseen for smart grid as well as a close integration with the energy storage and recharging systems of the electrified transportation era. Innovations at both algorithmic and hardware (i.e., power converters, electric drives, electronic control units (ECU), energy storage modules and charging stations) levels are proposed

The management of academic workloads: improving practice in the sector

Final report of HEFCE projec