Advanced Electronic Architecture Design for Next Electric Vehicle Generation

Vermesan, Ovidiu; Sans, Mariano; Hank, Peter; Farrall, Glenn; Packer, J. P.; Cesario, N.; Gall, Harald; Blystad, Lars-Cyril; Sciolla, Michele; Harrar, Ahmed

doi:10.1007/978-3-319-13656-1_8

Cited by 5 publications

(3 citation statements)

References 4 publications

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“…These sensors include position, wheel speed, accelerometer, gyroscopes, and other environmental and vehicle condition sensors. The VCU relies on accurate and real-time data from these sensors to make informed decisions regarding powertrain control, energy management, and vehicle stability [64]. Efficient powertrain control is also required to maximize the performance and range of EVs.…”

Section: Figure 8 Taxonomy Of the Development Strategies For Vcumentioning

confidence: 99%

Evolving Electric Mobility Energy Efficiency: In-Depth Analysis of Integrated Electronic Control Unit Development in Electric Vehicles

Naqvi,

Jamil,

Iqbal

et al. 2024

IEEE Access

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The integrated Electronic Control Unit (ECU) plays a pivotal role in optimizing energy efficiency within electric vehicles (EVs) by coordinating various subsystems, including the Vehicle Control Unit (VCU), Electrical Power Steering (EPS), Electronic Stability Control (ESC), and Body Control Unit (BCU). Our comprehensive review deeply explores the various aspects of integrated ECUs and their sub-disciplines, emphasizing development approaches and algorithms specifically designed for mobility energy efficiency.The study shows the effect of various control units on energy efficiency by carefully examining case studies and real-world applications. Employing a critical assessment, the paper examines the advantages and disadvantages of various control systems utilized in electric vehicles (EVs), providing insight into their effectiveness in various situations. The presented study is typically related to the relationship between ECUs and their sub-branches through an integrated approach. By comparing current control approaches, the study offers a deep knowledge of the role of these units in improving vehicle performance, stability, and overall control. Furthermore, we evaluate the strengths and limitations of integrated ECU algorithms in the context of EV control by comparing them in a fair amount of detail. We identify important research gaps by integrating knowledge of multiple control areas in EV. The research effort will give researchers a path forward and make it possible to pinpoint the prospective dimensions that need further exploration in advancing the area of integrated ECUs in EVs.

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Section: Figure 8 Taxonomy Of the Development Strategies For Vcumentioning

confidence: 99%

Evolving Electric Mobility Energy Efficiency: In-Depth Analysis of Integrated Electronic Control Unit Development in Electric Vehicles

Naqvi,

Jamil,

Iqbal

et al. 2024

IEEE Access

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“…The ECU design approaches are changing with these devices integrated into domain-specific controllers, vehicle embedded high-performance computing units, or virtualized and containerized software functions provided by the vehicle or edge. The evolution of the E/E architectures (Vermesan et al, 2015) drives the consolidation of ECUs into 6-7 domainspecific controllers for safety, perception, energy, powertrain, connectivity, body/comfort/interior, experience/infotainment. The complex autonomous driving functions demand secure, robust, energy-efficient, and real-time distributed computing resource-efficient operating systems to manage multi-tasking, coordinate operations, and eliminate excessive components.…”

Section: Ecas Vehicle Architecturesmentioning

confidence: 99%

Automotive Intelligence Embedded in Electric Connected Autonomous and Shared Vehicles Technology for Sustainable Green Mobility

Vermesan

John

Pype

et al. 2021

Front. Future Transp.

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The automotive sector digitalization accelerates the technology convergence of perception, computing processing, connectivity, propulsion, and data fusion for electric connected autonomous and shared (ECAS) vehicles. This brings cutting-edge computing paradigms with embedded cognitive capabilities into vehicle domains and data infrastructure to provide holistic intrinsic and extrinsic intelligence for new mobility applications. Digital technologies are a significant enabler in achieving the sustainability goals of the green transformation of the mobility and transportation sectors. Innovation occurs predominantly in ECAS vehicles’ architecture, operations, intelligent functions, and automotive digital infrastructure. The traditional ownership model is moving toward multimodal and shared mobility services. The ECAS vehicle’s technology allows for the development of virtual automotive functions that run on shared hardware platforms with data unlocking value, and for introducing new, shared computing-based automotive features. Facilitating vehicle automation, vehicle electrification, vehicle-to-everything (V2X) communication is accomplished by the convergence of artificial intelligence (AI), cellular/wireless connectivity, edge computing, the Internet of things (IoT), the Internet of intelligent things (IoIT), digital twins (DTs), virtual/augmented reality (VR/AR) and distributed ledger technologies (DLTs). Vehicles become more intelligent, connected, functioning as edge micro servers on wheels, powered by sensors/actuators, hardware (HW), software (SW) and smart virtual functions that are integrated into the digital infrastructure. Electrification, automation, connectivity, digitalization, decarbonization, decentralization, and standardization are the main drivers that unlock intelligent vehicles' potential for sustainable green mobility applications. ECAS vehicles act as autonomous agents using swarm intelligence to communicate and exchange information, either directly or indirectly, with each other and the infrastructure, accessing independent services such as energy, high-definition maps, routes, infrastructure information, traffic lights, tolls, parking (micropayments), and finding emergent/intelligent solutions. The article gives an overview of the advances in AI technologies and applications to realize intelligent functions and optimize vehicle performance, control, and decision-making for future ECAS vehicles to support the acceleration of deployment in various mobility scenarios. ECAS vehicles, systems, sub-systems, and components are subjected to stringent regulatory frameworks, which set rigorous requirements for autonomous vehicles. An in-depth assessment of existing standards, regulations, and laws, including a thorough gap analysis, is required. Global guidelines must be provided on how to fulfill the requirements. ECAS vehicle technology trustworthiness, including AI-based HW/SW and algorithms, is necessary for developing ECAS systems across the entire automotive ecosystem. The safety and transparency of AI-based technology and the explainability of the purpose, use, benefits, and limitations of AI systems are critical for fulfilling trustworthiness requirements. The article presents ECAS vehicles’ evolution toward domain controller, zonal vehicle, and federated vehicle/edge/cloud-centric based on distributed intelligence in the vehicle and infrastructure level architectures and the role of AI techniques and methods to implement the different autonomous driving and optimization functions for sustainable green mobility.

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“…자동차 또한 다양한 센서 및 액추에이터가 부착되므로 이들을 네트워크로 구성하고 효율성과 안전성을 고려하여 적절한 시간 내 에 감지하고 제어가 될 수 있도록 하는 연구가 많이 이 루어졌으며 이에 대한 연구주제로는 네트워크 구조 [2] , 성능 [3] , 실시간성 [4] 등이 있다. [8] .…”

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Study on the Optimization of Hybrid Network Topology for Railway Cars

Kim¹,

Yun²

2016

Journal of the Institute of Electronics and Information Enginee

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In the train system, railway vehicles are connected in a line. Therefore, this feature should be considered in composing network topology in a train system. Besides, inter-car communication should be distinguished from in-car communication.As for the inter-car communication, the hybrid topology was proposed to use rather than the conventional ring, star, daisy-chain, and bus topologies. In the hybrid topology, a number of cars are bound to be a group. Then star topology is used for the communication in a group and daisy-chain topology is used for the communication between groups. Hybrid topology takes the virtue of both star and daisy-chain topologies. Hence it maintains communication speed with reducing the number of connecting cables between cars. Therefore, it is important to choose the number of cars in a group to obtain higher performance. In this paper, we focus on the optimization of hybrid topology for railway cars. We first assume that the size of data and the frequency of data production for each car is identical. We also assume that the importance for the maximum number of cables to connect cars is variable as well as the importance of the communication speed. Separated weights are granted to both importance and we derive the optimum number of cars in a group for various number of cars and weights.

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Advanced Electronic Architecture Design for Next Electric Vehicle Generation

Abstract: Future generations of electric vehicles (EVs) require a scalable, layered architecture addressing different system aspects such as scalable modules, uniform communication, and hardware (HW) and software (SW) architectures. This will O. Vermesan

Cited by 5 publications

References 4 publications

Evolving Electric Mobility Energy Efficiency: In-Depth Analysis of Integrated Electronic Control Unit Development in Electric Vehicles

Evolving Electric Mobility Energy Efficiency: In-Depth Analysis of Integrated Electronic Control Unit Development in Electric Vehicles

Automotive Intelligence Embedded in Electric Connected Autonomous and Shared Vehicles Technology for Sustainable Green Mobility

Study on the Optimization of Hybrid Network Topology for Railway Cars

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