https://repositori.mypolycc.edu.my/jspui/handle/123456789/6667 2026-08-31T15:19:56Z https://repositori.mypolycc.edu.my/jspui/handle/123456789/10478 Title: OPTIMIZED AND ROUTED WIRING HARNESS BASED ON ZONAL CLUSTERING CONCEPT USING AI IN THE AUTOMOTIVE INDUSTRY Authors: Md Sanowar Hossain; Hafiz Abdul Quddus; Cevahir, Ziya; Jesser, Alexander Abstract: This paper presents an AI-driven approach for multi-zonal clustering and harness routing optimization in automotive electrical/electronic (E/E) systems. A methodology integrating K-means clustering with dynamic grid-based routing algorithms (A* and Bresenham’s line algorithm) is applied to optimize the wiring harness layout across the vehicle’s zones, covering six key domains (comfort, chassis, drive assist, infotainment, system, and powertrain). The proposed method accounts for realistic vehicle constraints, including restricted zones and diverse wire types, which are often overlooked in prior work. A12- Zone architecture under a high application-load scenario is examined, demonstrating that this configuration achieves an optimal trade-off between harness length, complexity, and cost efficiency. Compared to manual design approaches and existing zonal architectures in the literature, the AI-based method reduced design time, produced a leaner harness layout, and achieved measurable material and cost savings. Specifically, the 12-Zone cluster arrangement achieved a total wiring distance of 241.056 m and a total wire mass of 22.92 kg for 417 ECUs, outperforming both lower and higher zone counts in overall efficiency. Scalability tests from 1 to 14 zones (under Low, Mid, and High load variants) confirmed the robustness of the approach and identified the 12-Zone configuration as the best-balanced solution. These findings highlight the potential of AI-optimized zonal E/E architectures to significantly reduce wiring weight, complexity, and design effort while maintaining system performance and cost competitiveness, providing valuable insights for next-generation automotive E/E design. 2025-08-08T00:00:00Z https://repositori.mypolycc.edu.my/jspui/handle/123456789/10477 Title: PORT-BASED MODELING OF TRANSPORT PHENOMENAIN 1-D THERMOFLUID SYSTEMS AND OBJECT-ORIENTED IMPLEMENTATION WITH THE MODELICA LANGUAGE Authors: Márquez, Francisco M.; Zufiria, Pedro J.; Yebra, Luis J. Abstract: In this article, we present the physical foundations and the development of a Modelica library with components for modeling 1-D thermofluid systems. Modelica was selected because it is an object-oriented modeling language that facilitates the incremental design of the library. Modelica also allows the modeling of acausal components, where the input/output relation is defined by the boundary conditions under which models are simulated. We model single-substance systems that are macroscopically homogeneous, isotropic, and uncharged, which we call “simple systems” to model the behavior of these systems, we assume the postulate of classical irreversible thermodynamics. For the graphical representation of complex systems, we utilize a modified bond graph symbology to enhance the readability of the diagrams. We also implement the equations for viscosity and thermal conductivity to complete the IAPWS-95 model of water, enabling its use as a fluid in the piping systems presented in our examples. 2025-08-05T00:00:00Z https://repositori.mypolycc.edu.my/jspui/handle/123456789/10476 Title: RETHINKING VEHICLE ARCHITECTURE THROUGH SOFTWARIZATION AND SERVITIZATION Authors: Alaa khamis; Partha Goswami Abstract: The automotive industry is undergoing a foundational transformation driven by software defined vehicles (SDVs), where software not only orchestrates vehicle functionality but also redefines the value proposition through servitization, making services a key differentiator rather than the physical product. This paper explores the industry’s shift from a hardware-centric paradigm to one defined by software and presents a comprehensive review of SDVs, structured around a systematic analysis of scientific publications and patents. It lays out the evolution from a distributed to a centralized compute architecture and discusses the promises of SDVs and their challenges. In particular, the paper highlights critical obstacles including fragmented certification frameworks, lack of standardized toolchains, limited support for continuous validation, and rising complexity in software maintenance and repair. Additional concerns include vulnerability management, long-term software support, supply chain decentralization, regulatory compliance, and the absence of open datasets, simulators, and benchmarking tools. The software-centric nature of SDVs also imposes new demands on hardware scalability and future-proofing. Finally, we highlight future directions and research opportunities such as ecosystem collaboration, open innovation, open and standardized datasets, open education simulators and test-beds, quantifiable Quality of Experience (QoX), cognitive data sampling, and contextual observability. This work offers a structured and forward-looking perspective on how SDVs are fundamentally reshaping automotive technology and business models toward intelligent, service-centric mobility ecosystems. To fully realize this potential, several technical, operational, regulatory, and organizational challenges must be addressed across the SDV lifecycle. 2025-07-23T00:00:00Z https://repositori.mypolycc.edu.my/jspui/handle/123456789/10475 Title: REVOLUTIONIZING AUTOMOTIVE TECHNOLOGY: UNVEILING THE STATE OF VEHICULAR SENSORS AND BIOSENSORS Authors: Raparthi Yaswanth; M. Rajasekhara Babu Abstract: Vehicular sensors and biosensors have become integral to modern automotive technology, driving advancements in vehicle safety, performance optimization, and driver well-being. These technologies play a central role in the evolution of advanced driver assistance systems, autonomous vehicles, and vehicle health monitoring systems. This paper explores the development, applications, and challenges of vehicular sensors and biosensors, emphasizing their impact on transportation. It examines the historical context and evolution of these technologies, their role in monitoring environmental and internal vehicle parameters, and their contribution to assessing the driver’s physiological state. The study focuses on sensor fusion, integrating data from multiple sensors for more accurate and comprehensive insights. It also addresses challenges relate to accuracy, reliability, privacy, and environmental adaptability, proposing solutions such as advancements in sensor technology, improved data integration techniques, and robust privacy measures. Future research will focus on enhancing sensor performance and integration, incorporating machine learning and AI to refine data analysis and decision-making. This paper aims to provide a detailed analysis of the current and future landscape of vehicular sensors and biosensors, highlighting their essential role in advancing automotive technology and improving the driving experience. Vehicular sensors, biosensors, automotive technology, sensor fusion, driver well-being, data integration. 2024-12-09T00:00:00Z