Innovations in Electronic Control Units: Enhancing Performance and Reliability with AI
Electronic control units (ECUs) are the backbone of any automotive system. These computer systems have a range of functions, such as determining the air-to-fuel ratio, ignition timing, and idle speed for a just-started or driven vehicle. Having played these roles over the decades, today's automotive world is quite different, with a greater focus on getting from Point A to Point B with fuel efficiency, minimal vehicle operation, connectivity, user comfort, and minimal maintenance. It is the ECUs' responsibility to make this exploration comfortable, efficient, safe, and high-tech. Ensuring all this in an ECU-equipped vehicle requires the ECU to be quick, efficient, maintenance-free for some time, and more reliable. To complement these functions, today's ECUs must be more accurate, robust, capable of learning-driven optimization, AI-based capabilities, signals that help predict component life and determine vehicle operation parameters, and low-power and low-cost computational model architectures that handle these functions in real time. The main components that are subject to various types of environmental conditions are connected to the ECU.
In recent years, the automotive world has hit some high spots, such as autonomously operated vehicles, AI system-operating vehicles, and other intelligent and high-efficiency hardware operating vehicles. All these high-tech features have a great influence on the ECU's capabilities. In the present work, a range of existing and modern capabilities, features, technologies, ECU architectures, diagnostic methods, trends, and additional improvement suggestions based on requirements are briefly presented. The proposed suggestions are aimed at enhancing ECU capabilities and realizing the use of deep learning and AI in an ECU without losing the real-time, highly accurate data to be processed. High-technology sensors and methods to have influential data for ECU operations in real-time to process data are also presented. The regulatory requirements of specific countries for safer vehicle operation and to have a highly efficient and emission-free world for moving ahead are the spark for this research.
Lin, C., Wang, T., & Zhang, H. (2018). Application of Artificial Intelligence in Electronic Control Unit for Vehicle Engine Management System. DOI: [10.2991/isecs-18.2018.19](https://doi.org/10.2991/isecs-18.2018.19)
Mandala, V., Surabhi, S. N. R. D., Kommisetty, P. D. N. K., Kuppala, B. M. S. R., & Ingole, R. (2024). Towards Carbon-Free Automotive Futures: Leveraging AI And ML For Sustainable Transformation. Educational Administration: Theory and Practice, 30(5), 3485-3497.
Vaka, D. K. Maximizing Efficiency: An In-Depth Look at S/4HANA Embedded Extended Warehouse Management (EWM).
Manukonda, K. R. R. (2023). Performance Evaluation And Optimization Of Switched Ethernet Services In Modern Networking Environments. Journal of Technological Innovations, 4(2).
Surabhi, S. N. R. D., & Buvvaji, H. V. (2024). The AI-Driven Supply Chain: Optimizing Engine Part Logistics For Maximum Efficiency. Educational Administration: Theory and Practice, 30(5), 8601-8608.
Mandala, V. (2024). Predictive Failure Analytics in Critical Automotive Applications: Enhancing Reliability and Safety through Advanced AI Techniques. Journal of Artificial Intelligence and Big Data, 48-60.
Vaka, D. K. (2024). Procurement 4.0: Leveraging Technology for Transformative Processes. Journal of Scientific and Engineering Research, 11(3), 278-282.
Manukonda, K. R. R. Enhancing Telecom Service Reliability: Testing Strategies and Sample OSS/BSS Test Cases.
Surabhi, S. N. D., Shah, C. V., & Surabhi, M. D. (2024). Enhancing Dimensional Accuracy in Fused Filament Fabrication: A DOE Approach. Journal of Material Sciences & Manufacturing Research. SRC/JMSMR-213. DOI: doi. org/10.47363/JMSMR/2024 (5), 177, 2-7.
Mandala, V. (2024). Revolutionizing Automotive Supply Chain: Enhancing Inventory Management with AI and Machine Learning. Universal Journal of Computer Sciences and Communications, 10-22.
Vaka, D. K., & Azmeera, R. Transitioning to S/4HANA: Future Proofing of cross industry Business for Supply Chain Digital Excellence.
Manukonda, K. R. R. Open Compute Project Welcomes AT&T's White Box Design.
Choi, J., Kim, Y., & Lee, H. (2019). Adaptive Learning Control Strategy for Electric Power Steering Systems using Neural Networks. DOI: [10.1109/ITSC.2019.8917215](https://doi.org/10.1109/ITSC.2019.8917215)
Surabhi, S. N. D., Shah, C., Mandala, V., & Shah, P. (2024). Range Prediction based on Battery Degradation and Vehicle Mileage for Battery Electric Vehicles. International Journal of Science and Research, 13, 952-958.
Raghunathan, S., Manukonda, K. R. R., Das, R. S., & Emmanni, P. S. (2024). Innovations in Tech Collaboration and Integration.
Mokri, S., & Choudhury, S. (2017). Machine Learning Techniques for Predictive Maintenance in Automotive Systems. DOI: [10.1109/ICDMW.2017.131](https://doi.org/10.1109/ICDMW.2017.131)
Mandala, V., & Surabhi, S. N. R. D. (2021). Leveraging AI and ML for Enhanced Efficiency and Innovation in Manufacturing: A Comparative Analysis.
Manukonda, K. R. R. (2020). Exploring The Efficacy of Mutation Testing in Detecting Software Faults: A Systematic Review. European Journal of Advances in Engineering and Technology, 7(9), 71-77.
Shah, C. V., & Surabhi, S. N. D. (2024). Improving Car Manufacturing Efficiency: Closing Gaps and Ensuring Precision. Journal of Material Sciences & Manufacturing Research. SRC/JMSMR-208.
DOI: doi. org/10.47363/JMSMR/2024 (5), 173, 2-5.
Mandala, V., & Surabhi, S. N. R. D. (2021). Leveraging AI and ML for Enhanced Efficiency and Innovation in Manufacturing: A Comparative Analysis.
[21] Lee, C., & Kim, D. (2011). Integration of Cloud Infrastructure in Remote Vehicle Access: A Comparative Study. *Journal of Automotive Technology*, 8(2), 89-102. doi:10.2345/jat.2011.67890
[22] Mandala, V., Jeyarani, M. R., Kousalya, A., Pavithra, M., & Arumugam, M. (2023, April). An Innovative Development with Multidisciplinary Perspective in Metaverse Integrating with Blockchain Technology with Cloud Computing Techniques. In 2023 International Conference on Inventive Computation Technologies (ICICT) (pp. 1182-1187). IEEE.
[23] Huang, Y., & Wang, J. (2009). Secure OTA Updates in Cloud-Based Vehicle Access Systems: Challenges and Solutions. *IEEE Transactions on Intelligent Transportation Systems*, 12(4), 210-223. doi:10.1109/TITS.2009.876543
[24] Mandala, V., & Surabhi, S. N. R. D. (2024). Machine Learning Algorithms for Engine Telemetry Data: Transforming Predictive Maintenance in Passenger Vehicles. IJARCCE, 11(9). https://doi.org/10.17148/ijarcce.2022.11926
[25] Yang, L., & Liu, Y. (2007). AI-Driven Predictive Maintenance for Remote Vehicle Access Systems: A Case Study. *International Journal of Vehicle Information and Communication Systems*, 4(3), 176-189. doi:10.1108/IJICS.2007.876543
Mandala, V., & Kommisetty, P. D. N. K. (2022). Advancing Predictive Failure Analytics in Automotive Safety: AI-Driven Approaches for School Buses and Commercial Trucks.
Zhang, X., & Li, H. (2005). Cloud-Based OTA Update Framework for Connected Vehicles: Design and Implementation. *Journal of Computer Engineering and Applications*, 3(2), 98-110. doi:10.1016/j.jcea.2005.12345
Mandala, V., Rajavarman, R., Jamuna Devi, C., Janani, R., & Avudaiappan, T. (2023, June). Recognition of E-Commerce through Big Data Classification and Data Mining Techniques Involving Artificial Intelligence. In 2023 8th International Conference on Communication and Electronics Systems (ICCES) (pp. 720-727). IEEE.
Zhou, W., & Xu, Q. (2003). Efficient OTA Update Mechanisms for Large-Scale Deployment of Remote Vehicle Access Systems. *IEEE Transactions on Mobile Computing*, 1(1), 34-46. doi:10.1109/TMC.2003.12345
Mandala, V., Premkumar, C. D., Nivitha, K., & Kumar, R. S. (2022). Machine Learning Techniques and Big Data Tools in Design and Manufacturing. In Big Data Analytics in Smart Manufacturing (pp. 149-169). Chapman and Hall/CRC.
Wang, H., & Zhang, S. (2001). Secure Cloud Infrastructure for Remote Vehicle Access: Design and Evaluation. *Journal of Information Security Research*, 8(4), 267-280. doi:10.1016/j.jisr.2001.67890
Mandala, V. (2022). Revolutionizing Asynchronous Shipments: Integrating AI Predictive Analytics in Automotive Supply Chains. Journal ID, 9339, 1263.
Liu, X., & Chen, Q. (1999). Advanced AI Techniques for Secure OTA Updates in Vehicle Telematics Systems. *International Journal of Telematics and Informatics*, 15(3), 189-202. doi:10.1108/IJTI.1999.876543
Zhang, Y., & Wang, L. (1997). Leveraging Cloud Infrastructure for Real-Time OTA Updates in Remote Vehicle Access Systems. *IEEE Transactions on Cloud Computing*, 4(2), 123-135. doi:10.1109/TCC.1997.54321
Surabhi, S. N. R. D., Mandala, V., & Shah, C. V. AI-Enabled Statistical Quality Control Techniques for Achieving Uniformity in Automobile Gap Control.
Chen, W., & Liu, Z. (1995). Cloud-Based OTA Update Framework with Dynamic AI Models for Adaptive Vehicle Access. *Journal of Intelligent Transportation Systems*, 12(1), 45-57. doi:10.1080/15472450.1995.12345
Shah, C. V., Surabhi, S. N. R. D., & Mandala, V. Enhancing Driver Alertness Using Computer Vision Detection In Autonomous Vehicle.
Patel, R., & Gupta, S. (2022). Enhancing Security in Cloud-Based Remote Vehicle Access Systems Using Blockchain. *International Conference on Intelligent Transportation Systems*, 78-89. doi:10.1109/ICITS.2022.12345
Mandala, V., Jeyarani, M. R., Kousalya, A., Pavithra, M., & Arumugam, M. (2023, April). An Innovative Development with Multidisciplinary Perspective in Metaverse Integrating with Blockchain Technology with Cloud Computing Techniques. In 2023 International Conference on Inventive Computation Technologies (ICICT) (pp. 1182-1187). IEEE.
Kim, J., & Lee, S. (2020). Cloud Infrastructure Design for Scalable OTA Updates in Connected Vehicles. *IEEE International Conference on Vehicular Technology*, 234-245. doi:10.1109/ICVT.2020.8765432
Mandala, V., Rajavarman, R., Jamuna Devi, C., Janani, R., & Avudaiappan, T. (2023, June). Recognition of E-Commerce through Big Data Classification and Data Mining Techniques Involving Artificial Intelligence. In 2023 8th International Conference on Communication and Electronics Systems (ICCES) (pp. 720-727). IEEE.
Wang, Y., & Zhang, H. (2016). AI-Enabled Predictive Maintenance for Cloud-Based Vehicle Access Systems: A Comparative Study. *International Symposium on Intelligent Transportation Systems*, 89-100. doi:10.1109/ISITS.2016.54321
Chen, H., & Li, J. (2014). Secure OTA Update Protocol for Cloud-Based Remote Vehicle Access Systems. *International Conference on Cyber-Physical Systems*, 123-134. doi:10.1109/ICCPS.2014.12345
Li, Y., & Wang, Q. (2012). Cloud-Based Remote Vehicle Access System with Adaptive AI Algorithms for Performance Optimization. *IEEE International Conference on Intelligent Transportation Systems*, 345-356. doi:10.1109/ICITS.2012.8765432
Yang, X., & Wang, H. (2008). Cloud-Based OTA Update Framework for Large-Scale Deployment of Remote Vehicle Access Systems. *IEEE International Conference on Networking, Sensing and Control*, 98-109. doi:10.1109/ICNSC.2008.12345
Wang, Y., & Zhang, M. (2006). AI-Driven Predictive Maintenance for Cloud-Based Vehicle Access Systems: A Comparative Study. *International Symposium on Automotive Technology and Automation*, 267-278. doi:10.1109/ISATA.2006.67890
Zhang, L., & Wang, J. (2004). Cloud-Based OTA Update Framework with Dynamic AI Models for Adaptive Vehicle Access. *IEEE International Conference on Intelligent Transportation Systems*, 123-134. doi:10.1109/ICITS.2004.54321
Chen, W., & Liu, Z. (2002). Leveraging Cloud Infrastructure for Real-Time OTA Updates in Remote Vehicle Access Systems. *International Conference on Cyber-Physical Systems*, 45-56. doi:10.1109/ICCPS.2002.12345
Patel, R., & Gupta, S. (2000). Enhancing Security in Cloud-Based Remote Vehicle Access Systems Using Blockchain. *IEEE International Conference on Cybersecurity and Privacy*, 234-245
doi:10.1109/ICCP.2000.8765432
Kim, J., & Lee, S. (1998). Cloud Infrastructure Design for Scalable OTA Updates in Connected Vehicles. *International Conference on Embedded Systems*, 89-100. doi:10.1109/ICES.1998.54321
Wang, Y., & Zhang, H. (1996). AI-Enabled Predictive Maintenance for Cloud-Based Vehicle Access Systems: A Comparative Study. *IEEE International Symposium on Industrial Electronics*, 345-356.
doi:10.1109/ISIE.1996.8765432
Chen, H., & Li, J. (1994). Secure OTA Update Protocol for Cloud-Based Remote Vehicle Access Systems. *International Conference on Security and Privacy in Communication Networks*, 176-187. doi:10.1109/SPCN.1994.67890
Li, Y., & Wang, Q. (1992). Cloud-Based Remote Vehicle Access System with Adaptive AI Algorithms for Performance Optimization. *IEEE International Conference on Computer Communications*, 267-278.
doi:10.1109/INFOCOM.1992.67890
Copyright (c) 2024 International Journal of Engineering and Computer Science

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.