skip to main content
10.1145/3626562.3626829acmconferencesArticle/Chapter ViewAbstractPublication PagesmiddlewareConference Proceedingsconference-collections
research-article

Federated Computing in Electric Vehicles to Predict Coolant Temperature

Published: 11 December 2023 Publication History

Abstract

Reducing greenhouse gas emissions in mobility is paramount to achieving a carbon-neutral society. However, battery-electrical vehicles (BEV) introduce unique engineering challenges to protect expensive electrical components from overheating. A centralized architecture for model-driven predictions of coolant temperatures poses privacy and legal issues. Additionally, the applications in a vehicle compete for the available resources and must use them as sparingly as possible. Therefore, we introduce a new federated computing (FC) use case to help transform the mobility sector. We evaluate the performance of two FC approaches (linear regression and machine learning) on hardware and privacy metrics by leveraging a real-world dataset from BEVs. Our findings show trade-offs between hardware utilization and model accuracy. The linear regression model yields the best performance and prediction metrics. FC with ML shows up to 761 % variances when comparing vehicle-specific models with models trained with the entire fleet and clustering the data into velocity profiles partly improves prediction performance.

References

[1]
European Environment Agency. 2022. New registrations of electric vehicles in Europe. https://www.eea.europa.eu/ims/new-registrations-of-electric-vehicles Accessed March 20, 2023.
[2]
European Environment Agency. 2023. EEA greenhouse gases - data viewer. https://www.eea.europa.eu/data-and-maps/data/data-viewers/greenhouse-gases-viewer Accessed March 20, 2023.
[3]
Arne Albertsen. 2010. Electrolytic Capacitor Lifetime Estimation. Bodos Power Magazine (04 2010), 52--54.
[4]
Philip Arejola, Ondrej Burkacky, Johannes Deichmann, Gourav Ganguly, Asif Khan, and Martin Wrulich. 2022. The future of automotive computing: Cloud and edge.
[5]
David Basin, Søren Debois, and Thomas Hildebrandt. 2018. On Purpose and by Necessity: Compliance Under the GDPR. In Financial Cryptography and Data Security, Sarah Meiklejohn and Kazue Sako (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 20--37.
[6]
Andreas Billert, Stefan Erschen, Michael Frey, and Frank Gauterin. 2022. Predictive Battery Thermal Management using Quantile Convolutional Neural Networks. Transportation Engineering 10 (11 2022), 100150. https://doi.org/10.1016/j.treng.2022.100150
[7]
Andreas Billert, Michael Frey, and Frank Gauterin. 2022. A Method of Developing Quantile Convolutional Neural Networks for Electric Vehicle Battery Temperature Prediction Trained on Cross-Domain Data. 3 (05 2022), 1--1. https://doi.org/10.1109/OJITS.2022.3177007
[8]
BMW. 2023. BMW Group Technology Trend Radar. https://www.bmwgroup.com/en/innovation/company/technology-trend-radar.html Accessed September 26, 2023.
[9]
Ahmet M. Elbir, Burak Soner, Sinem Çöleri, Deniz Gündüz, and Mehdi Bennis. 2022. Federated Learning in Vehicular Networks. In 2022 IEEE International Mediterranean Conference on Communications and Networking (MeditCom). 72--77. https://doi.org/10.1109/MeditCom55741.2022.9928621
[10]
Ahmed Roushdy Elkordy, Yahya H. Ezzeldin, Shanshan Han, Shantanu Sharma, Chaoyang He, Sharad Mehrotra, and Salman Avestimehr. 2023. Federated Analytics: A survey. (2023). https://doi.org/10.48550/ARXIV.2302.01326
[11]
Pietro Ferrara and Fausto Spoto. 2018. Static Analysis for GDPR Compliance.
[12]
Jean Baptiste Joseph Fourier. 2009. The Analytical Theory of Heat. Cambridge University Press. https://doi.org/10.1017/CBO9780511693205
[13]
Scott Hardman, Amrit Chandan, Gil Tal, and Tom Turrentine. 2017. The effectiveness of financial purchase incentives for battery electric vehicles -- A review of the evidence. Renewable and Sustainable Energy Reviews 80 (2017), 1100--1111. https://doi.org/10.1016/j.rser.2017.05.255
[14]
Jonathan Hey, Adam C. Malloy, Ricardo Martinez-Botas, and Michael Lampérth. 2016. Online Monitoring of Electromagnetic Losses in an Electric Motor Indirectly Through Temperature Measurement. IEEE Transactions on Energy Conversion 31, 4 (2016), 1347--1355. https://doi.org/10.1109/TEC.2016.2562029
[15]
Tzu-Ming Harry Hsu, Hang Qi, and Matthew Brown. 2019. Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification. arXiv:1909.06335 [cs.LG]
[16]
Matthew Jagielski, Alina Oprea, Battista Biggio, Chang Liu, Cristina Nita-Rotaru, and Bo Li. 2018. Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning. In 2018 IEEE Symposium on Security and Privacy (SP). 19--35. https://doi.org/10.1109/SP.2018.00057
[17]
Baofeng Ji, Xueru Zhang, Shahid Mumtaz, Congzheng Han, Chunguo Li, Hong Wen, and Dan Wang. 2020. Survey on the Internet of Vehicles: Network Architectures and Applications. IEEE Communications Standards Magazine 4, 1 (2020), 34--41. https://doi.org/10.1109/MCOMSTD.001.1900053
[18]
Honghai Kuang, Qian Guo, Shengqing Li, and Hao Zhong. 2021. Short-term wind power forecasting model based on multi-feature extraction and CNN-LSTM. IOP Conference Series: Earth and Environmental Science 702, 1 (mar 2021), 012019. https://doi.org/10.1088/1755-1315/702/1/012019
[19]
Tian Li, Maziar Sanjabi, Ahmad Beirami, and Virginia Smith. 2020. Fair Resource Allocation in Federated Learning. arXiv:1905.10497 [cs.LG]
[20]
Xin Li, Yifan Dang, and Tefang Chen. 2018. Vehicular Edge Cloud Computing: Depressurize the Intelligent Vehicles Onboard Computational Power. In 2018 21st International Conference on Intelligent Transportation Systems (ITSC). 3421--3426. https://doi.org/10.1109/ITSC.2018.8569286
[21]
Yiran Li, Hongwei Li, Guowen Xu, Tao Xiang, and Rongxing Lu. 2022. Practical Privacy-Preserving Federated Learning in Vehicular Fog Computing. IEEE Transactions on Vehicular Technology 71, 5 (2022), 4692--4705. https://doi.org/10.1109/TVT.2022.3150806
[22]
Liangkai Liu, Sidi Lu, Ren Zhong, Baofu Wu, Yongtao Yao, Qingyang Zhang, and Weisong Shi. 2021. Computing Systems for Autonomous Driving: State of the Art and Challenges. IEEE Internet of Things Journal 8, 8 (2021), 6469--6486. https://doi.org/10.1109/JIOT.2020.3043716
[23]
Su Liu, Jiong Yu, Xiaoheng Deng, and Shaohua Wan. 2022. FedCPF: An Efficient-Communication Federated Learning Approach for Vehicular Edge Computing in 6G Communication Networks. IEEE Transactions on Intelligent Transportation Systems 23, 2 (2022), 1616--1629. https://doi.org/10.1109/TITS.2021.3099368
[24]
State of California Department of Justice. 2018. California Consumer Privacy Act of 2018 [1798.100 - 1798.199.100].
[25]
German Association of the Automotive Industry. 2022. ADAXO: Automotive Data Access -- Extended and Open: VDA concept for access to in-vehicle data.
[26]
Marc Sebastián Padrós, Pascal A. Schirmer, and Iosif Mporas. 2022. Estimation of Cooling Circuits' Temperature in Battery Electric Vehicles Using Karhunen Loeve Expansion and LSTM. In 2022 30th European Signal Processing Conference (EUSIPCO). 1546--1550. https://doi.org/10.23919/EUSIPCO55093.2022.9909690
[27]
Jonghyun Park and Youngjin Kim. 2020. Supervised-Learning-Based Optimal Thermal Management in an Electric Vehicle. IEEE Access 8 (2020), 1290--1302. https://doi.org/10.1109/ACCESS.2019.2961791
[28]
Sam G. Parler. 2004. Deriving Life Multipliers for Electrolytic Capacitors. https://api.semanticscholar.org/CorpusID:107675698
[29]
Sashank Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konečný, Sanjiv Kumar, and H. Brendan McMahan. 2021. Adaptive Federated Optimization. arXiv:2003.00295 [cs.LG]
[30]
Dierk Schröder. 2009. Elektrische Antriebe - Regelung von Antriebssystemen. https://doi.org/10.1007/978-3-540-89613-5
[31]
Personal Data Protection Commission Singapore. 2014. Personal Data Protection Act.
[32]
Soumya Sudhakar, Vivienne Sze, and Sertac Karaman. 2023. Data Centers on Wheels: Emissions From Computing Onboard Autonomous Vehicles. IEEE Micro 43, 1 (2023), 29--39. https://doi.org/10.1109/MM.2022.3219803
[33]
Jasper Tan, Blake Mason, Hamid Javadi, and Richard Baraniuk. 2022. Parameters or Privacy: A Provable Tradeoff Between Overparameterization and Membership Inference. In Advances in Neural Information Processing Systems, Alice H. Oh, Alekh Agarwal, Danielle Belgrave, and Kyunghyun Cho (Eds.). https://openreview.net/forum?id=7nypt7cjNL
[34]
Kang Tan, Duncan Bremner, Julien Le Kernec, and Muhammad Imran. 2020. Federated Machine Learning in Vehicular Networks: A summary of Recent Applications. In 2020 International Conference on UK-China Emerging Technologies (UCET). 1--4. https://doi.org/10.1109/UCET51115.2020.9205482
[35]
European Union. 2016. REGULATION (EU) 2016/679 OF THE EUROPEAN PAR-LIAMENT AND OF THE COUNCIL of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation).
[36]
Jayant Vyas, Debasis Das, and Sajal K. Das. 2020. Vehicular Edge Computing Based Driver Recommendation System Using Federated Learning. In 2020 IEEE 17th International Conference on Mobile Ad Hoc and Sensor Systems (MASS). 675--683. https://doi.org/10.1109/MASS50613.2020.00087
[37]
Oliver Wallscheid. 2021. Thermal Monitoring of Electric Motors: State-of-the-Art Review and Future Challenges. IEEE Open Journal of Industry Applications 2 (2021), 204--223. https://doi.org/10.1109/OJIA.2021.3091870
[38]
Oliver Wallscheid, Wilhelm Kirchgässner, and Joachim Böcker. 2017. Investigation of long short-term memory networks to temperature prediction for permanent magnet synchronous motors. In 2017 International Joint Conference on Neural Networks (IJCNN). 1940--1947. https://doi.org/10.1109/IJCNN.2017.7966088
[39]
Dan Wang, Siping Shi, Yifei Zhu, and Zhu Han. 2022. Federated Analytics: Opportunities and Challenges. IEEE Network 36, 1 (2022), 151--158. https://doi.org/10.1109/MNET.101.2100328
[40]
Mark Wellons. 2019. Stefan--Boltzmann Law. Introduction to Quantum Mechanics 1(2019).
[41]
Dongdong Ye, Rong Yu, Miao Pan, and Zhu Han. 2020. Federated Learning in Vehicular Edge Computing: A Selective Model Aggregation Approach. IEEE Access 8 (2020), 23920--23935. https://doi.org/10.1109/ACCESS.2020.2968399
[42]
Xuefei Yin, Yanming Zhu, and Jiankun Hu. 2021. A Comprehensive Survey of Privacy-Preserving Federated Learning: A Taxonomy, Review, and Future Directions. ACM Comput. Surv. 54, 6, Article 131 (jul 2021), 36 pages. https://doi.org/10.1145/3460427
[43]
ZF. 2021. ZF ProAI: The Source of Vehicle Intelligence. https://www.zf.com/products/en/cars/stories/proai.html Accessed March 21, 2023.
[44]
Yan Zhou, Michael Wang, Han Hao, Larry Johnson, and Hewu and Wang. 2015. Plug-in electric vehicle market penetration and incentives: a global review. (2015). https://doi.org/10.1007/s11027-014-9611-2

Cited By

View all
  • (2024)PyDTS: A Python Toolkit for Deep Learning Time Series ModellingEntropy10.3390/e2604031126:4(311)Online publication date: 31-Mar-2024

Index Terms

  1. Federated Computing in Electric Vehicles to Predict Coolant Temperature

        Recommendations

        Comments

        Information & Contributors

        Information

        Published In

        cover image ACM Conferences
        Middleware '23: Proceedings of the 24th International Middleware Conference: Industrial Track
        December 2023
        52 pages
        ISBN:9798400704277
        DOI:10.1145/3626562
        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

        Sponsors

        In-Cooperation

        • IFIP: International Federation for Information Processing

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        Published: 11 December 2023

        Permissions

        Request permissions for this article.

        Check for updates

        Author Tags

        1. Electric Vehicle
        2. Federated Computing
        3. Systems

        Qualifiers

        • Research-article
        • Research
        • Refereed limited

        Funding Sources

        • Bavarian Ministry of Economic Affairs, Regional Development and Energy

        Conference

        Middleware '23
        Sponsor:

        Acceptance Rates

        Overall Acceptance Rate 203 of 948 submissions, 21%

        Contributors

        Other Metrics

        Bibliometrics & Citations

        Bibliometrics

        Article Metrics

        • Downloads (Last 12 months)58
        • Downloads (Last 6 weeks)1
        Reflects downloads up to 10 Feb 2025

        Other Metrics

        Citations

        Cited By

        View all
        • (2024)PyDTS: A Python Toolkit for Deep Learning Time Series ModellingEntropy10.3390/e2604031126:4(311)Online publication date: 31-Mar-2024

        View Options

        Login options

        View options

        PDF

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader

        Figures

        Tables

        Media

        Share

        Share

        Share this Publication link

        Share on social media