Abstract
Artificial vision is a key factor for new generation automotive systems. This paper focuses on a module aimed at maximizing the energy flow between the transmitting and receiving grids, in the context of dynamic wireless charging of electrical vehicles. The output of the module helps the driver to keep a precise alignment between the vehicle and the charging grids in the road. The module was developed using low cost and open hardware and software components. This paper provides a characterization of the embedded system from a performance point of view, considering various parameters, such as CPU load, memory footprint, and energy consumption, in view of assessing the Raspberry Pi as a platform for embedded rapid prototyping and computing in automotive environment.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Falcini, F., Lami, G., Costanza, A.M.: Deep learning in automotive software. IEEE Softw. 34(3), 56–63 (2017)
Ruffo, R., Cirimele, V., Diana, M., Khalilian, M., La Ganga, A., Guglielmi, P.: Sensorless control of the charging process of a dynamic inductive power transfer system with an interleaved nine-phase boost converter. IEEE Trans. Industr. Electron. 65(10), 7630–7639 (2018)
Tavakoli, R., Pantic, Z., Analysis, design and demonstration of a 25-kW dynamic wireless charging system for roadway electric vehicles. IEEE J. Emerg. Sel. Topics Power Electron. https://doi.org/10.1109/jestpe.2017.2761763
Hwang, K., Park, J., Kim, D., Park, H.H., Kwon, J.H., Kwak, S.I., Ahn, S.: Autonomous coil alignment system using fuzzy steering control for electric vehicles with dynamic wireless charging. Math Probl. Eng. Article ID 205285, 14 p (2015). https://doi.org/10.1155/2015/205285
Cirimele, V., Smiai, O., Guglielmi, P., Bellotti, F., Berta, R., De Gloria, A.: Maximizing power transfer for dynamic wireless charging electric vehicles. In: International Conference on Applications in Electronics Pervading Industry, Environment and Society, APPLEPIES 2016, Rome. Lecture Notes in Electrical Engineering, vol. 429, pp. 59–65 (2017). https://doi.org/10.1007/978-3-319-55071-8_8
Amditis, A. Karaseitanidis, G., Damousis, I., Guglielmi, P., Cirimele, V.: Dynamic wireless charging for more efficient FEVS: the fabric project concept, MedPower 2014, Athens, pp. 1–6 (2014)
Raspberry Pi 3 Model B. https://www.raspberrypi.org/products/raspberry-pi-3-model-b/
Marosi, A.C., Lovas, R., Kisari, Á., Simonyi, E.: A novel IoT platform for the era of connected cars. In: 2018 IEEE international conference on future IoT technologies (Future IoT), Eger, pp. 1–11 (2018)
Shi, W., Cao, J., Zhang, Q., Li, Y., Xu, L.: Edge computing: vision and challenges. IEEE Internet Things J 3(5), 637–646 (2016)
Fan, Q., Ansari, N.: Application aware workload allocation for edge computing-based IoT. IEEE Internet Things J. 5(3), 2146–2153 (2018)
Hajdarevic, K., Konjicija, S., Subasi, A.: A low energy APRS-IS client-server infrastructure implementation using Raspberry Pi. In: 2014 22nd Telecommunications Forum Telfor (TELFOR), Belgrade, pp. 296–299 (2014)
Cimino, D., Ferrero, A., Queirolo, L., Bellotti, F., Berta, R., De Gloria, A.: A low-cost, open-source cyber physical system for automated, remotely controlled precision agriculture, In: Proceedings of Applications in Electronics Pervading Industry, Environment and Society (APPLEPIES), Lecture Notes in Electrical Engineering, Rome, Sept. 215. Springer, Cham
Hassan, Q.F.: A Tutorial Introduction to IoT Design and Prototyping with Examples, in Internet of Things A to Z: Technologies and Applications, vol. 1, Wiley-IEEE Press (2018)
He, Q., Segee B., Weaver, V.: Raspberry Pi 2 B+ GPU Power, Performance, and Energy Implications. In: 2016 International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas, NV, pp. 163–167 (2016)
Nunes, L.H., et al.: Performance and energy evaluation of RESTful web services in Raspberry Pi. In: 2014 IEEE 33rd International Performance Computing and Communications Conference (IPCCC), Austin, TX, pp. 1–9 (2014)
Jupyter Notebook. https://jupyter.org/
Beyeler, M., OpenCV with Python Blueprints, Packt (2015)
Kobeissi, A., Bellotti, F., Berta, R., De Gloria, A.: IoT grid alignment assistant system for dynamic wireless charging of electric vehicles. In: 5th International Workshop on Intelligent Transportation and Connected Vehicles Technologies (ITCVT 2018), Valencia, Spain (2018)
Acknowledgements
We would like to thank the FABRIC coordinator, Prof. Angelos Amditis and all the colleagues that allowed a successful performance of the project.
This work was supported in part by the EU, under the Feasibility Analysis and Development of on-road charging solutions for future electric vehicles (FABRIC) integrated project (FP7-SST-2013-RTD-1 605405).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Kobeissi, A., Bellotti, F., Berta, R., De Gloria, A. (2019). Raspberry Pi 3 Performance Characterization in an Artificial Vision Automotive Application. In: Saponara, S., De Gloria, A. (eds) Applications in Electronics Pervading Industry, Environment and Society. ApplePies 2018. Lecture Notes in Electrical Engineering, vol 573. Springer, Cham. https://doi.org/10.1007/978-3-030-11973-7_1
Download citation
DOI: https://doi.org/10.1007/978-3-030-11973-7_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-11972-0
Online ISBN: 978-3-030-11973-7
eBook Packages: EngineeringEngineering (R0)