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Low-Cost Data Acquisition System for Electric Vehicles

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Computer Vision and Robotics

Part of the book series: Algorithms for Intelligent Systems ((AIS))

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Abstract

As environmental pollution and energy crises are increasing towards a dangerous level, Climate security and energy emergencies have encouraged the development of electric vehicles. The primary explanation is that they cannot fulfill the purchasers’ requirements because of the high starting cost and low driving range. The development of electric vehicles is still in its early stages. Extensive testing of batteries, electric motors, and charging stations is required for continuous development of the performance of electric vehicle powertrains, charging systems, and batteries. Existing data loggers are suitable for low current and voltage. A reliable and low-cost data acquisition system (DAQ) is required to record and monitor the performance of the electric vehicle. This paper presents a data acquisition system for electric vehicles that can store and monitor high current and voltage. Data can be recorded continuously to determine current, voltage, electric vehicle driving patterns, and battery charging and discharging patterns. This reliable high current data acquisition system consists of an Arduino UNO, a current sensor, a voltage divider, and a DTH11 temperature sensor to analyze and store the performance record of the electric vehicle. Test results revealed that the developed data acquisition system is accurate and reliable.

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References

  1. International Energy Agency [Internet] Energy technology perspectives 2012: the wider benefits of the 2 1C scenario

    Google Scholar 

  2. Chan M. CBT online [Internet]. E V charging station launched at Bangsar Shopping Centre

    Google Scholar 

  3. European Commission (2011) Transport in figures’—statistical pocketbook. https://ec.europa.eu/transport/facts-fundings/statistics/pocketbook-2011_en/. Accessed 21 Feb 2021

  4. Darabi Z, Ferdowsi M (2011) Aggregated impact of plug-in hybrid electric vehicles on electricity demand profile. IEEE Trans Sustain Energy 2(4):501–508

    Google Scholar 

  5. Green Car Reports. Lithium-ion battery packs now 209 per kwh, will fall to 100 by 2025: Bloomberg analysis. https://www.greencarreports.com/news/1114245_lithium-ion-battery-packs-now-209-per-kwh-will-fall-to-100-by-2025-bloomberg-analysis. Accessed 18 Feb 2021

  6. Yong JY, Ramachandara Murthy VK, Tan KM, Mithulananthan N (2015) A review on the state-of-the-art technologies of electric vehicle, its impacts and prospects. Renew Sustain Energy Rev 49:365–385

    Google Scholar 

  7. Li Y, Liu K, Foley AM, ZĂĽlke A, Berecibar M, Nanini-Maury E, Van Mierlo J, Hoster HE (2019) Data-driven health estimation and lifetime prediction of lithium-ion batteries: a review. Renew Sustain Energy Rev 113:109254

    Article  Google Scholar 

  8. Liu K, Li Y, Hu X, Lucu M, Widanage WD (2020) Gaussian process regression with automatic relevance determination Kernel for calendar aging prediction of lithium-ion batteries. IEEE Trans Ind Inform 2020(16):3767–3777

    Article  Google Scholar 

  9. Hu X, Zhang K, Liu K, Lin X, Dey S, Onori S (2020) Advanced fault diagnosis for lithium-ion battery systems: a review of fault mechanisms, fault features, and diagnosis procedures. IEEE Ind Electron Mag 2020(14):65–91

    Article  Google Scholar 

  10. Kothandabhany SKM (2011) Electric vehicle roadmap for Malaysia: proceedings of the sustainable mobility: 1st Malaysian-German sustainable automotive mobility conference, Oct 18

    Google Scholar 

  11. Chan M (2013) CBT online [Internet]. E V charging station launched at Bangsar Shopping Centre

    Google Scholar 

  12. Wong D (2014) CBT online [Internet]. Electric cars and buses available to the public next year

    Google Scholar 

  13. Benghanem M (2009) Measurement of meteorological data based on wireless data acquisition system monitoring. Appl Energy 86:2651–2660

    Article  Google Scholar 

  14. Vinay G, Nishant S, Deepesh M, Himanshu P (2020) IOT enabled data acquisition system for electric vehicle. AIP Publishing

    Google Scholar 

  15. Bhatti AR, Salam Z, Aziz MJBA, Yee KP, Ashique RH (2016) Electric vehicles charging using photovoltaic: status and technological review. Renew Sustain Energy Rev 54:34–47

    Article  Google Scholar 

  16. Pachauri RK, Mahela OP, Khan B, Kumar A, Agarwal S, Alhelou HH, Bai J (2021) Development of Arduino assisted data acquisition system for solar photovoltaic array characterization under partial shading conditions. Comput Electr Eng 92:107175

    Article  Google Scholar 

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Correspondence to Vinay Gupta .

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Gupta, V., Lohani, T., Shekhawat, K.S. (2023). Low-Cost Data Acquisition System for Electric Vehicles. In: Shukla, P.K., Singh, K.P., Tripathi, A.K., Engelbrecht, A. (eds) Computer Vision and Robotics. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-19-7892-0_5

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