IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v9y2021i18p2266-d636060.html
   My bibliography  Save this article

Algorithm Execution Time and Accuracy of NTC Thermistor-Based Temperature Measurements in Time-Critical Applications

Author

Listed:
  • Marko Petkovšek

    (Faculty of Electrical Engineering, University of Ljubljana, Tržaška 25, SI-1000 Ljubljana, Slovenia)

  • Mitja Nemec

    (Faculty of Electrical Engineering, University of Ljubljana, Tržaška 25, SI-1000 Ljubljana, Slovenia)

  • Peter Zajec

    (Faculty of Electrical Engineering, University of Ljubljana, Tržaška 25, SI-1000 Ljubljana, Slovenia)

Abstract

This paper addresses the challenges of selecting a suitable method for negative temperature coefficient (NTC) thermistor-based temperature measurement in electronic devices. Although measurement accuracy is of great importance, the temperature calculation time represents an even greater challenge since it is inherently constrained by the control algorithm executed in the microcontroller (MCU). Firstly, a simple signal conditioning circuit with the NTC thermistor is introduced, resulting in a temperature-dependent voltage U T being connected to the MCU’s analog input. Next, a simulation-based approximation of the actual temperature vs. voltage curve is derived, resulting in four temperature notations: for a look-up table principle, polynomial approximation, B equation and Steinhart–Hart equation. Within the simulation results, the expected temperature error of individual methods is calculated, whereas in the experimental part, performed on a DC/DC converter prototype, required prework and available MCU resources are evaluated. In terms of expected accuracy, the look-up table and the Steinhart–Hart equation offer superior results over the polynomial approximation and B equation, especially in the nominal temperature range of the NTC thermistor. However, in terms of required prework, the look-up table is inferior compared to the Steinhart–Hart equation, despite the latter having far more complex mathematical functions, affecting the overall MCU algorithm execution time significantly.

Suggested Citation

  • Marko Petkovšek & Mitja Nemec & Peter Zajec, 2021. "Algorithm Execution Time and Accuracy of NTC Thermistor-Based Temperature Measurements in Time-Critical Applications," Mathematics, MDPI, vol. 9(18), pages 1-16, September.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:18:p:2266-:d:636060
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/9/18/2266/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/9/18/2266/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Salvina Gagliano & Fabiana Cairone & Angelo Amenta & Maide Bucolo, 2019. "A Real Time Feed Forward Control of Slug Flow in Microchannels †," Energies, MDPI, vol. 12(13), pages 1-11, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Luigi Fortuna & Arturo Buscarino, 2022. "Analog Circuits," Mathematics, MDPI, vol. 10(24), pages 1-4, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Salil Bharany & Sandeep Sharma & Sumit Badotra & Osamah Ibrahim Khalaf & Youseef Alotaibi & Saleh Alghamdi & Fawaz Alassery, 2021. "Energy-Efficient Clustering Scheme for Flying Ad-Hoc Networks Using an Optimized LEACH Protocol," Energies, MDPI, vol. 14(19), pages 1-20, September.
    2. José Francisco Sáez & Alfonso Baños, 2021. "Reset Control of Parallel MISO Systems," Mathematics, MDPI, vol. 9(15), pages 1-22, August.
    3. Bin Li & Zheng Cui & Qun Cao & Wei Shao, 2021. "Increasing Efficiency of a Finned Heat Sink Using Orthogonal Analysis," Energies, MDPI, vol. 14(3), pages 1-15, February.
    4. Ying Zhang & Bo Yin & Yanping Cong & Zehua Du, 2020. "Multi-State Household Appliance Identification Based on Convolutional Neural Networks and Clustering," Energies, MDPI, vol. 13(4), pages 1-12, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:9:y:2021:i:18:p:2266-:d:636060. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.