FPGA Implementation of Steinhart–Hart Equation for Accurate Thermistor Linearization

Rana, K.P.S.; Kumar, Vineet; Dagar, Amit Kumar; Chandel, Ankit; Kataria, Akshay

doi:10.1109/jsen.2018.2795098

Cited by 22 publications

(9 citation statements)

References 34 publications

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“…Many datasheets, in addition, offer parameters for the analytical description of R(T). The Steinhart-Hart equation [29] yields a very close match [22] with the actual measurement data in the whole nominal temperature range of the NTC thermistor:…”

Section: Ntc Thermistor Basicsmentioning

confidence: 70%

“…A distinctive feature of thermistors is the highly non-linear dependence of resistance with temperature, which is a fact to consider even with modern cutting-edge materials and manufacturing technologies [19]. To compensate for this, various approaches have been made to linearise the response-from hardware-based signal conditioning circuits to computer-based solutions or their combinations, implementing modern techniques, such as artificial neural networks [20] and field-programmable arrays (FPGA) [21][22][23]. On the hardware side, common solutions mainly rely on conditioning circuits implementing standalone operational amplifiers (OP ap) circuits [23,24] or voltage-controlled oscillators using dedicated timer ICs (555) [25][26][27], where a thermistor is connected to a frequency-determining input stage of the timing circuit, thus resulting in a more or less linear dependence of output frequency with temperature.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

2021

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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 UT 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.

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Section: Ntc Thermistor Basicsmentioning

confidence: 70%

Section: Introductionmentioning

confidence: 99%

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

2021

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“…The linearization of this equation allows the determination of the sensitivity of the thermistor, quantified by the β parameter (Equation (2)). An alternative model fitting the dependence of thermistor resistance with temperature is the Steinhart–Hart equation, a third order polynomial that can better fit the variation of resistance over a wider temperature range (Equation (3)) [ 19 , 20 ]. Accordingly, both models were used to fit the temperature dependent resistance of the BDD film ( Figure 3 ).…”

Section: Resultsmentioning

confidence: 99%

Boron Doped Diamond for Real-Time Wireless Cutting Temperature Monitoring of Diamond Coated Carbide Tools

et al. 2021

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Among the unique opportunities and developments that are currently being triggered by the fourth industrial revolution, developments in cutting tools have been following the trend of an ever more holistic control of manufacturing processes. Sustainable manufacturing is at the forefront of tools development, encompassing environmental, economic, and technological goals. The integrated use of sensors, data processing, and smart algorithms for fast optimization or real time adjustment of cutting processes can lead to a significant impact on productivity and energy uptake, as well as less usage of cutting fluids. Diamond is the material of choice for machining of non-ferrous alloys, composites, and ultrahard materials. While the extreme hardness, thermal conductivity, and wear resistance of CVD diamond coatings are well-known, these also exhibit highly auspicious sensing properties through doping with boron and other elements. The present study focuses on the thermal response of boron-doped diamond (BDD) coatings. BDD coatings have been shown to have a negative temperature coefficient (NTC). Several approaches have been adopted for monitoring cutting temperature, including thin film thermocouples and infrared thermography. Although these are good solutions, they can be costly and become impractical for certain finishing cutting operations, tool geometries such as rotary tools, as well as during material removal in intricate spaces. In the scope of this study, diamond/WC-Co substrates were coated with BDD by hot filament chemical vapor deposition (HFCVD). Scanning electron microscopy, Raman spectroscopy, and the van der Pauw method were used for morphological, structural, and electrical characterization, respectively. The thermal response of the thin diamond thermistors was characterized in the temperature interval of 20–400 °C. Compared to state-of-the-art temperature monitoring solutions, this is a one-step approach that improves the wear properties and heat dissipation of carbide tools while providing real-time and in-situ temperature monitoring.

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“…It was found that reduced graphene oxide (RGO) with outstanding carrier mobility and high surface-to-volume ratio had been applied in material science, energy, and biomedicine [21,22]. The composite material composed of RGO and NaYF 4 :Yb 3+ ,Er 3+ will show bi-functional properties such as fluorescence and negative temperature coefficient (NTC) resistive element [23,24,25]. Since the fluorescence features of NaYF 4 :Yb 3+ ,Er 3+ are various, and the conductive characteristics of RGO can be affected by external disturbance of composite on account of its low density state surrounding the Dirac point [22], the resistance of composite material (RGO-NaYF 4 :Yb 3+ ,Er 3+ ) will be detected by using the spectrum property of NaYF 4 :Yb 3+ ,Er 3+ nanoparticles.…”

Section: Introductionmentioning

confidence: 99%

“…The FIR between I U and I L will change regularly and the temperature dependent FIR can be obtained according to Boltzmann distributing law [10,11,12], as shown in Figure 2c. The relation between temperature and resistance can be obtained from dependence of resistance on temperature according to Steinhart–Hart formula [23,24,25] in Figure 2d. Based on the above analysis, the FIR and relative sensitivity as a function of resistance can be derived, as shown in Figure 2e,f.…”

Section: Introductionmentioning

confidence: 99%

Detecting Variable Resistance by Fluorescence Intensity Ratio Technology

Sheng

Wang

Tao³

et al. 2019

Sensors

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We report a new method for detecting variable resistance during short time intervals by using an optical method. A novel variable-resistance sensor composed of up-conversion nanoparticles (NaYF4:Yb3+,Er3+) and reduced graphene oxide (RGO) is designed based on characteristics of a negative temperature coefficient (NTC) resistive element. The fluorescence intensity ratio (FIR) technology based on green and red emissions is used to detect variable resistance. Combining the Boltzmann distributing law with Steinhart–Hart equation, the FIR and relative sensitivity SR as a function of resistance can be defined. The maximum value of SR is 1.039 × 10−3/Ω. This work reports a new method for measuring variable resistance based on the experimental data from fluorescence spectrum.

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FPGA Implementation of Steinhart–Hart Equation for Accurate Thermistor Linearization

Cited by 22 publications

References 34 publications

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

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

Boron Doped Diamond for Real-Time Wireless Cutting Temperature Monitoring of Diamond Coated Carbide Tools

Detecting Variable Resistance by Fluorescence Intensity Ratio Technology

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