Piezoelectric Energy Harvesting Prediction and Efficient Management for Industrial Wireless Sensor
Abstract
:Featured Application
Abstract
1. Introduction
1.1. Literature Overview and Main Problematic
1.2. Motivation and Main Contribution
1.3. The Method, Assumptions, and Paper Organization
- The memory for storing data in the IWS is assumed to be unlimited.
- The conditioning of the energy coming from the PT is not treated since, in most designs, Maximum Power Point Tracking (MPPT) is used to keep track of the maximum efficiency operating condition [43]. This issue is not discussed here as MPPT is now a classical function. The maximum power transfer is studied here by varying the resistive load of the PT.
2. Case Study and Measured Vibrations
2.1. Process Description
2.2. Measured Vibration
2.3. Mechanical-Electric Conversion
3. Prediction of the Harvestable Energy
3.1. Previous Linear Energy Prediction
- In Figure 11b, a prediction error of up to was obtained; such a difference can be prejudicial for the definition of the specifications of the autonomous IWS. Around the 383rd hour, a prediction error of 1017 uJ was also observed. The overall analysis from the results of Figure 11b gives a relative error of 25.25% and a Root Mean Square Error (RMSE) of 28.63%.
- In Figure 11c,d, there was a comparison between the actual power and the estimated power around the times when the most significant errors occurred. The results showed that the predicted power follows the same trend as the real power when it is monotonic.
- Figure 11e,f showed a larger zoom around the most significant errors to better visualize the previous aspect. These latest results show that the EWMA method fails when the power fluctuates a lot. This observation was in agreement with the result of Figure 10, for which it was obtained that the power value was better correlated with the power at the last instant. So many fluctuations in power lead to the failure of the EWMA method. A new predictor of the harvestable vibrational energy is proposed in the next subsection to overcome this limitation.
3.2. The Predictor of the Harvestable Energy from Vibrations
- Figure 13a compares the predicted power for the measurements recorded during the month. The result shows that the power estimated by the PHEV follows the power peaks of the real power.
- Figure 13b shows the evolution of the absolute prediction error during the month. The largest absolute error reached was , unlike obtained with the EWMA predictor. This error corresponded to a relative error of 21.2%, unlike 25.25% with the EWMA predictor.
- Figure 13d,e show that the power gaps between the real power and the predicted power were less pronounced with the PHEV than the EWMA algorithm.
- An analysis of the absolute prediction errors in the EWMA and PHEV algorithm was shown in Figure 13f, and it was obtained that the prediction error was lower in the case of PHEV. Based on these results, the RMSE obtained with PHEV was compared to with the EWMA algorithm. Performances of the two predictors on all the month’s data are summarized in Table 4.
4. Efficient Management of Harvested Energy
4.1. IWS Energy Consumption Model
4.2. Management of the Harvested Energy
- Figure 17a shows the transmitted data accumulated over the month for a transmission range of . The result also highlights the frequency of transmission of the IWS. Overall, it was achieved that the IWS depletes its energy reserve during the first hours of measurement. For example, for a transmission with a maximum size of , the average throughput at the start of the month was 4.36 bits / s, unlike 0.5 bits / s observed towards the end of the month.
- In Figure 17b, the maximum range was increased to 3 km, and it is observed that the amount of data transmitted over the whole month decreases, which was an expected result since the energy cost of the IWS has increased.
- In Figure 17c, the transmission range varies from 100 to 5000 km, with a data size set to the legend’s values. The amount of data transmitted over the month increases with increasing packet size. For example, for packets of size 4096 bits, it was obtained that 94.21 kbits of data were transmitted during the month under the basis of the energy harvested from the vibrations. This amount of data corresponds to an average throughput of 2.15 bits per second.
- For different packet sizes (512 bits, 1024 bits, 2048 bits, and 4096 bits), the evolution of the residual energy of the IWS is shown in Figure 17d. The evolution of the residual energy agrees with the previous results, in which it was noted that the transmission frequency is higher in the first hours of the month. It was also observed that the residual energy of the IWS increased after each transmission. This was justified because the harvestable energy calculation during each measurement cycle is based on the PHEV’s predicted power. This power was less than or equal to the real power, as shown in Figure 13a. This is another advantage of PHEV, which avoids overestimating the harvestable power, as is the EWMA predictor.
5. Comparison with Related Works
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Sensors | Microprocessor | Radio Chip | |||
---|---|---|---|---|---|
Components | Power consumption in active mode | Components | Power consumption in active mode | Components | Transmit power consumption |
ADXL35 (Accelerometer of Analog Devices) | [29] | MSP430G2553 of Texas Instrument | [30] | CC2430 of Texas Instrument | [28] |
STLM20 (Temperature sensor of ST) | [31] | MSP430L092 of Texas Instrument | [32] | CC2520 of Texas Instrument | [33] |
MPL115A (Pressure sensor of Freescale) | [34] | ATMega128 of Atmel | [35] | SX1211 of Semtech | [36] |
PT | Frequency Range (Hz) | Power | |
---|---|---|---|
Mide volture [37,39] | |||
Piezo System [40] | |||
MicroGen System [41] | and | ||
PMG Perpetuum [42] | 50 and 60 Hz | --- |
Parameter | Value |
---|---|
Stack area | |
Blocking Force | |
Test voltage | 40 |
No-load displacement at test voltage volts | 0.016 |
Electric capacitance | 145 |
Resonant Frequency | 1 Hz |
Predictor Metric | EWMA | PHEV | Improvement |
---|---|---|---|
Maximum absolute error | 1.02 | 0.857 | 16% |
Relative error | 25.25 | 21.2 | 16.03% |
RMSE | 28.63 | 19.52 | 31.82% |
Parameter | Symbol | Value and Ref |
---|---|---|
Path loss exponent | 3 or 4 [61] | |
LoRa transmitter/receiver frequency | [60] | |
LoRa SX1280 transceiver sensitivity | −99 dBm [60] | |
Spreading Factor | SF | [60] |
Bandwidth | [60] |
Ref (Year) | Applications (Acceleration @ Frequency) | Used Method | Throughput @Transmission Range@ Transmission Frequency |
---|---|---|---|
[64] (2005) | Data compression and HSU approach | − @ − @ | |
[65] (2017) | HSU | − @ − @ | |
[38] (2017) | HSU | − @ − @ | |
[66] (2018) | Vehicles | HSU | |
[19] (2020) | Mining locomotive | PT with SSHI and HSU | 1.21 bits/s @ 958 m @ 7 |
This Work (2020) | SAG | PT with PHEV and MDSP |
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Mouapi, A.; Hakem, N.; Kandil, N. Piezoelectric Energy Harvesting Prediction and Efficient Management for Industrial Wireless Sensor. Appl. Sci. 2020, 10, 8486. https://doi.org/10.3390/app10238486
Mouapi A, Hakem N, Kandil N. Piezoelectric Energy Harvesting Prediction and Efficient Management for Industrial Wireless Sensor. Applied Sciences. 2020; 10(23):8486. https://doi.org/10.3390/app10238486
Chicago/Turabian StyleMouapi, Alex, Nadir Hakem, and Nahi Kandil. 2020. "Piezoelectric Energy Harvesting Prediction and Efficient Management for Industrial Wireless Sensor" Applied Sciences 10, no. 23: 8486. https://doi.org/10.3390/app10238486
APA StyleMouapi, A., Hakem, N., & Kandil, N. (2020). Piezoelectric Energy Harvesting Prediction and Efficient Management for Industrial Wireless Sensor. Applied Sciences, 10(23), 8486. https://doi.org/10.3390/app10238486