Vibration energy harvesting in automobiles to power wireless sensors

Zhu, Qingyuan; Guan, Mingjie; He, Yuanqin

doi:10.1109/icinfa.2012.6246873

Cited by 26 publications

(21 citation statements)

References 20 publications

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“…Initially, the sensors used are wired, and when many sensors are needed, these wired connections can be troublesome for many industrial applications [6]. This has led to the advent of the wireless sensor (WS) [7], which by freeing wired connections offers more flexibility in their deployment.…”

Section: Introductionmentioning

confidence: 99%

Cantilevered Piezoelectric Micro Generator Design Issues and Application to the Mining Locomotive

2019

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This paper will present a complete discussion in recent design strategies for harvesting vibration energy using piezoelectric cantilever transducers. The interest in this primary energy source is due to its presence in non-negligible quantities in most of the engines used in the industrial process. Previous work has shown that it is possible to harvest significant amounts of energy capable of supplying a wireless sensor (WS) node. However, in most research, only one step of the energy conversion and utilization chain is studied. Starting from the definition of the different design issues for a piezoelectric micro generator (PMG), the leading optimization solutions will be reviewed in this paper. Based on the findings, the quantification of the data transmission range of wireless sensor nodes powered by a PMG is proposed to support the objectives envisioned by Industry 4.0. The vibration characteristics taken from mining locomotives that have not yet been treated previously are used to illustrate the improvement of the various optimization solutions. Through our objectives, this work offers a comprehensive discussion on the use of vibrational energy by wireless sensors, bringing together the fields of mechanics, electrical, electronics, and wireless communications. The theoretical basis for each design stage is provided through the design equations. Based on actual measurements of ambient vibration, it is demonstrated, considering an optimal design of the PMG, that a WS could transmit data beyond 1 km for physical phenomena to be controlled every 7 min.Energies 2020, 13, 63 2 of 28The data collected by WS are transmitted wirelessly to BS. Given the advantages offered by the WS, namely flexibility, ease of deployment, low cost, they are now playing a vital role in the industry 4.0 [8]. More specifically, in the industrial environment, WSs are used in the automation system [9], operations control, system monitoring (humidity, temperature, fire alarm, dust index, toxic gas, and pollution) [9,10], and motion control [11]. In most of these industrial applications, WSs are placed in difficult-to-access locations (mining crushers for example) and maintenance operations for the replacement of batteries can be costly.This issue of energy autonomy of WSs has given rise in recent years to a new field of research known as energy harvesting (EH) [12]. Energy harvesting is the process of converting ambient energy into electrical energy; the most targeted application is the power supply of the WSs.EH technologies differ from one another depending on the nature of the used primary energy source. The main considered sources are the internal light, electromagnetic energy, vibration, and heat [13]. Among these primary sources, vibration abounds in the industrial applications due to the many engines used in most processes [14]. Concerning the specific case of the mining industry, crushers that operate 24 h a day, mining locomotives for the transport of personnel and minerals, trucks, are examples of sources of vibration.A lot of rese...

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Section: Introductionmentioning

confidence: 99%

Cantilevered Piezoelectric Micro Generator Design Issues and Application to the Mining Locomotive

2019

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“…Vibration energy harvesting is used to generate power using the vibration energy generated in the process of battery charging and supplying power to onboard electrical equipment. [4][5][6][7] Vibration energy harvesting technology is not mature because, although energy reclaiming suspension 8,9 may be used to recover vibration energy and optimize damping, the mechanical properties of a vehicle will be affected due to its complicated structure. 10,11 On account of the difficulty in collecting vibration energy and the low conversion efficiency, it is difficult to recover vibration energy by other means.…”

Section: Discussionmentioning

confidence: 99%

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confidence: 99%

Vibration energy harvesting in vehicles by gear segmentation and a virtual displacement filtering algorithm

Xie

Quan

et al. 2018

Int J Energy Res

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Summary In this study, vibration straight‐line displacements occurring during electric vehicle operations are changed into reversely rotating displacements and divided into small segments by a gear unit. Piezoelectric bending elements are used to form a cantilever beam–based energy feedback mechanism for converting vibration energy into electrical power within an allowable displacement range. A virtual vibration displacement filtering algorithm is proposed to effectively filter virtual displacements that cannot excite the energy harvester and generate electrical power. The average speed of the gear exciting the bender has an accuracy 30%, which is increased using this algorithm compared with 2 other algorithms and directly affects the accuracy of calculating the average power generated by the calculation of piezoelectric bending elements. Theoretical and experimental analyses are conducted for the impact of gear pitch on the regeneration power value by changing the gear pitch at a fixed driving speed. Experiments show that when a vehicle is operated at a fixed speed, the proposed method can be used to obtain the maximum average power of a single piezoelectric bending element through determination of a rational gear pitch. Specifically, when the test vehicle operated at 20 and 60 km/h, the gear pitch should have been 7 and 10 mm.

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“…In these applications, sensors are often deployed in large-scale and in hostile areas making human maintenance and battery-replacement impractical. Due to these reasons, energy harvesting techniques for sensor nodes [2]- [4], which enable the conversion of ambient energy (such as solar [5], vibrational [6], or thermal energy [7]) to electric energy, are being developed and used to prolong sensor lifetime. By employing energy harvesting technology, the characteristics of the energy arrival and the efficiency of energy usage will have a significant impact on the sensing performance.…”

Section: Introductionmentioning

confidence: 99%

Optimized Random Deployment of Energy Harvesting Sensors for Field Reconstruction in Analog and Digital Forwarding Systems

Hsu

Hong

Wang

2015

IEEE Trans. Signal Process.

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This work examines the large-scale deployment of energy harvesting sensors for the purpose of sensing and reconstruction of a spatially correlated Gaussian random field. The sensors are powered solely by energy harvested from the environment and are deployed randomly according to a spatially nonhomogeneous Poisson point process whose density depends on the energy arrival statistics at different locations. Random deployment is suitable for applications that require deployment over a wide and/or hostile area. During an observation period, each sensor takes a local sample of the random field and reports the data to the closest data-gathering node if sufficient energy is available for transmission.The realization of the random field is then reconstructed at the fusion center based on the reported sensor measurements. For the purpose of field reconstruction, the sensors should, on the one hand, be more spread out over the field to gather more informative samples, but should, on the other hand, be more concentrated at locations with high energy arrival rates or large channel gains toward the closest data-gathering node. This tradeoff is exploited in the optimization of the random sensor deployment in both analog and digital forwarding systems. More specifically, given the statistics of the energy arrival at different locations and a constraint on the average number of sensors, the spatially-dependent sensor density and the energy-aware transmission policy at the sensors are determined for both cases by minimizing an upper bound on the average mean-square reconstruction error. The efficacy of the proposed schemes are demonstrated through numerical simulations.

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Vibration energy harvesting in automobiles to power wireless sensors

Cited by 26 publications

References 20 publications

Cantilevered Piezoelectric Micro Generator Design Issues and Application to the Mining Locomotive

Cantilevered Piezoelectric Micro Generator Design Issues and Application to the Mining Locomotive

Vibration energy harvesting in vehicles by gear segmentation and a virtual displacement filtering algorithm

Optimized Random Deployment of Energy Harvesting Sensors for Field Reconstruction in Analog and Digital Forwarding Systems

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