Shape morphing is one of the most appealing applications of adaptive structures. Among the various means of achieving shape morphing, origami-inspired folding is particularly advantageous, because folding is a powerful approach to induce three-dimensional and sophisticated shape changes. However, attaining large-amplitude folding is still a challenge in origami engineering. While promising, the use of active materials as a folding activation strategy is limited due to the constant voltage supply that is required to maintain the desired configuration of the structure. One possible solution is to embed bi-stability into the structure. Bi-stability can play two significant roles here: first, it can significantly reduce the actuation requirement to induce shape morphing, and second, it can maintain the shape change without demanding sustained energy supply. In a previous study by the authors, a unique shape morphing (or self-folding) method using harmonic excitation has been proposed for a bi-stable water-bomb base. However, this approach has some drawbacks because the nonlinear dynamic behaviors of origami are quite sensitive to different design parameters, such as initial conditions, excitation parameters, and inaccuracies in manufacturing. In this study, via numerical simulations, we show that by harnessing the intra-well resonance of the water-bomb structure and incorporating a relatively simple feedback control strategy, one can achieve a rapid and robust morphing using relatively low actuation magnitude. The results of this study can lay the foundation of a new category of morphing origami mechanisms with efficient and reliable embedded actuation.
The State of Health (SOH) of the battery is often represented either using the decrease in the capacity of the battery or the increase in the internal resistance of the battery. While these indices are commonly used, they do not provide any insight on the reasons for the degradation of the health of the battery. Understanding battery aging and the impact it has on the working/performance of the battery is required to determine the State of Function (SOF) of the battery for that particular application. The SOF of the battery can provide information on the current applicability of the battery to the application. The Remaining Useful Life (RUL) of the battery is also highly dependent on the current and past operating conditions. Determining the reason behind the degradation and the impact on the health can also help determine the RUL or provide feedback to the user on alternate usage patterns to prolong the RUL. This paper uses a first principle based degradation model to determine the sensitivity of the terminal voltage and capacity of the battery to the degradation of the concentration of lithium ions in the anode/negative electrode.
Rechargeable lithium-ion batteries are now widely adopted in our life. To fulfil various energy and power requirements in real-world applications, battery cells are connected to form battery packs. The cell-to-cell difference exists in the battery pack after manufactured, and this difference will further deteriorates when the battery cells are exposed and used in various operating conditions. This unavoidable cell-to-cell difference results in early cut-off on the battery pack, which influences the performance of the battery pack and makes accurately estimating the battery pack SOC challenging. This paper proposes a novel real-time algorithm to effectively identify the most significant cells in a serial-connected battery pack in order to accurately estimate the SOC of the battery pack. A battery pack composed of ten serial-connected battery cells is carried out in this paper to evaluate the performance of the proposed algorithm. The results show that the most significant cells are successfully identified, and the SOC of the battery pack is estimated accurately based on the identified most significant cell.
This paper discusses the commonly used techniques to estimate the state of health (SOH) and state of function (SOF) of lithium ion batteries and their limitations. Factors affecting the health and SOF of the battery are discussed in this paper. The SOH of the battery is mainly represented by the capacity degradation and the increase in the internal resistance. The other indices that could represent the battery's health are also briefly discussed. The different techniques that are used to estimate the capacity and internal resistance of the battery are discussed along with their limitations. The concept of SOF and its relationship with SOC, SOH and temperature are discussed along with the commonly used techniques to estimate the SOF of the battery. This paper also discusses the limitations in the definition and estimation of the SOF.
Smart sensors can be used in nuclear power plants to detect operational vibrations in order to predict plant maintenance schedules. The sensor design process includes characterization of the sensing element and determination of the optimal sensor location for nuclear applications. The selection of appropriate amplifiers, filters, and control elements are analyzed using the sensor's specifications such as output amplitude, frequency, etc. A mock-up experiment was designed to test the performance of the vibration sensor and the sensitivity of the sensing element in real time. The experimental tests showed the system tank experiencing vibrations due to various water flow rates. This research is used to validate the working of the smart sensor and highlight its capability to perform detailed calculations, make appropriate decisions, and communicate these decisions to a remote monitoring system.
Lithium-ion batteries are becoming the main energy storage in electric vehicles and electric grids. To elevate the battery capacity and the voltage supply, the battery cells are stacked to form a battery pack. The state-of charge (SOC) of the battery pack requires continuous monitoring for the operation safety. The current developed SOC estimation algorithms shows decent estimation accuracy but they are designed for individual cells. These algorithms stay in the battery cell level because they cannot capture the cell-to-cell difference which exists after manufactured. This paper proposed a battery pack SOC Co-Estimation algorithm based on the estimated battery cell SOC. The proposed battery pack SOC Co-Estimation algorithm can accurately estimates the SOC of a battery pack with three serial connected battery cells but without cell balancing. This algorithm also has the potential to reduce the computation effort on the battery management system (BMS) because it does not need to monitor every single cell in the battery pack.
Battery Energy Storage Systems (BESS) are key components in microgrids for reliable operations. To ensure the safety and reliability of the BESS, accurate State-of-Charge (SOC) estimation is crucial. Most battery SOC estimation algorithms are developed assuming the measured load current and terminal voltage data are trust-worthy. However, in the real-world BESS applications, the measured battery data contains abnormal data which result in poor SOC estimation accuracy and will be harmful to the safety of the BESS and the stability of the microgrid. In this paper, a practical abnormal data filtering framework for real-world BESS applications is proposed. This framework real-time detects and filters abnormal data in real-world applications. Three typical abnormal data filters are investigated and demonstrated in this paper to illustrate the effectiveness of using data filtering for accurate SOC estimation in real-world BESS applications.
This article proposes a noninvasive liquid level sensing technique using laser-generated ultrasound waves for nuclear power plant applications. Liquid level sensors play an important role of managing the coolant system safely and stably in the plant structure. Current sensing techniques are mostly intrusive, performing inside the fluidic structure, which is disadvantageous in terms of the regular maintenance of the plant system. Furthermore, typical intrusive sensors do not perform stably under varying environmental conditions such as temperature and radiation. In this study, sensing units are attached to the outer surface of a liquid vessel to capture guided ultrasound waves in a nonintrusive manner. The signal intensity of the guided wave dissipates when the signal interacts with the internal liquid media. The sensing mechanism is mathematically expressed as an index value to correlate the liquid level with the sensor signal. For the acoustic wave generation, laser-generated ultrasound was adopted instead of using typical contact type transducers. Following the simulation validation of the proposed concept, the performance of the developed sensor was confirmed through experimental results under elevated liquid temperature conditions. The nonlinear multivariable regression exhibited the best-fit to the datasets measured under the variable liquid level and temperature conditions.
Li-ion batteries are considered as main energy sources for next generation of transportation systems. This paper presents a systematic way to design an efficient hardware testbed for Battery Monitoring System (BMS) applications in Electric Vehicle (EV) industry following the standard industrial communication protocol. The hardware testbed performs both the battery voltage/current data acquisition and the Co-Estimation algorithm. Co-Estimation is an electric circuit model based SOC estimation algorithm which takes model parameter variations into account. In this paper, the Co-Estimation algorithm is firstly discussed. A battery hardware testbed design is then elaborated, and reasons for selecting main components, including microcontroller and voltage/current sensors are explained. The performance of the hardware testbed is compared with MATLAB simulation result using the same Co-Estimation algorithm, showing similar performance between two different platforms: hardware testbed and software simulation.
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