Dynamic Stress Test Research Articles

State‐of‐charge estimation of lithium‐ion batteries using composite multi‐dimensional features and a neural network

A novel method that uses composite multi-dimensional features data to estimate the state of charge (SOC) of a battery is presented to address the shortcomings of using single-dimensional feature data. Two types of data, the terminal voltage and the terminal current, which can be obtained directly by measuring, are selected as low-dimensional feature data. The open-circuit voltage (OCV), as high-dimensional feature data, cannot be directly measured, and can be used to estimate the SOC by the OCV-SOC method. Thus, in this study, the second-order RC equivalent model of a battery is used and the OCV is identified online by the forgetting factor recursive least-squares algorithm. The proposed method is implemented by first using a feed-forward neural network, followed by a time-series neural network. The dynamic stress test and urban dynamometer driving schedule discharging profiles are applied to train and test the two neural networks. The experimental results show that the proposed method can estimate the SOC more accurately than neural networks using only single-dimensional feature data. Moreover, the time series neural network can overcome the shortcomings of traditional estimation methods.

Architectural design and performance evaluation of a ZigBee technology based adaptive sprinkler irrigation robot

Irrigation is an assistive measure to salvage the problem of inadequate water for plantation and vegetation in dry places and during dry seasons. In this paper, the architectural design and performance evaluation of an adaptive sprinkler irrigation robot are presented. Two assemblies (transmitter’s and receiver’s modules) to be interfaced via a ZigBee communication were designed to enhance prototyping efficiency. The mechanical structure of the robot chassis was designed using SolidWorks and the assemblies with Arduino Uno microcontroller and ZigBee XBee module. Dynamic stress tests and fatigue analysis simulations were carried out on the designed chassis framework under varying loading conditions ranging for minimum to 50 N. The test results are consistent with the designed water carrying capacity of 5 L and can be deduced based on flow rate of the pump, that the water would be exhausted above 100 s. So a complete sprinkling cycle would last for about two minutes and thirty seconds. It was noticed that soil moisture content during day is lower than after irrigation has been carried out. The efficiency of the irrigation robot was also examined by the relationship between volume of water carried and the speed of the sprinkler robot. The presentation concludes with the system performance including the analysis of the water carrying capacity, distance of watering per cycle, and time requirements to water a given area of farmland.

Fatigue redesign of failed sub frame using stress measuring, FEA and British Standard 7608

Abstract The bridge inspection vehicle is a kind of special vehicle that can provide a work platform for the bridge maintenance. Under the working condition, 8 tires and 2 outriggers support on the ground. The supporting load of outrigger is much bigger than the tire, because of the stiffness distribution. After about six months in service, cracks tend to appear in the sub frame of bridge inspection vehicle, near the connection area between sub frame and front outrigger. To identify the cause of the crack failure and propose an approach to improve the design, a practical method using finite element analysis (FEA) and tests is carried out. The FEA model is established by the test of supporting load, and 5 load scenarios are considered. FEA and corresponding stress test of original sub frame are used to analyze the cause of the cracking. Analysis results indicate that the stresses are concentrated in the sub frame at the area of crack failure. Then, a solution for improvement of the sub frame is proposed by FEA. As the crack occurs near the weld seam, the fatigue assessment of welded joint is more scientific for the welded structure, so the fatigue of welded structure is discussed. According to BS 7608, the process of fatigue life assessment is proposed. Dynamic stress test is implemented to acquire the stress spectrum. By use of the software Ncode, the fatigue of the sub frame is calculated. The results indicate that the improved sub frame is much better than the original in the expected fatigue life. The improved sub frame has been in service for more than 15 months and no cracks appear up to date.

State-of-Charge Estimation of Lithium-Ion Batteries via Long Short-Term Memory Network

Accurate state-of-charge (SOC) estimation is critical for driving range prediction of electric vehicles and optimal charge control of batteries. In this paper, a stacked long short-term memory network is proposed to model the complex dynamics of lithium iron phosphate batteries and infer battery SOC from current, voltage, and temperature measurements. The proposed network is trained and tested using data collected from the dynamic stress test, US06 test, and federal urban driving schedule. The performance on SOC estimation is evaluated regarding tracking accuracy, computation time, robustness against unknown initial states, and compared with results from the model-based filtering approach (unscented Kalman filter). Moreover, different training and testing data sets are constructed to test its robustness against varying loading profiles. The experimental results show that the proposed network well captures the nonlinear correlation between SOC and measurable signals and provides better tracking performance than the unscented Kalman filter. In case of inaccurate initial SOCs, the proposed network presents quick convergence to the true SOC, with root mean square errors within 2% and mean average errors within 1%. Moreover, the estimation time at each time step is sub-millisecond, making it appropriate for real-time applications.

Convolutional Gated Recurrent Unit–Recurrent Neural Network for State-of-Charge Estimation of Lithium-Ion Batteries

For most deep learning practitioners, recurrent networks are often used for sequence modeling. However, recent researches indicate that convolutional architectures may be used to optimize recurrent networks on some machine translation tasks. Problems here are which architecture we should use for a new sequence modeling. By integrating and systematically evaluating the general convolution and recurrent architecture used for sequence modeling, a convolution gated recurrent unit (CNN-GRU) network is proposed for the state-of-charge (SOC) estimation of lithium-ion batteries in this paper. Deep-learning models are well suited for SOC estimation because a battery management system is time-varying and non-linear. The CNN-GRU model is trained using data collected from the battery-discharging processes, such as the dynamic stress test and the federal urban driving schedule. The experimental results show that the proposed method can achieve higher estimation accuracy than two commonly used deep learning models (recurrent neural network and gated recurrent unit) and two traditional machine learning approaches (support vector machine and extreme learning machine) for SOC estimation of lithium-ion batteries.

Dynamic Stress and Pressure Fluctuation Test on Model Unit of Huge Francis Turbine

Dynamic Stress and Pressure Fluctuation Test on Model Unit of Huge Francis Turbine

Combined CNN-LSTM Network for State-of-Charge Estimation of Lithium-Ion Batteries

State-of-charge (SOC), which indicates the remaining capacity at the current cycle, is the key to the driving range prediction of electric vehicles and optimal charge control of rechargeable batteries. In this paper, we propose a combined convolutional neural network (CNN) - long short-term memory (LSTM) network to infer battery SOC from measurable data, such as current, voltage, and temperature. The proposed network shares the merits of both CNN and LSTM networks and can extract both spatial and temporal features from input data. The proposed network is trained using data collected from different discharge profiles, including a dynamic stress test, federal urban driving schedule, and US06 test. The performance of the proposed network is evaluated using data collected from a new combined dynamic loading profile in terms of estimation accuracy and robustness against the unknown initial state. The experimental results show that the proposed CNN-LSTM network well captures the nonlinear relationships between SOC and measurable variables and presents better tracking performance than the LSTM and CNN networks. In case of unknown initial SOCs, the proposed network fast converges to true SOC and, then, presents smooth and accurate results, with maximum mean average error under 1% and maximum root mean square error under 2%. Moreover, the proposed network well learns the influence of ambient temperature and can estimate battery SOC under varying temperatures with maximum mean average error under 1.5% and maximum root mean square error under 2%.

An indicator for the electrode aging of lithium-ion batteries using a fractional variable order model

The fractional order expressed as a function of recursive factors defines the fractional morphology of charge distribution on porous electrodes. Electrode aging causes the fractality of charge distribution to gradually deviate from the original fractal dimension, consequently, the fractional order of the fractional order model varies with the cycle number of batteries. This paper aims to determine the relationship between the fractional order and electrode aging, and use the fractional order as an indicator for electrode aging. The transient discharge dataset of fully charged lithium-ion batteries is taken as the input signal of the identification system of the fractional order model. The identification of the fractional order is then performed with the least squares method. The results show that there is a stable monotonous relationship between the fractional order and the cycle number of batteries. Using the fractional order as an indicator for electrode aging can quickly evaluate the degradation level of electrodes without destroying the battery. Moreover, it is significant to find that the electrode life is terminated when the fractional order tends to be stable, and therefore, the fractional order is used to recognize the ongoing or abrupt degradation of batteries. The effectiveness of the proposed method is verified by the dataset of National Aeronautics and Space Administration (NASA) Ames Prognostics Center of Excellence and the dynamic stress test.

State-of-charge estimation method of lithium-ion batteries based on long-short term memory network

This paper presents a method to estimate the state-of-charge (SOC) of lithium-ion batteries based on long-short term memory network (LSTM). The method is mainly composed of two parts: (1) A linear neural network is used to identify the parameters of second-order equivalent circuit model. (2) A LSTM network is built to estimate the SOC of lithium-ion battery. The linear neural network is trained using the American Dynamic Stress Test Condition (DST) dataset of battery (1st cycle), while the LSTM network is trained using the Chinese Standard Operating Condition (QCT) datasets of battery (1st, 10th and 20th cycle). After that, the trained LSTM network is tested using DST datasets of the batteries and QCT datasets under different temperatures. The results show that the LSTM network can accurately estimate the SOC of lithium battery under different temperatures, different working conditions and lifespan.

Reliable measurements of physiologic ankle syndesmosis widening using dynamic 3D ultrasonography: a preliminary study.

PurposeThe purpose of this study was to present a technique for measuring physiologic distal tibiofibular syndesmosis widening using 3-dimensional ultrasonography (3D-US) with an evaluation of its reliability, and to determine whether there were differences in the measurements between different dynamic stress tests.MethodsWe retrospectively evaluated 3D-US of 20 subjects with normal ankle syndesmosis. 3D-US was performed in neutral (N), dorsiflexion with external rotation (DFER), and weight-bearing standing (WB) positions at the anterior inferior tibiofibular ligament level in both ankles for comparison. Using 3D-US volume data, axial images were reconstructed at the level of the lateral prominence of the anterior tibial tubercle to ensure consistent measurements of the tibiofibular clear space (TFCS) by two radiologists.ResultsThere was a wide range of TFCS values among the subjects (N, 1.2 to 4.2 mm; DFER, 2.3 to 4.8 mm; WB, 1.7 to 4.6 mm). When both ankles of each subject were evaluated, the side-to-side differences were less than 1 mm in all positions, with high intraclass correlation coefficient (ICC) values between both ankles (ICC, 0.85 to 0.93). The inter-rater agreement for all TFCS measurements between the two radiologists was excellent (ICC, 0.81 to 0.96). In comparisons between the two dynamic stress tests, the TFCS was significantly wider in the DFER position than in the WB position (DFER vs. WB, 3.3 mm vs. 2.9 mm; P<0.001).ConclusionUsing 3D-US, we were able to consistently evaluate the TFCS with good reliability. In a comparison of the two dynamic tests, there was more significant widening of the TFCS in the DFER position than in the WB position.

SOC estimation for lithium batteries based on the full parallel nonlinear autoregressive neural network with external inputs

Lithium-ion batteries are the core element in electric-vehicle systems. Because they provide a power source for the entire electric vehicle, their state of charge (SOC) is crucial to ensure the proper operation of the vehicle. Thus, a more precise estimate of the SOC should significantly improve the safety and utility of these batteries. Toward this end, we propose herein novel nonlinear autoregressive with exogenous input (NARX) and full-parallel (dual-close loop) structure NARX (FPNARX) networks to overcome the disadvantage of the traditional series-parallel (open-closed loop) structure NARX (SPNARX) network for estimating the SOC. To test the performance of the FPNARX network, we use the dynamic stress test and the urban dynamometer driving schedule discharge profiles in battery-discharge experiments. Furthermore, feed-forward neural networks and other time-series neural networks, including the time-delay neural network and SPNARX, are also implemented for the same experiments. The experimental results show that the time-series networks can estimate the SOC more precisely than the feed-forward network under non-constant-current discharge. Moreover, the applicability of the time-series networks depends on the current amplitude and frequency. However, the FPNARX network always gives the best results.

A State of Charge Estimation Method Based on Adaptive Extended Kalman-Particle Filtering for Lithium-ion Batteries

A state of charge (SOC) estimation method is proposed. An Adaptive Extended Kalman Particle filter (AEKPF) based on Particle Filter (PF) and Adaptive Kalman Filter (AKF) is used in order to decrease the error and reduce calculations. The second-order resistor-capacitor (RC) Equivalent Circuit Model (ECM) is used to identify dynamic parameters of the battery. After testing (include Dynamic Stress test (DST), New European Driving Cycle (NEDC), Federal Urban Dynamic Schedule (FUDS), Urban Dynamometer Driving Schedules (UDDS), etc.) at different temperatures and times, it was found that the AEKPF exhibits greater tolerance for high system noise (10% or higher) and provides more accurate estimations under common operating conditions.

Road Network Resilience: How to Identify Critical Links Subject to Day-to-Day Disruptions

Disruptive events occur on road networks on a daily basis and affect traffic flow. Resilience analysis aims to assess the consequences of such disruptions by quantifying the ability of a network to absorb and react to adverse events. In this paper, we advance a methodological approach based on resilience stress testing and a dynamic mesoscopic simulator. We aim to identify and rank the links most critical to the overall performance of the road network, taking into account the dynamic properties of road traffic and focusing on day-to-day disruptions. As a metric to quantify road network performance in the presence of such disruptions, we use the increase in overall travel cost. We then compare our approach with purely topological approaches. We discuss the advantages and drawbacks of the different analyzed metrics. We prove that link ranking varies greatly depending on the metric. Specifically, the proposed stress testing methodology can produce very accurate results by taking into account demand and congestion, but requires computationally intensive simulations, being therefore prohibitive even on medium-sized networks. Conversely, purely static topological metrics can be inaccurate if they do not take into account traffic demand and network dynamics. Our study highlights the need for joining traditional traffic-agnostic topological resilience analysis with demand-aware dynamic stress testing techniques.

Design of stable and durable polymer electrolyte membrane fuel cells by embedding hydrophobic cage-structured material in cell components

Performance stability and durability of polymer electrolyte membrane fuel cells (PEMFCs) depend on many factors of cell assembly that in turn determines the commercial applicability of PEMFC technology. In this study, an economically viable solution to improve the stability of PEMFCs at variable power is achieved with tailored membrane electrode assemblies (MEAs) by the incorporation of hydrophobic, cage-structured octasilane polyhedral oligomeric silsesquioxanes, (OSP) to Nafion™ in the MEA construction. In the Durability and Stability Dynamic Stress Testing (DST) studies over an operating time of 250 h; OSP modified MEAs exhibited a higher peak power density of 0.60 W cm−2 which dropped only 45%, compared to peak power density of neat Nafion™ which dropped 70% from the initial peak power density of 0.37 W cm−2. The H+ conductivity of OSP/Nafion™ composite MEAs remains stable during the full course of study and demonstrated sustained performance in harsh DST environment. The results illustrated that small amount of OSP (∼3%) in the MEA does prevent excess water accumulation in MEA and simultaneously allocate enough humidity for transporting H+, thus it advances the stability and durability of Nafion™ for manufacturing PEMFC and Direct Methanol Fuel Cells (DMFC).

Comparison of Battery Degradations Induced By Different Synthetic Driving Cycles and Real-World Traffic Conditions

Advances in lithium ion battery pack technology have led to significant market penetration for electric vehicles (EVs), but there is still a need to develop new battery technologies, better control algorithms, and faster charging strategies to enhance further the ownership experience. Once developed, these advances must be validated. Unfortunately, fleet testing is not often an option because of the associated cost. To address this issue and permit the validation of new technologies in the laboratory quickly, many studies are relying on synthetic driving cycles to mimic the usage cells would experience under actual utilization. Among the more popular ones are the Federal Urban Driving Schedule (FUDS), a US standard; the Dynamic Stress Test (DST) which is a simplified version of the FUDS cycle; and the New European Driving Cycle (NEDC), a European standard. The ability of these cycles to replicate EV usage is usually taken for granted; whereas, to the best of our knowledge, no study has scrutinized these cycles to establish if the induced degradation is indeed comparable with what is observed from real driving data. It is proposed here to investigate the impact of the most-used, standardized driving cycles on state-of-the art lithium-ion cells and evaluate them against the impact of real driving in a laboratory-controlled environment. Different analysis techniques and metrics, such as voltage response, capacity, rate capability, and resistance, are employed to compare the most common synthetic driving cycles (FUDS, DST and NEDC) with scaled-down real driving data selected from the Hawai'i Natural Energy Institute (HNEI) in-house database. Two different sets of real driving data was used, one using a single commute in a loop and one using 25 similar commutes in a loop, to reveal any effect of traffic. This study aimed to elucidate any significant discrepancies between simulated and real driving outcomes, determine the effect of traffic conditions, and propose possible improvements.

Morphological Analysis of Tibiofibular Disruption Using CT images in Ankle Fractures with Unstable Syndesmotic Injury

Category: Trauma Introduction/Purpose: We aimed to investigate the diagnostic value of CT scan and the morphologic pattern of distal tibiofibular disruption associated with fracture-type or concomitant avulsion fractures in cases of ankle fracture with unstable syndesmosis requiring surgical fixation. Methods: 104 cases images(preoperative plane radiographs and CT axial images) of ankle fractures needed syndesmotic fixation due to unstable syndesmosis on intraoperative stress test were retrospectively reviewed. We classified ankle fractures by Lauge-Hansen classification and AO-OTA classification. Also, associated tibiofibular avulsion fractures including anterior distal tibiofibular avulsion fractures and posterior malleolar fractures were identified. Classification of posterior malleolar fractures based on Bartonícek-Rammelt classification was conducted. We grouped morphologic patterns of distal tibiofibular disruption which is 10 mm proximal to the plafond on CT axial images. We divided them into no diastasis, symmetric diastasis, anterior open, and posterior open according to the diastasis pattern, and no translation, anterior translation, and posterior translation depending on sagittal translation. Chi-square test/Fisher’s exact test and adjusted residual analysis were used for statistical analysis. Results: On LH classification, SER in 78(75%), PER in 18(17.3%), and PA in 8(7.7%). AITFL avulsions were in 37(35.6%) and PM fractures in 58(55.8%). Diastasis patterns were recorded in 30(28.8%) of no diastasis, 41(39.4%) of symmetric diastasis, 3(2.9%) of anterior open, and 30(28.8%) of posterior open. Depending on sagittal translation, 53(51.0%) of no translation, 44(42.3%) of anterior translation, and 7(6.7%) of posterior translation. Neither diastasis nor sagittal translation was revealed by CT in 17.3% of all. Syndesmotic morphologic pattern was various in SER [no diastasis (35.9%, AR=2.7), posterior open (35.9%, AR=2.7), and anterior translation (48.7%, AR=2.3)] whereas PER had relatively consistent pattern (symmetric diastasis in 88.9%, AR=4.7). Tibiofibular avulsion fractures such as Charput and PM fracture did not create distinct feature of widening. Conclusion: In terms of syndesmotic disruption pattern by CT images, PER fractures tend to show simple symmetric diastasis pattern, but SER fractures have a variety of patterns including normal appearance. Even if associated avulsion fracture do not make certain diastasis feature on static CT images, we still have to be careful not to overlook the syndesmotic instability. Diagnostic value of static imaging such as CT may not be higher than expected if not accompanied with intraoperative dynamic stress test.

Proven accuracy for a new dynamic gap measurement in navigated TKA.

Clinical outcome of TKA remains unsatisfactory in 20% of the cases. Navigation has added accuracy in terms of alignment, but has improved clinical outcome only in small series with gap-balanced techniques. Reason for that could be that conventional gap balanced TKA determines gaps in extension and 90° of flexion only. Furthermore, measurement is only static. Therefore, the accuracy of a new dynamic navigation software which allows gap assessment throughout the entire range of motion was tested. The purpose of this study was to investigate the accuracy and reliability of dynamic gap testing during gap-balanced TKA. In two different centres, a total of 65 TKA procedures were performed in a tibia-first, gap-balanced technique using a new CAS software. At the same and at different time points of surgery, two different surgeons performed gap measurement to provide inter-observer reliability data and repeated gap measurement to provide intra-observer reliability data. These gap measurements were performed throughout the entire ROM under dynamic stress testing to detect maximum gap values. CAS surgery was able to produce correct coronal alignment in 96.4% of the cases (within 3° mechanical alignment). Both inter-observer and intra-observer reliabilities were excellent for gap values throughout the entire ROM. Inter-observer bias of deviation 0.05; 95% limits of agreement of - 2.1 to + 2.21mm. Intra-observer bias of deviation 0.09; 95% limits of agreement of - 2.27 to + 2.44mm. This new CAS software in combination with the presented dynamic gap measurement provides accurate gap values and therefore facilitates balancing TKA. This technique works reproducibly for different surgeons and has proven robustness also for repeated measurements of any surgeon in this study.

Multi-timescale power and energy assessment of lithium-ion battery and supercapacitor hybrid system using extended Kalman filter

The power capability and maximum charge and discharge energy are key indicators for energy management systems, which can help the energy storage devices work in a suitable area and prevent them from over-charging and over-discharging. In this work, a model based power and energy assessment approach is proposed for the lithium-ion battery and supercapacitor hybrid system. The model framework of the lithium-ion battery and supercapacitor hybrid system is developed based on the equivalent circuit model, and the model parameters are identified by regression method. Explicit analyses of the power capability and maximum charge and discharge energy prediction with multiple constraints are elaborated. Subsequently, the extended Kalman filter is employed for on-board power capability and maximum charge and discharge energy prediction to overcome estimation error caused by system disturbance and sensor noise. The charge and discharge power capability, and the maximum charge and discharge energy are quantitatively assessed under both the dynamic stress test and the urban dynamometer driving schedule. The maximum charge and discharge energy prediction of the lithium-ion battery and supercapacitor hybrid system with different time scales are explored and discussed.

Abstract #101 Effect of Norplant on the Adrenal Reserve Capacity Using a Dynamic Stress Test

Abstract #101 Effect of Norplant on the Adrenal Reserve Capacity Using a Dynamic Stress Test

Wood-Inspired High-Performance Ultrathick Bulk Battery Electrodes.

Ultrathick electrode design is a promising strategy to enhance the specific energy of Li-ion batteries (LIBs) without changing the underlying materials chemistry. However, the low Li-ion conductivity caused by ultralong Li-ion transport pathway in traditional random microstructured electrode heavily deteriorates the rate performance of ultrathick electrodes. Herein, inspired by the vertical microchannels in natural wood as the highway for water transport, the microstructures of wood are successfully duplicated into ultrathick bulk LiCoO2 (LCO) cathode via a sol-gel process to achieve the high areal capacity and excellent rate capability. The X-ray-based microtomography demonstrates that the uniform microchannels are built up throughout the whole wood-templated LCO cathode bringing in 1.5 times lower of tortuosity and ≈2 times higher of Li-ion conductivity compared to that of random structured LCO cathode. The fabricated wood-inspired LCO cathode delivers high areal capacity up to 22.7 mAh cm-2 (five times of the existing electrode) and achieves the dynamic stress test at such high areal capacity for the first time. The reported wood-inspired design will open a new avenue to adopt natural hierarchical structures to improve the performance of LIBs.