Estimated Energy Expenditure Research Articles

Improved Nitrate-to-Ammonia Electrocatalysis through Hydrogen Poisoning Effects.

Electrochemical conversion from nitrate to ammonia is a key step in sustainable ammonia production. However, it suffers from low productive efficiency or high energy consumption due to a lack of desired electrocatalysts. Here we report nickel cobalt phosphide (NiCoP) catalysts for nitrate-to-ammonia electrocatalysis that display a record-high catalytic current density of -702±7 mA cm-2, ammonia production rate of 5415±26 mmol gcat -1 h-1 and Faraday efficiency of 99.7±0.2 % at -0.3 V vs. RHE, affording the estimated energy consumption as low as 22.7 kWh kgammonia -1. Theoretical and experimental results reveal that these catalysts benefit from hydrogen poisoning effects, which leave behind catalytically inert adsorbed hydrogen species (HI*) at Co-hollow sites and thereupon enable ideally reactive HII* at secondary Co-P sites. The dimerization between HI* and HII* for H2 evolution is blocked due to the catalytic inertia of HI* thereby the HII* drives nitrate hydrogenation timely. With these catalysts, the continuous ammonia production is further shown in an electrolyser with a real energy consumption of 18.9 kWh kgammonia -1.

Dynamic soaring decouples dynamic body acceleration and energetics in albatrosses.

Estimates of movement costs are essential for understanding energetic and life-history trade-offs. Although overall dynamic body acceleration (ODBA) derived from accelerometer data is widely used as a proxy for energy expenditure (EE) in free-ranging animals, its utility has not been tested in species that predominately use body rotations or exploit environmental energy for movement. We tested a suite of sensor-derived movement metrics as proxies for EE in two species of albatrosses, which routinely use dynamic soaring to extract energy from the wind to reduce movement costs. Birds were fitted with a combined heart-rate, accelerometer, magnetometer and GPS logger, and relationships between movement metrics and heart rate-derived V̇O2, an indirect measure of EE, were analyzed during different flight and activity modes. When birds were exclusively soaring, a metric derived from angular velocity on the yaw axis provided a useful proxy of EE. Thus, body rotations involved in dynamic soaring have clear energetic costs, albeit considerably lower than those of the muscle contractions required for flapping flight. We found that ODBA was not a useful proxy for EE in albatrosses when birds were exclusively soaring. As albatrosses spend much of their foraging trips soaring, ODBA alone was a poor predictor of EE in albatrosses. Despite the lower percentage of time flapping, the number of flaps was a useful metric when comparing EE across foraging trips. Our findings highlight that alternative metrics, beyond ODBA, may be required to estimate energy expenditure from inertial sensors in animals whose movements involve extensive body rotations.

Energy Deficit and Factors Associated with Energy Balance during a Combat Deployment in U.S. Army Special Operation Forces Soldiers.

Forty-six SOF Soldiers (age: 30.1 ± 3.5 yrs, body mass index: 27.7 ± 4.1 kg/m2) completed surveys on demographic data, mission activity characteristics, gastrointestinal issues, ration consumption, resilience, mood state, and dietary intake using a 127-question food frequency questionnaire at the end of a six-month deployment. EE was estimated using a SOF-specific prediction equation with a physical activity factor of 2.1. A paired t-test compared reported energy intake (EI) with estimated energy expenditure (EE). Pearson correlations identified significant variables associated with energy balance, which were then incorporated into a multiple linear regression model. The regression analysis included Profile of Mood States (POMS) anger and POMS depression as predictor variables to determine their influence on energy balance. Reported mean EI was 2512 ± 1059 kcal·d-1, while estimated mean EE was 5272 ± 525 kcal·d-1. The mean energy imbalance was -2854 kcal/d (95% CI: -2655 to -3055, p < 0.001), with all participants in negative energy balance (range: -492 to -3813 kcal/d). POMS depression (r = 0.517, p < 0.01) and POMS anger (r = 0.363, p = 0.020) were associated with energy balance. The regression model was significant (R2 = 0.23, F (2, 38) = 7.02, p < 0.01), with POMS depression significantly predicting energy balance (β = 50.76, p = 0.011). Deployed SOF Soldiers reported high EE and limited EI, which may negatively impact performance. Higher POMS depression scores were associated with lower energy deficits. Future studies should investigate the relationship between mood and energy balance, using direct measures of EI and EE.

Composite activity type and stride-specific energy expenditure estimation model for thigh-worn accelerometry

BackgroundAccurately measuring energy expenditure during physical activity outside of the laboratory is challenging, especially on a large scale. Thigh-worn accelerometers have gained popularity due to the possibility to accurately detect physical activity types. The use of machine learning techniques for activity classification and energy expenditure prediction may improve accuracy over current methods. Here, we developed a novel composite energy expenditure estimation model by combining an activity classification model with a stride specific energy expenditure model for walking, running, and cycling.MethodsWe first trained a supervised deep learning activity classification model using pooled data from available adult accelerometer datasets. The composite energy expenditure model was then developed and validated using additional data based on a sample of 69 healthy adult participants (49% female; age = 25.2 ± 5.8 years) who completed a standardised activity protocol with indirect calorimetry as the reference measure.ResultsThe activity classification model showed an overall accuracy of 99.7% across all five activity types during validation. The composite model for estimating energy expenditure achieved a mean absolute percentage error of 10.9%. For running, walking, and cycling, the composite model achieved a mean absolute percentage error of 6.6%, 7.9% and 16.1%, respectively.ConclusionsThe integration of thigh-worn accelerometers with machine learning models provides a highly accurate method for classifying physical activity types and estimating energy expenditure. Our novel composite model approach improves the accuracy of energy expenditure measurements and supports better monitoring and assessment methods in non-laboratory settings.

Comprehensive Task Optimization Architecture for Urban UAV-Based Intelligent Transportation System

This paper tackles the problem of resource sharing and dynamic task assignment in a task scheduling architecture designed to enable a persistent, safe, and energy-efficient Intelligent Transportation System (ITS) based on multi-rotor Unmanned Aerial Vehicles (UAVs). The addressed task allocation problem consists of heterogenous pick-up and delivery tasks with time deadline constraints to be allocated to a heterogenous fleet of UAVs in an urban operational area. The proposed architecture is distributed among the UAVs and inspired by market-based allocation algorithms. By exploiting a multi-auctioneer behavior for allocating both delivery tasks and re-charge tasks, the fleet of UAVs is able to (i) self-balance the utilization of each drone, (ii) assign dynamic tasks with high priority within each round of the allocation process, (iii) minimize the estimated energy consumption related to the completion of the task set, and (iv) minimize the impact of re-charge tasks on the delivery process. A risk-aware path planner sampling a 2D risk map of the operational area is included in the allocation architecture to demonstrate the feasibility of deployment in urban environments. Thanks to the message exchange redundancy, the proposed multi-auctioneer architecture features improved robustness with respect to lossy communication scenarios. Simulation results based on Monte Carlo campaigns corroborate the validity of the approach.

Calibrating acceleration transmitters to quantify the seasonal energetic costs of activity in lake trout.

Bioenergetics models are powerful tools used to address a range of questions in fish biology. However, these models are rarely informed by free-swimming activity data, introducing error. To quantify the costs of activity in free-swimming fish, calibrations produced from standardized laboratory trials can be applied to estimate energy expenditure from sensor data for specific tags and species. Using swim tunnel respirometry, we calibrated acceleration sensor-equipped transmitting tags to estimate the aerobic metabolic rates (ṀO2) of lake trout (Salvelinus namaycush) at three environmentally relevant temperatures. Aerobic and swim performance were also assessed. Like other calibrations, we found strong relationships between ṀO2 and acceleration or swimming speed, and jackknife validations and data simulations suggest that our models accurately predict metabolic costs of activity in adult lake trout (~5% algebraic error and ~20% absolute error). Aerobic and swim performance metrics were similar to those reported in other studies, but their critical swimming speed was lower than expected. Additionally, lake trout exhibited a wide aerobic scope, suggesting that the avoidance of waters ≥15°C may be related to selection for optimal growing temperatures. The ability to quantify the free-swimming energetic costs of activity will advance our understanding of lake trout ecology and may yield improvements to bioenergetics model.

Drive Cycle‐Based Estimation of Energy Consumption for Electric Two‐Wheeler

ABSTRACTThe transportation sector is the backbone of the economic growth of any country. However, the heavy dependence of this sector on petroleum fuel is a matter of concern for sustainable development. To address this issue countries are working toward green energy‐based transportation, and among all viable solutions electric vehicles (EVs) are emerging as front runners. Range anxiety is one of the most prominent concerns in EV adoption. The range of a vehicle depends on the energy consumption so it becomes crucial to estimate it very precisely. There are many standard drive cycles such as the New European Driving Cycle (NEDC) and Worldwide harmonized Light‐duty Vehicle Test Cycles (WLTC) which are used for the estimation of energy consumption. However, these standard cycles fail to capture the driving behavior of real traffic. Due to this reason, these standard cycles underestimate the energy consumption compared with actual consumption. For more realistic energy requirement estimations, researchers are focusing on the development of real‐world drive cycles specific to a particular geography. In this paper, a real‐world drive cycle of electric two‐wheeler has been developed for the city of Lucknow, India, and compared with the driving characteristics and energy consumption estimates of WLTC. The energy requirement per km for the Lucknow drive cycle and WLTC are found as 14.89 Wh/km and 11.95 Wh/km, respectively, which indicates per km energy required estimation for LDC is 24.60% higher than WLTC.

Validity and Reliability of Wearable Technology Devices during Simulated Pickleball Game Play

Pickleball is a popular sport. Also popular is wearable technology usage. Because the validity and reliability of wearable technology during pickleball is unknown, the purpose of this research was to evaluate the ability of common devices to return heart rate and estimated energy expenditure during pickleball activity. Twenty adult participants were outfitted with a portable metabolic unit and heart rate monitor (criterion measures). Experimental devices were a Garmin Instinct, Polar Vantage M2, Polar OH1, and Polar Verity Sense. Participants played simulated pickleball for 10 min. Validity measures included mean absolute percent error (MAPE) and Lin’s Concordance Correlation Coefficient (CCC), whereas reliability measures included coefficient of variation (CV) and intraclass correlation coefficient (ICC). The heart rate returned lower than 10% MAPE across all devices (Instinct = 5.73–6.32%, Verity Sense = 2.92–2.97%, OH1 = 3.39–3.45%) and greater than 0.85 CCC (Instinct = 0.85–0.88, Verity Sense = 0.96–0.96, OH1 = 0.93–0.94). The CV was below 10% (Instinct = 9.30%, Verity Sense = 2.68%, OH1 = 5.01%), and ICC was above 0.7 (Instinct = 0.77, Verity Sense = 0.98, OH1 = 0.91). The energy expenditure MAPE was greater than 10% (Instinct = 27.67–28.08%, Vantage M2 = 18.87–23.38%) with CCC lower than 0.7 (Instinct = 0.47–0.49, Vantage M2 = 0.62–0.63). Reliability thresholds were met in the Vantage M2 (CV = 6%, ICC = 0.98) but not in the Instinct (CV = 15%, ICC = 0.86). The Instinct was neither valid nor reliable for estimated energy expenditure, while the Polar Vantage M2 was reliable but not valid. All devices returned valid and reliable heart rates during pickleball.

A review of machine learning approaches for electric vehicle energy consumption modelling in urban transportation

Global warming and carbon emissions have drawn attention to the need to decarbonize transport. Promoting electric vehicles (EVs) has become an important strategy towards this goal. Although EVs have significant advantages in emission reduction and energy saving, their wider adoption is limited by factors such as range and charging infrastructure. Accurate prediction and modeling of EV energy consumption is the key to solving these challenges. This review first summarizes traditional energy consumption estimation models and, thereafter explores the application of interpretable machine learning, emphasizing its importance in improving model transparency and practicality. The potential of neural networks to enhance prediction accuracy through feature extraction and pattern recognition is also discussed. This paper aims to provide a systematic review of the latest advances in EV energy consumption forecasting. It serves as a reference for researchers and practitioners to optimize and upgrade urban transport systems for sustainable development.

Validation of activity trackers to estimate energy expenditure in older adults with cardiovascular risk factors.

To compare different types of activity trackers recording physical activity energy expenditure (PAEE) and examine their criterion validity against indirect calorimetry (IC) as the gold standard in adults over 60 years of age with a special focus on women with cardiovascular risk. Synchronous registrations of PAEE were performed with up to four different devices to determine criterion validity against IC while participants performed a protocol of simulated activities in a laboratory setting. Thirty-four participants (25 women, 9 men) with at least a light cardiac risk performed a protocol of simulated activities in a laboratory setting (daily living activities, cycle ergometer test). PAEE was simultaneously assessed by IC, two research-grade activity trackers (ActiGraph-wGT3X-BT and Actiheart-4) and two consumer-level activity trackers (OMRON pedometer and Fitbit Charge-3). Tracker-derived PAEE was compared with PAEE calculated from IC descriptively and by Bland-Altman plots. The ActiGraph (0.7 ± 0.4 kcal/min), the Actiheart (1.1 ± 0.6 kcal/min) and the OMRON (0.8 ± 0.6 kcal/min) underestimated, while the Fitbit (3.4 ± 1.2 kcal/min) overestimated PAEE compared to IC-PAEE (2.0 ± 0.5 kcal/min). The Bland-Altman limits of agreement (LoA) against IC were +0.5/+2.2 kcal/min for the ActiGraph, -0.3/+2.1 kcal/min for the Actiheart, -3.7/+1.0 kcal/min for the Fitbit, and -0.5/+2.9 kcal/min for the OMRON. The magnitude of the deviation varied considerably depending on the activity (e.g. walking, cleaning, cycle ergometer test). The research-grade activity trackers estimated PAEE with higher validity than the commercially available activity trackers. The partly very wide LoA have to be critically considered when assessing PAEE in the context of health service research, as individual Physical Activity behaviour may be under- or overestimated.

An energy and information analysis method of logic gates based on stochastic thermodynamics.

To reduce the energy consumption of logic gates in digital circuits, the size of transistors approaches the mesoscopic scale, e.g. sub-7 nanometers. However, existing energy consumption analysis methods exhibit various deviation for logic gates when the nonequilibrium information processing of mesoscopic scale transistors with ultra-low voltages is analyzed. Based on the stochastic thermodynamics theory, an information energy ratio method is proposed for the energy consumption estimation of XOR gates composed of mesoscopic scale transistors. The proposed method provides a new insight to quantify the transformation between the information capacity and energy consumption for XOR gates and extending to other logic gates. Utilizing the proposed analysis method, the supply voltage of the parity check circuit can be optimized by numerical simulations without expensive and complex practical measurements. The information energy ratio is the first analytical method to quantify the energy and information transformation of logic gates at the mesoscopic scale.

Real-Time Implementable Reduced-Order Energy Model for an Electric Vehicle

&lt;div&gt;Accurate estimation of vehicle energy consumption plays an important role in developing advanced energy-saving connected automated vehicle technologies such as Eco Approach and Departure, PHEV mode blending, and Eco-route planning. The present study developed a reduced-order energy model with second-order response surfaces and torque estimation to estimate the energy consumption while just relying on the drive cycle information. The model is developed for fully electric Chevrolet Bolt using chassis dynamometer data. The dyno test data encompasses the various EPA test cycles, real-world, and aggressive maneuvers to capture most powertrain operating conditions. The developed model predicts energy consumption using vehicle speed and road-grade inputs for a drive cycle. The accuracy of the model is validated by comparing the prediction results against track and road test data. The developed model was able to accurately predict the energy consumption for track drive cycles within the error of ±4.0% of that measured from the experimental data. Finally, the model has been tested and verified for real-time implementation using the dSPACE MicroAutoBox II HIL test bench.&lt;/div&gt;

Energy Efficient Design of Building Based on Building Information Modelling (BIM)

Building energy analysis is rarely carried out due to the complexity of building shape and materials. On the other hand, the urgency of environmentally friendly construction is increasing through the vision of the SDGs and Architecture 2030. The development of BIM technology is expected to provide accurate estimates of building energy consumption for projects undertaken, as well as recommendations for alternative designs and specifications to increase the energy efficiency of a building. The use of BIM technology for energy analysis during building design helps to implement green building sustainable design based on the analysis of BIM energy simulation software. Data of three-dimensional BIM model with the attributes of materials, project schedule and location are used in building energy consumption simulation analysis. Several scenarios of different materials and layout are carried out to determine the most efficient scenario for energy consumption and followed by its cost estimation. The results of this study are expected to be able to provide the energy value that can be saved through predetermined scenarios as well as the value of the costs required to run a more energy-friendly design scenario in a comprehensive manner.

Optics‐Enabled Highly Scalable Inverter for Multi‐Valued Logic

AbstractThe rapid advancements in machine learning have exacerbated the interconnect bottleneck inherent in binary logic‐based computing architectures. An interesting approach to tackle this problem involves increasing the information density per interconnect, i.e., by switching from a two‐valued to a multi‐valued logic (MVL) architecture. However, current MVL implementations offer limited overall performance and face challenges in scaling to process data signals with radix (number of logic levels) even just above 3. In this work, a novel concept is introduced for implementation of a highly scalable and fully passive inverter based on the frequency‐domain phase‐only linear manipulation of the input MVL data signal, which is encoded in the amplitude variations of an electromagnetic wave along the time axis. As a key advantage, this solution is entirely independent of the input radix. The proposed design is implemented using an optical fibre Bragg grating device. Inversion of quaternary signals is experimentally demonstrated, as well as binary and ternary signals, at a remarkable operation speed of 32 GBaud, with an estimated energy consumption of 24 fJ/bit. The proposed method is universal and can be applied to any system that supports transmission and detection of coherent waves, such as microwave, plasmonic, mechanical, or quantum.

Potential sources of inaccuracy in the Apple watch series 4 energy expenditure estimation algorithm during wheelchair propulsion

BackgroundThe Apple Watch (AW) was the first smartwatch to provide wheelchair user (WCU) specific information on energy expenditure (EE), but was found to be inaccurate (i.e., it underestimated) and imprecise (i.e., the underestimation was variable). Insight is therefore needed into where these inaccuracies/imprecisions originate. Accordingly, the aim of this study was to investigate how much of the variation in AW EE is explained by heart rate (HR), in addition to other factors such as body mass and height, sex, age, physical activity level and disability.MethodsForty participants (20 WCU, 20 non-disabled) performed three 4-min treadmill wheelchair propulsion stages at different speed-incline combinations, on three separate days, while wearing an AW series 4 (setting: “outdoor push walking pace”). Linear mixed model analyses investigated how much of the variation in AW EE (kcal·min−1) is explained by the fixed effects AW HR (beats·min−1), body mass and height, sex, age, physical activity level and disability. Participant-ID was included as random-intercept effect. The same mixed model analyses were conducted for criterion EE and HR. Marginal R2 (R2m; fixed effects only) and conditional R2 (R2c; fixed and random effects) values were computed. An R2m close to zero indicates that the fixed effects alone do not explain much variation.ResultsAlthough criterion HR explained a significant amount of variation in criterion EE (R2m: 0.44, R2c: 0.92, p < 0.001), AW HR explained little variation in AW EE (R2m: 0.06, R2c: 0.86, p < 0.001). In contrast, body mass and sex explained a significant amount of variation in AW EE (R2m: 0.74, R2c: 0.79, p < 0.001). No further improvements in fit were achieved by adding body height, age, physical activity level or disability to the AW EE model (R2m: 0.75, R2c: 0.79, p = 0.659).ConclusionOur results remain inconclusive on whether AW heart rate is used as factor to adjust for exercise intensity in the black box AW EE estimation algorithms. In contrast, body mass explained much of the variation in AW EE, indicating that the AW EE estimation algorithm is very reliant on this factor. Future investigations should explore better individualization of EE estimation algorithms.

NN MODELLING OF THE ENERGY CONSUMPTION IN THE WELDING PROCESS

Energy consumption is different for various technological processes used in manufacturing (cutting, plastic deformation, sintering, welding, etc.) and nowadays is becoming a more and more important issue to be considered when planning certain manufacturing processes. Related to this, when releasing a quotation for a givenproduct request coming for the market, it is important to have a tool enabling to evaluate the estimated energy consumption that will be required. This paper proposes a method to predict the consumption of energy in the welding process depending on the same main parameters of the process. The method is based on causal identification and on the NN modelling technique. Method application is sampled in the case of a real database including information concerning pipes welding. The method proves to be fast and delivers results of reasonable precision.

Estimation and Sensitivity Analysis of Electric Buses Energy Consumption through Microscopic Simulations on Public Transport Lines

Estimation and Sensitivity Analysis of Electric Buses Energy Consumption through Microscopic Simulations on Public Transport Lines

Energy Consumption Estimation Method of Battery Electric Buses Based on Real-World Driving Data

Energy Consumption Estimation Method of Battery Electric Buses Based on Real-World Driving Data

Calibrating Car-Following Models Using SUMO-in-the-Loop and Vehicle Trajectories From Roadside Radar

This paper presents an innovative calibration method for car-following (CF) models in the Simulation of Urban MObility (SUMO) using real-world trajectory data from a 1.5 km signalized urban corridor, captured by roadside radars. By applying a sophisticated track-level association and fusion methodology, the study extends trajectory analysis beyond individual radar fields of view. The enhanced data is then utilized to refine the Krauss, IDM, and W99 CF models within SUMO, addressing the literature gap by integrating SUMO into the calibration loop, thereby accommodating the simulator's integration scheme and any model adaptations. The research identifies that default SUMO models tend to exhibit shorter time headways compared to real-world data, with calibration effectively reducing this discrepancy. Moreover, the W99 model, despite its unrealistic acceleration profiles when calibrated without considering acceleration, most accurately captures the higher-end energy consumption distribution. Conversely, the IDM model, with its default parameters, provides the closest approximation to observed acceleration behaviors, highlighting the nuanced performance of CF models in traffic simulation and their implications for energy consumption estimation. Detailed results of optimized parameters for each CF model are provided in appendix in addition to distribution information that may be useful for other modelers to use directly or other datasets to be compared in the future (including expansion of the work to include vehicle classification).

Preliminary field validity of ActiGraph-based energy expenditure estimation in wheelchair users with spinal cord injury.

Cross-sectional validation study. To develop a raw acceleration signal-based random forest (RF) model for predicting total energy expenditure (TEE) in manual wheelchair users (MWUs) and evaluate the preliminary field validity of this new model, along with four existing models published in prior literature, using the Doubly Labeled Water (DLW) method. General community and research institution in Pittsburgh, USA. A total of 78 participants' data from two previous studies were used to develop the new RF model. A seven-day cross-sectional study was conducted to collect participants' free-living physical activity and TEE data, resting metabolic rate, demographics, and anthropometrics. Ten MWUs with spinal cord injury (SCI) completed the study, with seven participants having valid data for evaluating the preliminary field validity of the five models. The RF model achieved a mean absolute error (MAE) of 0.59 ± 0.60 kcal/min and a mean absolute percentage error (MAPE) of 23.6% ± 24.3% on the validation set. For preliminary field validation, the five assessed models yielded MAE from 136 kcal/day to 1141 kcal/day and MAPE from 6.1% to 50.2%. The model developed by Nightingale et al. in 2015 achieved the best performance (MAE: 136 ± 96 kcal/day, MAPE: 6.1% ± 4.7%), while the RF model achieved comparable performance (MAE: 167 ± 99 kcal/day, MAPE: 7.4% ± 5.1%). Two existing models and our newly developed RF model showed good preliminary field validity for assessing TEE in MWUs with SCI and the potential to detect lifestyle change in this population. Future large-scale field validation studies and model iteration are recommended.