Measuring battery discharge characteristics for accurate UAV endurance estimation

2020 ◽  
Vol 124 (1277) ◽  
pp. 1099-1113
Author(s):  
L. Mariga ◽  
I. Silva Tiburcio ◽  
C.A. Martins ◽  
A.N. Almeida Prado ◽  
C. Nascimento
Keyword(s):  
Low Cost ◽  
Estimation Error ◽  
Flight Time ◽  
Time Estimates ◽  
Discharge Rates ◽  
Nonlinear Optimisation ◽  
Time Calculation ◽  
Discharge Profile ◽  
Battery Discharge ◽  
Battery Discharge Characteristics

ABSTRACTThe increasing use of unmanned aerial vehicles in areas such as rescue, mapping, and transportation have made it necessary to study more accurate techniques for calculating flight time estimates. Such calculations require knowing the battery discharge profile. Simplified flight time calculation methods provide data with uncertainties as they are based solely on manufacturer datasheet information. This study presents a setup to measure the battery discharge curve using a LabVIEW interface with a low-cost acquisition system. The acquired data passes through a nonlinear optimisation algorithm to find the battery coefficients, which enables the more precise estimation of its range and endurance. The great advantage of this model is that it makes it possible to predict how the battery will discharge at different rates using just one experimental curve. The methodology was applied to three different batteries and the model was validated with different discharge rates in a controlled environment, which resulted in endurance lower than 3.0% for most conditions and voltage estimation error lower than 3.0% in operational voltage. The work also presented a methodology for estimating cruise time based on the current used during each flight stage.

scholarly journals A Novel Longitudinal Speed Estimator for Four-Wheel Slip in Snowy Conditions

2021 ◽  
Vol 11 (6) ◽  
pp. 2809
Author(s):  
Dongmin Zhang ◽  
Qiang Song ◽  
Guanfeng Wang ◽  
Chonghao Liu
Keyword(s):  
Control Strategies ◽  
Low Cost ◽  
Estimation Error ◽  
Slip Rate ◽  
Vehicle Speed ◽  
Acceleration Sensor ◽  
Wheel Slip ◽  
Longitudinal Acceleration ◽  
Tire Rotation ◽  
Speed Estimator

This article proposes a novel longitudinal vehicle speed estimator for snowy roads in extreme conditions (four-wheel slip) based on low-cost wheel speed encoders and a longitudinal acceleration sensor. The tire rotation factor, η, is introduced to reduce the deviation between the rotation tire radius and the manufacturer’s marked tire radius. The Local Vehicle Speed Estimator is defined to eliminate longitudinal vehicle speed estimation error. It improves the tire slip accuracy of four-wheel slip, even with a high slip rate. The final vehicle speed is estimated using two fuzzy control strategies that use vehicle speed estimates from speed encoders and a longitudinal acceleration sensor. Experimental and simulation results confirm the algorithm’s validity for estimating longitudinal vehicle speed for four-wheel slip in snowy road conditions.

scholarly journals Practical Endurance Estimation for Minimizing Energy Consumption of Multirotor Unmanned Aerial Vehicles

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2221 ◽  
Author(s):  
Myeong-hwan Hwang ◽  
Hyun-Rok Cha ◽  
Sung Yong Jung
Keyword(s):  
Drag Coefficient ◽  
Unmanned Aerial Vehicles ◽  
Estimation Method ◽  
Flight Test ◽  
Flight Time ◽  
Average Error ◽  
Practical Applications ◽  
Aerial Vehicles ◽  
Vehicle Shape ◽  
Battery Discharge

The practically applicable endurance estimation method for multirotor unmanned aerial vehicles (UAVs) using a battery as a power source is proposed. The method considers both hovering and steady-level flights. The endurance, thrust, efficiency, and battery discharge are determined with generally available data from the manufacturer. The effects of the drag coefficient related to vehicle shape and payload weight are examined at various forward flight speeds. As the drag coefficient increases, the optimum speed at the minimum required power and the maximum endurance are reduced. However, the payload weight causes an opposite effect, and the optimal flying speed increases with an increase in the payload weight. For more practical applications for common users, the value of S × Cd is determined from a preliminary flight test. Given this value, the endurance is numerically estimated and validated with the measured flight time. The proposed method can successfully estimate the flight time with an average error of 2.3%. This method would be useful for designers who plan various missions and select UAVs.

scholarly journals A Deep Learning Approach for Table Tennis Forehand Stroke Evaluation System Using an IMU Sensor

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Sahar S. Tabrizi ◽  
Saeid Pashazadeh ◽  
Vajiheh Javani
Keyword(s):  
Evaluation System ◽  
Low Cost ◽  
Estimation Error ◽  
Weighted Average ◽  
Principal Component ◽  
Training System ◽  
Table Tennis ◽  
Sports Activity ◽  
Nonlinear Regression Models ◽  
Shadow Play

Psychological and behavioral evidence suggests that home sports activity reduces negative moods and anxiety during lockdown days of COVID-19. Low-cost, nonintrusive, and privacy-preserving smart virtual-coach Table Tennis training assistance could help to stay active and healthy at home. In this paper, a study was performed to develop a Forehand stroke’ performance evaluation system as the second principal component of the virtual-coach Table Tennis shadow-play training system. This study was conducted to show the effectiveness of the proposed LSTM model, compared with 2DCNN and RBF-SVR time-series analysis and machine learning methods, in evaluating the Table Tennis Forehand shadow-play sensory data provided by the authors. The data was generated, comprising 16 players’ Forehand strokes racket’s movement and orientation measurements; besides, the strokes’ evaluation scores were assigned by the three coaches. The authors investigated the ML models’ behaviors changed by the hyperparameters values. The experimental results of the weighted average of RMSE revealed that the modified LSTM models achieved 33.79% and 4.24% estimation error lower than 2DCNN and RBF-SVR, respectively. However, the R ¯ 2 results show that all nonlinear regression models are fit enough on the observed data. The modified LSTM is the most powerful regression method among all the three Forehand types in the current study.

scholarly journals Assessment of Breathing Parameters Using an Inertial Measurement Unit (IMU)-Based System

Sensors ◽  
2018 ◽  
Vol 19 (1) ◽  
pp. 88 ◽  
Author(s):  
Ambra Cesareo ◽  
Ylenia Previtali ◽  
Emilia Biffi ◽  
Andrea Aliverti
Keyword(s):  
Inertial Sensors ◽  
Low Cost ◽  
Estimation Error ◽  
Processing Algorithm ◽  
Measurement Unit ◽  
Breathing Patterns ◽  
Independent Manner ◽  
Inertial Measurement ◽  
Optoelectronic Plethysmography ◽  
The One

Breathing frequency (fB) is an important vital sign that—if appropriately monitored—may help to predict clinical adverse events. Inertial sensors open the door to the development of low-cost, wearable, and easy-to-use breathing-monitoring systems. The present paper proposes a new posture-independent processing algorithm for breath-by-breath extraction of breathing temporal parameters from chest-wall inclination change signals measured using inertial measurement units. An important step of the processing algorithm is dimension reduction (DR) that allows the extraction of a single respiratory signal starting from 4-component quaternion data. Three different DR methods are proposed and compared in terms of accuracy of breathing temporal parameter estimation, in a group of healthy subjects, considering different breathing patterns and different postures; optoelectronic plethysmography was used as reference system. In this study, we found that the method based on PCA-fusion of the four quaternion components provided the best fB estimation performance in terms of mean absolute errors (<2 breaths/min), correlation (r > 0.963) and Bland–Altman Analysis, outperforming the other two methods, based on the selection of a single quaternion component, identified on the basis of spectral analysis; particularly, in supine position, results provided by PCA-based method were even better than those obtained with the ideal quaternion component, determined a posteriori as the one providing the minimum estimation error. The proposed algorithm and system were able to successfully reconstruct the respiration-induced movement, and to accurately determine the respiratory rate in an automatic, position-independent manner.

Ion transport and battery discharge characteristics of polymer electrolyte based on PEO complexed with NaFeF4 salt

Ionics ◽  
2006 ◽  
Vol 12 (3) ◽  
pp. 219-226 ◽  
Author(s):  
V. M. Mohan ◽  
V. Raja ◽  
A. K. Sharma ◽  
V. V. R. Narasimha Rao
Keyword(s):  
Ion Transport ◽  
Polymer Electrolyte ◽  
Discharge Characteristics ◽  
Battery Discharge ◽  
Battery Discharge Characteristics

Convergent Validity of 3 Low Cost Motion Sensors With the ActiGraph Accelerometer

2010 ◽  
Vol 7 (5) ◽  
pp. 662-670 ◽  
Author(s):  
James J. McClain ◽  
Teresa L. Hart ◽  
Renee S. Getz ◽  
Catrine Tudor-Locke
Keyword(s):  
Physical Activity ◽  
Convergent Validity ◽  
Lower Cost ◽  
Low Cost ◽  
Assessment Instruments ◽  
Motion Sensors ◽  
Activity Time ◽  
Free Living ◽  
Time Estimates ◽  
Actigraph Accelerometer

Background:This study evaluated the utility of several lower cost physical activity (PA) assessment instruments for detecting PA volume (steps) and intensity (time in MVPA or activity time) using convergent methods of assessment.Methods:Participants included 26 adults (9 male) age 27.3 ± 7.1 years with a BMI of 23.8 ± 2.8 kg/m2. Instruments evaluated included the Omron HJ-151 (OM), New Lifestyles NL-1000 (NL), Walk4Life W4L Pro (W4L), and ActiGraph GT1M (AG). Participants wore all instruments during a laboratory phase, consisting of 10 single minute treadmill walking bouts ranging in speed from 40 to 112 m/min, and immediate following the laboratory phase and during the remainder of their free-living day (11.3 ± 1.5 hours). Previously validated AG MVPA cutpoints were used for comparison with OM, NL, and W4L MVPA or activity time outputs during the laboratory and free-living phase.Results:OM and NL produced similar MVPA estimates during free-living to commonly used AG walking cutpoints, and W4L activity time estimates were similar to one AG lifestyle cutpoint evaluated.Conclusion:Current findings indicate that the OM, NL, and W4L, ranging in price from $15 to $49, can provide reasonable estimates of free-living MVPA or activity time in comparison with a range of AG walking and lifestyle cutpoints.

scholarly journals A computational approach for generating smooth estimates of motor unit discharge rates and visualizing population discharge characteristics

2021 ◽  
Author(s):  
James A Beauchamp ◽  
Obaid U Khurram ◽  
Julius PA Dewald ◽  
CJ Heckman ◽  
Gregory EP Pearcey
Keyword(s):  
Motor Unit ◽  
Neural Control ◽  
Polynomial Regression ◽  
Discharge Rate ◽  
Medial Gastrocnemius ◽  
Support Vector ◽  
Discharge Characteristics ◽  
Discharge Rates ◽  
Entire Duration ◽  
Discharge Profile

Objective: Successive improvements in high density surface electromyography and decomposition techniques have facilitated an increasing yield in decomposed motor unit (MU) spike times. Though these advancements enhance the generalizability of findings and promote the application of MU discharge characteristics to inform the neural control of motor output, limitations remain. Specifically, 1) common approaches for generating smooth estimates of MU discharge rates introduce artifacts in quantification, which may bias findings, and 2) discharge characteristics of large MU populations are often difficult to visualize. Approach: In the present study, we propose support vector regression (SVR) as an improved approach for generating continuous estimates of discharge rate and compare the fit characteristics of SVR to traditionally used methods, including Hanning window filtering and polynomial regression. Furthermore, we introduce ensembles as a method to visualize the discharge characteristics of large MU populations. We define ensembles as the average discharge profile of a subpopulation of MUs, composed of a time normalized ensemble average of all units within this subpopulation. Analysis was conducted with MUs decomposed from the tibialis anterior (N = 2128), medial gastrocnemius (N = 2673), and soleus (N = 1190) during isometric plantarflexion and dorsiflexion contractions. Main Result: Compared to traditional approaches, we found SVR to alleviate commonly observed inaccuracies and produce significantly less absolute fit error in the initial phase of MU discharge and throughout the entire duration of discharge. Additionally, we found the visualization of MU populations as ensembles to intuitively represent population discharge characteristics with appropriate accuracy for visualization. Significance: The results and methods outlined here provide an improved method for generating smooth estimates of MU discharge rate with SVR and present a unique approach to visualizing MU populations with ensembles. In combination, the use of SVR and generation of ensembles represent an efficient method for rendering population discharge characteristics.

scholarly journals A Practical Load Disaggregation Approach for Monitoring Industrial Users Demand with Limited Data Availability

Energies ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 4880
Author(s):  
Sara Tavakoli ◽  
Kaveh Khalilpour
Keyword(s):  
Large Scale ◽  
Service Providers ◽  
Low Cost ◽  
Estimation Error ◽  
Data Availability ◽  
Smart Meters ◽  
Industrial Sectors ◽  
Load Monitoring ◽  
Load Disaggregation ◽  
Electrical Loads

The emergence of smart sensors has had a significant impact on the utility industry. In particular, it has made the planning and implementation of demand-side management (DSM) programmes easier. Nevertheless, for various reasons, some users may not implement smart meters for load monitoring. This paper addresses such cases, particularly large-scale industrial users, which, despite heavy electrical loads coming from many different processes, implement only simple energy measuring equipment for billing purposes. This necessitates the utilisation of novel methodologies for load disaggregation, often referred to as nonintrusive load monitoring (NILM). The availability of such tools can create multifold benefits for industrial park management, utility service providers, regulators, and policymakers. Here, we introduce an optimisation algorithm for nonintrusive load disaggregation that is low-cost, speedy, and acceptably accurate. As a case study, we used real network data of three industrial sectors: food processing, stonecutting, and glassmaking. For all cases, the optimisation framework developed a desegregated profile and estimated the load with an error of less than 5%. For non-workdays, given the higher uncertainty for the continuity of different processes, the estimation error was higher but still in an acceptable range of around 3.63–15.09% with an average of 8.10%.

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