Development of a driver model to study the effects of real-world driver behaviour on the fuel consumption

2011 ◽  
Vol 225 (11) ◽  
pp. 1518-1530 ◽  
Author(s):  
A McGordon ◽  
J E W Poxon ◽  
C Cheng ◽  
R P Jones ◽  
P A Jennings
Keyword(s):  
Fuel Economy ◽  
Real World ◽  
High Speed ◽  
Driver Model ◽  
Good Representation ◽  
Vehicle Speed ◽  
Data Logging ◽  
Real World Data ◽  
Driver Behaviour ◽  
The Individual

The real-world fuel economy of vehicles is becoming increasingly important to manufacturers and customers. One of the major influences in this is driver behaviour, but it is difficult to study in a controlled and repeatable manner. An assessment of driver models for studying real-world driver behaviour has been carried out. It has been found that none of the currently existing driver models has sufficient fidelity for studying the effects of real-world driver behaviour on the fuel economy of the individual vehicle. A decision-making process has been proposed which allows a driver model with a range of driving tasks to be developed. This paper reports the initial results of a driver model as applied to the conceptually straightforward scenario of high-speed cruising. Data for the driver model have been obtained through real-world data logging. It has been shown that the simulation driver model can provide a good representation of real-world driving behaviour in terms of the vehicle speed, and this is compared with a number of logged driver speed traces. A comparison of the modelled fuel economy for logged and driver model real-world drivers shows good agreement.

Benchmarking Fuel Economy of Connected and Automated Vehicles in Real World Driving Conditions via Monte Carlo Simulation

2020 ◽  
Author(s):  
Shreshta Rajakumar Deshpande ◽  
Shobhit Gupta ◽  
Dennis Kibalama ◽  
Nicola Pivaro ◽  
Marcello Canova
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Fuel Economy ◽  
Real World ◽  
Human Subjects ◽  
Driver Model ◽  
Traffic Information ◽  
Vehicle Speed ◽  
Instrumented Vehicle ◽  
The Impact

Abstract Connectivity and automation have accelerated the development of algorithms that use route and real-time traffic information for improving energy efficiency. The evaluation of such benefits, however, requires establishing a reliable baseline that is representative of a real-world driving environment. In this context, virtual driver models are generally adopted to predict the vehicle speed based on route data and presence of lead vehicles, in a way that mimics the response of human drivers. This paper proposes an Enhanced Driver Model (EDM) that forecasts the human response when driving in urban conditions, considering the effects of Signal Phasing and Timing (SPaT) by introducing the concept of Line-of-Sight (LoS). The model was validated against data collected on an instrumented vehicle driven on public roads by different human subjects. Using this model, a Monte Carlo simulation is conducted to determine the statistical distribution of fuel consumption and travel time on a given route, varying driver behavior (aggressiveness), traffic conditions and SPaT. This allows one to quantify the impact of uncertainties associated to real-world driving in fuel economy estimates.

scholarly journals An Enhanced Driver Model for Evaluating Fuel Economy on Real-World Routes

2019 ◽  
Vol 52 (5) ◽  
pp. 574-579 ◽  
Author(s):  
Shobhit Gupta ◽  
Shreshta R. Deshpande ◽  
Punit Tulpule ◽  
Marcello Canova ◽  
Giorgio Rizzoni
Keyword(s):  
Fuel Economy ◽  
Real World ◽  
Driver Model

VP07 Collaboratively Modelling The Impact Of Interventions Retrospectively

2017 ◽  
Vol 33 (S1) ◽  
pp. 149-149
Author(s):  
Gordon Bache ◽  
Sukh Tatla ◽  
Deborah Simpson
Keyword(s):  
Real World ◽  
Real Data ◽  
System Level ◽  
Local Data ◽  
Real World Data ◽  
Total Saving ◽  
Retrospective Assessment ◽  
Administration Time ◽  
The Individual ◽  
The Impact

INTRODUCTION:A conventional approach to communicating value is to model the budget impact of a medicine and the associated formulations in which it is available to be prescribed. However, such an approach does not demonstrate the actual realization of the proposed impact. This abstract outlines an approach to presenting retrospective data back to healthcare professionals (HCP) that blends assumptions and real-world data. For illustrative purposes, we present the results of an application of the model for subcutaneously delivered trastuzumab in an anonymized trust in Yorkshire and Humber.METHODS:The authors developed a model that examined one calendar year (from April 2014) of redistributed sales data for both the intravenous and subcutaneous formulations of trastuzumab for every National Health Service (NHS) trust in England. A series of baseline assumptions (1) were used to model the resource impact of different formulations such as chair time, HCP time, pharmacy preparation time, consumables, wastage, and other considerations. Impacts were estimated at the individual attendance level and scaled to the caseload. These baseline assumptions could then be overwritten by the individual trust using local data.RESULTS:The site delivered approximately 985 doses of subcutaneous trastuzumab over a period of 12 months from April 2014, which represented about 76 percent of the total number of doses delivered. Chair time is estimated to have reduced by 22 minutes per attendance, resulting in a total saving of 361hours. HCP administration time is estimated to have reduced by 23 minutes per attendance, resulting in a total saving of 378 hours based on changing 985 IV doses to SC therapy.CONCLUSIONS:Blending real data and assumptions to provide a retrospective assessment of actual benefits realized back to HCPs is a powerful tool for demonstrating real-world value at both an individual trust and system level.

Real-World Robustness for Hybrid Vehicle Optimal Energy Management Strategies Incorporating Drivability Metrics

2014 ◽  
Vol 136 (6) ◽  
Author(s):  
Daniel F. Opila ◽  
Xiaoyong Wang ◽  
Ryan McGee ◽  
R. Brent Gillespie ◽  
Jeffrey A. Cook ◽  
...  
Keyword(s):  
Energy Management ◽  
Fuel Economy ◽  
Real World ◽  
Power Flow ◽  
Design Method ◽  
Management Strategies ◽  
Hybrid Vehicle ◽  
Stochastic Dynamic ◽  
Real World Data ◽  
Industrial Controller

Hybrid vehicle fuel economy and drive quality are coupled through the “energy management” controller that regulates power flow among the various energy sources and sinks. This paper studies energy management controllers designed using shortest path stochastic dynamic programming (SP-SDP), a stochastic optimal control design method which can respect constraints on drivetrain activity while minimizing fuel consumption for an assumed distribution of driver power demand. The performance of SP-SDP controllers is evaluated through simulation on large numbers of real-world drive cycles and compared to a baseline industrial controller provided by a major auto manufacturer. On real-world driving data, the SP-SDP-based controllers yield 10% better fuel economy than the baseline industrial controller, for the same engine and gear activity. The SP-SDP controllers are further evaluated for robustness to the drive cycle statistics used in their design. Simplified drivability metrics introduced in previous work are validated on large real-world data sets.

scholarly journals The everyday acoustic environment and its association with human heart rate: evidence from real-world data logging with hearing aids and wearables

2020 ◽  
Author(s):  
Jeppe H. Christensen ◽  
Gabrielle H. Saunders ◽  
Michael Porsbo ◽  
Niels H. Pontoppidan
Keyword(s):  
Heart Rate ◽  
Hearing Aids ◽  
Real World ◽  
Data Logging ◽  
Environmental Sound ◽  
Real World Data ◽  
Heart Rates ◽  
World Data ◽  
Ambient Sound ◽  
Mean Heart Rate

AbstractWe investigate the longitudinal association between multidimensional characteristics of everyday ambient sound and continuous mean heart rate. We used in-market data from hearing aid users who logged ambient acoustics via smartphone-connected hearing aids and continuous heart rate from their own wearables.We find that ambient acoustic characteristics explain approximately 4% of the fluctuation in mean heart rate throughout the day. Specifically, increases in ambient sound pressure intensity are significantly related to increases in mean heart rates, corroborating prior laboratory and short-term real-world data. In addition, however, and not previously recognized, increases in the ambient sound quality - that is, the difference between sound signal and noise - are associated with decreases in mean heart rates.Our findings document a mixed influence of everyday sounds on cardiovascular stress, and that the relationship is more complex than is seen from examination of sound intensity alone. Thus, our findings highlight the relevance of ambient environmental sound in models of human ecophysiology.

scholarly journals The everyday acoustic environment and its association with human heart rate: evidence from real-world data logging with hearing aids and wearables

2021 ◽  
Vol 8 (2) ◽  
pp. 201345
Author(s):  
Jeppe H. Christensen ◽  
Gabrielle H. Saunders ◽  
Michael Porsbo ◽  
Niels H. Pontoppidan
Keyword(s):  
Heart Rate ◽  
Hearing Aids ◽  
Real World ◽  
Data Logging ◽  
Short Term ◽  
Real World Data ◽  
Acoustic Characteristics ◽  
World Data ◽  
Ambient Sound ◽  
Mean Heart Rate

We investigate the short-term association between multidimensional acoustic characteristics of everyday ambient sound and continuous mean heart rate. We used in-market data from hearing aid users who logged ambient acoustics via smartphone-connected hearing aids and continuous mean heart rate in 5 min intervals from their own wearables. We find that acoustic characteristics explain approximately 4% of the fluctuation in mean heart rate throughout the day. Specifically, increases in ambient sound pressure intensity are significantly related to increases in mean heart rate, corroborating prior laboratory and short-term real-world data. In addition, increases in ambient sound quality—that is, more favourable signal to noise ratios—are associated with decreases in mean heart rate. Our findings document a previously unrecognized mixed influence of everyday sounds on cardiovascular stress, and that the relationship is more complex than is seen from an examination of sound intensity alone. Thus, our findings highlight the relevance of ambient environmental sound in models of human ecophysiology.

A gamma Bayesian exponential model for computing and updating residual life distribution of bearings

2013 ◽  
Vol 227 (11) ◽  
pp. 2620-2633 ◽  
Author(s):  
Bing Li ◽  
Zhen Gao ◽  
ZhongJie Shen ◽  
XueFeng Chen ◽  
ZhengJia He
Keyword(s):  
Real World ◽  
Fatigue Testing ◽  
Residual Life ◽  
Bayesian Updating ◽  
Life Distribution ◽  
Real World Data ◽  
Important Place ◽  
Life Estimation ◽  
Rejection Algorithm ◽  
The Individual

Residual life estimation occupies an important place in modern mechanical design and condition-based maintenance programs. Condition monitoring information can reflect the health status of the individual device, and the effective use of this information can help continuously predict the individual residual life. In this study, an exponential degradation model is developed to describe the degradation characteristics of devices for residual life estimation. This model is based on a gamma-prior Bayesian updating approach and an acceptance–rejection algorithm. With the gamma distribution representing the degradation rate differences among individuals, the real-world data can be described flexibly. By aid of Bayesian updating approach, the model can be updated with both the historical data and real-time monitoring signals. Furthermore, on the basis of the updated model and by means of acceptance–rejection algorithm, the residual life distribution can be computed without redundant computation. Consequently, the residual life can be estimated using the results of the residual life distribution. Finally, the proposed method is applied to real-world vibration-based degradation signals resulting from the accelerated fatigue testing of conical roller bearings. The results show that this method can avoid redundant computation and effectively estimate and update the bearing’s residual life. Therefore, the engineering value and general application of this novel method has been validated.

Battery Sizing as a Function of Powertrain Component Efficiencies for Various Drive Cycles

2011 ◽  
Author(s):  
Lynn R. Gantt ◽  
R. Jesse Alley ◽  
Douglas J. Nelson
Keyword(s):  
Real World ◽  
Hybrid Electric Vehicle ◽  
Energy Use ◽  
Net Energy ◽  
Vehicle Speed ◽  
Drive Cycle ◽  
Component Efficiency ◽  
Technical Specifications ◽  
Hybrid Electric ◽  
The Individual

The market segment of hybrid-electric and full function electric vehicles is growing within the automotive transportation sector. While many papers exist concerning fuel economy or fuel consumption and the limitations of conventional powertrains, little published work is available for vehicles which use grid electricity as an energy source for propulsion. Generally, the emphasis is put solely on the average drive cycle efficiency for the vehicle with very little thought given to propelling and braking powertrain losses for individual components. The modeling section of this paper will take basic energy loss equations for vehicle speed and acceleration, along with component efficiency information to predict the grid energy consumption in AC Wh/km for a given drive cycle. An electric-only range target is established as part of the vehicle technical specifications. This set range along with component characteristics will impact the sizing of the energy storage subsystem. To demonstrate the usefulness in understanding powertrain losses, the energy use is described in propelling, braking, idle, and charging cases. A simulation focusing on battery sizing to meet power and range requirements shows the impacts of friction brakes, regenerative braking fraction, and average motor efficiency. Vehicle characteristics such as, but not limited to, a range extender application, electric-only vehicle range, and acceleration performance are explained as well. The model is correlated to real world vehicle data for a custom-built plug-in hybrid electric vehicle. By using the Virginia Tech Range Extended Crossover (VTREX) and collecting data from testing, the parameters that the model is based on will be correlated with real world test data. The paper presents a propelling, braking, and net energy weighted drive cycle averaged efficiency that can be used to calculate the losses for a given cycle. In understanding the losses at each component, not just the individual efficiency, areas for future vehicle improvement can be identified to reduce petroleum energy use and greenhouse gases.

scholarly journals Clustering Users Based on Hearing Aid Use: An Exploratory Analysis of Real-World Data

2021 ◽  
Vol 3 ◽  
Author(s):  
Alessandro Pasta ◽  
Tiberiu-Ioan Szatmari ◽  
Jeppe Høy Christensen ◽  
Kasper Juul Jensen ◽  
Niels Henrik Pontoppidan ◽  
...  
Keyword(s):  
Hearing Aids ◽  
Real World ◽  
Hearing Aid ◽  
Objective Data ◽  
Data Logging ◽  
Real World Data ◽  
Hearing Aid Use ◽  
User Clustering ◽  
Usage Patterns ◽  
Typical Day

While the assessment of hearing aid use has traditionally relied on subjective self-reported measures, smartphone-connected hearing aids enable objective data logging from a large number of users. Objective data logging allows to overcome the inaccuracy of self-reported measures. Moreover, data logging enables assessing hearing aid use with a greater temporal resolution and longitudinally, making it possible to investigate hourly patterns of use and to account for the day-to-day variability. This study aims to explore patterns of hearing aid use throughout the day and assess whether clusters of users with similar use patterns can be identified. We did so by analyzing objective hearing aid use data logged from 15,905 real-world users over a 4-month period. Firstly, we investigated the daily amount of hearing aid use and its within-user and between-user variability. We found that users, on average, used the hearing aids for 10.01 h/day, exhibiting a substantial between-user (SD = 2.76 h) and within-user (SD = 3.88 h) variability. Secondly, we examined hearing aid use hourly patterns by clustering 453,612 logged days into typical days of hearing aid use. We identified three typical days of hearing aid use: full day (44% of days), afternoon (27%), and sporadic evening (26%) day of hearing aid use. Thirdly, we explored the usage patterns of the hearing aid users by clustering the users based on the proportion of time spent in each of the typical days of hearing aid use. We found three distinct user groups, each characterized by a predominant (i.e., experienced ~60% of the time) typical day of hearing aid use. Notably, the largest user group (49%) of users predominantly had full days of hearing aid use. Finally, we validated the user clustering by training a supervised classification ensemble to predict the cluster to which each user belonged. The high accuracy achieved by the supervised classifier ensemble (~86%) indicated valid user clustering and showed that such a classifier can be successfully used to group new hearing aid users in the future. This study provides a deeper insight into the adoption of hearing care treatments and paves the way for more personalized solutions.

scholarly journals Predicting time to relapse in patients with schizophrenia according to patients’ relapse history: a historical cohort study using real-world data in Sweden

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Kristian Tore Jørgensen ◽  
Martin Bøg ◽  
Madhu Kabra ◽  
Jacob Simonsen ◽  
Michael Adair ◽  
...  
Keyword(s):  
Disease Progression ◽  
Real World ◽  
Real World Data ◽  
Historical Cohort ◽  
World Data ◽  
Drug Register ◽  
Increased Risk ◽  
History Of ◽  
The Individual ◽  
Time To Relapse

Abstract Background For patients with schizophrenia, relapse is a recurring feature of disease progression, often resulting in substantial negative impacts for the individual. Although a patient’s relapse history (specifically the number of prior relapses) has been identified as a strong risk factor for future relapse, this relationship has not yet been meticulously quantified. The objective of this study was to use real-world data from Sweden to quantify the relationship of time to relapse in schizophrenia with a patient’s history of prior relapses. Methods Data from the Swedish National Patient Register and Swedish Prescribed Drug Register were used to study relapse in patients with schizophrenia with a first diagnosis recorded from 2006–2015, using proxy definitions of relapse. The primary proxy defined relapse as a psychiatric hospitalisation of ≥7 days’ duration. Hazard ratios (HRs) were calculated for risk of each subsequent relapse, and Aalen-Johansen estimators were used to estimate time to next relapse. Results 2,994 patients were included, and 5,820 relapse episodes were identified using the primary proxy. As the number of previous relapses increased, there was a general trend of decreasing estimated time between relapses. Within 1.52 years of follow-up, 50% of patients with no history of relapse were estimated to have suffered their first relapse episode. 50% of patients with one prior relapse were estimated to have a second relapse within 1.23 years (HR: 1.84 [1.71–1.99]) and time to next relapse further decreased to 0.89 years (HR: 2.77 [2.53–3.03]) and 0.22 years (HR: 18.65 [15.42–22.56]) for 50% of patients with two or ten prior relapses, respectively. Supplementary analyses using different inclusion/exclusion criteria for the study population and redefined proxies of relapse reflected the pattern observed with the primary analyses of a higher number of prior relapses linked with increased risk of/reduced estimated time to the next relapse. Conclusions The results suggested a trend of accelerating disease progression in schizophrenia, each relapse episode predisposing an individual to the next within a shorter time period. These results emphasise the importance of providing early, effective, and tolerable treatments that better meet a patient’s individual needs.

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