Least-Squares Estimates of Automotive-Drag Coefficients from Coast-Down Tests

Griffith, James M.; Venhuizen, J.R.

doi:10.23919/acc.1986.4788934

Cited by 3 publications

(2 citation statements)

References 5 publications

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“…Notable previous work has attempted to fit parameters from linear dynamics and powertrain models using least-squares [1], [2] and more exotic and complicated methods [3], [4]. This work extends previous work presented by the authors pushing the state-of-theart of identifying electric vehicle mass in realtime [5].…”

Section: Introductionmentioning

confidence: 54%

Real-time electric vehicle mass identification

Wilhelm

Rodgers

Bornatico

2013

2013 World Electric Vehicle Symposium and Exhibition (EVS27)

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A technique capable of identifying electric vehicle (EV) mass in real-time has been a topic of research for several years due to the advantages it presents, such as the ability to dramatically improve range estimates, perform more effective torque vectoring for ABS/ESC, track delivery vehicle weight, etc.. Some crucial issues in mass identification impede an easy implementation of such an algorithm, however, and this work introduces a simple method to calculate EV mass on-the-fly using standard data available on most CAN buses and therefore without the need of additional sensors. The results presented here are achieved using an eight step technique suitable for accurate mass estimations during wide-open-throttle acceleration events.The algorithm's instantaneous error is less than 10%, and converges to better than 3% absolute accuracy performance with subsequent measurements. A preliminary analysis of trips lacking hard acceleration presented in this paper show an inability to differentiate between loaded and unloaded conditions.

show abstract

Section: Introductionmentioning

confidence: 54%

Real-time electric vehicle mass identification

Wilhelm

Rodgers

Bornatico

2013

2013 World Electric Vehicle Symposium and Exhibition (EVS27)

View full text Add to dashboard Cite

show abstract

“…Much attention has been given to the problem of identifying vehicle loss parameters, particularly vehicle mass. Notable previous work has attempted to fit parameters from linear dynamics and powertrain models using least-squares [1], [2] and more exotic and complicated methods [3], [4]. This work extends previous work presented by the authors pushing the state-of-theart of identifying electric vehicle mass in realtime [5].…”

Section: Introductionmentioning

confidence: 54%

Real-time electric vehicle mass identification

Wilhelm

Rodgers

Bornatico

2013

WEVJ

View full text Add to dashboard Cite

A technique capable of identifying electric vehicle (EV) mass in real-time has been a topic of research for several years due to the advantages it presents, such as the ability to dramatically improve range estimates, perform more effective torque vectoring for ABS/ESC, track delivery vehicle weight, etc.. Some crucial issues in mass identification impede an easy implementation of such an algorithm, however, and this work introduces a simple method to calculate EV mass on-the-fly using standard data available on most CAN buses and therefore without the need of additional sensors. The results presented here are achieved using an eight step technique suitable for accurate mass estimations during wide-open-throttle acceleration events. The algorithm's instantaneous error is less than 10%, and converges to better than 3% absolute accuracy performance with subsequent measurements. A preliminary analysis of trips lacking hard acceleration presented in this paper show an inability to differentiate between loaded and unloaded conditions.

show abstract