Name
Abstract | ||
|---|---|---|
Energy consumption of a vehicle is a factor of several environmental and driving conditions, such as air flow density, road grade, and vehicle weight. Accurate estimation of these factors influences the control performance, diagnostics, and the vehicle's overall energy consumption. Individual vehicle dynamics, as part of a large convoy governing principles, will expand to include the states that are shared between vehicles. The controller performance relies on the estimated parameters to minimize energy consumption. The estimation of environmental and driving conditions for individual vehicles as part of a convoy is a challenging task. This paper introduces an adaptive model-based energy factor estimation in large-scale convoys. These factors are influenced by vehicle parameters and driving condition uncertainties. These uncertainties, if not estimated correctly, shift the predicted energy consumption and result in low control performance. Mathematical formulation of the proposed estimator in the context of large-scale system is studied through several case study scenarios, and their effectiveness is demonstrated. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/TITS.2015.2440426 | Intelligent Transportation Systems, IEEE Transactions |
Keywords | Field | DocType |
energy factor estimation,intelligent convoy of vehicles,multi expanded-models,vehicle parameter estimation,parameter estimation,vehicle dynamics,kalman filters | Automotive engineering,Control theory,Simulation,Kalman filter,Vehicle dynamics,Airflow,Estimation theory,Engineering,Energy consumption,Energy factor,Estimator | Journal |
Volume | Issue | ISSN |
PP | 99 | 1524-9050 |
Citations | PageRank | References |
0 | 0.34 | 15 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pardis Khayyer | 1 | 0 | 0.34 |
| Ümit Özgüner | 2 | 1014 | 166.59 |