Hegde et al., 2020 - Google Patents
Velocity and energy trajectory prediction of electrified powertrain for look ahead controlHegde et al., 2020
- Document ID
- 5795268731353645523
- Author
- Hegde B
- Ahmed Q
- Rizzoni G
- Publication year
- Publication venue
- Applied Energy
External Links
Snippet
Energy management strategies for hybrid and electric vehicles require accurate prediction of future velocity and energy consumption trajectories. This paper presents a parametric, model-based methodology to utilize look-ahead data provided by sensors and connectivity …
- 238000005265 energy consumption 0 abstract description 39
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/50—Computer-aided design
- G06F17/5009—Computer-aided design using simulation
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Hegde et al. | Velocity and energy trajectory prediction of electrified powertrain for look ahead control | |
Li et al. | Deep reinforcement learning enabled decision-making for autonomous driving at intersections | |
Alcala et al. | Gain‐scheduling LPV control for autonomous vehicles including friction force estimation and compensation mechanism | |
EP3859459A1 (en) | A computer implemented machine learning system and a method for operating the machine learning system for determining a time series | |
Shen et al. | Energy-efficient connected cruise control with lean penetration of connected vehicles | |
Sadid et al. | Modelling and simulation of (connected) autonomous vehicles longitudinal driving behavior: A state‐of‐the‐art | |
Lim et al. | A distance-based two-stage ecological driving system using an estimation of distribution algorithm and model predictive control | |
Németh et al. | Optimised speed profile design of a vehicle platoon considering road inclinations | |
Gupta et al. | Eco-driving of connected and autonomous vehicles with sequence-to-sequence prediction of target vehicle velocity | |
Hayat et al. | A holistic approach to the energy-efficient smoothing of traffic via autonomous vehicles | |
Zhu et al. | A GPU implementation of a look-ahead optimal controller for eco-driving based on dynamic programming | |
Wegener et al. | Longitudinal vehicle motion prediction in urban settings with traffic light interaction | |
Hyeon et al. | Influence of speed forecasting on the performance of ecological adaptive cruise control | |
Klingbeil et al. | Centralized model‐predictive cooperative and adaptive cruise control of automated vehicle platoons in urban traffic environments | |
Hyeon et al. | Data-driven forgetting and discount factors for vehicle speed forecasting in ecological adaptive cruise control | |
Hu et al. | Research on vehicle speed prediction model based on traffic flow information fusion | |
Hegde et al. | On quantifying the utility of look-ahead data for energy management | |
Shao et al. | Optimal eco-approach control with traffic prediction for connected vehicles | |
Thorat et al. | Modelling and Simulation-based Design Approach to Assisted and Automated Driving Systems Development | |
Musa et al. | Mpc-based cooperative longitudinal control for vehicle strings in a realistic driving environment | |
Chen et al. | Optimal control methods in intelligent vehicles | |
Villani et al. | Optimal Eco-Driving with Infrastructure-to-Vehicle Communication for Speed Adaptation Based on Real-Time Dynamic Macroscopic Traffic Conditions | |
Wu et al. | An optimal longitudinal control strategy of platoons using improved particle swarm optimization | |
Hegde et al. | Leveraging real-world driving data for design and impact evaluation of energy efficient control strategies | |
Hyeon | Speed Forecasting Strategies for the Energy-Optimal Car-Following of Connected and Automated Vehicles |