Optimal energy management in series hybrid electric vehicles

By A. Brahma, Y. Guezennec, Giorgio Rizzoni. Proceedings of the 2000 American Control Conference. ACC (IEEE Cat. No.00CH36334), 2000

361 citations recorded by the source corpus.

Subfields: Automotive Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/bdd04812e92b94b19bee440767de3901754cd85d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Automotive Engineering

1. Scott Jason Moura
2. Farzad Rajaei Salmasi
3. Hosam K. Fathy
4. Jeffrey L. Stein
5. Duncan S. Callaway
6. Ardalan Vahidi
7. Ilya V. Kolmanovsky
8. Huei Peng

In Electrical and Electronic Engineering

1. Scott Jason Moura
2. Hosam K. Fathy
3. Jeffrey L. Stein
4. Duncan S. Callaway
5. Huei Peng
6. Anna G. Stefanopoulou
7. Alberto Bemporad
8. Ilya V. Kolmanovsky

Citations in the corpus

• Current Management in a Hybrid Fuel Cell Power System: A Model-Predictive Control Approach (2006, background): “In most scenarios, dynamic programming is used offline for a given load cycle and, therefore, is cycle specific [11], [ 12 ].”
• Stochastic MPC With Learning for Driver-Predictive Vehicle Control and its Application to HEV Energy Management (2014, background): “Optimal solutions are based on dynamic programming (DP) [11], [33], [34] and assume full knowledge of the future power request, which ultimately defines the vehicle speed.”
• Optimal control of parallel hybrid electric vehicles (2004, background): “future driving conditions or of a scheduled driving cycle [ 2 ]–[5].”
• Control of a parallel hybrid powertrain: optimal control (2004, methodology): “• Dynamic programming has been applied to energy-management optimization for hybrid vehicles [8], [9].”
• A Stochastic Optimal Control Approach for Power Management in Plug-In Hybrid Electric Vehicles (2011, methodology): “Within the specific context of PHEVs, power management research has generally focused on fuel economy improvement, subject to constraints on battery state of charge, using either the rule-based [16] [17] or DDP approach [9] [10].”
• Intelligent energy management agent for a parallel hybrid vehicle-part I: system architecture and design of the driving situation identification process (2005, background): “IEMA FOR HEVS The proposed IEMA for HEVs is an extension of the previous research by the authors on fuzzy torque distribution control (FTDC) for a parallel hybrid vehicle [8].”