Control of a parallel hybrid powertrain: optimal control

By Sebastien Delprat, J. Lauber, Thierry-Marie Guerra, J. Rimaux. IEEE Transactions on Vehicular Technology, 2004

486 citations recorded by the source corpus.

Subfields: Automotive Engineering, Biomedical Engineering, Fluid Flow and Transfer Processes

View Paper: https://www.semanticscholar.org/paper/a6b49ebc60cde66f1f5c0ecb1f375ed453a6e4db

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. Farzad Rajaei Salmasi
2. Ali Emadi
3. Sheldon S. Williamson
4. Alain Bouscayrol
5. Huei Peng
6. Ching Chuen Chan
7. Sukwon Cha
8. Namwook Kim

In Biomedical Engineering

1. Paul van den Bosch
2. Ali Emadi
3. Farzad Rajaei Salmasi
4. Alain Bouscayrol
5. Sanjaka G. Wirasingha
6. Ching Chuen Chan
7. W. M. Heemels

In Fluid Flow and Transfer Processes

1. Ali Emadi
2. Farzad Rajaei Salmasi
3. Alain Bouscayrol
4. Sanjaka G. Wirasingha
5. Ching Chuen Chan

Citations in the corpus

• Topological overview of hybrid electric and fuel cell vehicular power system architectures and configurations (2005, methodology): “With this technique, it is possible to choose the most efficient operating points for the ICE at given torque demands freely and continuously [10]–[12].”
• Energy management strategies for vehicular electric power systems (2005, background): “To find the global optimal solution, control techniques such as linear programming [10], optimal control [11], and especially dynamic programming (DP) [12]–[15] have been studied.”
• Optimal Control of Hybrid Electric Vehicles Based on Pontryagin's Minimum Principle (2011, methodology): “Another approach based on optimal control theory which, basically, realizes the minimization of the Hamiltonian has been applied to the optimal control problem for HEVs in [6] and [7].”
• Control Strategies for Hybrid Electric Vehicles: Evolution, Classification, Comparison, and Future Trends (2007, methodology): “theory [28], dynamic and stochastic programming [29], [30], and genetic algorithms [31] are utilized to solve the aforementioned problem.”
• Electric, Hybrid, and Fuel-Cell Vehicles: Architectures and Modeling (2010, methodology): “Depending on their interactions, vehicle subsystems can be modeled by both their steady-state relationships and their dynamic relationships [44].”
• Classification and Review of Control Strategies for Plug-In Hybrid Electric Vehicles (2011, methodology): “The control theory approach takes advantage of this relationship to define a cost function using only two decision variables [12].”