Title :
Toyota Prius HEV neurocontrol
Author :
Prokhorov, Danil
Author_Institution :
Toyota Motor Eng. & Manuf. North America, Ann Arbor
Abstract :
The author propose a neural network controller for improved fuel efficiency of the Toyota Prius hybrid electric vehicle. The approach is based on recurrent neural networks and an effective combination of off-line and on-line training methods including the extended Kalman filter and the simultaneous perturbation stochastic approximation (SPSA). The proposed approach is quite general and applicable to other control systems.
Keywords :
control systems; fuel optimal control; hybrid electric vehicles; neurocontrollers; recurrent neural nets; Toyota Prius neurocontrol; control system; extended Kalman filter; fuel efficiency; hybrid electric vehicle; neural network controller; recurrent neural network; simultaneous perturbation stochastic approximation; Batteries; Computational intelligence; Energy management; Hybrid electric vehicles; Internal combustion engines; Mechanical power transmission; Neural networks; Neurocontrollers; Propulsion; Recurrent neural networks;
Conference_Titel :
Neural Networks, 2007. IJCNN 2007. International Joint Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
978-1-4244-1379-9
Electronic_ISBN :
1098-7576
DOI :
10.1109/IJCNN.2007.4371287
