Real-time Predictive Energy Management for Fuel Cell Electric Vehicles
Real-time Predictive Energy Management for Fuel Cell Electric Vehicles
- New search for: Zhou, Yang
- New search for: Ravey, Alexandre
- New search for: Pera, Marie-Cecile
- New search for: Zhou, Yang
- New search for: Ravey, Alexandre
- New search for: Pera, Marie-Cecile
This paper presents the design and application of a model predictive control-based energy management for a fuel cell hybrid electric vehicle. To estimate the upcoming vehicle speed within each receding horizon, a speed-forecast method is proposed using the layer recurrent neural network (LRNN). Then, the power-allocating decisions are derived via minimizing the multicriteria cost function by considering the predicted speed sequence. It has been verified that the LRNN predictor has a higher accuracy versus the benchmark methods. Software-in-the-Loop testing results have indicated that the proposed control strategy can improve fuel economy and fuel cell durability versus a rule-based benchmark, with an acceptable online computational burden.
Document information
Real-time Predictive Energy Management for Fuel Cell Electric Vehicles
2021-06-21
6628521 byte
Conference paper
Electronic Resource
English
Similar titles
Real-time simulation of fuel cell hybrid electric vehicles
| Tema Archive | 2004
Real-time model predictive control of connected electric vehicles
| Taylor & Francis Verlag | 2019
Predictive Real-Time Energy Management
| Springer Verlag | 2017
Optimal energy management of hybrid fuel cell electric vehicles
| Automotive engineering | 2015
Optimal Energy Management of Hybrid Fuel Cell Electric Vehicles
| SAE Technical Papers | 2015