ALL_17-Tfs-0598.pdf (811.99 kB)
Download file

Fuzzy optimal energy management for fuel cell and supercapacitor systems using neural network based driving pattern recognition

Download (811.99 kB)
journal contribution
posted on 09.08.2018, 15:00 by Ridong Zhang, Jili Tao, Huiyu Zhou
A novel adaptive energy management strategy is proposed for real time power split between fuel cells and supercapacitors in a hybrid electric vehicle in view of the fact that driving patterns greatly affect fuel economy. The driving pattern recognition (DPR) is achieved based on the features extracted from the historical velocity window with a multi-layer perceptron neural network. After the DPR has been obtained, an adaptive fuzzy energy management controller is utilized for power split according to the required power for vehicle running. In order to prolong the fuel cell lifetime whilst decreasing the hydrogen consumption, a genetic algorithm is applied to optimize critical factors such as adaptive gains and fuzzy membership function parameters for several standard driving cycles. In the proposed method, the future driving cycles are not required and the current driving pattern can be successfully recognized, demonstrating that less current fluctuations and fuel consumption can be achieved under various driving conditions. Compared with conventional energy management systems, the proposed framework can ensure the state of charge of supercapacitors within the desired limit.


This work was supported in part by the National Natural Science Foundation of China under Grant (61603337). H. Zhou was supported by UK EPSRC under Grant EP/N011074/1 and Royal Society-Newton Advanced Fellowship under Grant NA160342.



IEEE Transactions on Fuzzy Systems, 2018

Author affiliation

/Organisation/COLLEGE OF SCIENCE AND ENGINEERING/Department of Informatics


AM (Accepted Manuscript)

Published in

IEEE Transactions on Fuzzy Systems


Institute of Electrical and Electronics Engineers (IEEE)



Acceptance date


Copyright date


Available date


Publisher version