Multi-mode energy management strategy for fuel cell electric vehicles based on driving pattern identification using learning vector quantization neural network algorithm

JOURNAL OF POWER SOURCES

卷:389

Article

Song, Ke^1,2  Li, Feiqiang³  Hu, Xiao^1,2  He, Lin⁴  Niu, Wenxu^1,2  Lu, Sihao^1,2  Zhang, Tong^1,2 

[1] Tongji Univ, Sch Automot Studies, Shanghai 201804, Peoples R China

[2] Tongji Univ, Natl Fuel Cell Vehicle & Powertrain Syst Engn Res, Shanghai 201804, Peoples R China

[3] Zhengzhou Yutong Bus Co Ltd, Yutong Ind Pk,Yutong Rd, Zhengzhou 450061, Henan, Peoples R China

[4] HeFei Univ Technol, Automot Res Inst, Hefei 230009, Anhui, Peoples R China

关键词: Fuel cell electric vehicle;    Multi-mode energy management strategy;    Driving patterns identification;    LVQ neural network;   

DOI  :  10.1016/j.jpowsour.2018.04.024

来源: Elsevier

【 摘 要 】

The development of fuel cell electric vehicles can to a certain extent alleviate worldwide energy and environmental issues. While a single energy management strategy cannot meet the complex road conditions of an actual vehicle, this article proposes a multi-mode energy management strategy for electric vehicles with a fuel cell range extender based on driving condition recognition technology, which contains a patterns recognizer and a multi-mode energy management controller. This paper introduces a learning vector quantization (LVQ) neural network to design the driving patterns recognizer according to a vehicles driving information. This multi-mode strategy can automatically switch to the genetic algorithm optimized thermostat strategy under specific driving conditions in the light of the differences in condition recognition results. Simulation experiments were carried out based on the model's validity verification using a dynamometer test bench. Simulation results show that the proposed strategy can obtain better economic performance than the single -mode thermostat strategy under dynamic driving conditions.

【 授权许可】

Free