scholarly journals Fuzzy Optimal Energy Management for Fuel Cell and Supercapacitor Systems Using Neural Network Based Driving Pattern Recognition

2019 ◽  
Vol 27 (1) ◽  
pp. 45-57 ◽  
Author(s):  
Ridong Zhang ◽  
Jili Tao ◽  
Huiyu Zhou
Author(s):  
Pengfei Zou ◽  
Fazhan Tao ◽  
Zhumu Fu ◽  
Pengju Si ◽  
Chao Ma

In this paper, the hybrid electric vehicle is equipped with fuel cell/battery/supercapacitor as the research object, the optimal energy management strategy (EMS) is proposed by combining wavelet transform (WT) method and equivalent consumption minimization strategy (ECMS) for reducing hydrogen consumption and prolonging the lifespan of power sources. Firstly, the WT method is employed to separate power demand of vehicles into high-frequency part supplied by supercapacitor and low-frequency part allocated to fuel cell and battery, which can effectively reduce the fluctuation of fuel cell and battery to prolong their lifespan. Then, considering the low-frequency power, the optimal SOC of battery is used to design the equivalent factor of the ECMS method to improve the fuel economy. The proposed hierarchical EMS can realize a trade-off between the lifespan of power sources and fuel economy of vehicles. Finally, the effectiveness of the proposed EMS is verified by ADVISOR, and comparison results are given compared with the traditional ECMS method and ECMS combining the filter.


Author(s):  
Han Zhang ◽  
Jibin Yang ◽  
Jiye Zhang ◽  
Pengyun Song ◽  
Ming Li

Achieving an optimal operating cost is a challenge for the development of hybrid tramways. In the past few years, in addition to fuel costs, the lifespan of the power source is being increasingly considered as an important factor that influences the operating cost of a tramway. In this work, an optimal energy management strategy based on a multi-mode strategy and optimisation algorithm is described for a high-power fuel cell hybrid tramway. The objective of optimisation is to decrease the operating costs under the conditions of guaranteeing tramway performance. Besides the fuel costs, the replacement cost and initial investment of all power units are also considered in the cost model, which is expressed in economic terms. Using two optimisation algorithms, a multi-population genetic algorithm and an artificial fish swarm algorithm, the hybrid system's power targets for the energy management strategy were acquired using the multi-objective optimisation. The selected case study includes a low-floor light rail vehicle, and experimental validations were performed using a hardware-in-the-loop workbench. The results testify that an optimised energy management strategy can fulfil the operational requirements, reduce the daily operation costs and improve the efficiency of the fuel cell system for a hybrid tramway.


Author(s):  
Kai Wu ◽  
Ming Kuang ◽  
Milos Milacic ◽  
Xiaowu Zhang ◽  
Jing Sun

Dynamic characteristics of a proton exchange membrane fuel cell (PEMFC) system can impact fuel economy and load following performance of a fuel cell vehicle, especially if those dynamics are ignored in designing top-level energy management strategy. To quantify the effects of fuel cell system (FCS) dynamics on optimal energy management, dynamic programming (DP) is adopted in this study to derive optimal power split strategies at two levels: Level 1, where the FCS dynamics are ignored, and Level 2, where the FCS dynamics are incorporated. Analysis is performed to quantify the differences of these two resulting strategies to understand the effects of FCS dynamics. While Level 1 DP provides significant computational advantages, the resulting strategy leads to load following errors that need to be mitigated using battery or FCS itself. Our analysis shows that up to 5% fuel economy penalty on New York city cycle (NYCC) and 3% on supplemental federal test procedure (US06) can be resulted by ignoring FCS dynamics, when the dominant dynamics of the FCS has settling time as slow as 8 seconds.


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