scholarly journals Optimization Analysis of the Energy Management Strategy of the New Energy Hybrid 100% Low-Floor Tramcar Using a Genetic Algorithm

2018 ◽  
Vol 8 (7) ◽  
pp. 1144 ◽  
Author(s):  
Minggao Li ◽  
Ming Li ◽  
Guopeng Han ◽  
Nan Liu ◽  
Qiumin Zhang ◽  
...  

Performance and economic efficiency of the fuel cell (FC)/battery/super capacitor (SC) hybrid 100% low-floor tramcar is mainly determined by its energy management strategy. In this paper, a train traction model was built to calculate the power output and energy consumption properties of the hybrid tramcar. With the purpose of reducing hydrogen consumption, the genetic algorithm was adopted to optimize the original energy management strategy. The results before and after the optimization show that the power requirement of the tramcar can be satisfied in both situations with the fuel cell (FC) module non-stopped. The maximum output power of the FC is reduced from 170 kW to 101.21 kW. As for the SC, a two-parallel connection module is used instead of the three-parallel one, and the power range changes from −125~250 kW to −67~153 kW. Under the original energy management strategy, the battery cannot be used efficiently with less exporting and absorbent power. Its utilization ratio is improved greatly after optimization. In sum, the equivalent total hydrogen consumption is reduced from 3.3469 kg to 2.8354 kg, dropping by more than 15%.

2020 ◽  
Vol 10 (18) ◽  
pp. 6541
Author(s):  
Ali Castaings ◽  
Walter Lhomme ◽  
Rochdi Trigui ◽  
Alain Bouscayrol

This paper deals with the real-time energy management of a fuel cell/battery/supercapacitors energy storage system for electric vehicles. The association of the battery and the supercapacitors with the fuel cell aims to reduce the hydrogen consumption while limiting the constraints on the fuel cell and the battery. In this paper, a real-time optimization-based energy management strategy by λ-control is proposed. Simulation results on a standard driving cycle show that the hydrogen consumption is reduced by 7% in comparison with a fuel-cell-based electric vehicle without any secondary energy storage source. Moreover, the energy management strategy ensures the system safety while preserving the fuel cell and the battery. Experimental results show that the developed energy management strategy is well-suited for the real-time requirements, applicability, and safety.


2021 ◽  
Vol 29 (3) ◽  
pp. 299-313
Author(s):  
Shiyong Tao ◽  
Weirong Chen ◽  
Rui Gan ◽  
Luoyi Li ◽  
Guorui Zhang ◽  
...  

AbstractThis paper proposes an energy management strategy for a fuel cell (FC) hybrid power system based on dynamic programming and state machine strategy, which takes into account the durability of the FC and the hydrogen consumption of the system. The strategy first uses the principle of dynamic programming to solve the optimal power distribution between the FC and supercapacitor (SC), and then uses the optimization results of dynamic programming to update the threshold values in each state of the finite state machine to realize real-time management of the output power of the FC and SC. An FC/SC hybrid tramway simulation platform is established based on RT-LAB real-time simulator. The compared results verify that the proposed EMS can improve the durability of the FC, increase its working time in the high-efficiency range, effectively reduce the hydrogen consumption, and keep the state of charge in an ideal range.


2020 ◽  
pp. 1-13
Author(s):  
Wenguang Li ◽  
Guosheng Feng ◽  
Sumei Jia

This paper studies a hybrid power system composed of fuel cells, super capacitors and batteries. Super capacitors are used as auxiliary energy sources to provide the required high power when the car starts and accelerate, while absorbing braking energy when the car is braking. Fuzzy control is used to optimize its energy management strategy. The fuzzy controller of the three-energy source system takes the battery, super capacitor, and bus demand power as the input of the fuzzy controller, and the battery demand power and the fuel cell demand power as the fuzzy controller output. The system realizes the energy distribution of super capacitors, fuel cells and storage batteries according to power requirements, thereby improving the performance of the system and extending the life of components. And with hydrogen consumption as the optimization goal, the particle swarm algorithm is used to optimize the parameters of the fuzzy membership function. Compared with the fuzzy control strategy without particle swarm optimization, the optimized fuzzy energy management strategy reduces the hydrogen consumption of fuel cell vehicles. 10 L/(100 km)-1, which improves the economy of the vehicle.


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