scholarly journals Energy management strategy based on dynamic programming with durability extension for fuel cell hybrid tramway

2021 ◽  
Vol 29 (3) ◽  
pp. 299-313
Author(s):  
Shiyong Tao ◽  
Weirong Chen ◽  
Rui Gan ◽  
Luoyi Li ◽  
Guorui Zhang ◽  
...  
Keyword(s):  
Dynamic Programming ◽  
Fuel Cell ◽  
Real Time ◽  
Energy Management ◽  
Power Distribution ◽  
Management Strategy ◽  
High Efficiency ◽  
State Machine ◽  
Energy Management Strategy ◽  
Hydrogen Consumption

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.

scholarly journals Energy Management of a Multi-Source Vehicle by λ-Control

2020 ◽  
Vol 10 (18) ◽  
pp. 6541
Author(s):  
Ali Castaings ◽  
Walter Lhomme ◽  
Rochdi Trigui ◽  
Alain Bouscayrol
Keyword(s):  
Energy Storage ◽  
Fuel Cell ◽  
Real Time ◽  
Energy Management ◽  
Management Strategy ◽  
Storage System ◽  
Energy Storage System ◽  
Energy Management Strategy ◽  
The Real ◽  
Hydrogen Consumption

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.

scholarly journals Mapping Fuzzy Energy Management Strategy for PEM Fuel Cell–Battery–Supercapacitor Hybrid Excavator

Energies ◽  
2020 ◽  
Vol 13 (13) ◽  
pp. 3387 ◽  
Author(s):  
Hoai Vu Anh Truong ◽  
Hoang Vu Dao ◽  
Tri Cuong Do ◽  
Cong Minh Ho ◽  
Xuan Dinh To ◽  
...  
Keyword(s):  
Fuel Cell ◽  
Energy Management ◽  
Power Distribution ◽  
Management Strategy ◽  
Internal Combustion Engines ◽  
High Efficiency ◽  
Proton Exchange ◽  
High Peak Power ◽  
Energy Management Strategy ◽  
Power Sources

By replacing conventional supplies such as fossil fuels or internal combustion engines (ICEs), this paper presents a new configuration of hybrid power sources (HPS) based on the integration of a proton-exchange membrane fuel cell (PEMFC) with batteries (BATs) and supercapacitors (SCs) for hydraulic excavators (HEs). In contrast to conventional architectures, the PEMFC in this study functions as the main power supply, whereas the integrated BAT–SC is considered as an auxiliary buffer. Regarding shortcomings existing in the previous approaches, an innovative energy management strategy (EMS) was designed using a new mapping fuzzy logic control (MFLC) for appropriate power distribution. Comparisons between the proposed strategy with available approaches are conducted to satisfy several driving cycles with different load demands and verify the strategy’s effectiveness. Based on the simulation results, the efficiency of the PEMFC when using the MFLS algorithm increased up to 47% in comparison with the conventional proposed EMS and other approaches. With the proposed strategy, the HPS can be guaranteed to not only sufficiently support power to the system even when the endurance process or high peak power is required, but also extend the lifespan of the devices and achieves high efficiency.

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 ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Fuel Cell ◽  
Energy Management ◽  
Management Strategy ◽  
Maximum Output Power ◽  
Maximum Output ◽  
Energy Management Strategy ◽  
Super Capacitor ◽  
Power Requirement ◽  
Hydrogen Consumption

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%.

scholarly journals Smart Energy Management for Series Hybrid Electric Vehicles Based on Driver Habits Recognition and Prediction

Energies ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 2954
Author(s):  
Loïc Joud ◽  
Rui Da Silva ◽  
Daniela Chrenko ◽  
Alan Kéromnès ◽  
Luis Le Moyne
Keyword(s):  
Experimental Study ◽  
Dynamic Programming ◽  
Real Time ◽  
Energy Management ◽  
Control Strategy ◽  
Management Strategy ◽  
Energetic Efficiency ◽  
Energy Management Strategy ◽  
Common Cycles ◽  
Hybrid Electric

The objective of this work is to develop an optimal management strategy to improve the energetic efficiency of a hybrid electric vehicle. The strategy is built based on an extensive experimental study of mobility in order to allow trips recognition and prediction. For this experimental study, a dedicated autonomous acquisition system was developed. On working days, most trips are constrained and can be predicted with a high level of confidence. The database was built to assess the energy and power needed based on a static model for three types of cars. It was found that most trips could be covered by a 10 kWh battery. Regarding the optimization strategy, a novel real time capable energy management approach based on dynamic vehicle model was created using Energetic Macroscopic Representation. This real time capable energy management strategy is done by a combination of cycle prediction based on results obtained during the experimental study. The optimal control strategy for common cycles based on dynamic programming is available in the database. When a common cycle is detected, the pre-determined optimum strategy is applied to the similar upcoming cycle. If the real cycle differs from the reference cycle, the control strategy is adapted using quadratic programming. To assess the performance of the strategy, its resulting fuel consumption is compared to the global optimum calculated using dynamic programming and used as a reference; its optimality factor is above 98%.

scholarly journals System Design and Energy Management for a Fuel Cell/Battery Hybrid Forklift

Energies ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 3440 ◽  
Author(s):  
Zhiyu You ◽  
Liwei Wang ◽  
Ying Han ◽  
Firuz Zare
Keyword(s):  
Fuel Cell ◽  
Energy Management ◽  
Power Distribution ◽  
Management Strategy ◽  
Operating Conditions ◽  
Working Time ◽  
Lead Acid Battery ◽  
Energy Management Strategy ◽  
Hybrid Power ◽  
Lead Acid

Electric forklifts, dominantly powered by lead acid batteries, are widely used for material handling in factories, warehouses, and docks. The long charging time and short working time characteristics of the lead acid battery module results in the necessity of several battery modules to support one forklift. Compared with the cost and time consuming lead acid battery charging system, a fuel cell/battery hybrid power module could be more convenient for a forklift with fast hydrogen refueling and long working time. In this paper, based on the characteristics of a fuel cell and a battery, a prototype hybrid forklift with a fuel cell/battery hybrid power system is constructed, and its hardware and software are designed in detail. According to the power demand of driver cycles and the state of charge (SOC) of battery, an energy management strategy based on load current following for the hybrid forklift is proposed to improve system energy efficiency and dynamic response performance. The proposed energy management strategy will fulfill the power requirements under typical driving cycles, achieve reasonable power distribution between the fuel cell and battery and, thus, prolong its continuous working time. The proposed energy management strategy is implemented in the hybrid forklift prototype and its effectiveness is tested under different operating conditions. The results show that the forklift with the proposed hybrid powered strategy has good performance with different loads, both lifting and moving, in a smooth and steady way, and the output of the fuel cell meets the requirements of its output characteristics, its SOC of battery remaining at a reasonable level.

scholarly journals Energy Management of Fuel Cell Vehicles Based on Model Prediction Control Using Radial Basis Functions

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Weiwei Xin ◽  
Weiguang Zheng ◽  
Jirong Qin ◽  
Shangjun Wei ◽  
Chunyu Ji
Keyword(s):  
Fuel Cell ◽  
Real Time ◽  
Energy Management ◽  
Model Prediction ◽  
Management Strategy ◽  
Energy Management Strategy ◽  
Time Performance ◽  
Prediction Horizon ◽  
Model Prediction Control ◽  
Prediction Control

Energy management strategies can improve fuel cell hybrid electric vehicles’ dynamic and fuel economy, and the strategies based on model prediction control show great advantages in optimizing the power split effect and in real time. In this paper, the influence of prediction horizon on prediction error, fuel consumption, and real time was studied in detail. The framework of energy management strategy was proposed in terms of the model prediction control theory. The radial basis function neural network was presented as the predictor to obtain the short-term velocity in the future. A dynamic programming algorithm was applied to obtain optimized control laws in the prediction horizon. Considering the onboard controller’s real-time performance, we established a simple fuel cell vehicle mathematical model for simulation. Different prediction horizons were adopted on UDDS and HWFET to test the influence on prediction and energy management strategy. Simulation results showed the strategy performed well in fuel economy and real-time performance, and the prediction horizon of around 20 s was appropriate for this strategy.

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