Interpretation of GB/T 26779-2021,Hydrogen fuel cell electric vehicles refueling receptacle

With the development of fuel cell electric vehicle industry in China,the 70-MPa hydrogen storage cylinders have been widely applied on vehicles in recent years.The revised standard,GB/T 26779-2021,Hydrogen fuel cell electric vehicle refueling receptacle,was released on March 9,2021 with added stipulations for the 70-MPa hydrogen refuelling receptacle.The main technical contents of GB/T 26779-2021 and its similarities and differences with GB/T 26779-2011 are discussed in this paper.

Interpretation of GB/T 24549-2009 Fuel Cell Electric Vehicle—Safety Requirements

The national standard GB/T 24549—2009 Fuel Cell Electric Vehicle—Safety Requirements specifies the general safety requirements for whole vehicle and key parts of Fuel Cell Electric Vehicle (FCEV).It is of great significance for the development of FCEV in china.This paper discusses the main contents and the background of its development.

A Novel Method for the Application of the ECMS(Equivalent Consumption Minimization Strategy)to Reduce Hydrogen Consumption in Fuel Cell Hybrid Electric Vehicles

Fuel cell hybrid electric vehicles are currently being considered as ideal means to solve the energy crisis and global warming in today’s society.In this context,this paper proposes a method to solve the problem related to the dependence of the so-called optimal equivalent factor(determined in the framework of the equivalent consumption minimum strategy-ECMS)on the working conditions.The simulation results show that under typical conditions(some representative cities being considered),the proposed strategy can maintain the power balance;for different initial battery’s states of charge(SOC),after the SOC stabilizes,the fuel consumption is 5.25 L/100 km.

Machine learning modeling for fuel cell-battery hybrid power system dynamics in a Toyota Mirai 2 vehicle under various drive cycles

Electrification is considered essential for the decarbonization of mobility sector, and understanding and modeling the complex behavior of modern fuel cell-battery electric-electric hybrid power systems is challenging, especially for product development and diagnostics requiring quick turnaround and fast computation. In this study, a novel modeling approach is developed, utilizing supervised machine learning algorithms, to replicate the dynamic characteristics of the fuel cell-battery hybrid power system in a 2021 Toyota Mirai 2nd generation (Mirai 2) vehicle under various drive cycles. The entire data for this study is collected by instrumenting the Mirai vehicle with in-house data acquisition devices and tapping into the Mirai controller area network bus during chassis dynamometer tests. A multi-input - multi-output, feed-forward artificial neural network architecture is designed to predict not only the fuel cell attributes, such as average minimum cell voltage, coolant and cathode air outlet temperatures, but also the battery hybrid system attributes, including lithium-ion battery pack voltage and temperature with the help of 15 system operating parameters. Over 21,0000 data points on various drive cycles having combinations of transient and near steady-state driving conditions are collected, out of which around 15,000 points are used for training the network and 6,000 for the evaluation of the model performance. Various data filtration techniques and neural network calibration processes are explored to condition the data and understand the impact on model performance. The calibrated neural network accurately predicts the hybrid power system dynamics with an R-squared value greater than 0.98, demonstrating the potential of machine learning algorithms for system development and diagnostics.

Function approximation reinforcement learning of energy management with the fuzzy REINFORCE for fuel cell hybrid electric vehicles

In the paper,a novel self-learning energy management strategy(EMS)is proposed for fuel cell hybrid electric vehicles(FCHEV)to achieve the hydrogen saving and maintain the battery operation.In the EMS,it is proposed to approximate the EMS policy function with fuzzy inference system(FIS)and learn the policy parameters through policy gradient reinforcement learning(PGRL).Thus,a so-called Fuzzy REINFORCE algorithm is first proposed and studied for EMS problem in the paper.Fuzzy REINFORCE is a model-free method that the EMS agent can learn itself through interactions with environment,which makes it independent of model accuracy,prior knowledge,and expert experience.Meanwhile,to stabilize the training process,a fuzzy baseline function is adopted to approximate the value function based on FIS without affecting the policy gradient direction.More-over,the drawbacks of traditional reinforcement learning such as high computation burden,long convergence time,can also be overcome.The effectiveness of the proposed methods were verified by Hardware-in-Loop ex-periments.The adaptability of the proposed method to the changes of driving conditions and system states is also verified.

An adaptive fuel cell hybrid vehicle propulsion sizing model

As we enter the age of electrochemical propulsion,there is an increasing tendency to discuss the viability or otherwise of different electrochemical propulsion systems in zero-sum terms.These discussions are often grounded in a specific use case;however,given the need to electrify the wider transport sector it is evident that we must consider systems in a holistic fashion.When designed adequately,the hybridisation of power sources within automotive applications has been demonstrated to positively impact fuel cell efficiency,durability,and cost,while having potential benefits for the safety of vehicles.In this paper,the impact of the fuel cell to battery hybridisation degree is explored through the key design parameter of system mass.Different fuel cell electric hybrid vehicle(FCHEV)scenarios of various hydridisation degrees,including light-duty vehicles(LDVs),Class 8 heavy goods vehicles(HGVs),and buses are modelled to enable the appropriate sizing of the proton exchange membrane(PEMFC)stack and lithium-ion battery(LiB)pack and additional balance of plant.The operating conditions of the modelled PEMFC stack and battery pack are then varied under a range of relevant drive cycles to identify the relative performance of the systems.By extending the model further and incorporating a feedback loop,we are able to remove the need to include estimated vehicle masses a priori enabling improving the speed and accuracy of the model as an analysis tool for vehicle mass and performance estimation.

Utilization of Energy Storage and Hydrogen in Power and Energy Systems:Viewpoints from Five Aspects

Decarbonizing power systems is crucial to mitigating climate change impacts and achieving carbon neutrality.Increasing renewable energy supply can reduce greenhouse gas emissions and accelerate the decarbonization process.However,renewable energy sources(RESs)such as wind and solar power are characterized by intermittency and often non-dispatchability,significantly challenging their high-level integration into power systems.Energy storage is acknowledged as a vital indispensable solution for mitigating the intermittency of renewables such as wind and solar power and boosting the penetrations of renewables.In the CSEE JPES Forum,five well-known experts were invited to give keynote speeches,and the participating experts and scholars had comprehensive exchanges and discussions on energy storage technologies.Specifically,the views on the design,control,performance,and applications of new energy storage technologies,such as the fuel cell vehicle,water electrolysis,and flow battery,in the coordination and operation of power and energy systems were analyzed.The experts also provided experience that could be used to develop energy storage for constructing and decarbonizing new power systems.