Research on ECMS Based on Segmented Path Braking Energy Recovery in a Fuel Cell Vehicle

Proton exchange membrane fuel cells are widely regarded as having the potential to replace internal combustion engines in vehicles.Since fuel cells cannot recover energy and have a slow dynamic response,they need to be used with different power sources.Developing efficient energy management strategies to achieve excellent fuel economy is the goal of research.This paper proposes an adaptive equivalent fuel minimum consumption strategy(AECMS)to solve the problem of the poor economy of the whole vehicle caused by the wrong selection of equivalent factors(EF)in traditional ECMS.In this method,the kinematics interval is used to update the equivalent factor by considering the penalty term of energy recovery on SOC changes.Finally,the optimized equivalent factor is substituted into the optimization objective function to achieve efficient energy regulation.Simulation results under the New European Driving Cycle show that compared with the traditional ECMS based on fixed SOC benchmarks,the proposed method improves fuel economy by 1.7%while ensuring vehicle power and increases SOC by 30%.

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.

Modeling and Control of Parallel Hybrid Electric Vehicle Using Sea-Lion Optimization

This paper develops a parallel hybrid electric vehicle(PHEV)propor-tional integral controller with driving cycle.To improve fuel efficiency and reduce hazardous emissions in hybrid electric vehicles(HEVs)combine an electric motor(EM),a battery and an internal combustion engine(ICE).The electric motor assists the engine when accelerating,driving longer highways or climbing hills.This enables the use of a smaller,more efficient engine.It also makes use of the concept of regenerative braking to maximize energy efficiency.In a Hybrid Electric Vehicle(HEV),energy dissipated while braking is utilized to charge the battery.The proportional integral controller was used in this paper to analyze engine,motor performance and the New European Driving Cycle(NEDC)was used in the vehicle driving test using Matlab/Simulink.The proportional integral controllers were designed to track the desired vehicle speed and manage the vehi-cle’s energyflow.The Sea Lion Optimization(SLnO)methods were created to reduce fuel consumption in a parallel hybrid electric vehicle and the results were obtained for the New European Driving Cycle.

Realization and Analysis of Good Fuel Economy and Kinetic Performance of a Low-cost Hybrid Electric Vehicle

By using high-power and high-efficiency propulsion systems,current hybrid electric vehicles（HEVs） in market can achieve excellent fuel economy and kinetic performance.However,it is the cost of current HEVs that hinders HEVs coming into widespread use.A novel hybrid electric propulsion system is designed to balance HEV cost and performance for developing markets.A battery/supercapacitor-based hybrid energy storage system（HESS） is used to improve energy conversion efficiency and reduce battery size and cost.An all-in-one-controller（AIOC） which integrates engine electronic control unit（ECU）,motor ECU,and HESS management system is developed to save materials and energy,and reduce the influence of distribution parameters on circuit.As for the powertrain configuration,four schemes are presented：belt-driven starter generator（BSG） scheme,four-wheel drive HEV scheme,full HEV scheme,and ranger-extender electric vehicle（EV） scheme.Component selection and parameter matching for the propulsion system are performed,and an energy management strategy is developed based on powertrain configuration and selected components.Forward-facing simulation models are built,comprehending the control strategy based on the optimal engine torque for the low-cost hybrid electric propulsion system.Co-simulation of AVL CRUISE and Matlab/Simulink is presented and the best scheme is selected.The simulation results indicate that,for the best design,fuel consumption in urban driving condition is 4.11 L/（100 km） and 0-50 km/h accelerating time is 10.95 s.The proposed research can realize low-cost concept for HEV while achieving satisfactory fuel economy and kinetic performance,and help to improve commercialization of HEVs.

A comparative study of hybrid electric vehicle fuel consumption over diverse driving cycles

Environmental pollution and declining resources of fossil fuels in recent years,have increased demand for better fuel economy and less pollution for ground transportation.Among the alternative solutions provided by researchers in recent decades,hybrid electric vehicles consisted of an internal combustion engine and an electric motor have been considered as a promising solution in the short-term.In the present study,fuel economy characteristics of a parallel hybrid electric vehicle are investigated by using numerical simulation.The simulation methodology is based on a fast forward facing simulation model of a parallel hybrid and an internal combustion engine powertrains.The objective of this study is to present the main parameters which result in an optimum combination of hybrid powertrain components in order to obtain a better fuel economy of hybrid powertrains regarding different driven cycles and hybridization factors.Then,the fuel consumption of the parallel hybrid electric vehicles are compared considering various driven cycles and hybridization factors.The results showed that the better fuel economy of hybrid powertrains increases by decreasing average load of the test cycle and the point of the best fuel economy for a particular average load of the cycle moves towards higher hybridization factors when the average load of the test cycle is reduced.

Multi-objective comprehensive optimization of fuel consumption and emission for hybrid electric vehicles

Aiming to reduce fuel consumption and emissions of a dual-clutch hybrid electric vehicle during cold start, multiobjective optimization for fuel consumption and HC/CO emission from a TWC(three-way catalytic converter) outlet is presented in this paper. DP(dynamic programming) considering dual-state variables is proposed based on the Bellman optimality principle. Both the battery SOC(state of charge) and the temperature of TWC monolith are considered in the algorithm simultaneously. In this way the global optimal control strategy and the Pareto optimal solution of multi-objective function are derived. Simulation results show that the proposed method is able to promote the TWC light-off significantly by decreasing the engine's load and improving exhaust temperature from the outlet of the engine, in comparison with original DP considering the single battery SOC. Compared to the results achieved by rule-based control strategy, fuel economy and emission of TWC outlet for cold start are optimized comprehensively. Each indicator of Pareto solution set shows the significant improvement.

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 Resonant Dual Full-Bridge Class-E Bidirectional DC-DC Converter for Fuel Cell Electric Vehicle

The interests on energy storage schemes, bidirectional dc-dc converter and uninterruptible power supplies have been increasing nowadays as there wide researches are undertaken in the area of electric vehicles. A modified bi directional class-E resonant dc-dc converter is introduced here in this proposed topology for the application in electric vehicles. The advantages of soft switching techniques have been utilized for making analysis simple. The main advantage here in this system is that it can operate in a wide range of frequencies with minimal switching loss in transistors. This paper elaborates a detailed analysis on converter design and the same has been simulated and verified in Matlab/Simulink.

An optimal energy management development for various configuration of plug-in and hybrid electric vehicle

Due to soaring fuel prices and environmental concerns, hybrid electric vehicle(HEV) technology attracts more attentions in last decade. Energy management system, configuration of HEV and traffic conditions are the main factors which affect HEV's fuel consumption, emission and performance. Therefore, optimal management of the energy components is a key element for the success of a HEV. An optimal energy management system is developed for HEV based on genetic algorithm. Then, different powertrain system component combinations effects are investigated in various driving cycles. HEV simulation results are compared for default rule-based, fuzzy and GA-fuzzy controllers by using ADVISOR. The results indicate the effectiveness of proposed optimal controller over real world driving cycles. Also, an optimal powertrain configuration to improve fuel consumption and emission efficiency is proposed for each driving condition. Finally, the effects of batteries in initial state of charge and hybridization factor are investigated on HEV performance to evaluate fuel consumption and emissions. Fuel consumption average reduction of about 14% is obtained for optimal configuration data in contrast to default configuration. Also results indicate that proposed controller has reduced emission of about 10% in various traffic conditions.

Power Matching and Simulation Analysis of ISG Hybrid Electric Vehicle

Aiming at the limitation of ISG light hybrid electric vehicle, the power matching design of ISG moderate hybrid electric vehicle was studied. According to the requirements of vehicle performance indicators, the parameters of vehicle power drive system including engine, ISG motor, battery and related power transmission system were designed;the basic control strategy of ISG moderate hybrid electric vehicle was put forward;the dynamic model of vehicle was established by using MATLAB/Simulink software platform, and the vehicle performance was simulated under selected cycle conditions. The simulation results show that the parameter matching of the drive system is reasonable, the power performance of the vehicle meets the corresponding requirements, and the fuel economy has been significantly improved.

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.

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.

A New Topology of a Variable Output-Voltage DC-DC Converter for Fuel Cell Vehicles

The aim of this paper is to present a new topology of a DC-DC power converter for conditioning the current and voltages behaviors of embarked energy sources used in electrical vehicles. The fuel cells in conjunction with ultra-capacitors have been chosen as the power supply. The originality of the proposed converter is to use a variable voltage of the DC bus of the vehicle. The goal is to allow a better energy management of the embedded sources onboard the vehicle by improving its energy efficiency. After presenting and explaining the topology of the converter, some simulation and experiments results are shown to highlight its different operation modes.

A multivariable output neural network approach for simulation of plug-in hybrid electric vehicle fuel consumption

This study is laser focused on the simulation of fuel consumption and fuel economy label parameters of plug-in hybrid electric vehicles.While fuel economy is a key factor in the design of plug-in hybrid electric vehicles,a fuel economy label can educate customers about the economic advantage of purchasing a particular car.The fuel economy label of a PHEV consists of parameters like driving range,electrical energy consumption,fuel economy for city,highway,and combined use,battery recharge time,and fuel consumption rates.The study used an inverse function model of an artificial neural network to simulate and calculate the parameters of the fuel economy labels of PHEVs.Firstly,the selected parameters of the fuel economy label of plug-in hybrid electric vehicles were used to develop a single output model.The output variable of the single output model was then merged with dummy functions to form input variables for the inverse function model.The output variables simulated were engine size in litres;estimated driving range when the battery is fully charged in km,battery recharged time in hours,city fuel consumption(L/100 km),highway fuel consumption(L/100 km),combined fuel consumption(L/100 km),estimated driving range when the tank is full,carbon dioxide(CO_(2))emission in grams/km,electric motor power in kW,number of cylinders,and electrical charges consumed in kWh/100 km.Different cases of input variables were considered for the inverse function model.The accuracy of the model was 29.1 times greater than that of the conventional inverse artificial neural network model.

A hybrid fuel cell for water purification and simultaneously electricity generation

The development of highly efficient energy conversion technologies to extract energy from wastewater is urgently needed,especially in facing of increasing energy and environment burdens.Here,we successfully fabricated a novel hybrid fuel cell with BiOCl-NH_(4)PTA as photocatalyst.The polyoxometalate(NH_(4)PTA)act as the acceptor of photoelectrons and could retard the recombination of photogenerated electrons and holes,which lead to superior photocatalytic degradation.By utilizing BiOCl-NH_(4)PTA as photocatalysts and Pt/C air-cathode,we successfully constructed an electron and mass transfer enhanced photocatalytic hybrid fuel cell with flow-through field(F-HFC).In this novel fuel cell,dyes and biomass could be directly degraded and stable power output could be obtained.About 87%of dyes could be degraded in 30 min irradiation and nearly 100%removed within 90 min.The current density could reach up to~267.1μA/cm^(2);with maximum power density(Pmax)of~16.2μW/cm^(2) with Rhodamine B as organic pollutant in F-HFC.The power densities were 9.0μW/cm^(2),12.2μW/cm^(2),and 13.9μW/cm^(2) when using methyl orange(MO),glucose and starch as substrates,respectively.This hybrid fuel cell with BiOCl-NH_(4)PTA composite fulfills the purpose of decontamination of aqueous organic pollutants and synchronous electricity generation.Moreover,the novel design cell with separated photodegradation unit and the electricity generation unit could bring potential practical application in water purification and energy recovery from wastewater.

Closed-form solution to the dynamic programming for a heavy-duty parallel hybrid vehicle energy management

Dynamic programming(DP)is frequently used to obtain the optimal solution to the hybrid electric vehicle(HEV)energy management.The trade-off between the accuracy and the computational effort is the biggest problem for the DP method.The closed-form solution to the DP is proposed to solve this problem.Firstly,the affine linear model of the engine fuel rate is obtained based on engine test data.The piecewise linear approximation of the motor power demand is obtained considering the different energy flows in the charging and discharging stages of the battery.Then,the second-order Taylor expansion for the cost matrix at each time and state grid point is introduced to get the closed-form solution of the optimal torque split.The results show that this method can greatly reduce the computing burden by 93%while ensuring near-optimal fuel economy compared with the conventional DP method.

A review of hydrogen technologies and engineering solutions for railway vehicle design and operations

Interest in hydrogen-powered rail vehicles has gradually increased worldwide over recent decades due to the global pressure on reduction in greenhouse gas emissions,technology availability,and multiple options of power supply.In the past,research and development have been primarily focusing on light rail and regional trains,but the interest in hydrogen-powered freight and heavy haul trains is also growing.The review shows that some technical feasibility has been demonstrated from the research and experiments on proof-of-concept designs.Several rail vehicles powered by hydrogen either are currently operating or are the subject of experimental programmes.The paper identifies that fuel cell technology is well developed and has obvious application in providing electrical traction power,while hydrogen combustion in traditional IC engines and gas turbines is not yet well developed.The need for on-board energy storage is discussed along with the benefits of energy management and control systems.

Deep reinforcement learning based energy management strategy for fuel cell/battery/supercapacitor powered electric vehicle

Vehicles using a single fuel cell as a power source often have problems such as slow response and inability to recover braking energy.Therefore,the current automobile market is mainly dominated by fuel cell hybrid vehicles.In this study,the fuel cell hybrid commercial vehicle is taken as the research object,and a fuel cell/battery/supercapacitor energy topology is proposed,and an energy management strategy based on a double-delay deep deterministic policy gradient is designed for this topological structure.This strategy takes fuel cell hydrogen consumption,fuel cell life loss,and battery life loss as the optimization goals,in which supercapacitors play the role of coordinating the power output of the fuel cell and the battery,providing more optimization ranges for the optimization of fuel cells and batteries.Compared with the deep deterministic policy gradient strategy(DDPG)and the nonlinear programming algorithm strategy,this strategy has reduced hydrogen consumption level,fuel cell loss level,and battery loss level,which greatly improves the economy and service life of the power system.The proposed EMS is based on the TD3 algorithm in deep reinforcement learning,and simultaneously optimizes a number of indicators,which is beneficial to prolong the service life of the power system.

Ordered mesoporous carbon spheres assisted Ru nanoclusters/RuO_(2) with redistribution of charge density for efficient CO_(2) methanation in a novel H2/CO_(2)fuel cell

Efficiently reducing carbon dioxide(CO_(2))into carbon chemicals and fuels is highly desirable due to the rapid growth of atmospheric CO_(2)ncentration.In prior work,we described a unique H/CO_(2)fuel cell driven by low-valued waste heat,which not only CO_(2)nverts CO_(2)to methane(CH_(4))but also outputs electrical energy,yet the CO_(2)reduction rate needs to be urgently improved.Here,a novel Ru-RuOcatalyst with heterostructure was grafted on mesoporous carbon spheres by in situ partially reducing RuOinto ultrasmall Ru clusters(~1 nm),in which heteroatom-doped carbon spheres as a matrix with excellent CO_(2)nductivity and abundant pores can not only easily CO_(2)nfine the formation of Ru nanocluster but also are beneficial to the exposed active sites of Ru CO_(2)mplex and the mass transport.CO_(2)mpared to pure RuOnanoparticles supported on carbon spheres,our CO_(2)mposite catalyst boosts the CO_(2) nversion rate by more than 5-fold,reaching a value of 382.7μmol gcat.h-1at 170℃.Moreover,a decent output power density of 2.92 W mwas obtained from this H2/CO_(2)fuel cell using Ru-RuOembedded carbon spheres as a cathode catalyst.The Ru-RuOheterostructure can modify the adsorption energy of CO_(2)and induce the redistribution of charge density,thus boosting CO_(2)reduction significantly.This work not only offers an efficient catalyst for this novel H_(2)/CO_(2)fuel cell but also presents a facile method to prepare Ru nanoclusters.