WO2020200214A1 - 一种质子交换膜燃料电池系统温度主动容错控制方法 - Google Patents

一种质子交换膜燃料电池系统温度主动容错控制方法 Download PDF

Info

Publication number
WO2020200214A1
WO2020200214A1 PCT/CN2020/082646 CN2020082646W WO2020200214A1 WO 2020200214 A1 WO2020200214 A1 WO 2020200214A1 CN 2020082646 W CN2020082646 W CN 2020082646W WO 2020200214 A1 WO2020200214 A1 WO 2020200214A1
Authority
WO
WIPO (PCT)
Prior art keywords
model
fuel cell
temperature
stack
temperature control
Prior art date
Application number
PCT/CN2020/082646
Other languages
English (en)
French (fr)
Inventor
陈剑
严驰洲
Original Assignee
浙江大学
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 浙江大学 filed Critical 浙江大学
Priority to US17/311,700 priority Critical patent/US11916270B2/en
Publication of WO2020200214A1 publication Critical patent/WO2020200214A1/zh

Links

Images

Classifications

    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M8/00—Fuel cells; Manufacture thereof
    • H01M8/04—Auxiliary arrangements, e.g. for control of pressure or for circulation of fluids
    • H01M8/04298—Processes for controlling fuel cells or fuel cell systems
    • H01M8/04694—Processes for controlling fuel cells or fuel cell systems characterised by variables to be controlled
    • H01M8/04701—Temperature
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M8/00—Fuel cells; Manufacture thereof
    • H01M8/04—Auxiliary arrangements, e.g. for control of pressure or for circulation of fluids
    • H01M8/04007—Auxiliary arrangements, e.g. for control of pressure or for circulation of fluids related to heat exchange
    • H01M8/04014—Heat exchange using gaseous fluids; Heat exchange by combustion of reactants
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M8/00—Fuel cells; Manufacture thereof
    • H01M8/04—Auxiliary arrangements, e.g. for control of pressure or for circulation of fluids
    • H01M8/04007—Auxiliary arrangements, e.g. for control of pressure or for circulation of fluids related to heat exchange
    • H01M8/04029—Heat exchange using liquids
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M8/00—Fuel cells; Manufacture thereof
    • H01M8/04—Auxiliary arrangements, e.g. for control of pressure or for circulation of fluids
    • H01M8/04298—Processes for controlling fuel cells or fuel cell systems
    • H01M8/04313—Processes for controlling fuel cells or fuel cell systems characterised by the detection or assessment of variables; characterised by the detection or assessment of failure or abnormal function
    • H01M8/0432—Temperature; Ambient temperature
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M8/00—Fuel cells; Manufacture thereof
    • H01M8/04—Auxiliary arrangements, e.g. for control of pressure or for circulation of fluids
    • H01M8/04298—Processes for controlling fuel cells or fuel cell systems
    • H01M8/04313—Processes for controlling fuel cells or fuel cell systems characterised by the detection or assessment of variables; characterised by the detection or assessment of failure or abnormal function
    • H01M8/0432—Temperature; Ambient temperature
    • H01M8/04358—Temperature; Ambient temperature of the coolant
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M8/00—Fuel cells; Manufacture thereof
    • H01M8/04—Auxiliary arrangements, e.g. for control of pressure or for circulation of fluids
    • H01M8/04298—Processes for controlling fuel cells or fuel cell systems
    • H01M8/04313—Processes for controlling fuel cells or fuel cell systems characterised by the detection or assessment of variables; characterised by the detection or assessment of failure or abnormal function
    • H01M8/04537—Electric variables
    • H01M8/04544—Voltage
    • H01M8/04559—Voltage of fuel cell stacks
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M8/00—Fuel cells; Manufacture thereof
    • H01M8/04—Auxiliary arrangements, e.g. for control of pressure or for circulation of fluids
    • H01M8/04298—Processes for controlling fuel cells or fuel cell systems
    • H01M8/04992—Processes for controlling fuel cells or fuel cell systems characterised by the implementation of mathematical or computational algorithms, e.g. feedback control loops, fuzzy logic, neural networks or artificial intelligence
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M8/00—Fuel cells; Manufacture thereof
    • H01M8/10—Fuel cells with solid electrolytes
    • H01M2008/1095—Fuel cells with polymeric electrolytes
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02E60/30—Hydrogen technology
    • Y02E60/50—Fuel cells

Definitions

  • the invention belongs to a battery system control method in the fuel cell application field, and in particular relates to a proton exchange membrane fuel cell system temperature active fault-tolerant control method.
  • Fuel cells Due to environmental pollution and energy crisis, new energy sources led by proton exchange membrane fuel cells are receiving more and more attention. Fuel cells have the advantages of high power density, high conversion efficiency, and pollution-free. They are increasingly used in distributed power generation, new energy vehicles, energy storage and other fields. However, according to the annual report released by the US DOE, the economy and durability of fuel cells have not fully reached the commercialization indicators. Among them, stack manufacturing process and system control are important means to solve its commercialization, and temperature control is an important factor that affects stack performance. The stack will generate a lot of heat during the actual operation. Too high temperature will cause the membrane to dry and affect the activity of the membrane; too low temperature will cause flooding and affect the efficiency of the stack. Abnormal temperature control will affect the performance of the stack.
  • Active fault-tolerant control is a fault-tolerant control based on fault diagnosis.
  • Fault diagnosis can be divided into two categories: model-based and data-based.
  • Data-based fault diagnosis requires a large amount of data training and lacks a certain degree of robustness; model-based fault diagnosis requires the establishment of an accurate mathematical model, but it has better robustness Sex.
  • Temperature control is an important research field of fuel cell control. The existing research results include PID control, feedforward control, model predictive control, fuzzy control, etc. However, none of the above methods takes into account system failures and the control accuracy is not high. There are certain limitations in actual operation. In summary, the research on model-based active fault-tolerant temperature control of proton exchange membrane fuel cells is of great value.
  • the present invention proposes an active fault-tolerant temperature control method for a proton exchange membrane fuel cell system, which uses structural analysis and Dulmage-Mendelsohn methods to decompose to obtain the redundant part of the model, thereby generating system residuals to diagnose sensors Failure, and the use of active fault-tolerant control based on sliding mode realizes the fault-tolerant control of the temperature of the proton exchange membrane fuel cell system, which improves the robustness of the temperature control system.
  • the technical scheme adopted by the present invention includes the following steps:
  • a system structure matrix with the abscissa as the system unknown variable and the ordinate as the system equation is established by the structural analysis method for the fuel cell system temperature control model; for any element in the system structure matrix, if the system equation of the ordinate corresponding to the matrix element contains If there is an unknown system variable with the abscissa corresponding to the matrix element, mark the matrix element as 1, otherwise mark it as 0;
  • step S4 On the basis of identifying sensor faults in step S3, design an active fault-tolerant control structure based on the sliding mode controller to achieve fault-tolerant control of the fuel cell outlet temperature.
  • the fuel cell system temperature control model in step S1 is composed of a fuel cell temperature model, a stack voltage model, and a semi-empirical model of the auxiliary system;
  • M st is the stack mass
  • C st is the stack heat capacity
  • T st,out is the stack temperature
  • the stack temperature is the stack cooling water outlet temperature
  • the stack is the abbreviation of fuel cell
  • the auxiliary system is the radiator and water pump connected to the stack.
  • the semi-empirical model includes the water pump model and the radiator model;
  • the pump model is obtained by fitting the pump voltage V pump and the flow rate W cl .
  • the specific form is as follows:
  • the radiator model is obtained by fitting the radiator outlet temperature difference T diff , the flow rate W cl , the fan speed ⁇ , and the room temperature T 0.
  • the specific form is as follows:
  • F 1 ( ⁇ ) is the nonlinear transfer function of the fan speed ⁇ , which is defined as follows:
  • F 2 (T 0 ) is an empirical heat dissipation function, defined as follows:
  • the transient response can be ignored compared with the large time lag of the fuel cell temperature, and the semi-empirical model of the auxiliary system is obtained through least square fitting;
  • the system equations include all the model equations in the fuel cell temperature model, the stack voltage model, and the auxiliary system semi-empirical model;
  • the system unknown variables include the fuel cell temperature model, the stack voltage model, and the auxiliary system semi-empirical model equations. All variables that change over time.
  • the residual is for the unknown variables of the system, the analytical solution of the unknown variables of the system is obtained through the system equation, and the corresponding sensor value is subtracted to construct it;
  • the sensor in the fuel cell system temperature control model is the stack inlet Sensors for temperature, stack outlet temperature, cooling water flow, fan outlet temperature and stack voltage.
  • the active fault-tolerant control structure of step S4 is mainly composed of a sliding mode controller, a fault detection module, and a control module; the fault detection module judges the model according to the temperature, air pressure, volume or mass flow, and voltage parameters in the fuel cell system temperature control model After the fault detection module detects that the sensor in the model is faulty, the control module reconstructs the faulty sensor signal according to the fuel cell system temperature control model and feeds it back to the sliding mode controller, and finally the sliding mode controller The fault-tolerant control of the outlet temperature of the fuel cell is realized through feedback.
  • the sliding mode controller is established according to the fuel cell system temperature control model, the input of the sliding mode controller is the set temperature of the fuel cell, the output of the sliding mode controller is the cooling water flow, and the cooling water flow is the control variable of the sliding mode controller;
  • the sliding mode surface of the sliding mode controller is designed according to the temperature control model of the fuel cell system. In order to avoid the shock of the water pump, a smoothly processed switching function is adopted; in addition, considering the excessive overshoot caused by the integral effect in the sliding mode controller, The integral separation type anti-integral saturation method is added to the sliding surface to speed up the system's response ability and stability.
  • the invention solves the problem of sensor failure during the operation of the proton exchange membrane fuel cell temperature control system, and applies the model-based fault-tolerant control method to temperature control, which can effectively improve the reliability and durability of the fuel cell system.
  • Figure 1 is the structure matrix obtained after Dulmage-Mendelsohn decomposition in the present invention.
  • Figure 2 is the experimental result of setting temperature change under normal conditions in the embodiment of the present invention.
  • Fig. 3 is the experimental result of the load power change in the normal state in the embodiment of the present invention.
  • Fig. 4 is a temperature control experiment result in a fault state in the embodiment of the present invention.
  • Fig. 5 is an apparatus diagram of the proton exchange membrane fuel cell of the present invention.
  • the electronic load is connected to the fuel cell to provide a load for the fuel cell; in the right loop, water flows into the radiator through the water pump, and the fan provides cooling airflow for the radiator, and the water is finally injected after passing through the water tank The fuel cell dissipates heat for it.
  • the model-based active fault-tolerant temperature control method of proton exchange membrane fuel cell includes the following steps:
  • the temperature control model of the fuel cell system in step S1 is composed of a fuel cell temperature model, a stack voltage model and a semi-empirical model of the auxiliary system;
  • M st is the stack mass
  • C st is the stack heat capacity
  • T st,out is the stack temperature
  • the stack temperature is the stack cooling water outlet temperature
  • the auxiliary system is the radiator and water pump connected to the stack.
  • the semi-empirical model includes the water pump model and the radiator model;
  • the pump model is obtained by fitting the pump voltage V pump and the flow rate W cl .
  • the specific form is as follows:
  • the radiator model is obtained by fitting the radiator outlet temperature difference T diff , the flow rate W cl , the fan speed ⁇ , and the room temperature T 0.
  • the specific form is as follows:
  • F 1 ( ⁇ ) is the nonlinear transfer function of the fan speed ⁇ , which is defined as follows:
  • F 2 (T 0 ) is an empirical heat dissipation function, defined as follows:
  • the transient response can be ignored compared with the large time lag of the fuel cell temperature, and the semi-empirical model of the auxiliary system is obtained through least square fitting;
  • the battery system temperature control model establishes a system structure matrix with the abscissa as the system unknown variables and the ordinate as the system equation; for any element in the system structure matrix, if the system equation corresponding to the ordinate of the matrix element contains the corresponding matrix element For system unknown variables of the abscissa, mark the matrix element as 1, otherwise mark it as 0;
  • the system equations include all the model equations in the fuel cell temperature model, the stack voltage model, and the semi-empirical model of the auxiliary system;
  • the unknown variables of the system include all the equations in the fuel cell temperature model, the stack voltage model and the semi-empirical model of the auxiliary system Variables that change over time, such as chemical energy, internal energy, volume or mass flow, voltage, and air pressure.
  • step S3 Use the Dulmage-Mendelsohn method to decompose the system structure matrix of step S2 to obtain the redundant part, and construct the system residual in the redundant part to identify the sensor fault in the model;
  • the system equation e1 in the ordinate of Fig. 1 is the energy conservation equation
  • e2 is the theoretical exothermic equation
  • e3 is the theoretical hydrogen consumption equation
  • e4 is the input internal energy equation
  • e5 is the input hydrogen equation
  • e6 is the anode input saturated steam equation.
  • E7 is the input air equation
  • e8 is the theoretical oxygen consumption equation
  • e9 is the cathode input saturated steam equation
  • e10 is the output internal energy equation
  • e11 is the output hydrogen equation
  • e12 is the anode output saturated steam equation
  • e13 is the oxygen output equation
  • e14 Is the nitrogen output equation
  • e15 is the input oxygen equation
  • e16 is the cathode output saturated steam equation
  • e17 is the reaction generated water equation
  • e18 is the cooling water heat dissipation equation
  • e19 is the fuel cell heat dissipation equation
  • e20 is the battery load equation
  • e21 is the voltage equation
  • E22 is the Nernst equation
  • e23 is the hydrogen partial pressure equation
  • e24 is the oxygen partial pressure equation
  • e25 is the activation loss equation
  • e26 is the oxygen concentration equation
  • e27 is the ohmic loss equation
  • e28 is the internal resistance equation
  • the redundant part is the lower right part of the matrix in Figure 1, and the redundant part is the area where the number of system equations in the decomposed system structure matrix is greater than the number of system unknown variables;
  • the residual is for the unknown variables of the system.
  • the analytic solution of the unknown variables of the system is obtained through the system equation, and the corresponding sensor value is subtracted to construct it; the fuel cell system is detected by the residual online Sensor failure in the temperature control model: If the residual error is lower than the set threshold, it indicates that the sensor in the fuel cell system temperature control model has no fault; if the residual error exceeds the set threshold, it indicates that the fuel cell system temperature control model is The sensor is malfunctioning.
  • the sensors in the fuel cell system temperature control model are the stack inlet temperature, stack outlet temperature, cooling water flow, fan outlet temperature and stack voltage.
  • the 5 dotted lines at the bottom of the matrix in Figure 1 respectively indicate the five types of sensor failures: stack inlet temperature, stack outlet temperature, cooling water flow, fan outlet temperature, and stack voltage. All faults are located in the redundant part of the model, and Separated from each other, so faults can be detected and do not interfere with each other, that is, fault isolation.
  • step S4 On the basis of identifying sensor faults in step S3, design an active fault-tolerant control structure based on the sliding mode controller to achieve fault-tolerant control of the fuel cell outlet temperature.
  • the active fault-tolerant control structure of step S4 is mainly composed of a sliding mode controller, a fault detection module, and a control module; the fault detection module judges the model according to the temperature, air pressure, volume or mass flow, and voltage parameters in the fuel cell system temperature control model After the fault detection module detects that the sensor in the model is faulty, the control module reconstructs the faulty sensor signal according to the fuel cell system temperature control model and feeds it back to the sliding mode controller, and finally the sliding mode controller The fault-tolerant control of the outlet temperature of the fuel cell is realized through feedback.
  • the effect of the fault-tolerant controller is detected by adding a deviation fault to the fuel cell inlet temperature sensor. As shown in Figure 4, a fault is added in about 200 seconds.
  • the fault collection module calculates the system residual error corresponding to the fault. After the system residual error exceeds the threshold, it is judged as a sensor fault, and the reconstructed sensor signal is input to the sliding mode controller. Fault-tolerant control. It can be seen from the experimental results that the controller can still accurately control the outlet temperature at the set value even when the temperature of the inlet sensor is deviating.

Landscapes

  • Engineering & Computer Science (AREA)
  • Chemical & Material Sciences (AREA)
  • Chemical Kinetics & Catalysis (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • General Chemical & Material Sciences (AREA)
  • Electrochemistry (AREA)
  • Sustainable Energy (AREA)
  • Sustainable Development (AREA)
  • Manufacturing & Machinery (AREA)
  • Medical Informatics (AREA)
  • Theoretical Computer Science (AREA)
  • Software Systems (AREA)
  • Automation & Control Theory (AREA)
  • Fuzzy Systems (AREA)
  • Artificial Intelligence (AREA)
  • Health & Medical Sciences (AREA)
  • Evolutionary Computation (AREA)
  • Computing Systems (AREA)
  • Fuel Cell (AREA)
  • Combustion & Propulsion (AREA)

Abstract

本发明公开了一种质子交换膜燃料电池系统温度主动容错控制方法。首先建立质子交换膜燃料电池温度控制系统的模型，通过结构分析法针对模型建立系统结构矩阵，并使用Dulmage-Mendelsohn分解系统结构矩阵，获得模型冗余部分，并构建系统余差以反映温度控制系统传感器故障，在故障识别基础上，设计了基于滑模的主动容错控制，对电堆出口温度实现精确控制。本发明解决了质子交换膜燃料电池系统温度控制模型在运行过程中的传感器失效问题，将基于模型的容错控制运用到温度控制中，可以有效提高燃料电池系统的可靠性与耐久性。

Description

一种质子交换膜燃料电池系统温度主动容错控制方法 技术领域
本发明属于燃料电池应用领域的一种电池系统控制方法，尤其是涉及了一种质子交换膜燃料电池系统温度主动容错控制方法。
背景技术
由于环境污染与能源危机，以质子交换膜燃料电池为首的新能源正在受到人们越来越多的重视。燃料电池具有功率密度大，转化效率高，无污染等优点，在分布式发电，新能源汽车，储能等领域都得到了越来越多的运用。但是根据美国DOE发布的年度报告，燃料电池的经济性与耐久性均未完全达到商业化的指标。在这其中，电堆制造工艺与系统控制是解决其商业化的重要手段，而温度控制更是影响电堆性能的重要因素。电堆在实际的运行过程中会产生大量热量，过高的温度会导致膜干，影响膜的活性；过低的温度会造成水淹，影响电堆的效率。温度控制失常会影响电堆性能，反之，电堆或辅助系统发生故障也会影响温度控制。主动容错控制是一种基于故障诊断的容错控制。故障诊断可以分为基于模型与基于数据两大类，基于数据的故障诊断需要大量数据训练，缺乏一定的鲁棒性；基于模型的故障诊断需要建立精确的数学模型，但具有较好的鲁棒性。温度控制是燃料电池控制类的重要研究领域，现有研究成果包括PID控制，前馈控制，模型预测控制，模糊控制等，但上述方法都未考虑到系统故障，且控制精度不高，因此在实际操作过程中具有一定的局限性。综上所述，基于模型的质子交换膜燃料电池温度主动容错控制的研究是具有重要价值。
发明内容
为了解决背景技术中的问题，本发明提出了一种质子交换膜燃料电池系统温度主动容错控制方法，使用结构分析法与Dulmage-Mendelsohn法分解获得模型冗余部分，从而产生系统余差以诊断传感器故障，并采用基于滑模的主动容错控制实现了质子交换膜燃料电池系统温度的容错控制，提高了温度控制系统的鲁棒性。
本发明采用的技术方案包括以下步骤：
S1：建立燃料电池系统温度控制模型；
S2：通过结构分析法(Krysander M,
Figure PCTCN2020082646-appb-000001
J,Frisk E.A structural algorithm for finding testable sub-models and multiple fault isolability analysis[C]//21st International Workshop on Principles of Diagnosis(DX-10),Portland,Oregon,USA. 2010:17-18.)建立系统结构矩阵；
通过结构分析法针对燃料电池系统温度控制模型建立横坐标为系统未知变量、纵坐标为系统方程的系统结构矩阵；对于系统结构矩阵中的任一元素，若矩阵元素所对应纵坐标的系统方程包含有该矩阵元素所对应横坐标的系统未知变量，则将该矩阵元素标记为1，反之则标记为0；
S3：使用Dulmage-Mendelsohn法分解系统结构矩阵，分解后的系统结构矩阵呈现为上三角形态；获得分解后的系统结构矩阵的冗余部分，冗余部分为分解后的系统结构矩阵中系统方程数量大于系统未知变量数量的区域；并在冗余部分中构建余差，通过余差在线检测燃料电池系统温度控制模型中的传感器故障：若余差低于设定阈值，则表明燃料电池系统温度控制模型中的传感器未发生故障，若余差超过设定阈值，则表明燃料电池系统温度控制模型中的传感器发生故障；
S4：在步骤S3识别传感器故障基础上，设计基于滑模控制器的主动容错控制结构，对燃料电池出口温度实现容错控制。
所述步骤S1中的燃料电池系统温度控制模型由燃料电池温度模型、电堆电压模型和辅助系统半经验模型共同构成；
1)根据能量守恒定律以及热力学原理，建立如下燃料电池温度模型；
Figure PCTCN2020082646-appb-000002
其中，M st为电堆质量，C st为电堆热容，T st,out为电堆温度，电堆温度采用电堆冷却水出口温度；
Figure PCTCN2020082646-appb-000003
为电堆反应物带入的化学能，
Figure PCTCN2020082646-appb-000004
为电堆的输入气体带入的能量，
Figure PCTCN2020082646-appb-000005
为输出气体带出的能量，
Figure PCTCN2020082646-appb-000006
为电堆中的负载输出功率，
Figure PCTCN2020082646-appb-000007
为电堆冷却水带走的能量，
Figure PCTCN2020082646-appb-000008
为电堆自身散热；
所述电堆为燃料电池简称；
2)根据电化学原理，建立电堆电压模型(Wu X,Zhou B.Fault tolerance control for proton exchange membrane fuel cell systems[J].Journal of Power Sources,2016,324:804-829.)；
3)针对辅助系统，建立辅助系统的半经验模型；
辅助系统为与电堆相连的散热器、水泵，半经验模型包括水泵模型与散热器模型；
水泵模型通过水泵电压V pump与流量W cl拟合获得，具体形式如下：
Figure PCTCN2020082646-appb-000009
散热器模型通过散热器出口温差T diff、流量W cl、风扇转速ω、室温T 0拟合 获得，具体形式如下：
Figure PCTCN2020082646-appb-000010
其中，F 1(ω)是风扇转速ω的非线性转换函数，定义如下：
Figure PCTCN2020082646-appb-000011
F 2(T 0)是经验散热函数，定义如下：
F 2(T 0)＝(T 0-25)/ln(T 0/25)-25
由于辅助系统响应速度快，相比于燃料电池温度大时滞可以忽略暂态响应，通过最小二乘拟合得出辅助系统半经验模型；
所述步骤S2中，系统方程包括燃料电池温度模型、电堆电压模型以及辅助系统半经验模型中的所有模型方程；系统未知变量包括燃料电池温度模型、电堆电压模型以及辅助系统半经验模型方程中所有随时间变化发生变化的变量。
所述步骤S3中，余差是针对系统未知变量，通过系统方程获得对应系统未知变量的解析解，并与对应的传感器数值相减构建得到；燃料电池系统温度控制模型中的传感器为电堆进口温度、电堆出口温度、冷却水流量、风扇出口温度和电堆电压的传感器。
所述步骤S4的主动容错控制结构主要由滑模控制器、故障检测模块以及控制模块构成；故障检测模块根据燃料电池系统温度控制模型中温度、气压、体积或质量流量、电压这些参数判断模型中的传感器是否发生故障，在故障检测模块检测到模型中的传感器发生故障后，控制模块根据燃料电池系统温度控制模型重构发生故障的传感器信号并反馈至滑模控制器，最终由滑模控制器通过反馈实现燃料电池出口温度的容错控制。
根据燃料电池系统温度控制模型建立所述的滑模控制器，滑模控制器输入为燃料电池设定温度，滑模控制器输出为冷却水流量，冷却水流量作为滑模控制器的控制变量；根据燃料电池系统温度控制模型设计滑模控制器的滑模面，为避免水泵产生震荡，采用平滑处理的切换函数；此外考虑到滑模控制器中积分效果带来的过大超调作用，在滑模面上加入积分分离式抗积分饱和方法，以加快系统响应能力与稳定性。
本发明的有益效果：
本发明解决了质子交换膜燃料电池温度控制系统在运行过程中的传感器失效问题，将基于模型的容错控制方法运用到温度控制中，可以有效提高燃料电池系统的可靠性与耐久性。
附图说明
图1是本发明中经过Dulmage-Mendelsohn分解后得到的结构矩阵。
图2是本发明实施例中正常状态下设定温度改变实验结果。
图3是本发明实施例中正常状态下负载功率改变实验结果。
图4是本发明实施例中故障状态下温度控制实验结果。
图5是本发明质子交换膜燃料电池的装置图。
具体实施方式
下面结合附图和具体实施例对本发明进行详细说明。
如图5所示，左侧回路中，电子负载与燃料电池相连，为燃料电池提供负载；右侧回路中，水通过水泵流入散热器，风扇为散热器提供冷却气流，水经过水箱后最终注入燃料电池为其散热。
基于模型的质子交换膜燃料电池温度主动容错控制方法包括以下步骤：
S1：建立燃料电池系统温度控制模型；
步骤S1中的燃料电池系统温度控制模型由燃料电池温度模型、电堆电压模型和辅助系统半经验模型共同构成；
1)根据能量守恒定律以及热力学原理，建立如下燃料电池温度模型；
Figure PCTCN2020082646-appb-000012
其中，M st为电堆质量，C st为电堆热容，T st,out为电堆温度，电堆温度采用电堆冷却水出口温度；
Figure PCTCN2020082646-appb-000013
为电堆反应物带入的化学能，
Figure PCTCN2020082646-appb-000014
为电堆的输入气体带入的能量，
Figure PCTCN2020082646-appb-000015
为输出气体带出的能量，
Figure PCTCN2020082646-appb-000016
为电堆中的负载输出功率，
Figure PCTCN2020082646-appb-000017
为电堆冷却水带走的能量，
Figure PCTCN2020082646-appb-000018
为电堆自身散热；
2)根据电化学原理，建立电堆电压模型(Wu X,Zhou B.Fault tolerance control for proton exchange membrane fuel cell systems[J].Journal of Power Sources,2016,324:804-829.)；
3)针对辅助系统，建立辅助系统的半经验模型；
辅助系统为与电堆相连的散热器、水泵，半经验模型包括水泵模型与散热器模型；
水泵模型通过水泵电压V pump与流量W cl拟合获得，具体形式如下：
Figure PCTCN2020082646-appb-000019
散热器模型通过散热器出口温差T diff、流量W cl、风扇转速ω、室温T 0拟合获得，具体形式如下：
Figure PCTCN2020082646-appb-000020
其中，F 1(ω)是风扇转速ω的非线性转换函数，定义如下：
Figure PCTCN2020082646-appb-000021
F 2(T 0)是经验散热函数，定义如下：
F 2(T 0)＝(T 0-25)/ln(T 0/25)-25
由于辅助系统响应速度快，相比于燃料电池温度大时滞可以忽略暂态响应，通过最小二乘拟合得出辅助系统半经验模型；
S2：通过结构分析法建立系统结构矩阵；
通过结构分析法(Krysander M,
Figure PCTCN2020082646-appb-000022
J,Frisk E.A structural algorithm for finding testable sub-models and multiple fault isolability analysis[C]//21st International Workshop on Principles of Diagnosis(DX-10),Portland,Oregon,USA.2010:17-18.)针对燃料电池系统温度控制模型建立横坐标为系统未知变量、纵坐标为系统方程的系统结构矩阵；对于系统结构矩阵中的任一元素，若矩阵元素所对应纵坐标的系统方程包含有该矩阵元素所对应横坐标的系统未知变量，则将该矩阵元素标记为1，反之则标记为0；
步骤S2中，系统方程包括燃料电池温度模型、电堆电压模型以及辅助系统半经验模型中的所有模型方程；系统未知变量包括燃料电池温度模型、电堆电压模型以及辅助系统半经验模型方程中所有随时间变化发生变化的变量，如化学能、内能、体积或质量流量、电压和气压。
S3：使用Dulmage-Mendelsohn法分解步骤S2的系统结构矩阵，从而获得冗余部分，并在冗余部分中构建系统余差以识别模型中的传感器故障；
3.1)使用Dulmage-Mendelsohn法分解系统结构矩阵，分解后的系统结构矩阵如图1所示，呈现为上三角形态，上三角形态是系统结构矩阵中主对角线以下都是零的一种形态；
其中，图1纵坐标中的系统方程e1为能量守恒方程，e2为理论放热方程，e3为理论氢气消耗方程，e4为输入内能方程，e5为输入氢气方程，e6为阳极输入饱和蒸汽方程，e7为输入空气方程，e8为理论氧气消耗方程，e9为阴极输入饱和蒸汽方程，e10为输出内能方程，e11为输出氢气方程，e12为阳极输出饱和蒸汽方程，e13为氧气输出方程，e14为氮气输出方程，e15为输入氧气方程，e16为阴极输出饱和蒸汽方程，e17为反应生成水方程，e18为冷却水散热方程，e19为燃料电池散热方程，e20为电池负载方程，e21为电压方程，e22为能斯特方程，e23为氢气分压方程，e24为氧气分压方程，e25为活化损耗方程，e26为氧气浓度方程，e27为欧姆损耗方程，e28为内阻方程，e29为内阻辨识方程，e30为浓度损耗方程，e31为水箱方程，e32为散热器方程，e33为水泵方程，e34 燃料电池出口温度传感器故障方程，e35为散热器出口温度传感器故障方程，e36为燃料电池进口温度传感器故障方程，e37为流量传感器故障方程，e38为电压传感器故障方程，e39为氢气流量传感器方程，e40为空气流量传感器方程，e41为阳极压力传感器方程，e42为阴极压力传感器方程，e43为负载传感器方程；
图1横坐标中的未知变量
Figure PCTCN2020082646-appb-000023
为反应理论放热，
Figure PCTCN2020082646-appb-000024
为输入内能，
Figure PCTCN2020082646-appb-000025
为输出内能，
Figure PCTCN2020082646-appb-000026
为冷却水散热，
Figure PCTCN2020082646-appb-000027
为燃料电池散热，N W,ai为阳极输入饱和蒸汽量，N W,ci为阴极输入饱和蒸汽量，
Figure PCTCN2020082646-appb-000028
为阳极输出氢气流量，N W,ao为阳极输出饱和蒸汽流量，
Figure PCTCN2020082646-appb-000029
为阴极输出氧气流量，
Figure PCTCN2020082646-appb-000030
为阴极输出氮气流量，N W,cg为阴极生成水量，
Figure PCTCN2020082646-appb-000031
为阴极输入氧气流量，V act为活化损耗，V ohmic为欧姆损耗，V con为浓度损耗，
Figure PCTCN2020082646-appb-000032
为氢气分压，
Figure PCTCN2020082646-appb-000033
为氧气分压，
Figure PCTCN2020082646-appb-000034
为氧气浓度，R为欧姆内阻，ξ为欧姆内阻参数，E nernst为能斯特电压，
Figure PCTCN2020082646-appb-000035
为反应消耗氢气，
Figure PCTCN2020082646-appb-000036
为反应消耗氧气，T st,out为燃料电池出口温度，
Figure PCTCN2020082646-appb-000037
为负载功率，V cell为燃料电池电压，T ex,out为散热器出口温度，T st,in为燃料电池进口温度，W cl为冷却水流量，
Figure PCTCN2020082646-appb-000038
为阳极输入氢气流量，N Air,ci为阴极输入空气流量，P a为阳极压力，P c为阴极压力。
3.2)获得分解后的系统结构矩阵的冗余部分，冗余部分为图1矩阵中的右下部分，冗余部分为分解后的系统结构矩阵中系统方程数量大于系统未知变量数量的区域；
3.3)在冗余部分中构建余差，余差是针对系统未知变量，通过系统方程获得对应系统未知变量的解析解，并与对应的传感器数值相减构建得到；通过余差在线检测燃料电池系统温度控制模型中的传感器故障：若余差低于设定阈值，则表明燃料电池系统温度控制模型中的传感器未发生故障，若余差超过设定阈值，则表明燃料电池系统温度控制模型中的传感器发生故障。
燃料电池系统温度控制模型中的传感器为电堆进口温度、电堆出口温度、冷却水流量、风扇出口温度和电堆电压的传感器。图1矩阵中下方5条虚线分别表示电堆进口温度、电堆出口温度、冷却水流量、风扇出口温度、电堆电压这5类传感器故障，由于所有的故障都位于模型的冗余部分，且彼此分离，因此故障可以实现检测并且做到故障之间互不干扰，即故障隔离。
S4：在步骤S3识别传感器故障基础上，设计基于滑模控制器的主动容错控制结构，对燃料电池出口温度实现容错控制。
所述步骤S4的主动容错控制结构主要由滑模控制器、故障检测模块以及控制模块构成；故障检测模块根据燃料电池系统温度控制模型中温度、气压、体积或质量流量、电压这些参数判断模型中的传感器是否发生故障，在故障检测 模块检测到模型中的传感器发生故障后，控制模块根据燃料电池系统温度控制模型重构发生故障的传感器信号并反馈至滑模控制器，最终由滑模控制器通过反馈实现燃料电池出口温度的容错控制。
实施例：
通过实验验证该容错控制策略的有效性，分正常实验与故障实验。实验在一3kW质子交换膜燃料电池实验平台上进行，使用的燃料电池具有18片单体电池。
在正常实验中，燃料电池温度控制器的高控制精度以及较好的鲁棒性得到了体现。如图2所示，在设定温度发生改变的时候，燃料电池出口温度能够跟踪设定温度，且控制精度在±0.5℃以内；得益于积分分离式抗积分饱和，在设定温度发生阶跃的时候并没有产生大的超调，提高了控制器的稳定性。控制器在负载变动下的表现如图3所示，系统负载变化导致电堆内部产热变化，但控制器能够较好抑制电堆出口温度变化。
在故障实验中，通过对燃料电池入口温度传感器添加偏差故障以检测容错控制器效果。如图4所示，在约200秒加入故障，故障采集模块通过计算该故障对应的系统余差，系统余差超过阈值后判定为传感器故障，并将重构传感器信号输入至滑模控制器进行容错控制。从实验结果可以看出，即使在入口传感器温度出现偏差的状况下，该控制器仍旧能够将出口温度精确控制在设定值上。

Claims (6)

  1. 一种质子交换膜燃料电池系统温度主动容错控制方法，其特征在于，包括以下步骤：
    S1：建立燃料电池系统温度控制模型；
    S2：通过结构分析法建立系统结构矩阵；
    通过结构分析法针对燃料电池系统温度控制模型建立横坐标为系统未知变量、纵坐标为系统方程的系统结构矩阵；对于系统结构矩阵中的任一元素，若矩阵元素所对应纵坐标的系统方程包含有该矩阵元素所对应横坐标的系统未知变量，则将该矩阵元素标记为1，反之则标记为0；
    S3：使用Dulmage-Mendelsohn法分解系统结构矩阵，分解后的系统结构矩阵呈现为上三角形态；获得分解后的系统结构矩阵的冗余部分，冗余部分为分解后的系统结构矩阵中系统方程数量大于系统未知变量数量的区域；并在冗余部分中构建余差，通过余差在线检测燃料电池系统温度控制模型中的传感器故障：若余差低于设定阈值，则表明燃料电池系统温度控制模型中的传感器未发生故障，若余差超过设定阈值，则表明燃料电池系统温度控制模型中的传感器发生故障；
    S4：在步骤S3识别传感器故障基础上，设计基于滑模控制器的主动容错控制结构，对燃料电池出口温度实现容错控制。
  2. 根据权利要求1所述的一种质子交换膜燃料电池系统温度主动容错控制方法，其特征在于，所述步骤S1中的燃料电池系统温度控制模型由燃料电池温度模型、电堆电压模型和辅助系统半经验模型共同构成；
    1)根据能量守恒定律以及热力学原理，建立如下燃料电池温度模型；
    Figure PCTCN2020082646-appb-100001
    其中，M st为电堆质量，C st为电堆热容，T st,out为电堆温度，电堆温度采用电堆冷却水出口温度；
    Figure PCTCN2020082646-appb-100002
    为电堆反应物带入的化学能，
    Figure PCTCN2020082646-appb-100003
    为电堆的输入气体带入的能量，
    Figure PCTCN2020082646-appb-100004
    为输出气体带出的能量，
    Figure PCTCN2020082646-appb-100005
    为电堆中的负载输出功率，
    Figure PCTCN2020082646-appb-100006
    为电堆冷却水带走的能量，
    Figure PCTCN2020082646-appb-100007
    为电堆自身散热；
    所述电堆为燃料电池简称；
    2)根据电化学原理，建立电堆电压模型；
    3)针对辅助系统，建立辅助系统的半经验模型；
    辅助系统为与电堆相连的散热器、水泵，半经验模型包括水泵模型与散热 器模型；水泵模型通过水泵电压V pump与流量V cl拟合获得，具体形式如下：
    Figure PCTCN2020082646-appb-100008
    散热器模型通过散热器出口温差T diff、流量W cl、风扇转速ω、室温T 0拟合获得，具体形式如下：
    Figure PCTCN2020082646-appb-100009
    其中，F 1(ω)是风扇转速ω的非线性转换函数，定义如下：
    Figure PCTCN2020082646-appb-100010
    F 2(T 0)是经验散热函数，定义如下：
    F 2(T 0)＝(T 0-25)/ln(T 0/25)-25。
  3. 根据权利要求2所述的一种质子交换膜燃料电池系统温度主动容错控制方法，其特征在于，所述步骤S2中，系统方程包括燃料电池温度模型、电堆电压模型以及辅助系统半经验模型中的所有模型方程；系统未知变量包括燃料电池温度模型、电堆电压模型以及辅助系统半经验模型方程中所有随时间变化发生变化的变量。
  4. 根据权利要求1所述的一种质子交换膜燃料电池系统温度主动容错控制方法，其特征在于，所述步骤S3中，燃料电池系统温度控制模型中的传感器为电堆进口温度、电堆出口温度、冷却水流量、风扇出口温度和电堆电压的传感器。
  5. 根据权利要求1所述的一种质子交换膜燃料电池系统温度主动容错控制方法，其特征在于，所述步骤S4的主动容错控制结构主要由滑模控制器、故障检测模块以及控制模块构成；故障检测模块根据燃料电池系统温度控制模型中温度、气压、体积或质量流量、电压这些参数判断模型中的传感器是否发生故障，在故障检测模块检测到模型中的传感器发生故障后，控制模块根据燃料电池系统温度控制模型重构发生故障的传感器信号并反馈至滑模控制器，最终由滑模控制器通过反馈实现燃料电池出口温度的容错控制。
  6. 根据权利要求5所述的一种质子交换膜燃料电池系统温度主动容错控制方法，其特征在于，根据燃料电池系统温度控制模型建立所述的滑模控制器，滑模控制器输入为燃料电池设定温度，滑模控制器输出为冷却水流量，冷却水流量作为滑模控制器的控制变量；根据燃料电池系统温度控制模型设计滑模控制器的滑模面，为避免水泵产生震荡，采用平滑处理的切换函数；由于滑模控制器中积分效果带来的过大超调作用，在滑模面上加入积分分离式抗积分饱和方法，以加快系统响应能力与稳定性。
PCT/CN2020/082646 2019-04-02 2020-04-01 一种质子交换膜燃料电池系统温度主动容错控制方法 WO2020200214A1 (zh)

Priority Applications (1)

Application Number Priority Date Filing Date Title
US17/311,700 US11916270B2 (en) 2019-04-02 2020-04-01 Active fault-tolerant temperature control method for proton exchange membrane fuel cell system

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN201910261414.2A CN109950587A (zh) 2019-04-02 2019-04-02 一种质子交换膜燃料电池系统温度主动容错控制方法
CN201910261414.2 2019-04-02

Publications (1)

Publication Number Publication Date
WO2020200214A1 true WO2020200214A1 (zh) 2020-10-08

Family

ID=67013505

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2020/082646 WO2020200214A1 (zh) 2019-04-02 2020-04-01 一种质子交换膜燃料电池系统温度主动容错控制方法

Country Status (3)

Country Link
US (1) US11916270B2 (zh)
CN (2) CN109950587A (zh)
WO (1) WO2020200214A1 (zh)

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113721457A (zh) * 2021-05-24 2021-11-30 上海申风投资管理有限公司 基于lmi的氢燃料电池最优参数可变控制器的设计方法
CN114325398A (zh) * 2021-11-08 2022-04-12 淮阴工学院 一种针对质子交换膜燃料电池系统的故障检测方法
CN114840969A (zh) * 2022-03-11 2022-08-02 合肥工业大学 一种非线性机电系统的主动容错控制方法
CN115117391A (zh) * 2022-07-08 2022-09-27 新源动力股份有限公司 一种基于模糊逻辑与模型相结合的燃料电池热管理控制方法

Families Citing this family (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109950587A (zh) * 2019-04-02 2019-06-28 浙江大学 一种质子交换膜燃料电池系统温度主动容错控制方法
DE102020124076A1 (de) 2020-09-16 2022-03-17 Audi Aktiengesellschaft Temperatursteuervorrichtung mit Sensorplausibilisierung
CN112448005B (zh) * 2020-11-11 2022-02-01 湖北工业大学 一种燃料电池发动机出堆温度传感器失效的温度容错控制方法
CN113346111B (zh) * 2021-05-08 2022-11-22 中汽研汽车检验中心（天津）有限公司 一种质子交换膜燃料电池系统的建模方法
CN114420979B (zh) * 2021-12-09 2024-04-02 浙江大学 一种基于模型的质子交换膜燃料电池温度解耦控制方法
CN114492087B (zh) * 2022-04-02 2022-07-19 国网浙江省电力有限公司电力科学研究院 氢储能电站质子交换膜燃料电池故障诊断方法及装置
CN114725449A (zh) * 2022-04-22 2022-07-08 湖北工业大学 一种燃料电池发动机出堆温度传感器故障信号重构及预测方法
CN114976133B (zh) * 2022-06-30 2024-04-02 浙江大学 基于模型的质子交换膜燃料电池水管理容错控制方法
CN115275286A (zh) * 2022-08-16 2022-11-01 中国第一汽车股份有限公司 燃料电池的故障检测方法以及装置
CN115513489B (zh) * 2022-09-14 2023-07-25 武汉雄韬氢雄燃料电池科技有限公司 一种燃料电池散热子系统仿真方法

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20060063048A1 (en) * 2004-09-23 2006-03-23 Kolodziej Jason R Optimal temperature tracking for necessary and accurate thermal control of a fuel cell system
CN102968056A (zh) * 2012-12-07 2013-03-13 上海电机学院 质子交换膜燃料电池的建模系统及其智能预测控制方法
CN105206852A (zh) * 2015-09-30 2015-12-30 上海电机学院 质子交换膜燃料电池的温度控制方法
CN107317045A (zh) * 2017-07-28 2017-11-03 电子科技大学 一种固体氧化物燃料电池系统的最优容错控制方法
CN109950587A (zh) * 2019-04-02 2019-06-28 浙江大学 一种质子交换膜燃料电池系统温度主动容错控制方法

Family Cites Families (15)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6652967B2 (en) * 2001-08-08 2003-11-25 Nanoproducts Corporation Nano-dispersed powders and methods for their manufacture
JP4682801B2 (ja) * 2005-10-26 2011-05-11 トヨタ自動車株式会社 均質化法を用いる構造解析方法、均質化法を用いる構造解析装置及び均質化法を用いる構造解析プログラム
CN1915703A (zh) * 2006-08-31 2007-02-21 上海燃料电池汽车动力系统有限公司 具有冗余容错的燃料电池车辆动力驱动系统
US8906570B2 (en) * 2009-01-28 2014-12-09 GM Global Technology Operations LLC System and method for observing anode fluid composition during fuel cell start-up
CN102097636B (zh) * 2011-01-07 2013-07-17 武汉理工大学 一种燃料电池系统的故障诊断装置及方法
US9012101B2 (en) * 2012-06-07 2015-04-21 Relion, Inc. Fuel cell stacks
CN103384014A (zh) * 2013-05-29 2013-11-06 西南交通大学 基于最大净功率策略的质子交换膜燃料电池空气供应系统控制
KR101670727B1 (ko) * 2014-11-27 2016-11-09 한국과학기술연구원 농도 센서를 사용하지 않는 전압진폭 제어 기반의 피드백 제어 방식에 의한 액체형 연료전지의 연료농도 제어 방법 및 제어 장치, 이를 이용한 액체형 연료전지 장치
GB2541933B (en) * 2015-09-04 2017-11-22 Ceres Ip Co Ltd Temperature measuring method
FR3041479A1 (fr) * 2015-09-21 2017-03-24 Commissariat Energie Atomique Determination d’une distribution spatiale de l’activite catalytique d’une electrode de cellule electrochimique
US10622652B2 (en) * 2015-10-30 2020-04-14 Lg Chem, Ltd. Fuel cell system and control method therefor
CN105680071B (zh) * 2016-03-16 2018-04-13 华中科技大学 基于分数阶滑模变结构sofc系统热电协同控制方法
CN106229531B (zh) * 2016-09-29 2019-01-29 清华大学 燃料电池车辆容错控制方法及装置
CN108615917B (zh) * 2018-04-11 2020-08-18 华中科技大学 一种固体氧化物燃料电池系统的故障检测系统及方法
CN109524693B (zh) * 2018-11-13 2021-04-09 吉林大学 燃料电池空气供给系统模型预测控制方法

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20060063048A1 (en) * 2004-09-23 2006-03-23 Kolodziej Jason R Optimal temperature tracking for necessary and accurate thermal control of a fuel cell system
CN102968056A (zh) * 2012-12-07 2013-03-13 上海电机学院 质子交换膜燃料电池的建模系统及其智能预测控制方法
CN105206852A (zh) * 2015-09-30 2015-12-30 上海电机学院 质子交换膜燃料电池的温度控制方法
CN107317045A (zh) * 2017-07-28 2017-11-03 电子科技大学 一种固体氧化物燃料电池系统的最优容错控制方法
CN109950587A (zh) * 2019-04-02 2019-06-28 浙江大学 一种质子交换膜燃料电池系统温度主动容错控制方法

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113721457A (zh) * 2021-05-24 2021-11-30 上海申风投资管理有限公司 基于lmi的氢燃料电池最优参数可变控制器的设计方法
CN114325398A (zh) * 2021-11-08 2022-04-12 淮阴工学院 一种针对质子交换膜燃料电池系统的故障检测方法
CN114840969A (zh) * 2022-03-11 2022-08-02 合肥工业大学 一种非线性机电系统的主动容错控制方法
CN114840969B (zh) * 2022-03-11 2024-02-09 合肥工业大学 一种非线性机电系统的主动容错控制方法
CN115117391A (zh) * 2022-07-08 2022-09-27 新源动力股份有限公司 一种基于模糊逻辑与模型相结合的燃料电池热管理控制方法

Also Published As

Publication number Publication date
CN111244509B (zh) 2021-08-03
CN109950587A (zh) 2019-06-28
US20220029184A1 (en) 2022-01-27
US11916270B2 (en) 2024-02-27
CN111244509A (zh) 2020-06-05

Similar Documents

Publication Publication Date Title
WO2020200214A1 (zh) 一种质子交换膜燃料电池系统温度主动容错控制方法
Chen et al. The reactant starvation of the proton exchange membrane fuel cells for vehicular applications: A review
Gu et al. Data-based flooding fault diagnosis of proton exchange membrane fuel cell systems using LSTM networks
Liu et al. Robust model-based fault diagnosis for PEM fuel cell air-feed system
Rotondo et al. Robust fault diagnosis of proton exchange membrane fuel cells using a Takagi-Sugeno interval observer approach
CN108344947A (zh) 一种非侵入式的燃料电池故障诊断方法
Lebreton et al. Fault Tolerant Control Strategy applied to PEMFC water management
Liu et al. PEM fuel cell air-feed system observer design for automotive applications: An adaptive numerical differentiation approach
Zuo et al. Data-driven flooding fault diagnosis method for proton-exchange membrane fuel cells using deep learning technologies
Chen et al. Thermodynamic and economic study of PEMFC stack considering degradation characteristic
Xu et al. Nonlinear observation of internal states of fuel cell cathode utilizing a high-order sliding-mode algorithm
Kazmi et al. Parameter estimation of proton exchange membrane fuel cell system using sliding mode observer
Oh et al. Residual-based fault diagnosis for thermal management systems of proton exchange membrane fuel cells
Li et al. An LSTM and ANN fusion dynamic model of a proton exchange membrane fuel cell
Yang et al. Fault detection and isolation of pem fuel cell system by analytical redundancy
Xu et al. Interactions between a polymer electrolyte membrane fuel cell and boost converter utilizing a multiscale model
Song et al. AI-based proton exchange membrane fuel cell inlet relative humidity control
KR102231125B1 (ko) 연료전지 시스템의 다단계 고장 진단 방법 및 장치
Grondin-Perez et al. Mechanistic model versus artificial neural network model of a single-cell pemfc
CN116231013A (zh) 燃料电池故障嵌入模型的建立方法
Li Data-driven fault diagnosis for PEMFC systems
Salim et al. A review on fault diagnosis tools of the proton exchange membrane fuel cell
Gong et al. High-precision and efficiency diagnosis for polymer electrolyte membrane fuel cell based on physical mechanism and deep learning
Sun et al. PEM fuel cell thermal management strategy based on multi-model predictive control
Yang et al. Model based fault detection and isolation of PEM fuel cell

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 20784755

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 20784755

Country of ref document: EP

Kind code of ref document: A1