A hydrogen fuel cell vehicle battery thermal management and life prediction collaborative control method

Abstract

The application discloses a hydrogen fuel cell vehicle battery thermal management and life prediction collaborative control method, comprising the following steps: collecting operating temperature, current density and reaction gas partial pressure, constructing an electrochemical thermal coupling model to obtain output voltage and heat generation power; establishing an aging attenuation model based on temperature, current and voltage to obtain a health state and feedback correction voltage model; constructing a three-mode switching state machine according to temperature dynamics, environmental temperature and the best working interval to determine the operating mode; combining thermal management energy consumption, health state and mode to establish a thermal-electric life multi-objective collaborative optimization model; and adopting rolling optimization solving of model predictive control to obtain optimal control quantity and issue execution. Through the cooperation of electrochemistry, thermodynamics and aging degradation mechanism, the application realizes the balance of battery temperature accurate regulation and control and life attenuation inhibition, and significantly improves the comprehensive efficiency of the thermal management system and the durability of the fuel cell.

Description

Technical Field

[0001] This invention relates to the field of hydrogen fuel cell vehicles, and in particular to a method for coordinated control of battery thermal management and life prediction in hydrogen fuel cell vehicles. Background Technology

[0002] Hydrogen fuel cell vehicles, as a key technological route for new energy vehicles, have become a major development direction for the global automotive industry's green and low-carbon transformation due to their significant advantages such as zero emissions, long driving range, and short refueling time. In recent years, with advancements in fuel cell technology and the construction of hydrogen energy infrastructure, hydrogen fuel cell vehicles, with their outstanding characteristics of zero emissions and high energy conversion efficiency, have become a core technology choice for deep decarbonization in the transportation sector. However, in the process of their gradual promotion to the commercial vehicle market, existing technological solutions have revealed several shortcomings that need optimization: the short lifespan and poor durability of current automotive fuel cell systems have become core bottlenecks restricting their large-scale application, and existing thermal management technologies... The following shortcomings exist: thermal management strategies and lifespan predictions are disconnected; aging status information is not fed back to the thermal management controller in real time to participate in online optimization decisions, resulting in a lack of adaptive capability of the control strategy throughout the battery's entire lifespan; the optimization objective is singular, with temperature control accuracy as the primary goal, failing to incorporate thermal management energy consumption, output electrical performance, and lifespan degradation into a unified multi-objective collaborative optimization framework; the control mode is singular, lacking a multi-mode collaborative control architecture that adaptively switches according to operating conditions and environmental conditions; existing MPC-based methods mostly use open-loop settings in the prediction model, failing to consider the dynamic correction of electrochemical model parameters and heat generation characteristics by aging conditions, resulting in a gradual decrease in prediction accuracy as the battery ages. Summary of the Invention

[0003] This invention provides a collaborative control method for thermal management and life prediction of hydrogen fuel cell vehicles, which overcomes the problems of fragmented thermal management strategies and life prediction, single optimization objective dimension, single control mode, and gradual decrease in prediction accuracy as the battery ages.

[0004] To achieve the above objectives, the technical solution of the present invention is as follows: A method for coordinated control of thermal management and lifespan prediction in hydrogen fuel cell vehicle batteries includes: S1. Collect real-time operating temperature, current density, and partial pressure of reactant gases of hydrogen fuel cells to construct an electrochemical-thermal coupling model; The electrochemical-thermal coupling model includes a voltage sub-model and a thermal management energy consumption sub-model. S2. Based on the voltage sub-model, obtain the battery output voltage, and combine it with the real-time operating temperature and current density of the battery to construct an aging degradation model to obtain aging state parameters and battery health status; and feed the aging state parameters back to the voltage sub-model in step S1 to correct the voltage sub-model. The aging degradation model includes a temperature-voltage joint acceleration sub-model, an ohmic impedance evolution sub-model, and a mass transfer degradation sub-model. S3. Based on the real-time operating temperature of the battery and the preset optimal operating temperature range, construct a three-mode switching state machine to obtain the operating mode state of the current control cycle. S4. Construct a heat dissipation management energy consumption model, and combine the battery health status and the current control cycle operation mode status to construct a multi-objective collaborative optimization model of thermal-electrical-lifetime. S5. The multi-objective collaborative optimization model of thermal-electrical-lifetime is solved by the rolling optimization solution method based on model predictive control to obtain the optimal control quantity at the current moment. The vehicle controller then sends the optimal control quantity to each actuator for execution via the CAN bus to achieve collaborative control of battery thermal management and battery life prediction.

[0005] Furthermore, the expression for the voltage sub-model is:

[0006] In the formula, This refers to the battery output voltage. The Nernst electromotive force; To activate the overpotential; For Ohm overpotential; This is the concentration overpotential; The expression for the Nernst electromotive force is:

[0007] In the formula, Battery operating temperature; This refers to the partial pressure of hydrogen gas. The partial pressure of oxygen; It stands for volt, or voltage. The expression for the activation overpotential is:

[0008] In the formula, It is the ideal gas constant; This represents the number of electrons transferred in a single reaction. It is Faraday's constant; The charge transfer coefficient; The current current density; Internal leakage current density; The initial current density; The expression for the Ohmic overpotential is:

[0009] In the formula, For membrane resistance; Contact resistance; The expression for the concentration overpotential is:

[0010] In the formula, This is the concentration polarization coefficient. This represents the limiting current density.

[0011] Furthermore, the expression for the thermal management energy consumption sub-model is:

[0012] In the formula, This is the equivalent heat capacity of the battery. This represents the total heat output of the battery. The heat power carried away by the coolant circulation; This refers to the heat power lost from the battery surface to the surrounding environment. The rate of change of battery temperature; The expression for the total heat generation power of the battery is as follows:

[0013] In the formula, This represents the number of individual cell modules connected in series in a hydrogen fuel cell. The effective reaction area of ​​a single cell; The enthalpy change for the electrochemical reaction that produces liquid water; The expression for the heat power carried away by the coolant circulation is:

[0014] In the formula, This refers to the coolant mass flow rate; This refers to the specific heat capacity of the coolant. This refers to the outlet temperature of the battery coolant flow channel. This refers to the inlet temperature of the battery coolant flow channel; The expression for the heat power lost from the battery surface to the surrounding environment is:

[0015] In the formula, The equivalent heat transfer coefficient between the battery casing and the environment; This refers to the surface area for heat dissipation. The ambient temperature.

[0016] Furthermore, the expression for the temperature-voltage joint accelerator model is as follows:

[0017]

[0018] In the formula, For operation t The exchange current density at any given time; This represents the initial exchange current density; This is the activation aging rate coefficient; Reference voltage; This is a function reflecting the accelerating effect of temperature rise and voltage fluctuation on catalyst aging; This is the voltage acceleration factor.

[0019] Furthermore, the expression for the Ohmic impedance evolution sub-model is:

[0020]

[0021] In the formula, For operation t Ohmic impedance at time t; The initial ohmic impedance; The ohmic aging rate coefficient; This refers to the relative humidity inside the membrane. The optimal humidity level for durability; Humidity sensitivity coefficient; This is a function that reflects how both excessive dryness and excessive wetness of the membrane accelerate degradation; This is the time integration variable.

[0022] Furthermore, the expression for the mass transfer degradation sub-model is:

[0023] In the formula, For operation t The limiting current density at time t; The initial limiting current density; This is the mass transfer aging rate coefficient; It is the current density acceleration factor.

[0024] Furthermore, the mode switching rules of the three-mode switching state machine are as follows:

[0025] In the formula, The mode state for the next control cycle; This represents the mode state of the current control cycle, and when... The state at that time is low-temperature heating, when The state is normal temperature control, when The state at that time was high temperature cooling; This is the lower limit of the battery's optimal operating temperature; This is the upper limit of the battery's optimal operating temperature; This is a function for switching the direction of temperature decrease; A function for switching the direction of temperature increase; To predict temperature.

[0026] Furthermore, the thermal-electrical-lifetime multi-objective collaborative optimization model aims to minimize the weighted multi-objective cost of the total energy consumption of the thermal management system and the battery health state decay, and is constructed with constraints on battery operating temperature and coolant inlet / outlet temperature difference. The objective function of the thermo-electric-lifetime multi-objective collaborative optimization model is expressed as follows:

[0027] In the formula, For weighted multi-objective cost functions; For control vectors; For the set of control vectors; and These are the weighting coefficients; Total energy consumption of the thermal management system; The energy consumption for low-temperature auxiliary heating is determined based on the operating mode status of the current control cycle. Normalized baseline energy consumption; This represents the amount of battery health degradation. The maximum allowable attenuation; The expression for the total energy consumption of the thermal management system is as follows:

[0028] In the formula, To predict the length of the time domain; This is the initial running time; This refers to the coolant mass flow rate; This refers to the air mass flow rate through the radiator. Power of the cooling water pump; This refers to the power of the cooling fan; The expression for the energy consumption of the low-temperature auxiliary heating is:

[0029] In the formula, For indicator functions; For auxiliary heater power; The expression for the battery health state degradation is:

[0030] In the formula, for The battery health status at any time; for The battery health status at any time; The expression for the weighting coefficient is:

[0031] In the formula, for The battery health status at any time; This is the battery failure threshold.

[0032] Furthermore, the specific steps of the rolling optimization solution method based on model predictive control are as follows: S51. Discretize the thermal management energy consumption model and aging decay model using the forward Euler method to obtain discrete state equations; based on the current discretized state as the initial state, obtain the prediction sequence through the discretized state equations; obtain the output variables through the prediction sequence. S52. The objective function of the thermo-electric-lifetime multi-objective collaborative optimization model is transformed into a finite-time-domain cost function under the model predictive control framework, and the objective function is to minimize the finite-time-domain cost function. The expression of the objective function is as follows:

[0033] In the formula, For the finite-time cost function under the model predictive control framework; For the first The power of the cooling water pump in step; For the first The power of the cooling fan in the step; For the first The auxiliary heater power of the step; For the first k The control sequence to be optimized step; For the first The battery health status of the step; For the first The step indicator function; and For discrete time intervals; S53. Convert the battery operating temperature constraint and the coolant inlet / outlet temperature difference constraint into state constraints and input constraints, expressed as follows:

[0034] In the formula, For the first The battery operating temperature of the step; This refers to the battery's maximum safe operating temperature. This refers to the minimum safe operating temperature of the battery. For the first The coolant mass flow rate at step; This is the lower limit of the coolant mass flow rate; This represents the upper limit of the coolant mass flow rate; For the first The mass flow rate of air passing through the radiator; This represents the upper limit of the air mass flow rate through the radiator. This is the maximum power of the auxiliary heater; S54. Based on the output variables, state constraints, and input constraints, solve the objective function using a sequential quadratic programming algorithm to obtain the optimal control sequence. S55. Take the first element in the optimal control sequence as the optimal control quantity at the current moment.

[0035] Beneficial effects: This invention constructs an electrochemical thermal coupling model that includes a voltage sub-model and a thermal management energy consumption sub-model, and collects real-time operating temperature, current density, and reactant gas partial pressure data. This model can accurately describe the strong coupling relationship between electrochemical reactions, mass transport, and heat generation inside the fuel cell, overcoming the shortcomings of traditional empirical models or single-physics field models in terms of insufficient prediction accuracy under variable load conditions. This model can obtain the battery output voltage and heat generation power in real time, providing high-fidelity input for subsequent thermal management decisions, thereby significantly improving the adaptability and reliability of the control strategy under different operating conditions. By establishing an aging degradation model that includes a temperature-voltage joint acceleration sub-model, an ohmic impedance evolution sub-model, and a mass transfer degradation sub-model, the battery aging process can be comprehensively characterized from three dimensions: electrochemical polarization, ohmic polarization, and concentration polarization. Furthermore, the obtained aging state parameters can be fed back to the voltage model for correction, forming a closed-loop architecture of "aging perception - model update - control decision". This allows the control model to adaptively adjust as the battery usage time increases, effectively solving the problem of model mismatch worsening with aging in traditional methods and ensuring control accuracy throughout the entire life cycle. By constructing a three-mode switching state machine based on battery operating temperature, dynamic temperature change value, ambient temperature, and preset optimal operating temperature range, thermal management requirements are divided into different priority levels. This state machine not only considers the current absolute temperature value but also incorporates temperature change trends, enabling it to respond to sudden temperature changes in advance and avoid frequent actuator switching or mode oscillation. Compared with conventional single threshold switch control, the three-mode switching state machine has hysteresis characteristics and state memory function, which can effectively reduce the start-stop frequency of actuators such as cooling fans and water pumps, reducing accessory energy consumption while ensuring temperature control effect. A multi-objective collaborative optimization model for thermal, electrical, and lifespan was constructed based on thermal management energy consumption models, battery health status, and current operating mode. This model incorporates the minimization of instantaneous energy consumption, temperature deviation, and aging rate into a unified optimization framework, overcoming the drawbacks of traditional strategies that either "unilaterally pursue cooling effects while ignoring lifespan loss" or "overly conservative temperature control leading to high energy consumption." By introducing health status as one of the optimization objectives, it ensures that the long-term durability of the battery stack is not sacrificed for short-term cooling effects under high-temperature or high-current conditions, achieving a dynamic balance between battery performance, lifespan, and vehicle economy. Attached Figure Description

[0036] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0037] Figure 1 This is a flowchart illustrating the battery thermal management and lifespan prediction collaborative control method of the present invention. Figure 2 This is a schematic diagram of a hydrogen fuel cell structure applicable to the synergistic control method for battery thermal management and lifespan prediction in an embodiment of the present invention; Figure 3 This invention relates to a battery control system for a hydrogen fuel cell vehicle that is applicable to a synergistic control method for battery thermal management and lifespan prediction. Detailed Implementation

[0038] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0039] This embodiment provides a method for coordinated control of battery thermal management and lifespan prediction in hydrogen fuel cell vehicles, such as... Figure 1 As shown, it includes: S1. Collect real-time operating temperature, current density, and partial pressure of reactant gases of hydrogen fuel cells to construct an electrochemical-thermal coupling model; The electrochemical-thermal coupling model includes a voltage sub-model and a thermal management energy consumption sub-model. S2. Based on the voltage sub-model, obtain the battery output voltage, and combine it with the real-time operating temperature and current density of the battery to construct an aging degradation model to obtain aging state parameters and battery health status; and feed the aging state parameters back to the voltage sub-model in step S1 to correct the voltage sub-model. The aging degradation model includes a temperature-voltage joint acceleration sub-model, an ohmic impedance evolution sub-model, and a mass transfer degradation sub-model. S3. Based on the real-time operating temperature of the battery and the preset optimal operating temperature range, construct a three-mode switching state machine to obtain the operating mode state of the current control cycle. S4. Construct a heat dissipation management energy consumption model, and combine the battery health status and the current control cycle operation mode status to construct a multi-objective collaborative optimization model of thermal-electrical-lifetime. S5. The multi-objective collaborative optimization model of thermal-electrical-lifetime is solved by the rolling optimization solution method based on model predictive control to obtain the optimal control quantity at the current moment. The vehicle controller then sends the optimal control quantity to each actuator for execution via the CAN bus to achieve collaborative control of battery thermal management and battery life prediction.

[0040] S1. Collect real-time operating temperature, current density, and partial pressure of reactant gases of hydrogen fuel cells to construct an electrochemical-thermal coupling model; The electrochemical-thermal coupling model includes a voltage sub-model and a thermal management energy consumption sub-model. Preferably, in order to quantitatively analyze the voltage response of the hydrogen fuel cell under different temperatures, currents and pressures from the mechanistic perspective, a voltage sub-model is constructed; the hydrogen fuel cell is composed of multiple single cells connected in series, and its total output voltage is the sum of the voltages of all single cells; the actual operating voltage of a single cell, determined by the thermodynamic reversible electromotive force, is affected by polarization losses. The expression for the voltage sub-model is:

[0041] In the formula, This refers to the battery output voltage. The Nernst electromotive force; To activate the overpotential; For Ohm overpotential; This is the concentration overpotential; The real-time operating temperature is used to construct the Nernst electromotive force and the activation overpotential. The current density is used to construct the activation overpotential, ohmic overpotential, and concentration overpotential. The partial pressure of the reactant gas is used to construct the Nernst electromotive force; The Nernst electromotive force represents the maximum voltage output during the electrochemical reaction process of the hydrogen fuel cell, which is equivalent to the theoretical maximum limit of its output voltage; it can provide a benchmark for other polarization losses, and thus determine the degree to which the current operating point deviates from the ideal state. To reflect the influence of temperature and reactant gas pressure on the reversible electromotive force, and to rapidly calculate the theoretical voltage reference based on real-time temperature and pressure data collected by sensors, the expression for the Nernst electromotive force is as follows:

[0042] In the formula, Battery operating temperature; This refers to the partial pressure of hydrogen gas. The partial pressure of oxygen; It stands for volt, or voltage. Where 1.229V is the reversible electromotive force of a hydrogen fuel cell under standard conditions of 298.15K temperature and 1atm pressure; 0.85 is the temperature coefficient of the electromotive force, in mV / K; and 298.15 is the standard reference temperature. Gas constant (take 8.314) ) and Faraday constant (take 96485) and the number of electrons transferred in a single reaction The ratio is used to describe the effect of gas partial pressure on electromotive force; The activation overpotential is the activation energy required for the electrode reaction. In order to describe the nonlinear characteristics of the battery's output voltage dropping sharply at low current density, the exchange current density directly determines the level of activation loss. The expression for the activation overpotential is:

[0043] In the formula, It is the ideal gas constant; This represents the number of electrons transferred in a single reaction. It is Faraday's constant; The charge transfer coefficient; The current current density; Internal leakage current density; The initial current density; The ohmic overpotential is affected by the proton exchange membrane's resistance to proton conduction and the contact resistance between various components. The ohmic impedance directly generates heat, thus affecting the thermal balance, and also reflects the effect of temperature on voltage through the temperature dependence of the membrane resistance. The expression for the Ohmic overpotential is:

[0044] In the formula, For film resistance, the unit is . Its value is affected by the film thickness. and membrane conductivity It is affected by temperature and is very sensitive to temperature, and follows an Arrhenius-type relationship; Contact resistance, unit: ; The expression for membrane resistance is as follows:

[0045] In the formula, For film thickness; For membrane conductivity; For reference temperature, the standard state temperature of 298.15K is usually taken; The concentration overpotential is the additional voltage loss caused by the limited mass transfer of the reactant gas in the porous electrode under high current density. The expression for the concentration overpotential is:

[0046] In the formula, This is the concentration polarization coefficient. The limiting current density is the maximum current density that corresponds to the limit of the mass transfer rate of the reacting gas.

[0047] Preferably, since the change in battery temperature is determined by heat generation and internal and external heat dissipation, in order to obtain the change law of battery temperature and predict the temperature trajectory in subsequent models, a thermal management energy consumption sub-model is now established based on energy conservation. The expression for the thermal management energy consumption sub-model is:

[0048] In the formula, This is the equivalent heat capacity of the battery. This represents the total heat output of the battery. The heat power carried away by the coolant circulation; This refers to the heat power lost from the battery surface to the surrounding environment. The rate of change of battery temperature, in units of ; The total heat generation power of the battery is used to describe the irreversible heat of the electrochemical reaction; The expression for the total heat generation power of the battery is:

[0049] In the formula, This represents the number of individual cell modules connected in series in a hydrogen fuel cell. The effective reaction area of ​​a single cell; The enthalpy change for the electrochemical reaction that produces liquid water, expressed in units of . ; In this context, the first term in parentheses represents the theoretical equivalent thermal power based on the thermal neutral voltage, the second term represents the actual electrical power output, and the difference between the two represents the portion dissipated as heat. The heat power carried away by the coolant circulation is determined by the coolant flow rate and the temperature difference between the inlet and outlet. In order to link the coolant flow rate with the heat dissipation capacity of the battery management system; The expression for the heat power carried away by the coolant circulation is:

[0050] In the formula, This refers to the coolant mass flow rate; This refers to the specific heat capacity of the coolant. This refers to the outlet temperature of the battery coolant flow channel. This refers to the inlet temperature of the battery coolant flow channel; The heat power lost from the battery surface to the surrounding environment is used to evaluate the impact of environmental factors on battery temperature under low temperature cold start and high temperature heat dissipation conditions. The lumped parameter method is used to analyze the heat loss from the battery to the environment. The expression for the heat power lost from the battery surface to the surrounding environment is:

[0051] In the formula, The equivalent heat transfer coefficient between the battery casing and the environment, in units of... ; This refers to the surface area for heat dissipation. Ambient temperature; The equivalent heat transfer coefficient between the battery casing and the environment is affected by the vehicle's speed, and is corrected in real time based on vehicle speed during online calculation; specifically, the vehicle speed signal is acquired in real time via the CAN bus in the vehicle controller. According to the preset and The mapping relationship is used to look up a table or interpolate to obtain the value at the current vehicle speed. Value; the higher the vehicle speed, the stronger the convective heat transfer. As the value increases, the heat power lost by the battery to the environment... The corresponding increase.

[0052] S2. Based on the voltage sub-model, obtain the battery output voltage, and combine it with the real-time operating temperature and current density of the battery to construct an aging degradation model to obtain aging state parameters and battery health status; and feed the aging state parameters back to the voltage sub-model in step S1 to correct the voltage sub-model. The aging degradation model includes a temperature-voltage joint acceleration sub-model, an ohmic impedance evolution sub-model, and a mass transfer degradation sub-model. The aging state parameters include: the exchange current density at time t, the ohmic resistance at time t, and the limiting current density at time t. Preferably, since the increase in activation overpotential is mainly caused by the irreversible decay of the catalyst's electrochemical active area, it is now necessary to establish an aging model for the catalyst, expressing the physicochemical process as a mathematical expression driven by real-time state variables such as temperature and voltage, thereby achieving online prediction. The expression for the temperature-voltage joint accelerator sub-model is:

[0053]

[0054] In the formula, For operation t The exchange current density at time t, in units of ; The initial exchange current density, in units of ; This is the activation aging rate coefficient; Reference voltage; This is a function reflecting the accelerating effect of temperature rise and voltage fluctuation on catalyst aging; This is the voltage acceleration factor.

[0055] Preferably, since the proton exchange membrane undergoes chemical degradation and mechanical damage, resulting in an increase in ohmic impedance, and membrane degradation is sensitive to humidity, an ohmic impedance evolution sub-model incorporating a humidity factor is constructed to reflect the impact of the thermal management subsystem employing humidification and cooling strategies on membrane lifetime. The expression for the Ohmic impedance evolution sub-model is:

[0056]

[0057] In the formula, For operation t Ohmic impedance at time t; The initial ohmic impedance; This is the ohmic aging rate coefficient, in units of... ; This refers to the relative humidity inside the membrane. The optimal reference humidity for durability; Humidity sensitivity coefficient; This is a function that reflects how both excessive dryness and excessive wetness of the membrane accelerate degradation; This is the time integration variable.

[0058] Preferably, when the porosity of the gas diffusion layer decreases, the limiting current density... The degradation of the diffusion layer structure caused by high-load cycling will gradually decrease. In order to distinguish this aging from the previous two types of electrochemical and ohmic aging, a mass transfer degradation sub-model is constructed so as to impose reasonable lifetime constraints on high-current conditions in optimized control. The expression for the mass transfer degradation sub-model is:

[0059] In the formula, For operation t The limiting current density at time t; The initial limiting current density; This is the mass transfer aging rate coefficient; It is the current density acceleration factor.

[0060] Specifically, the ratio of the current maximum available power to the initial maximum available power is defined as the battery's health state, expressed as:

[0061] In the formula, For operation t The battery health status at any time; For operation t Maximum available power at any given time; This represents the initial maximum available power. The operation t The maximum available power at any given time is calculated using a combination of offline polarization curve lookup and online interpolation. The initial maximum available power is obtained during initialization through standard polarization curve testing and stored in the non-volatile memory of the vehicle controller, serving as a fixed benchmark for the entire lifecycle calculation of SOH. After obtaining the exchange current density, ohmic impedance, and limiting current density at the operating time through the temperature-voltage joint accelerator sub-model, the ohmic impedance evolution sub-model, and the mass transfer degradation sub-model, respectively, these aging state parameters are substituted into the voltage sub-model in step S1 to update the calculation parameters of activation overpotential, ohmic overpotential, and concentration overpotential, and the polarization curve of the battery is recalculated. Based on the updated polarization curve, the maximum available power at the current moment is obtained by solving the maximum value of the battery's polarization curve. This calculation adopts a combination of offline polarization curve lookup and online interpolation. Specifically, a polarization curve database is pre-generated under different aging state parameter combinations. During runtime, multi-dimensional interpolation is performed based on the current polarization curve to obtain the current polarization curve. The operating point corresponding to the maximum power is obtained through numerical optimization algorithm. The battery health status can be obtained by comparing the current maximum available power with the initial maximum available power; The remaining battery life (RUL) is calculated based on the battery's health status; the remaining battery life (RUL) is defined as the time from the current moment when the state of health (SOH) decays to a preset failure threshold. The time elapsed; The expression for the remaining service life of the battery is:

[0062] In the formula, For operation t The remaining battery life at any given time; The infimum is all the conditions that are satisfied. The minimum value in; This is the time increment starting from the current moment; This is a preset failure threshold.

[0063] In this embodiment, the exchange current density, ohmic impedance, and limiting current density at the operating time are calculated by using the temperature-voltage joint accelerator sub-model, the ohmic impedance evolution sub-model, and the mass transfer degradation sub-model, respectively. These three parameters constitute the aging state parameters in this invention. Specifically, the temperature-voltage joint acceleration sub-model calculates the exchange current density based on the real-time operating temperature of the battery and the voltage of a single cell through an integral evolution equation; the ohmic impedance evolution sub-model calculates the ohmic impedance based on the relative humidity inside the membrane and the operating time, which reflects the degree of chemical degradation and mechanical damage to the proton exchange membrane; and the mass transfer degradation sub-model calculates the limiting current density based on the current density i and the operating time, which reflects the decrease in mass transfer capacity caused by the reduction in the porosity of the gas diffusion layer. The three aging state parameters mentioned above are fed back into the voltage sub-model in step S1: the initial exchange current density in the activation overpotential expression is replaced by the exchange current density; the contact resistance in the ohmic overpotential expression is replaced by the ohmic impedance; and the limiting current density in the concentration overpotential expression is replaced by the limiting current density. Through this online parameter update mechanism, the voltage sub-model can be adaptively adjusted with the changes in battery aging state, ensuring the model prediction accuracy throughout the entire life cycle.

[0064] S3. Based on the real-time operating temperature of the battery and the preset optimal operating temperature range, construct a three-mode switching state machine to obtain the operating mode state of the current control cycle. Preferably, the mode switching rules of the three-mode switching state machine are as follows:

[0065] In the formula, The mode state for the next control cycle; This represents the mode state of the current control cycle, and when... The state at that time is low-temperature heating, when The state is normal temperature control, when The state at that time was high temperature cooling; This is the lower limit of the battery's optimal operating temperature; This is the upper limit of the battery's optimal operating temperature; This is a function for switching the direction of temperature decrease; A function for switching the direction of temperature increase; To predict temperature.

[0066] Among them, by integrating various criteria and prediction mechanisms, the mode state of the current and next control cycles is determined. , Between low-temperature heating mode and normal temperature control mode, based on Hysteresis switching is performed, switching between normal temperature control mode and high-temperature cooling mode based on predicted temperature. Switching ahead of time provides a clear basis for enabling different objective functions and constraint sets for the multi-objective optimization problem in step S4 according to different modes.

[0067] Specifically, the steps for constructing the three-mode switching state machine are as follows: S31. Based on the battery's optimal operating temperature range, thermal management control is divided into three operating modes: low-temperature heating mode, normal temperature control mode, and high-temperature cooling mode. When the temperature is below the lower limit of the range, auxiliary heating is performed to restore reactivity; when the temperature is above the upper limit of the range, forced cooling is implemented to prevent accelerated aging of the membrane electrode. Based on the current temperature of the fuel cell stack Based on the core criteria, the basic classification of the three operating modes—low-temperature heating mode, normal temperature control mode, and high-temperature cooling mode—is as follows:

[0068] In the formula, Operating mode; This is the lower limit of the battery's optimal operating temperature; This is the upper limit of the battery's optimal operating temperature; By dividing the problem into three basic operating modes, the thermal management control problem is transformed into a finite state machine, which allows us to design optimization objectives and constraints for different modes. S32. During actual operation, battery temperature may fluctuate near the mode boundary due to load variations, causing repeated mode switching and resulting in frequent start-stop cycles of actuators such as heaters, water pumps, and fans. Therefore, a hysteresis interval is introduced based on the basic criteria, and a hysteresis switching function is defined. The switching function when the temperature rises above the threshold is:

[0069] In the formula, This is the switching function when the temperature rises above a threshold. The baseline threshold; For hysteresis bandwidth; when This indicates that the trigger condition for switching to a higher temperature mode has been met; The switching function when the temperature drops below the threshold is:

[0070] In the formula, This is the switching function when the temperature drops below a threshold. S33: In low-temperature environments, a feedforward trigger condition based on ambient temperature is added. When the ambient temperature is lower than the preset cold start threshold, the system enters the preparatory state for low-temperature heating mode, and the heater begins to preheat the coolant. The feedforward logic is as follows:

[0071] In the formula, This is a preheating enable indicator; Ambient temperature; The cold start temperature threshold is set to 0℃. S34: When the battery is in the high load ramp-up phase, the heat generated will increase rapidly. If the current temperature threshold is relied upon for judgment, the response of the cooling system may be delayed, causing the battery to exceed the maximum safe temperature allowed by the battery in a short time, resulting in irreversible heat loss. Therefore, a predictive switching mechanism based on the temperature change rate is introduced. The predicted temperature is defined as follows:

[0072] In the formula, To predict temperature; For prediction time constant; Simultaneously, a predictive switching mechanism for the low-temperature direction is set up, when... In this case, the high-temperature cooling mode is switched in advance to avoid high-temperature damage to the membrane electrode caused by peaks when the battery is rapidly loaded. Correspondingly, the low-temperature direction also introduces a basis based on An early exit mechanism; in the low-temperature heating mode, when When entering the exit heating mode, the heating power is gradually reduced to achieve a smooth transition and avoid temperature overshoot; both exit mechanisms are based on the same predicted temperature. Two-way forecasting; The early exit judgment in the low-temperature heating mode can be expressed as:

[0073] In the formula, To enable the low-temperature heating mode early, when... When the value is 1, the heating power is gradually reduced to achieve a smooth temperature transition; S35: Integrate steps S31 to S34 into a complete mode switching state machine to obtain the mode switching rules of the three-mode switching state machine.

[0074] S4. Construct a heat dissipation management energy consumption model, and combine the battery health status and the current control cycle operation mode status to construct a multi-objective collaborative optimization model of thermal-electrical-lifetime. Preferably, the thermal-electrical-lifetime multi-objective collaborative optimization model aims to minimize the weighted multi-objective cost of the total energy consumption of the thermal management system and the battery health state decay, and is constructed with constraints on battery operating temperature and coolant inlet-outlet temperature difference. The objective function of the thermo-electric-lifetime multi-objective collaborative optimization model is expressed as follows:

[0075] In the formula, For weighted multi-objective cost functions; For control vectors; For the set of control vectors; and These are the weighting coefficients; Total energy consumption of the thermal management system; The energy consumption for low-temperature auxiliary heating is determined based on the operating mode status of the current control cycle. Normalized baseline energy consumption; This represents the amount of battery health degradation. The maximum allowable attenuation; The expression for the total energy consumption of the thermal management system is as follows:

[0076] In the formula, To predict the length of the time domain; This is the initial running time; This refers to the coolant mass flow rate. This refers to the air mass flow rate through the radiator. Power of the cooling water pump; This refers to the power of the cooling fan; Specifically, the expression for the power of the cooling water pump is:

[0077] In the formula, This refers to the total pressure drop in the coolant circuit. The density of the coolant; For water pump efficiency; The power of the cooling fan is proportional to the cube of the airflow through the radiator, as expressed by:

[0078] In the formula, This refers to the air mass flow rate through the radiator. This refers to the fan power factor; The energy consumption for low-temperature auxiliary heating is only included in the low-temperature heating mode and is determined by the integral of the auxiliary heater power in the prediction time domain; Only when That is, when the current heating mode is at a low temperature, the energy consumption for low-temperature auxiliary heating. Perform value calculations; otherwise, low-temperature auxiliary heating energy consumption. The value is 0; The expression for the energy consumption of the low-temperature auxiliary heating is:

[0079] In the formula, For indicator functions; For auxiliary heater power; The expression for the battery health state degradation is:

[0080] In the formula, for The battery health status at any time; for The battery health status at any time; The weighting coefficients are weighting adjustment rules for energy consumption and lifespan extension. The weighting coefficients are expressed as linear functions with non-step switching. The weighting coefficients are calculated online according to the following rules. The expression for the weighting coefficient is:

[0081] In the formula, for The battery health status at any time; This is the battery failure threshold.

[0082] Specifically, the battery operating temperature constraint ensures that the battery operating temperature and the temperature difference between the coolant inlet and outlet are within a safe range, expressed as:

[0083] In the formula, This refers to the lowest permissible operating temperature of the battery. This is the highest safe temperature allowed by the battery. The coolant inlet and outlet temperature difference constraint is used to prevent thermal stress damage caused by excessive temperature gradients and to limit the degree of uneven temperature distribution inside the fuel cell stack. The expression is:

[0084] In the formula, This represents the maximum permissible temperature difference between the coolant inlet and outlet.

[0085] S5. The multi-objective collaborative optimization model of thermal-electrical-lifetime is solved by the rolling optimization solution method based on model predictive control to obtain the optimal control quantity at the current moment. The vehicle controller then sends the optimal control quantity to each actuator for execution via the CAN bus to achieve collaborative control of battery thermal management and battery life prediction.

[0086] Preferably, the specific steps of the rolling optimization solution method based on model predictive control are as follows: S51. Discretize the thermal management energy consumption model and aging decay model using the forward Euler method to obtain discrete state equations; based on the current discretized state as the initial state, obtain the prediction sequence through the discretized state equations; obtain the output variables through the prediction sequence. Specifically, in order to provide a unified description of battery temperature dynamics and health degradation dynamics within the model predictive control framework, the thermal management energy consumption sub-model and the aging degradation model are discretized using the forward Euler method, resulting in discrete state equations, expressed as follows:

[0087] In the formula, For the first The state vector at each sampling time; For the first The state vector at each sampling time, and ; Let be the decision variables to be solved, and ; Let be a measurable external disturbance vector, and ,in, For the corresponding load current, For ambient temperature, This refers to the relative humidity inside the membrane. The forward Euler method is used for continuous-time state equations. After discretization, the state update expression is as follows: That is, based on the current state Based on this, using its rate of change and sampling period The product of the two is used to make a first-order approximate prediction of the state at the next time step, thereby obtaining the discretized state equation of the system. In each control cycle, the current measurement value is... Using the discretized state equation as the initial state, the state trajectory for several future steps is recursively predicted to obtain the prediction sequence. Simultaneously, based on the predicted state sequence, the output variables, including battery temperature, are obtained through an electrochemical-thermal coupling model and an aging degradation model. and health status ; S52. The objective function of the thermo-electric-lifetime multi-objective collaborative optimization model is transformed into a finite-time-domain cost function under the model predictive control framework, and the objective function is to minimize the finite-time-domain cost function. The expression of the objective function is as follows:

[0088] In the formula, For the finite-time cost function under the model predictive control framework; For the first The power of the cooling water pump in step; For the first The power of the cooling fan in the step; For the first The auxiliary heater power of the step; For the first k The control sequence to be optimized in the next step; For the first The battery health status of the step; For the first The step indicator function; and For discrete time intervals; S53. Convert the battery operating temperature constraint and the coolant inlet / outlet temperature difference constraint into state constraints and input constraints, expressed as follows:

[0089] In the formula, For the first The battery operating temperature of the step; This refers to the battery's maximum safe operating temperature. This refers to the minimum safe operating temperature of the battery. For the first The coolant mass flow rate at step; This is the lower limit of the coolant mass flow rate; This represents the upper limit of the coolant mass flow rate; For the first The mass flow rate of air passing through the radiator; This represents the upper limit of the air mass flow rate through the radiator. This is the maximum power of the auxiliary heater; S54. Based on the output variables, state constraints, and input constraints, solve the objective function using a sequential quadratic programming algorithm to obtain the optimal control sequence. The optimal control sequence can be represented as: ; Specifically, at each sampling time, the sequential quadratic programming algorithm approximates the original nonlinear constraint optimization problem as a quadratic programming subproblem at the current iteration point, and approximates the optimal solution of the original problem by iteratively solving the quadratic programming subproblem; in this embodiment, the control vector dimension is 3, and the prediction time domain... Typically, a value of 10 to 20 is chosen. The optimal control sequence obtained by solving this problem... In the middle, only the first control variable It was actually issued and implemented; S55. Take the first element in the optimal control sequence as the optimal control quantity at the current moment.

[0090] The optimal control quantity can be expressed as:

[0091] In the formula, The first term of the optimal control sequence obtained by solving the optimization problem at the current sampling time; It is the actual control quantity applied to the actuator; At the next sampling time Update the state measurement values As new initial conditions, the solution process from S51 to S54 is repeated.

[0092] In this embodiment, the vehicle controller transmits the optimal control quantity via the CAN bus. The commands are sent to each actuator to adjust the operating status, including the coolant flow rate command in the optimal control parameters. The signal is sent to the thermal management controller, which converts it into a speed control signal for the electronic water pump, and then sends the airflow command. This is converted into a speed control signal for the cooling fan, and the auxiliary heating power command is then transferred. This is converted into a switch or power regulation signal for the heater, thereby achieving closed-loop regulation of battery temperature and thermal management energy consumption.

[0093] In this embodiment, as Figure 2 As shown, the working process of a hydrogen fuel cell is as follows: air and hydrogen from the hydrogen storage tank enter the bipolar plates from the cathode and anode sides, respectively. Then, hydrogen flows into the anode channel and air flows into the cathode channel. Hydrogen and oxygen pass through the catalyst layer, undergo an electrochemical reaction, generate water and release electrical energy. like Figure 3 As shown, the battery control system of a hydrogen fuel cell vehicle includes a hydrogen fuel cell, a hydrogen supply subsystem, an air supply subsystem, a thermal management subsystem, a DC / DC converter, a DC / AC converter, a power battery pack, a motor, and a vehicle controller; among which, the thermal management subsystem consists of a coolant circulation loop (including an electronic water pump, an electronic thermostat, a radiator, and a cooling fan), an auxiliary electric heater, a temperature sensor, a flow sensor, and a thermal management controller. The connection relationships of each component are as follows: (a) Connection relationship of energy transfer section: The power output terminal of the hydrogen fuel cell is electrically connected to the input terminal of DC / DC converter 1, the output terminal of DC / DC converter 1 is electrically connected to the input terminal of DC / AC converter, the output terminal of DC / AC converter is electrically connected to the power input terminal of motor for driving motor operation; the input terminal of DC / DC converter 2 is electrically connected to the output terminal of power battery pack for auxiliary power supply and energy recovery. (II) Physical connection relationship of thermal management subsystem: The coolant outlet of the hydrogen fuel cell is connected to the radiator, coolant tank, auxiliary heater and electronic thermostat in sequence through pipeline to form a coolant circulation loop; the cooling fan is fixed on one side of the radiator for forced air cooling of the radiator; temperature sensors are respectively set at the coolant inlet, outlet and battery body of the hydrogen fuel cell; flow sensor is set in the coolant circulation loop for real-time acquisition of coolant flow rate; (III) Control and signal connection relationship: The vehicle controller communicates bidirectionally with the hydrogen supply subsystem, air supply subsystem, thermal management controller, DC / DC converter, DC / AC converter, power battery pack and motor through CAN bus; the thermal management controller is connected to the electronic water pump, electronic thermostat, cooling fan, auxiliary electric heater, temperature sensor and flow sensor through hard-wired signal lines to collect sensor data and send control commands to the actuators.

[0094] The present invention has the following beneficial effects: This invention constructs an electrochemical-thermal coupling model that includes a voltage sub-model and a thermal management energy consumption sub-model, and collects real-time operating temperature, current density, and reactant gas partial pressure data. This model can accurately describe the strong coupling relationship between electrochemical reactions, mass transport, and heat generation inside a fuel cell, overcoming the shortcomings of traditional empirical models or single-physics field models in terms of insufficient prediction accuracy under variable load conditions. The model can obtain the battery output voltage and heat generation power in real time, providing high-fidelity input for subsequent thermal management decisions, thereby significantly improving the adaptability and reliability of the control strategy under different operating conditions. By establishing an aging degradation model that includes a temperature-voltage joint acceleration sub-model, an ohmic impedance evolution sub-model, and a mass transfer degradation sub-model, the battery aging process can be comprehensively characterized from three dimensions: electrochemical polarization, ohmic polarization, and concentration polarization. Furthermore, the obtained aging state parameters can be fed back to the voltage model for correction, forming a closed-loop architecture of "aging perception - model update - control decision". This allows the control model to adaptively adjust as the battery usage time increases, effectively solving the problem of model mismatch worsening with aging in traditional methods and ensuring control accuracy throughout the entire life cycle. By constructing a three-mode switching state machine based on battery operating temperature, dynamic temperature change value, ambient temperature, and preset optimal operating temperature range, thermal management requirements are divided into different priority levels. This state machine not only considers the current absolute temperature value but also incorporates temperature change trends, enabling it to respond to sudden temperature changes in advance and avoid frequent actuator switching or mode oscillation. Compared with conventional single threshold switch control, the three-mode switching state machine has hysteresis characteristics and state memory function, which can effectively reduce the start-stop frequency of actuators such as cooling fans and water pumps, reducing accessory energy consumption while ensuring temperature control effect. A multi-objective collaborative optimization model for thermal, electrical, and lifespan was constructed based on thermal management energy consumption models, battery health status, and current operating mode. This model incorporates the minimization of instantaneous energy consumption, temperature deviation, and aging rate into a unified optimization framework, overcoming the drawbacks of traditional strategies that either "unilaterally pursue cooling effects while ignoring lifespan loss" or "overly conservative temperature control leading to high energy consumption." By introducing health status as one of the optimization objectives, it ensures that the long-term durability of the battery stack is not sacrificed for short-term cooling effects under high-temperature or high-current conditions, achieving a dynamic balance between battery performance, lifespan, and vehicle economy.

[0095] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for coordinated control of thermal management and lifespan prediction in hydrogen fuel cell vehicles, characterized in that, include: S1. Collect real-time operating temperature, current density, and partial pressure of reactant gases of hydrogen fuel cells to construct an electrochemical-thermal coupling model; The electrochemical-thermal coupling model includes a voltage sub-model and a thermal management energy consumption sub-model. S2. Based on the voltage sub-model, obtain the battery output voltage, and combine it with the real-time operating temperature and current density of the battery to construct an aging degradation model to obtain aging state parameters and battery health status; and feed the aging state parameters back to the voltage sub-model in step S1 to correct the voltage sub-model. The aging degradation model includes a temperature-voltage joint acceleration sub-model, an ohmic impedance evolution sub-model, and a mass transfer degradation sub-model. S3. Based on the real-time operating temperature of the battery and the preset optimal operating temperature range, construct a three-mode switching state machine to obtain the operating mode state of the current control cycle. S4. Construct a heat dissipation management energy consumption model, and combine the battery health status and the current control cycle operation mode status to construct a multi-objective collaborative optimization model of thermal-electrical-lifetime. S5. The multi-objective collaborative optimization model of thermal-electrical-lifetime is solved by the rolling optimization solution method based on model predictive control to obtain the optimal control quantity at the current moment. The vehicle controller then sends the optimal control quantity to each actuator for execution via the CAN bus to achieve collaborative control of battery thermal management and battery life prediction.

2. The method for coordinated control of thermal management and lifespan prediction of a hydrogen fuel cell vehicle battery according to claim 1, characterized in that, The expression for the voltage sub-model is: In the formula, This refers to the battery output voltage. The Nernst electromotive force; To activate the overpotential; For Ohm overpotential; This is the concentration overpotential; The expression for the Nernst electromotive force is: In the formula, Battery operating temperature; This refers to the partial pressure of hydrogen gas. The partial pressure of oxygen; It stands for volt, or voltage. The expression for the activation overpotential is: In the formula, It is the ideal gas constant; This represents the number of electrons transferred in a single reaction. It is Faraday's constant; The charge transfer coefficient; The current current density; Internal leakage current density; The initial current density; The expression for the Ohmic overpotential is: In the formula, For membrane resistance; Contact resistance; The expression for the concentration overpotential is: In the formula, This is the concentration polarization coefficient. This represents the limiting current density.

3. The method for coordinated control of thermal management and lifespan prediction of a hydrogen fuel cell vehicle battery according to claim 2, characterized in that, The expression for the thermal management energy consumption sub-model is: In the formula, This is the equivalent heat capacity of the battery. This represents the total heat output of the battery. The heat power carried away by the coolant circulation; This refers to the heat power lost from the battery surface to the surrounding environment. The rate of change of battery temperature; The expression for the total heat generation power of the battery is as follows: In the formula, This represents the number of individual cell modules connected in series in a hydrogen fuel cell. The effective reaction area of ​​a single cell; The enthalpy change for the electrochemical reaction that produces liquid water; The expression for the heat power carried away by the coolant circulation is: In the formula, This refers to the coolant mass flow rate; This refers to the specific heat capacity of the coolant. This refers to the outlet temperature of the battery coolant flow channel. This refers to the inlet temperature of the battery coolant flow channel; The expression for the heat power lost from the battery surface to the surrounding environment is: In the formula, The equivalent heat transfer coefficient between the battery casing and the environment; This refers to the surface area for heat dissipation. The ambient temperature.

4. The method for coordinated control of thermal management and lifespan prediction of a hydrogen fuel cell vehicle battery according to claim 2, characterized in that, The expression for the temperature-voltage joint accelerator sub-model is: In the formula, For operation t The exchange current density at any given time; This represents the initial exchange current density; This is the activation aging rate coefficient; Reference voltage; This is a function reflecting the accelerating effect of temperature rise and voltage fluctuation on catalyst aging; This is the voltage acceleration factor.

5. The method for coordinated control of thermal management and lifespan prediction of a hydrogen fuel cell vehicle battery according to claim 2, characterized in that, The expression for the Ohmic impedance evolution sub-model is: In the formula, For operation t Ohmic impedance at time t; The initial ohmic impedance; The ohmic aging rate coefficient; This refers to the relative humidity inside the membrane. The optimal humidity level for durability; Humidity sensitivity coefficient; This is a function that reflects how both excessive dryness and excessive wetness of the membrane accelerate degradation; This is the time integration variable.

6. The method for coordinated control of thermal management and lifespan prediction of a hydrogen fuel cell vehicle battery according to claim 2, characterized in that, The expression for the mass transfer degradation sub-model is: In the formula, For operation t The limiting current density at time t; The initial limiting current density; This is the mass transfer aging rate coefficient; It is the current density acceleration factor.

7. The method for coordinated control of thermal management and lifespan prediction of a hydrogen fuel cell vehicle battery according to claim 1, characterized in that, The mode switching rules of the three-mode switching state machine are as follows: In the formula, The mode state for the next control cycle; This represents the mode state of the current control cycle, and when... The state at that time is low-temperature heating, when The state is normal temperature control, when The state at that time was high temperature cooling; This is the lower limit of the battery's optimal operating temperature; This is the upper limit of the battery's optimal operating temperature; This is a function for switching the direction of temperature decrease; A function for switching the direction of temperature increase; To predict temperature.

8. The method for coordinated control of thermal management and lifespan prediction of a hydrogen fuel cell vehicle battery according to claim 7, characterized in that, The thermo-electric-lifetime multi-objective collaborative optimization model aims to minimize the weighted multi-objective cost of the total energy consumption of the thermal management system and the battery health state decay, and is constructed with constraints on battery operating temperature and coolant inlet and outlet temperature difference. The objective function of the thermo-electric-lifetime multi-objective collaborative optimization model is expressed as follows: In the formula, For weighted multi-objective cost functions; For control vectors; For the set of control vectors; and These are the weighting coefficients; Total energy consumption of the thermal management system; The energy consumption for low-temperature auxiliary heating is determined based on the operating mode status of the current control cycle. Normalized baseline energy consumption; This represents the amount of battery health degradation. The maximum allowable attenuation; The expression for the total energy consumption of the thermal management system is as follows: In the formula, To predict the length of the time domain; This is the initial running time; This refers to the coolant mass flow rate; This refers to the air mass flow rate through the radiator. Power of the cooling water pump; This refers to the power of the cooling fan; The expression for the energy consumption of the low-temperature auxiliary heating is: In the formula, For indicator functions; For auxiliary heater power; The expression for the battery health state degradation is: In the formula, for The battery health status at any time; for The battery health status at any time; The expression for the weighting coefficient is: In the formula, for The battery health status at any time; This is the battery failure threshold.

9. The method for coordinated control of thermal management and lifespan prediction of a hydrogen fuel cell vehicle battery according to claim 8, characterized in that, The specific steps of the rolling optimization solution method based on model predictive control are as follows: S51. Discretize the thermal management energy consumption model and aging decay model using the forward Euler method to obtain discrete state equations; based on the current discretized state as the initial state, obtain the prediction sequence through the discretized state equations; obtain the output variables through the prediction sequence. S52. The objective function of the thermo-electric-lifetime multi-objective collaborative optimization model is transformed into a finite-time-domain cost function under the model predictive control framework, and the objective function is to minimize the finite-time-domain cost function. The expression of the objective function is as follows: In the formula, For the finite-time cost function under the model predictive control framework; For the first The power of the cooling water pump in step; For the first The power of the cooling fan in the step; For the first The auxiliary heater power of the step; For the first k The control sequence to be optimized step; For the first The battery health status of the step; For the first The step indicator function; and For discrete time intervals; S53. Convert the battery operating temperature constraint and the coolant inlet / outlet temperature difference constraint into state constraints and input constraints, expressed as follows: In the formula, For the first The battery operating temperature of the step; This refers to the battery's maximum safe operating temperature. This refers to the minimum safe operating temperature of the battery. For the first The coolant mass flow rate at step; This is the lower limit of the coolant mass flow rate; This represents the upper limit of the coolant mass flow rate; For the first The mass flow rate of air passing through the radiator; This represents the upper limit of the air mass flow rate through the radiator. This is the maximum power of the auxiliary heater; S54. Based on the output variables, state constraints, and input constraints, solve the objective function using a sequential quadratic programming algorithm to obtain the optimal control sequence. S55. Take the first element in the optimal control sequence as the optimal control quantity at the current moment.

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