CN115339330B - Energy output management method for hybrid electric vehicles based on battery aging - Google Patents

Abstract

The present invention proposes an energy output management method for hybrid electric vehicles based on battery aging. The hybrid electric vehicle uses fuel cells and lithium batteries as power batteries, and supercapacitors provide instantaneous high power demand. In energy output management, the current distribution of the hybrid electric vehicle system composed of fuel cells, lithium batteries and supercapacitors is divided into two layers: the top layer uses an adaptive low-pass filter based on fuzzy control to decouple the low-frequency current from the load current to obtain the bottom layer demand current and the top layer supercapacitor output current; the bottom layer obtains the bottom layer fuel cell and lithium battery output current based on the bottom layer demand current and the adaptive equivalent power consumption minimum strategy considering the aging of each battery. The present invention can effectively and reliably manage the energy output of hybrid electric vehicles, and also ensure the charging support of lithium batteries, improve the utilization rate of fuel cells, and extend the service life of power batteries.

Description

Energy output management method for hybrid electric vehicles based on battery aging

Technical Field

The invention belongs to the technical field of automobile control and relates to a method for managing energy output of a hybrid electric vehicle.

Background Art

Previously, the power source of automobiles mainly relied on fossil fuels. However, with the increasing exploitation of fossil fuels, this non-renewable energy will eventually run out, and the use of fossil fuels will inevitably cause environmental pollution. Therefore, new energy vehicles will be a major trend in the current and future transportation systems. For fuel cell hybrid vehicles, in order to achieve the best operating effect, it is particularly important to design a real-time, efficient, and adaptive energy output management method so that fuel cells, lithium batteries, and supercapacitors can coordinate and distribute energy under different working conditions.

At present, the energy output management methods of fuel cell hybrid vehicles can be divided into two categories: rule-based strategies and optimization-based strategies. The rule-based strategy uses direct rules or fuzzy rules to manage the normal operation of the vehicle. It has a simple structure and is easy to use. It has been widely used in electric vehicles, but it is difficult to achieve the optimal power distribution. The optimization-based strategy adopts the equivalent consumption minimization strategy. As a real-time optimization strategy, it realizes the instantaneous distribution of energy among various energy sources by calculating the equivalent fuel cost function. It does not rely on the prior knowledge of driving conditions and can achieve a setting point close to the optimal. However, during the life cycle of a fuel cell hybrid vehicle, the battery will age with the use of the vehicle and the power will degrade. As the main power source of a fuel cell hybrid vehicle, the output power of the fuel cell at the corresponding current will decrease with aging, and its maximum working efficiency point will also change; at the same time, the capacity of the lithium battery will decrease with the use of the vehicle, and the resistance of the lithium battery will increase with aging, thereby reducing the output voltage of the lithium battery. Therefore, if the battery aging is not taken into account in the energy output management method of the fuel cell hybrid vehicle, it is impossible to effectively and reliably manage the energy output of the fuel cell hybrid vehicle.

Summary of the invention

In order to solve the problems described in the background art, the present invention proposes an energy output management method for a hybrid electric vehicle based on battery aging.

The technical solution of the present invention comprises the following steps:

Step 1: Obtain the load current on the DC bus of the hybrid electric vehicle, the supercapacitor charge state value and the vehicle speed through the sensor, obtain the adjustment frequency through the fuzzy controller and the supercapacitor charge state value optimization module, and input the adjustment frequency into the Butterworth filter to obtain the bottom demand current and the top supercapacitor output current;

Step 2: Establish a fuel cell aging model, use unscented Kalman filtering to estimate aging parameters, and calculate the health status of the fuel cell and lithium battery;

Step 3, obtaining the lithium battery equivalent factor and the maximum current dynamic change rate of the fuel cell through the health status of the fuel cell and the lithium battery;

Step 4: Input the underlying demand current, lithium battery equivalent factor and fuel cell dynamic current change rate into the constructed adaptive equivalent power consumption minimization strategy to calculate the output current of the fuel cell and lithium battery.

Furthermore, in step 1, in the fuzzy controller, the input variables are the supercapacitor state of charge value SOC _sc and the load current I _LOAD , and the output variable is the initial adjustment frequency f _s ', 0.5<SOC _sc <0.9. In the supercapacitor SOC optimization module, the supercapacitor reference state of charge value related to the driving condition The definition is as follows:

where v _max is the maximum speed of the vehicle, is the maximum state of charge of the supercapacitor; v is the current speed of the vehicle.

In order to ensure that the supercapacitor state of charge value SOC _sc is close to its supercapacitor reference state of charge value And it changes with the driving conditions. The adjustable frequency increment Δf is defined to correct the fuzzy controller output, which realizes the change of the supercapacitor with the driving conditions. The final corrected adjustment frequency _fs is expressed as follows:

Where k is the adjustment factor, SOC _sc is the supercapacitor state of charge value, It is the reference state of charge value of the supercapacitor.

Input the adjustment frequency _fs into the Butterworth filter, the demand current at the bottom layer and the supercapacitor output current at the top layer are:

I _SC = I _LOAD - I _RE

Among them, I _RE (k) is the bottom layer demand current of the current cycle, I _RE (k-1) is the bottom layer demand current of the previous cycle, I _LOAD (k) is the load current of the current cycle, T is the filter sampling period, I _RE is the bottom layer demand current, and I _SC is the supercapacitor output current of the top layer.

Furthermore, in step 2, the health status of the fuel cell is defined as follows:

Wherein, α _min is the minimum value of the degradation deviation, and α _max is the maximum value of the degradation deviation; α _min and α _max are calculated from the internal resistance limit value and the maximum current limit value of the factory parameters of the fuel cell.

The health status of lithium batteries is defined as follows:

in, It is the initial maximum capacity of the battery in the lithium battery parameters. is the power threshold for battery life termination; q(t) is the maximum power of the battery measured at time t.

Furthermore, in step three,

The lithium battery equivalent factor λ _BA is:

λ _BA =λ _BA0 (1+0.195SOH _FC )(1+0.187SOH _BA ),

Wherein, λ _BA0 is the initial equivalent factor of the lithium battery, SOH _FC is the health state of the fuel cell, and SOH _BA is the health state of the lithium battery;

The maximum dynamic current change rate of the fuel cell dI _FC is:

dI _FC =dI ₀ *(1-0.5*SOH _BA ),

Among them, dI ₀ represents the dynamic change rate of the initial current of the fuel cell, and SOH _BA is the health state of the lithium battery.

Furthermore, in the step 4,

The adaptive equivalent power consumption minimum strategy is:

Where, I _FC represents the output current of the fuel cell, I _BA represents the output current of the lithium battery, I _RE represents the underlying demand current, m _w represents the total hydrogen consumption of the fuel cell hybrid vehicle system, m _FC represents the hydrogen consumption of the fuel cell, K _BA is the lithium battery efficiency penalty coefficient, K _FC is the fuel cell efficiency penalty coefficient, λ _BA is the equivalent factor of the lithium battery, and U _bus is the DC bus voltage; Indicates the upper and lower limits of the fuel cell current output, It represents the upper and lower limits of the lithium battery current output, I _FC (t) and I _FC (t-1) represent the fuel cell current at time t and time t-1, and dI _FC represents the maximum dynamic change rate of the fuel cell current.

The fuel cell efficiency penalty coefficient K _FC is:

Wherein, η is the instantaneous efficiency of the fuel cell, η _opt is the optimal efficiency of the fuel cell, η _max is the maximum efficiency of the fuel cell, and η _min is the minimum efficiency of the fuel cell;

The lithium battery efficiency penalty coefficient K _BA is defined as:

Among them, u is the instantaneous charge of the lithium battery, B _int is the initial charge of the lithium battery, B _max is the maximum charge of the lithium battery, and B _min is the minimum charge of the lithium battery.

The hybrid vehicle of the present invention uses fuel cells and lithium batteries as power batteries, and supercapacitors provide instantaneous high power demand. In the energy output management of the hybrid vehicle, the current distribution of the hybrid vehicle system composed of fuel cells, lithium batteries and supercapacitors is divided into two layers: the top layer uses an adaptive low-pass filter based on fuzzy control to decouple the low-frequency current from the load current to obtain the bottom layer demand current and the top layer supercapacitor output current; the bottom layer obtains the bottom layer fuel cell and lithium battery output current based on the bottom layer demand current and the adaptive equivalent power consumption minimum strategy considering the aging of each battery. The present invention can effectively and reliably manage the energy output of the hybrid vehicle, and also ensure the charging support of the lithium battery, improve the utilization rate of the fuel cell, and extend the service life of the power battery.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a flow chart of the energy output management method of the present invention.

FIG. 2 is a design rule diagram of the membership function of the fuzzy controller.

Figure 3 shows the top and bottom current curves of a hybrid vehicle.

FIG. 4 is a graph showing the output current of a fuel cell and a lithium battery of a hybrid vehicle.

Figure 5 shows the state of charge values of the supercapacitor and lithium battery of a hybrid vehicle.

DETAILED DESCRIPTION

The following is a detailed description of the implementation of the present invention in conjunction with the accompanying drawings, but they do not constitute a limitation of the present invention, but are only examples. At the same time, through the description, the advantages of the present invention will be more clearly understood. All deformations that can be directly derived or associated with the contents disclosed by ordinary technicians in the field should be considered as the protection scope of the present invention. The positional relationships described in the embodiments are consistent with those shown in the drawings, and other parts not described in detail in the embodiments are all prior art.

1. Get the demand current at the bottom layer and the supercapacitor current at the top layer

The load current on the DC bus of the hybrid electric vehicle, the supercapacitor charge state value and the vehicle speed are obtained through sensors, and the adjustment frequency is obtained through the fuzzy controller and the supercapacitor charge state value optimization module. The adjustment frequency is input into the Butterworth filter to obtain the bottom-level demand current and the top-level supercapacitor current.

In the fuzzy controller, the input variables are the supercapacitor state of charge value SOC _sc and the load current I _LOAD , and the output variable is the initial adjustment frequency f _s ', 0.5<SOC _sc <0.9;

The calculation rules of the fuzzy controller are based on the input-output fuzzy membership function and the fuzzy rule base. The "triangle + trapezoid" membership function is selected. The design rules of the membership function of the fuzzy controller input and output variables SOC _sc , I _LOAD and f _s ' are shown in Figure 1, and the fuzzy rule control table is shown in Table 1.

Table 1 Fuzzy rule control table

Note: NH: negative high; NL: negative low; ZE: zero; PL: positive low; PH: positive high; VL: very low; L: low; M: medium; H: high; VH: very high.

The initial adjustment frequency _fs ' is adjusted according to different driving conditions. The adjustment rules of the fuzzy controller are as follows:

(1) When the vehicle is accelerating or starting, that is, when I _LOAD is PL or PH, if the supercapacitor charge is high, reduce f _s ' to allow the supercapacitor to provide a large amount of energy;

(2) When the vehicle is decelerating, that is, when I _LOAD is NL or NH, if the supercapacitor charge is low, increase f _s ' to allow the supercapacitor to recover most of the regenerated energy;

(3) When the power required by the car is low, that is, when I _LOAD is PL or PH, only fuel cells and lithium batteries can meet the power demand well. Supercapacitors only need to provide a lower level of energy to avoid small power fluctuations to fuel cells and lithium batteries. f _s ' needs to maintain a higher value;

(4) To avoid over-discharge/over-charge of the supercapacitor, f _s ' is appropriately adjusted when the charge of the supercapacitor increases or decreases.

The ultimate goal of fuzzy controller design is to calculate the accurate value of the adjustment frequency. The characteristic of the centroid method is that when the input variable has a slight change, the output result will also change accordingly, and the output value changes smoothly. Therefore, the centroid method is selected as the defuzzification calculation method. In the case of discrete domain, the centroid method calculation formula is:

z ₀ = f _s '

Among them, i is the number of quantization levels, is the corresponding membership degree value, zi _is the corresponding quantization level value, and _z0 is the precise value after the fuzzy controller is decoded, that is, the initial adjustment frequency _fs '.

In the supercapacitor SOC optimization module, the supercapacitor reference state of charge value related to the driving conditions The definition is as follows:

where v _max is the maximum speed of the vehicle, is the maximum state of charge of the supercapacitor; v is the current speed of the vehicle;

In order to ensure that the supercapacitor state of charge value SOC _sc is close to its supercapacitor reference state of charge value And it changes with the driving conditions. The adjustable frequency increment Δf is defined to correct the fuzzy controller output, which realizes the change of the supercapacitor with the driving conditions. The final corrected adjustment frequency _fs is expressed as follows:

Where k is the adjustment factor, SOC _sc is the supercapacitor state of charge value, It is the reference state of charge value of the supercapacitor.

Input the adjustment frequency _fs into the Butterworth filter, the demand current at the bottom layer and the supercapacitor current at the top layer are:

I _SC = I _LOAD - I _RE

Among them, I _RE (k) is the bottom layer demand current of the current cycle, I _RE (k-1) is the bottom layer demand current of the previous cycle, I _LOAD (k) is the load current of the current cycle, T is the filter sampling period, I _RE is the bottom layer demand current, and I _SC is the supercapacitor output current of the top layer.

2. Calculate the health status of fuel cells and lithium batteries

In the health status estimation of the fuel cell, the static voltage model of the proton exchange membrane fuel cell is used as the aging model of the fuel cell. The equation is as follows:

Where E represents the total voltage of the fuel cell stack, N _cell represents the number of fuel cell layers, E _rev represents the thermodynamic reversible potential, E _act represents the activation loss, R represents the internal resistance of the fuel cell, I _FC represents the fuel cell current, B represents the empirical constant, and I _max represents the maximum allowable current.

The fuel cell unscented Kalman filter estimation model is as follows:

Wherein, x _k =[α,β] ^T is the unscented Kalman filter state variable, A=[1,T;0,1], y _k is the fuel cell voltage, w _k and v _k are the process and observation noises, _uk is the fuel cell input reference current, and g(x _k , _uk ) is the static voltage model; given the fuel cell input reference current _uk and the actual output voltage y _k measured by the sensor, the unscented Kalman filter as a state observer can estimate the unobserved state parameter x _k+1 in the fuel cell aging model to obtain the fuel cell internal resistance R(t) and the maximum current I _max (t) at time t;

The changes of the fuel cell internal resistance R and the maximum current I _max over time are described as follows:

Wherein, R(t) represents the internal resistance of the fuel cell at time t, I _max (t) represents the maximum current of the fuel cell at time t, R ₀ and I _max0 represent their initial values respectively, and α(t) represents the degradation deviation that changes with time.

Therefore, it can be obtained through formula (7).

The health status of a fuel cell is defined as follows:

Wherein, α(t) can be obtained by formula (7), α _min is the minimum value of degradation deviation, and α _max is the maximum value of degradation deviation; α _min and α _max are calculated from the internal resistance limit value and the maximum current limit value of the fuel cell factory parameters.

In the health status estimation of lithium batteries, the electrochemical-based lithium-ion battery model is used as the lithium battery aging model. The equation is as follows:

V(t)＝V _U,p -V _U,n -V _S,p -V _S,n -V _e -V _O,n -V _O,p (9),

Among them, V _U,p and V _U,n are the equilibrium voltages of the positive and negative collectors respectively, V _O,p and V _O,n are the surface overvoltages caused by the charge transfer resistance of the positive and negative collectors respectively, V _S,p and V _S,n are the voltage drops caused by the solid phase ohmic resistance of the positive and negative collectors respectively, and _Ve is the voltage drop caused by the ohmic resistance of the electrolyte.

The equilibrium voltage can be calculated using the Nernst equation:

Wherein, _xi represents the molar fraction, i represents the electrode (n represents the negative electrode, p represents the positive electrode), _U0 is the reference voltage, R is the universal gas constant, T is the electrode temperature, N is the number of electrons transferred in the reaction (for lithium ions N = 1), and _Vact,i represents the activity correction term, _which is 0 under ideal conditions.

_xi is represented by:

Wherein, _qi is the amount of lithium ions in electrode i, _qmax = _qp + _qn , i.e., _qmax is the maximum charge of lithium ions; for lithium batteries, the molar fractions of the positive and negative electrodes are _xp + _xn =1, when fully charged, _xp =0.4 and _xn =0.6; when fully discharged, _xp =1 and _xn =0.

The relationship between the voltage and internal resistance of a lithium battery is:

V _o =V _S,p +V _S,n +V _e =i _app R ₀ (12),

The total voltage caused by the solid phase ohmic resistance, electrolyte ohmic resistance and resistance at the collector of the lithium battery can be regarded as V _o , and the internal resistance of the lithium battery can be expressed as R ₀ , where i _app is the lithium battery input reference current.

The unscented Kalman filter estimation model for lithium batteries is as follows:

Among them, _xk =[α,β] ^T is the unscented Kalman filter state variable, A=[1,T;0,1], _yk is the lithium battery voltage, _wk and _vk are process and observation noise, _uk is the lithium battery input reference current, and h( _xk , _uk ) is the lithium battery electrochemical model. Given the lithium battery input reference current _uk and the actual output voltage _yk measured by the sensor, the unscented Kalman filter as a state observer can estimate the unobserved state parameter xk ₊₁ in the lithium battery aging model to obtain the maximum charge _qmax and internal resistance _R0 of the lithium battery at time t.

The health status of lithium batteries is defined as follows:

in, It is the initial maximum capacity of the battery in the lithium battery parameters. is the battery life end threshold, The value of 50%; q(t) is the maximum battery capacity measured at time t.

3. Obtain the lithium battery equivalent factor and the maximum current dynamic change rate of the fuel cell

The lithium battery equivalent factor λ _BA is:

λ _BA =λ _BA0 (1+0.195SOH _FC )(1+0.187SOH _BA ) (15);

Wherein, λ _BA0 is the initial equivalent factor of the lithium battery, SOH _FC is the health state of the fuel cell, and SOH _BA is the health state of the lithium battery;

The aging of the fuel cell leads to an increase in the current change of the fuel cell in the entire driving cycle, which means an increase in the degradation rate of the fuel cell. In order to increase the life of the fuel cell, its dynamic change rate needs to be limited. The maximum dynamic change rate of the fuel cell current dI _FC is:

The maximum dynamic current change rate of the fuel cell dI _FC is:

dI _FC =dI ₀ *(1-0.5*SOH _BA ),

Among them, dI ₀ represents the dynamic change rate of the initial current of the fuel cell, and SOH _BA is the health state of the lithium battery.

4. Obtain the output current of fuel cells and lithium batteries

The underlying demand current, lithium battery equivalent factor and fuel cell dynamic current change rate are input into the constructed adaptive equivalent power consumption minimization strategy to calculate the output current of the fuel cell and lithium battery.

The adaptive equivalent power consumption minimum strategy is:

Where, I _FC represents the output current of the fuel cell, I _BA represents the output current of the lithium battery, I _RE represents the underlying demand current, m _w represents the total hydrogen consumption of the fuel cell hybrid vehicle system, m _FC represents the hydrogen consumption of the fuel cell, K _BA is the lithium battery efficiency penalty coefficient, K _FC is the fuel cell efficiency penalty coefficient, λ _BA is the equivalent factor of the lithium battery, and U _bus is the DC bus voltage; Indicates the upper and lower limits of the fuel cell current output, It represents the upper and lower limits of the lithium battery current output, I _FC (t) and I _FC (t-1) represent the fuel cell current at time t and time t-1, and dI _FC represents the maximum current dynamic change rate of the fuel cell;

The fuel cell efficiency penalty coefficient K _FC is:

Wherein, η is the instantaneous efficiency of the fuel cell, η _opt is the optimal efficiency of the fuel cell, η _max is the maximum efficiency of the fuel cell, and η _min is the minimum efficiency of the fuel cell;

The lithium battery efficiency penalty coefficient K _BA is defined as:

Among them, u is the instantaneous charge of the lithium battery, B _int is the initial charge of the lithium battery, B _max is the maximum charge of the lithium battery, and B _min is the minimum charge of the lithium battery.

Example

A hybrid vehicle that combines three power sources, namely a 4.5kW proton exchange membrane fuel cell, a 48V 40Ah lithium battery, and a 165F supercapacitor, is selected as the simulation object. Urban road conditions are used as test conditions. The above energy output management method is used to simulate it, and the results are shown below.

Figure 3 is a graph of the top and bottom currents of a hybrid vehicle, where the solid line is the load current, the thin dashed line is the bottom demand current, and the thick dashed line is the top supercapacitor output current. It can be seen that the supercapacitor as an auxiliary power source can play the role of "peak shaving and valley filling", making the bottom demand current in a relatively stable state.

Figure 4 is a graph of the output current of the fuel cell and the lithium battery of the hybrid vehicle, where the solid line is the output current of the fuel cell and the dotted line is the output current of the lithium battery. It is known that the high efficiency range of a 4.5kW proton exchange membrane fuel cell is between 0.5kW and 2kW, and the bus voltage of the hybrid vehicle is 48V. In this embodiment, the output current of the fuel cell ranges from 10A to 40A. It can be known through calculation that the output power of the fuel cell is maintained within the high efficiency range.

Figure 5 shows the state of charge of the supercapacitor and lithium battery of the hybrid vehicle, where the solid line is the state of charge of the supercapacitor and the dotted line is the state of charge of the lithium battery. After a complete operating cycle, the state of charge of the supercapacitor and the state of charge of the lithium battery are basically maintained within the expected range, without overcharging or over-discharging.

The preferred embodiments of the present invention are described in detail above in combination with the accompanying drawings and specific embodiments. However, the present invention is not limited to the specific details in the above embodiments. Within the technical concept of the present invention, a variety of simple modifications can be made to the technical solution of the present invention, and these simple modifications all belong to the protection scope of the present invention.

Claims (10)

1. The energy output management method of the hybrid electric vehicle based on battery aging is characterized by comprising the following steps of:

Step one, acquiring a load current, a super-capacitor charge state value and an automobile speed on a direct current bus of a hybrid electric automobile through a sensor, acquiring an adjusting frequency through a fuzzy controller and a super-capacitor charge state value optimizing module, and inputting the adjusting frequency into a Butterworth filter to acquire a required current of a bottom layer and a super-capacitor output current of a top layer;

Step two, establishing an aging model of the fuel cell and the lithium battery, estimating aging parameters by using unscented Kalman filtering, and calculating the health states of the fuel cell and the lithium battery;

Step three, obtaining a lithium battery equivalent factor and a maximum current dynamic change rate of the fuel battery according to the health states of the fuel battery and the lithium battery;

And step four, inputting the required current of the bottom layer, the lithium battery equivalent factor and the dynamic current change rate of the fuel battery into a built self-adaptive equivalent power consumption minimum strategy, and calculating to obtain the output currents of the fuel battery and the lithium battery.

2. The energy output management method of a hybrid vehicle based on battery aging according to claim 1, characterized in that: in the first step of the process,

In the fuzzy controller, the input variables are the super-capacitor charge state value SOC _sc and the load current I _LOAD, and the output variables are the initial regulation frequency f _s',0.5<SOC_sc <0.9;

In the super capacitor SOC optimization module, a super capacitor reference state of charge value related to driving conditions The definition is as follows:

Where v _max is the maximum speed of the vehicle, Is the maximum state of charge value of the super capacitor; v is the current vehicle speed;

To ensure that the super-capacitor state of charge value SOC _sc approaches its super-capacitor reference state of charge value And the adjustable frequency increment delta f is defined to correct the output of the fuzzy controller according to the driving condition, so that the change of the super capacitor according to the driving condition is realized, and the final corrected adjusting frequency f _s is expressed as follows:

Where k is the adjustment factor, SOC _sc is the super-capacitor state-of-charge value, Is the reference state of charge value of the super capacitor;

the adjusting frequency f _s is input into the Butterworth filter, and the required current of the bottom layer and the output current of the super capacitor of the top layer are respectively as follows:

I_SC＝I_LOAD-I_RE

The current period of I _RE (k), the current period of I _RE (k-1), the current period of I _LOAD (k), T is the filter sampling period, I _RE is the current of the bottom layer, and I _SC is the super capacitor output current of the top layer.

3. The energy output management method of a hybrid vehicle based on battery aging according to claim 2, characterized in that: in the fuzzy controller, the calculation rule of the fuzzy controller is based on an input-output fuzzy membership function and a fuzzy rule base, a triangular and trapezoidal membership function is selected, the adjusting frequency f _s' is adjusted according to different driving conditions, a gravity center method is taken as a defuzzification calculation method, and under the discrete domain, the gravity center method has the calculation formula:

Wherein i is the number of quantization levels, For the corresponding membership degree value, z _i is the corresponding quantization level value, and z ₀ is the precise value of the fuzzy controller after the de-modeling, namely the initial adjustment frequency f _s'.

4. The energy output management method of a hybrid vehicle based on battery aging according to claim 2 or 3, characterized in that: in the second step, the first step is performed,

The state of health of the fuel cell is defined as follows:

Where α _min is the minimum value of the degradation bias and α _max is the maximum value of the degradation bias; alpha _min and alpha _max are calculated from the internal resistance limit value and the maximum current limit value of the factory parameters of the fuel cell;

The state of health of a lithium battery is defined as follows:

Wherein, For the initial maximum charge of the battery among the lithium battery parameters,A power threshold value that is the end of battery life; q (t) is the measured maximum battery charge at time t.

5. The energy output management method of a battery aging-based hybrid vehicle according to claim 4, characterized in that: in the state of health of the fuel cell, a static voltage model of the proton exchange membrane fuel cell is used as an aging model of the fuel cell, and the equation is as follows:

wherein E represents the total voltage of the fuel cell stack, N _cell represents the number of fuel cell layers, E _rev represents the thermodynamic reversible potential, E _act represents the activation loss, R represents the internal resistance of the fuel cell, I _FC represents the current of the fuel cell, B represents the empirical constant, and I _max represents the maximum allowable current;

the unscented Kalman filter estimation model for the fuel cell is as follows:

Wherein x _k＝[α,β]^T is an unscented kalman filter state variable, a= [1, t;0,1], y _k is fuel cell voltage, w _k and v _k are process and observation noise, u _k is fuel cell input reference current, g (x _k,u_k) is a static voltage model; knowing the fuel cell input reference current u _k and the actual output voltage y _k measured by the sensor, the unscented kalman filter as a state observer is used to estimate the unobserved state parameter x _k+1 in the fuel cell aging model to find the internal resistance R (t) and the maximum current I _max (t) of the fuel cell at time t;

the change over time of the internal resistance R of the fuel cell and the maximum current I _max is described as follows:

Wherein R (t) represents the internal resistance of the fuel cell at time t, I _max (t) represents the maximum current of the fuel cell at time t, R ₀ and I _max0 each represent its initial value, and α (t) represents the degradation deviation over time.

6. The energy output management method of a battery aging-based hybrid vehicle according to claim 4, characterized in that: in the calculation of the state of health of the lithium battery, an electrochemical-based lithium ion battery model is used as a lithium battery aging model, and the equation is as follows:

V(t)＝V_U,p-V_U,n-V_S,p-V_S,n-V_e-V_O,n-V_O,p，

Wherein V _U,p,V_U,n is the equilibrium voltage of the positive and negative current collectors, V _O,p,V_O,n is the surface overvoltage caused by the charge transfer resistance of the positive and negative current collectors, V _S,p,V_S,n is the voltage drop caused by the solid phase ohmic resistance of the positive and negative current collectors, and V _e is the voltage drop caused by the ohmic resistance of the electrolyte;

the equilibrium voltage is calculated using the nernst equation:

Wherein x _i represents a mole fraction, i represents an electrode, i=n represents a negative electrode, and i=p represents a positive electrode; u ₀ is the reference voltage, R is the universal gas constant, T is the electrode temperature; n is the number of electrons transferred in the reaction, n=1 for lithium batteries; v _act,i represents an activity correction term, V _act,i being ideally 0;

x _i is represented as:

Wherein q _i is the amount of lithium ions in the electrode i, q _max＝q_p+q_n, i.e. q _max is the maximum electric quantity of lithium ions; for a lithium battery, the molar fraction of the positive electrode and the negative electrode of the lithium battery is x _p+x_n =1, when the lithium battery is fully charged, x _p =0.4 and x _n =0.6; at full discharge, x _p =1 and x _n =0;

The relation between the voltage and the internal resistance of the lithium battery is as follows:

V_o＝V_S,p+V_S,n+V_e＝i_appR₀，

The total voltage caused by the solid phase ohmic resistance, the electrolyte ohmic resistance, and the resistance at the current collector of the lithium battery can be regarded as V _o, and the internal resistance of the lithium battery can be expressed as R ₀, where i _app is the lithium battery input reference current;

the unscented Kalman filter estimation model of the lithium battery is as follows:

Wherein x _k＝[α,β]^T is an unscented kalman filter state variable, a= [1, t;0,1], y _k is lithium battery voltage, w _k and v _k are process and observation noise, u _k is lithium battery input reference current, h (x _k,u_k) is lithium battery electrochemical model; the input reference current u _k of the lithium battery and the actual output voltage y _k measured by the sensor are known, and unscented Kalman filtering serving as a state observer is used for estimating an unobserved state parameter x _k+1 in the aging model of the lithium battery so as to obtain the maximum electric quantity q _max and the internal resistance R ₀ of the lithium battery at the moment t.

7. The energy output management method of a battery aging-based hybrid vehicle according to claim 5 or 6, characterized in that: in the third step, the step of the method,

The lithium battery equivalent factor lambda _BA is:

λ_BA＝λ_BA0(1+0.195SOH_FC)(1+0.187SOH_BA)，

Wherein lambda _BA0 is the initial equivalent factor of the lithium battery, SOH _FC is the health state of the fuel battery, and SOH _BA is the health state of the lithium battery;

The maximum current dynamic change rate dI _FC of the fuel cell is:

dI_FC＝dI₀*(1-0.5*SOH_BA)，

Where dI ₀ represents the initial current dynamic change rate of the fuel cell, and SOH _BA is the state of health of the lithium battery.

8. The energy output management method of a battery aging-based hybrid vehicle according to claim 7, characterized in that: in the fourth step, the first step is performed,

The adaptive equivalent power consumption minimum strategy is:

Wherein I _FC represents the output current of the fuel cell, I _BA represents the output current of the lithium battery, I _RE represents the demand current of the bottom layer, m _w represents the total hydrogen consumption of the fuel cell hybrid vehicle system, m _FC represents the fuel cell hydrogen consumption, K _BA is the lithium battery efficiency penalty coefficient, K _FC is the fuel cell efficiency penalty coefficient, λ _BA is the equivalent factor of the lithium battery, and U _bus is the dc bus voltage; indicating upper and lower limits of the fuel cell current output, The upper and lower limits of the current output of the lithium battery are indicated, I _FC (t) and I _FC (t-1) indicate the fuel cell currents at time t and time t-1, and dI _FC indicates the maximum current dynamic change rate of the fuel cell.

9. The energy output management method of a battery aging-based hybrid vehicle according to claim 8, characterized in that: the fuel cell efficiency penalty coefficient K _FC is:

Where η is the fuel cell instantaneous efficiency, η _opt is the fuel cell optimum efficiency, η _max is the fuel cell maximum efficiency, and η _min is the fuel cell minimum efficiency.

10. The energy output management method of a battery aging-based hybrid vehicle according to claim 8, characterized in that: the lithium battery efficiency penalty coefficient K _BA is defined as:

Where u is the instantaneous charge of the lithium battery, B _int is the initial charge of the lithium battery, B _max is the maximum charge of the lithium battery, and B _min is the minimum charge of the lithium battery.