CN112925209B - Fuel cell automobile model-interference double-prediction control energy management method and system - Google Patents
Fuel cell automobile model-interference double-prediction control energy management method and system Download PDFInfo
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Abstract
The invention relates to a fuel cell automobile model-interference double-prediction control energy management method and system, which are used for realizing the minimization of equivalent hydrogen consumption of a fuel cell hybrid power system. The fuel cell automobile hybrid power system consists of a speed sensor, a speed predictor, an energy management controller, a DC/DC converter, a fuel cell engine and a power battery, wherein the speed predictor predicts the future speed by utilizing historical speed information and corrects the future speed by a Markov model. The model-interference double-prediction control energy management is combined with the power demand of the whole vehicle in the future, and the power of the fuel cell engine and the power cell is distributed through the energy management controller, wherein the system prediction model predicts the state-of-charge parameter of the power cell, the interference prediction calculates the future load disturbance by using the predicted vehicle speed, and the future load disturbance is input into the system prediction model, so that the accuracy of the system prediction model in the traditional model prediction control is enhanced, and the optimal control action of rolling optimization output is improved accordingly.
Description
Technical Field
The invention relates to a fuel cell hybrid power system, in particular to a fuel cell automobile model-interference double-prediction control energy management method and system.
Background
With the continuous development of global science and technology and economy, the consumption of energy is gradually increased, and the energy crisis of environmental pollution is increasingly serious. New energy power generation devices conforming to the concept of sustainable development have become a research hotspot in the energy field. Fuel cell engines are receiving great attention in the automotive field for their advantages of low pollution and high energy conversion efficiency. However, when the variation in the output power of the fuel cell engine is large, the membrane electrode assembly of the fuel cell stack is easily degraded, resulting in a shorter service life of the fuel cell engine. In order to solve the application problem of the fuel cell engine, a structure of a fuel cell hybrid power system is generally adopted. And a power battery with better dynamic response capability is added into the power system, so that the defect of the response capability of the fuel cell engine is overcome. Therefore, how to adjust the power output of the fuel cell engine and the power battery under different levels of power requirements to achieve efficient and stable operation of the power system is an important issue to be resolved urgently.
In a plurality of energy management methods, the model predictive control energy management strategy can effectively solve the problems of multiple variables and constraint, has stronger robustness and stability, and is widely applied to control management of strong nonlinear systems such as fuel cells and the like. However, the system response prediction in the conventional model predictive control is calculated in the prediction time domain based on the state variable values and the disturbance values acquired in real time. When external disturbances change, the system response prediction of the traditional model predictive control is not completely accurate, so that the calculated control actions are only approximate to the optimal solution, and the economy and durability of the fuel cell hybrid system are affected. Therefore, conventional model predictive control has the potential and necessity for improvement when applied to fuel cell vehicle energy management.
Disclosure of Invention
The invention aims to provide a fuel cell automobile model-interference double-prediction control energy management method and system, which are used for distributing power demands of a fuel cell engine and a power cell in real time based on minimum equivalent hydrogen consumption so as to enable a power system to work stably and efficiently.
In order to achieve the above purpose, the technical scheme of the invention is as follows: a fuel cell car model-disturbance bi-predictive control energy management method comprising the steps of:
s1, predicting a future vehicle speed by utilizing historical vehicle speed information, calculating prediction errors of the historical vehicle speed information and the future vehicle speed information, establishing a vehicle speed error correction model, and correcting the predicted future vehicle speed information;
s2, inputting future vehicle speed information into an energy management controller, and calculating future vehicle power requirements by combining a vehicle dynamics model;
and S3, combining the power demand information of the whole vehicle in the future, predicting important system parameters including the state of charge of the power battery based on a linear prediction model of the power system, establishing an equivalent hydrogen consumption objective function, and calculating an optimal solution of the equivalent hydrogen consumption objective function through an optimization algorithm to obtain optimal power distribution of the fuel battery engine and the power battery.
In an embodiment of the present invention, in step S1, a vehicle speed prediction method for predicting a future vehicle speed by using historical vehicle speed information is a third-order exponential smoothing method, and is input as historical vehicle speed information and output as predicted future vehicle speed information.
In an embodiment of the present invention, in step S1, the vehicle speed error correction model is a markov model.
In one embodiment of the present invention, in step S3, an optimization algorithm for calculating an optimal solution of the objective function of the equivalent hydrogen consumption is an active set algorithm.
In an embodiment of the invention, the model-interference double prediction refers to state quantity prediction and interference prediction respectively, the important system parameters including the state of charge of the power battery are predicted by establishing a system prediction model, and the interference prediction refers to calculation of future load disturbance information by predicting the vehicle speed and combining with a whole vehicle dynamics model.
The invention also provides a fuel cell automobile model-interference double-prediction control energy management system, which comprises: the system comprises a vehicle speed sensor, a vehicle speed predictor, an energy management controller, a DC/DC converter, a fuel cell engine and a power battery;
the fuel cell engine is connected with the DC/DC converter in series and then connected with the power cell in parallel on the DC bus;
the vehicle speed predictor inputs historical vehicle speed information and outputs predicted future vehicle speed information;
the energy management controller inputs predicted future vehicle speed information, and adopts model-interference double prediction control to output energy distribution of the fuel cell engine and the power cell based on the minimum equivalent hydrogen consumption;
the model-interference double prediction control is to predict and correct future vehicle speed information to improve the prediction accuracy of model prediction control.
In one embodiment of the invention, the vehicle speed predictor predicts future vehicle speed information based on an exponential smoothing method-Markov correction model.
In an embodiment of the invention, the exponential smoothing method-markov correction model calculates the future vehicle speed by combining the historical vehicle speed information with the third-order exponential smoothing method, calculates the prediction error of the historical vehicle speed information and the future vehicle speed information, establishes the markov model vehicle speed error correction model, and corrects the vehicle speed information predicted based on the exponential smoothing method.
In an embodiment of the present invention, the energy management controller uses a model-interference double prediction control strategy to perform energy management on the calculated future power demand of the whole vehicle, so as to obtain optimal power distribution of the fuel cell engine and the power cell based on the minimum equivalent hydrogen consumption.
In an embodiment of the invention, the model-interference double-prediction control strategy is combined with future power demand information, the important system parameters including the state of charge of the power battery are predicted based on the linear prediction model of the power system, an equivalent hydrogen consumption objective function is established, and an objective function optimal solution is calculated through an active set algorithm, so that the optimal power distribution of the fuel battery engine and the power battery is obtained.
Further, the vehicle speed predictor and the energy management controller are embedded in a singlechip for the whole vehicle controller.
Furthermore, the energy management controller is a singlechip for a whole vehicle controller, the input signal has real-time vehicle speed information, and the output signal comprises the optimal power distribution of the fuel cell engine and the power cell.
The invention also provides a vehicle using the method or system as described above.
Compared with the prior art, the invention has the following beneficial effects:
the invention manages the power demand of the fuel cell hybrid power system, distributes the output power of the fuel cell engine and the power cell based on the power demand information of the whole vehicle in the future, ensures the minimum equivalent hydrogen consumption of the whole vehicle in the running process, and keeps the stable and efficient running of the power system.
The future whole vehicle power demand information is calculated based on an exponential smoothing method-Markov correction model and a whole vehicle dynamics model by taking historical vehicle speed information acquired in real time as parameters and is used as the predicted disturbance variable input of an energy management strategy.
The energy management strategy based on model-disturbance biprediction control is utilized to distribute the power of the fuel cell engine and the power cell. The model-interference double-prediction control method is a control method capable of effectively solving the problems of multiple variables and constraint, has strong anti-interference capability, has strong robustness and stability, can realize advanced control management, and can limit the economical efficiency and durability of a fuel cell hybrid power system.
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FIG. 1 is a schematic diagram of the overall architecture of a fuel cell hybrid system based on an MPC5604B microcontroller in accordance with the present invention;
FIG. 2 is a schematic diagram of a fuel cell hybrid system controller according to the present invention;
FIG. 3 is a schematic diagram of a fuel cell hybrid system vehicle speed prediction according to the present invention;
FIG. 4 is a schematic diagram of the power distribution of the fuel cell hybrid system of the present invention;
FIG. 5 is a schematic representation of the state of charge of a fuel cell hybrid system power cell of the present invention;
fig. 6 is a schematic diagram of the integrated equivalent hydrogen consumption of the fuel cell hybrid system of the present invention.
Marking:
1-a vehicle speed sensor; 2-a vehicle speed predictor; 3-an energy management controller; 4-MPC5604B microcontroller; 5-a power cell; a 6-DC/DC converter; 7-fuel cell engine.
Detailed Description
The technical scheme of the invention is specifically described below with reference to the accompanying drawings.
As shown in fig. 1, the fuel cell automobile model-interference double-prediction control energy management system provided by the invention comprises a vehicle speed sensor 1, a vehicle speed predictor 2, an energy management controller 3, an MPC5604B microcontroller 4, a power cell 5, a DC/DC converter 6 and a fuel cell engine 7, wherein the overall structure schematic diagram of the fuel cell hybrid power system based on the MPC5604B microcontroller is shown in fig. 1.
The DC/DC converter converts the voltage of the output end of the fuel cell engine and is connected with the power cell in parallel to the direct current bus;
the vehicle speed predictor collects vehicle speed information in real time and outputs predicted future vehicle speed information;
the energy management controller combines the predicted future vehicle speed information, calculates and obtains the energy distribution of the fuel cell engine and the power cell, and transmits the power requirement to the fuel cell engine and the power cell.
In this embodiment, an energy management system of a fuel cell vehicle power system based on a model-disturbance bi-predictive control of an MPC5604B microcontroller is designed, referring to fig. 1, and includes a vehicle speed sensor, a vehicle speed predictor, an energy management controller, a DC/DC converter, a fuel cell engine, and a power cell, which are sequentially connected. The whole vehicle unit adopts an MPC5604B microcontroller. The MPC5604B in the control system downloads codes generated in the third party CodeWarrior compiling environment to the MPC5604B singlechip by using a standard JTAG emulation debug interface PC [0 ].
In this embodiment, MPC5604B is connected to the vehicle speed sensor through GPIO ports PA 0-PA 2 to read the real-time vehicle speed, and after the internal optimization management algorithm, outputs control logic through eMIOS ports PB 11 and PB 12 and is connected to the DC/DC converter to control the output power of the fuel cell engine. And outputting power distribution signals of the power battery through eMIOS ports PB [13] and PB [14 ].
In this embodiment, the energy management controller based on the MPC5604B takes the measured data of the vehicle speed sensor as input, and outputs a control signal through a model-interference bi-predictive control algorithm, so that the fuel cell engine and the power battery output at optimal power, and the goal of low hydrogen consumption of the system is ensured.
Aiming at the fuel cell hybrid power system, the invention distributes the output power of the fuel cell engine and the power cell of the power system under different power level requirements, realizes the energy management control based on the minimum equivalent hydrogen consumption, and keeps the high-efficiency stable operation of the power system.
The invention relates to a set of vehicle speed predictors and a set of energy management controllers, and a schematic diagram of a power system controller is shown in fig. 2.
The vehicle speed predictor calculates the future vehicle speed information based on an exponential smoothing method-Markov correction model through the whole vehicle speed information acquired in real time by the sensor, and the adopted exponential smoothing method-Markov correction model is built offline through historical data.
Specifically, a third-order exponential smoothing method model is built in consideration of the vehicle speed change characteristic, as shown in formula (1):
wherein S is t A is a smoothed value at time t, a is a smoothing factor, x is t Time-of-day vehicle speed sequence value.
Building a vehicle speed prediction model based on an exponential smoothing method, as shown in a formula (2):
wherein the method comprises the steps of
According to the historical vehicle speed information, a vehicle speed prediction sequence based on an exponential smoothing method can be obtained. And calculating the prediction error of the exponential smoothing prediction model by comparing the prediction error with the historical vehicle speed information. The sequence of vehicle speed prediction errors may be considered as a discrete Markov chain from which a Markov correction model is built. The transition probability matrix of the vehicle speed prediction error is shown in formula (3):
wherein the method comprises the steps of
Wherein N is ij The number of times the speed prediction error is transferred from the state i to the state j is the number, and the speed prediction error of the exponential smoothing method can be predicted and corrected based on a Markov correction model.
The vehicle speed predictor calculates future vehicle speed information based on an exponential smoothing method-Markov correction model by utilizing the historical vehicle speed information acquired by the sensor, and the obtained vehicle speed prediction correction result is shown in fig. 3.
The energy management controller is combined with the future vehicle speed information output by the vehicle speed predictor to calculate and obtain a whole vehicle power demand prediction sequence, the interference quantity serving as a model prediction control strategy is used for predicting state variables of the power system, and the optimal power distribution strategy is calculated based on the minimum equivalent hydrogen consumption objective function.
Specifically, in combination with future vehicle speed information, a vehicle power demand prediction sequence is calculated based on a vehicle dynamics model, as shown in a formula (4):
wherein eta is tran C for the efficiency of the whole vehicle transmission system D The air resistance coefficient is A is the windward area of the whole vehicle, a is the acceleration, delta is the rotating mass conversion coefficient, m v For the whole car quality, C f And v is the vehicle speed, and v is the rolling resistance coefficient.
And (3) inputting the calculated whole vehicle power demand sequence into a system prediction model as the disturbance quantity of a model prediction control strategy, and calculating the predicted value of the state variable of the power system. The linear incremental state space equation of the system is shown in formula (5):
ΔX(k+1)＝A·ΔX(k)+B·ΔU(k)+D·Δd(k) (5)
wherein X is a system state variable, which is set as a state of charge of a power battery, U is a system control variable, which is set as output power of a fuel cell engine, D is a system disturbance variable, which is set as a power demand of the whole vehicle, and A, B and D are system state space coefficient matrixes.
The system prediction model of the model-interference double-prediction control strategy based on the predicted interference quantity is shown in a formula (6):
X p (k+1|k)＝A p ΔX(k)+B p ΔU(k)+D p Δd(k)+X s (k) (6)
wherein A is P 、B P And D P Is a prediction model coefficient matrix calculated from the state space coefficient matrix. X is X s (k) The system state value is acquired in real time at the time k. And feedback correction is carried out based on the actual state value of the system, and the system response increment at the future moment is calculated through a model-interference double-prediction model, so that the influence caused by the nonlinear and model mismatch uncertainty factors of the system can be well processed. The model predictive control strategy establishes an objective function based on the minimum equivalent hydrogen consumption, as shown in equation (7):
wherein the method comprises the steps of
In the method, in the process of the invention,
for equivalent hydrogen consumption mass flow of hybrid system,/->
Hydrogen mass flow for fuel cell engine consumption, +.>
The equivalent hydrogen consumption mass flow of the power battery is represented by kappa, the state of charge balance coefficient of the power battery, P, the model predictive control prediction time domain and P fc For fuel cell engine output power, eta fc LHV for fuel cell engine efficiency H2 Is hydrogen with low calorific value, P bat And gamma is the charge and discharge efficiency coefficient of the power battery.
The model-interference bi-predictive control adopts a rolling finite time domain optimization strategy. At the kth moment, predicting the future system state quantity change in a set prediction time domain by a system prediction model based on the predicted interference quantity, updating the related parameters of the objective function, solving the optimal power distribution result at the kth moment by an active set method, and applying the optimal power distribution result to an actual system. And at the next moment, predicting the time domain to roll forward, and carrying out optimal power allocation calculation again based on the system state quantity and the interference quantity at the k+1 moment obtained by feedback. The model-disturbance bi-predictive control implements a rolling optimization process by constantly solving for optimal power allocation at each moment.
And the energy management controller outputs the optimal power distribution strategy to the fuel cell engine and the power battery, so that the high-efficiency and stable operation of the whole vehicle power system is realized. The schematic diagrams of the output power of the fuel cell engine and the state of charge of the power cell based on the actual working condition of the whole vehicle are shown in fig. 4 and 5. In addition, an energy management strategy is designed for dynamically planning the purpose of reducing the hydrogen consumption to the maximum extent, the energy management strategy is used as an optimization reference to compare two model predictive control, and fig. 6 is the accumulated equivalent hydrogen consumption of the hybrid power system under the working condition. And comparing the energy management strategy based on the model-interference double-prediction control with the management control result of the traditional model prediction control energy management strategy, and reflecting the optimization effect and application potential of the energy management strategy based on the model-interference double-prediction control.
The above is a preferred embodiment of the present invention, and all changes made according to the technical solution of the present invention belong to the protection scope of the present invention when the generated functional effects do not exceed the scope of the technical solution of the present invention.
Claims (8)
1. A fuel cell automobile model-disturbance bipredictive control energy management method, characterized by comprising the steps of:
s1, predicting a future vehicle speed by utilizing historical vehicle speed information, calculating prediction errors of the historical vehicle speed information and the future vehicle speed information, establishing a vehicle speed error correction model, and correcting the predicted future vehicle speed information; the vehicle speed prediction method adopted for predicting the future vehicle speed is a third-order exponential smoothing method, is input as historical vehicle speed information, and is output as predicted future vehicle speed information:
taking the vehicle speed change characteristics into consideration, a third-order exponential smoothing method model is built, as shown in a formula (1):
wherein S is t A is a smoothed value at time t, a is a smoothing factor, x is t A time-of-day vehicle speed sequence value;
building a vehicle speed prediction model based on an exponential smoothing method, as shown in a formula (2):
wherein the method comprises the steps of
Obtaining a vehicle speed prediction sequence based on an exponential smoothing method according to the historical vehicle speed information; calculating a prediction error of an exponential smoothing method prediction model through comparison with historical vehicle speed information; the vehicle speed prediction error sequence is regarded as a discrete Markov chain, and a Markov correction model is built according to the discrete Markov chain; the transition probability matrix of the vehicle speed prediction error is shown in formula (3):
wherein the method comprises the steps of
Wherein N is ij The number of times that the speed prediction error is transferred from the state i to the state j is the number, and the speed prediction error of the exponential smoothing method can be predicted and corrected based on a Markov correction model;
step S2, inputting the future vehicle speed information into an energy management controller, and calculating the future vehicle power demand by combining a vehicle dynamics model:
and (3) calculating a whole vehicle power demand prediction sequence based on a whole vehicle dynamics model by combining future vehicle speed information, wherein the whole vehicle power demand prediction sequence is shown in a formula (4):
wherein eta is tran C for the efficiency of the whole vehicle transmission system D The air resistance coefficient is A is the windward area of the whole vehicle, a is the acceleration, delta is the rotating mass conversion coefficient, m v For the whole car quality, C f The rolling resistance coefficient, v is the vehicle speed;
step S3, predicting system parameters including the state of charge of the power battery based on a linear prediction model of the power system according to the power demand information of the whole vehicle in the future, establishing an equivalent hydrogen consumption objective function, and calculating an optimal solution of the equivalent hydrogen consumption objective function through an optimization algorithm to obtain optimal power distribution of the fuel battery engine and the power battery:
inputting the calculated whole vehicle power demand sequence as the disturbance quantity of a model prediction control strategy into a system linear prediction model, and calculating the predicted value of a power system state variable; the system linear increment state space equation is shown in a formula (5):
ΔX(k+1)＝A·ΔX(k)+B·ΔU(k)+D·Δd(k)(5)
wherein X is a system state variable, which is set as a state of charge of a power battery, U is a system control variable, which is set as output power of a fuel cell engine, D is a system disturbance variable, which is set as a power demand of the whole vehicle, and A, B and D are system state space coefficient matrixes;
the system linear prediction model of the model-interference double-prediction control strategy based on the predicted interference quantity is shown in a formula (6):
X p (k+1|k)＝A p ΔX(k)+B p ΔU(k)+D p Δd(k)+X s (k)(6)
wherein A is P 、B P And D P The system linear prediction model coefficient matrix is calculated by the state space coefficient matrix; x is X s (k) The system state value is acquired in real time at the time k; and (3) carrying out feedback correction based on the actual state value of the system, calculating the system response increment at the future moment through a model-interference double-prediction model, and establishing an objective function based on the minimum equivalent hydrogen consumption by a model prediction control strategy, wherein the objective function is shown in a formula (7):
wherein the method comprises the steps of
In the method, in the process of the invention,
for equivalent hydrogen consumption mass flow of hybrid system,/->
Hydrogen mass flow for fuel cell engine consumption, +.>
The equivalent hydrogen consumption mass flow of the power battery is represented by kappa, the state of charge balance coefficient of the power battery, P, the model predictive control prediction time domain and P fc For fuel cell engine output power, eta fc LHV for fuel cell engine efficiency H2 Is hydrogen with low calorific value, P bat And gamma is the charge and discharge efficiency coefficient of the power battery.
2. The fuel cell automobile model-disturbance biprediction control energy management method according to claim 1, wherein in step S3, the optimization algorithm for calculating the optimal solution of the equivalent hydrogen consumption objective function is an active set algorithm.
3. The energy management method of fuel cell vehicle model-disturbance biprediction control according to claim 1, wherein the method is characterized in that the energy management of a fuel cell vehicle power system is realized through model-disturbance biprediction, wherein the model-disturbance biprediction is respectively state quantity prediction and disturbance prediction, the prediction of important system parameters including the state of charge of a power cell is carried out through establishing a power system linear prediction model, the disturbance prediction is that future load disturbance information is calculated through predicting future vehicle speed and combining a whole vehicle dynamics model.
4. A fuel cell automobile model-disturbance bipredictive control energy management system, the management system comprising: the system comprises a vehicle speed sensor, a vehicle speed predictor, an energy management controller, a DC/DC converter, a fuel cell engine and a power battery;
the vehicle speed sensor is used for detecting current vehicle speed information;
the fuel cell engine is connected with the DC/DC converter in series and then connected with the power cell in parallel on the DC bus;
the vehicle speed predictor inputs historical vehicle speed information and outputs predicted future vehicle speed information;
the energy management controller inputs predicted future vehicle speed information, and adopts model-interference double prediction control to output energy distribution of the fuel cell engine and the power cell based on the minimum equivalent hydrogen consumption;
the model-interference double-prediction control is to obtain future load disturbance information in advance by predicting and correcting future vehicle speed information, so that the prediction accuracy of model prediction control is improved;
the vehicle speed predictor inputs historical vehicle speed information and outputs predicted future vehicle speed information by the following implementation modes:
taking the vehicle speed change characteristics into consideration, a third-order exponential smoothing method model is built, as shown in a formula (1):
wherein S is t A is a smoothed value at time t, a is a smoothing factor, x is t A time-of-day vehicle speed sequence value;
building a vehicle speed prediction model based on an exponential smoothing method, as shown in a formula (2):
wherein the method comprises the steps of
Obtaining a vehicle speed prediction sequence based on an exponential smoothing method according to the historical vehicle speed information; calculating a prediction error of an exponential smoothing method prediction model through comparison with historical vehicle speed information; the vehicle speed prediction error sequence is regarded as a discrete Markov chain, and a Markov correction model is built according to the discrete Markov chain; the transition probability matrix of the vehicle speed prediction error is shown in formula (3):
wherein the method comprises the steps of
Wherein N is ij The number of times that the speed prediction error is transferred from the state i to the state j is the number, and the speed prediction error of the exponential smoothing method can be predicted and corrected based on a Markov correction model;
the energy management controller inputs predicted future vehicle speed information, and the implementation mode of outputting the energy distribution of the fuel cell engine and the power cell based on the minimum equivalent hydrogen consumption by adopting model-interference double prediction control is as follows:
1) And (3) calculating a whole vehicle power demand prediction sequence based on a whole vehicle dynamics model by combining future vehicle speed information, wherein the whole vehicle power demand prediction sequence is shown in a formula (4):
wherein eta is tran C for the efficiency of the whole vehicle transmission system D The air resistance coefficient is A is the windward area of the whole vehicle, a is the acceleration, delta is the rotating mass conversion coefficient, m v For the whole car quality, C f The rolling resistance coefficient, v is the vehicle speed;
2) According to the power demand information of the whole vehicle in the future, predicting system parameters including the state of charge of the power battery based on a linear prediction model of the power system, establishing an equivalent hydrogen consumption objective function, and calculating an optimal solution of the equivalent hydrogen consumption objective function through an optimization algorithm to obtain optimal power distribution of the fuel battery engine and the power battery:
inputting the calculated whole vehicle power demand sequence as the disturbance quantity of a model prediction control strategy into a system linear prediction model, and calculating the predicted value of a power system state variable; the system linear increment state space equation is shown in a formula (5):
ΔX(k+1)＝A·ΔX(k)+B·ΔU(k)+D·Δd(k)(5)
wherein X is a system state variable, which is set as a state of charge of a power battery, U is a system control variable, which is set as output power of a fuel cell engine, D is a system disturbance variable, which is set as a power demand of the whole vehicle, and A, B and D are system state space coefficient matrixes;
the system linear prediction model of the model-interference double-prediction control strategy based on the predicted interference quantity is shown in a formula (6):
X p (k+1|k)＝A p ΔX(k)+B p ΔU(k)+D p Δd(k)+X s (k)(6)
wherein A is P 、B P And D P The system linear prediction model coefficient matrix is calculated by the state space coefficient matrix; x is X s (k) The system state value is acquired in real time at the time k; and (3) carrying out feedback correction based on the actual state value of the system, calculating the system response increment at the future moment through a model-interference double-prediction model, and establishing an objective function based on the minimum equivalent hydrogen consumption by a model prediction control strategy, wherein the objective function is shown in a formula (7):
wherein the method comprises the steps of
In the method, in the process of the invention,
for equivalent hydrogen consumption mass flow of hybrid system,/->
Hydrogen mass flow for fuel cell engine consumption, +.>
The equivalent hydrogen consumption mass flow of the power battery is represented by kappa, the state of charge balance coefficient of the power battery, P, the model predictive control prediction time domain and P fc For fuel cell engine output power, eta fc LHV for fuel cell engine efficiency H2 Is hydrogen with low calorific value, P bat And gamma is the charge and discharge efficiency coefficient of the power battery.
5. The fuel cell vehicle model-disturbance biprediction control energy management system of claim 4, wherein the vehicle speed predictor predicts future vehicle speed information based on an exponential smoothing method-markov correction model.
6. The fuel cell vehicle model-disturbance bi-predictive control energy management system of claim 5, wherein the exponential smoothing method-markov correction model calculates a future vehicle speed based on the historical vehicle speed information in combination with the third-order exponential smoothing method, and calculates a prediction error between the historical vehicle speed information and the future vehicle speed information, thereby creating a markov model vehicle speed error correction model for correcting the vehicle speed information predicted based on the exponential smoothing method.
7. The energy management system of claim 4, wherein the energy management controller adopts a model-interference bi-predictive control strategy, and combines future power demand information, predicts important system parameters including the state of charge of the power battery based on a linear predictive model of the power system, establishes an equivalent hydrogen consumption objective function, calculates an objective function optimal solution through an active set algorithm, and obtains optimal power distribution of the fuel cell engine and the power battery.
8. A vehicle characterized by the use of a method according to any one of claims 1-3 or a system according to any one of claims 4-7.
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