CN113815437B - Predictive energy management method for fuel cell hybrid electric vehicle - Google Patents
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Abstract
A predictive energy management method for a fuel cell hybrid electric vehicle belongs to the technical field of new energy vehicle power supplies. The present invention is directed to a predictive energy management method for a fuel cell hybrid vehicle that ensures high-efficiency operation of a fuel cell while maintaining the SOC of the lithium battery near a reference value, and minimizes hydrogen consumption in the case where the above conditions are satisfied. The method comprises the following steps: topology of the hybrid powertrain; establishing a fuel cell hybrid electric vehicle model; vehicle speed prediction and vehicle driving mode identification; the fuel cell hybrid electric vehicle on-line energy management method based on model predictive control is designed. The invention achieves the relative optimization among the three of economy, state of charge and fuel cell efficiency. A multi-objective fuel cell hybrid electric vehicle energy management method is designed. On the premise of stably maintaining the SOC of the lithium battery, the efficiency of the fuel battery is greatly improved, and the hydrogen consumption is remarkably reduced.
Description
Technical Field
The invention belongs to the technical field of new energy automobile power supplies.
Background
With the proposal of the national 'double carbon' target, the emission of automobiles is subject to more severe restriction, and the current electric and low emission of automobiles is an irreversible development trend. Among various vehicle-mounted power supply types, the fuel cell has the characteristics of zero emission, high efficiency, high energy density and long duration, so that the fuel cell is a good vehicle-mounted power supply selection. However, since the fuel cell generates electric energy by means of electrochemical reaction, and air as a reactant is delivered to the electric pile to participate in the reaction through the air compressor and the air intake manifold, a single fuel cell power supply often has a characteristic of slow response, and is difficult to cope with a rapidly changing vehicle energy demand. By establishing a hybrid power system of the fuel cell and the lithium battery, the energy response of the whole power system can be greatly improved, and the lithium battery plays a role in recovering braking energy in the power supply process and serves as an auxiliary power supply when the power of the fuel cell is insufficient, so that the power performance of the vehicle is greatly improved.
In the fuel cell and lithium battery hybrid power system, the fuel cell is a main energy source, and can stably output electric energy, and the lithium battery is an energy storage device which can serve as an auxiliary power supply. During the running process of the fuel cell hybrid electric vehicle, the electric energy required by the driving motor is determined by the vehicle speed and the vehicle parameters, and is provided by the fuel cell and the lithium cell together. If the output of the fuel cell is too large, the consumption of hydrogen is excessive, and the economical efficiency of the hybrid power system is deteriorated; if the output of the lithium battery is too large, the SOC (State of Charge) of the lithium battery is too low, which affects the cruising ability of the vehicle. Obviously, during the running of the vehicle, the reasonable energy management method can improve the economical efficiency of the hybrid system and the dynamic performance of the vehicle. There is therefore a need to formulate efficient energy management methods to address the real-time energy distribution optimization problem between fuel cells and lithium cells.
Generally, energy management methods of hybrid vehicles are classified into two types, a rule-based energy management method and an optimization-based energy management method. The rule-based method distributes the power of the fuel cell and the lithium cell according to the pre-designed energy management rule according to different types of running conditions of the automobile and different ranges of the SOC of the lithium cell. The rule-based method is simple and feasible, the operation amount is low, but the formulation of the energy management rule often depends on expert experience, and an optimal solution of energy distribution cannot be obtained. The optimization-based methods can be subdivided into offline optimization methods and online optimization methods. Dynamic planning, a minimum principle and a series of bionic optimization algorithms such as genetic algorithm all belong to an offline optimization method, and a global optimal solution of energy distribution can be obtained, but the offline optimization method has a larger limitation in practical application. The online optimization method obtains a local optimal solution and updates the solution in real time in each sampling period of the system, compared with the global optimization, the optimization solution of the online optimization method can update in real time, is more practical to the engineering, is a development trend at present and in the future, but the online optimization energy management method still has a plurality of problems to be solved:
1. in the design of the optimization target, the traditional method only considers the SOC of the lithium battery, is single-target optimization, and does not design an energy management method from the economical point of view, which increases unnecessary hydrogen consumption.
2. Ohmic polarization, activation polarization and concentration polarization phenomena exist in the fuel cell, and the energy-efficiency characteristic relationship exists. When the fuel cell is operated in the low efficiency region, the polarization phenomenon causes a large voltage loss, thereby reducing the energy utilization rate.
3. In the method following the principle of rolling time domain optimization, a local optimal solution in the future short-time domain is obtained in each sampling period. In practical applications, future vehicle speed information is unpredictable. The conventional processing method for this problem is to calculate the current vehicle speed as the future vehicle speed assuming that the vehicle is traveling at a constant speed, which obviously leads to a deterioration of the optimization effect.
In the process of establishing a vehicle speed prediction model, establishing a transition probability matrix is an important step. Because the running data of the vehicles under different road conditions have great difference, the transition probability matrix established by the traditional method is often high in divergence degree, so that the prediction accuracy is insufficient.
Disclosure of Invention
The present invention is directed to a predictive energy management method for a fuel cell hybrid vehicle that ensures high-efficiency operation of a fuel cell while maintaining the SOC of the lithium battery near a reference value, and minimizes hydrogen consumption in the case where the above conditions are satisfied.
The method comprises the following steps:
s1, topological structure of a hybrid power system;
s2, establishing a fuel cell hybrid electric vehicle model;
s3, vehicle speed prediction and vehicle driving mode identification
1. Vehicle driving road condition identification
Intercepting running condition section by adopting random number method
T 0 ＝τ(T-ΔT) (21)
Wherein T is 0 Is the start time of each sampling period, τ is a random number from 0 to 1, T is the length of each packet, Δt is the length of each sampling segment;
extracting feature vectors in each sample segment
l(k)＝[v ave ,ε,a max ,a min ] T (22)
Wherein v is ave Epsilon is the idle time ratio, a, for the average speed of each segment max At the maximum value of acceleration, a min Is the minimum value of acceleration;
normalizing feature vectors
Wherein l * For normalized characteristic value, l is the original characteristic, l min And/l max Original features which are minimum and maximum respectively; 2. vehicle speed prediction
Discrete division of acceleration and velocity of a dataset into finite length sequences
Wherein the method comprises the steps ofRepresents N a Discrete acceleration data,/->Represents N r A plurality of discrete velocity data;
the transition probability matrix is
Wherein,indicating when the vehicle speed is equal to v i The acceleration is represented by a in n sampling periods i Becomes a j Probability of (2);
computing using maximum likelihood estimation
Wherein,indicating when the vehicle speed is equal to v i At the time of acceleration a i Data number of->Indicating when the vehicle speed is equal to v i The acceleration is represented by a in n sampling periods i Becomes a j Data number of (2);
satisfy the following requirements
S4, designing an online energy management method of a fuel cell hybrid electric vehicle based on model predictive control
Defining performance index of battery SOC
Wherein,is a binary norm of the matrix; s (SOC (k)) is a penalty term in the cost function, and the weight of the penalty term can be adjusted according to the balance relation between the SOC and other performance indexes;
defining a second performance index
Wherein N-1 is the length of the predicted time domain;
defining a third performance index
Wherein eta fc (k) Is fuel cell efficiency;
according to the performance index and the constraint condition, the final objective function is that
Wherein x is 1 (k) Is SOC (k), x 2 (k) Is P fc (k) θ (k) is the vehicle speed at time k;
solving the objective function to obtain a control sequence
Wherein,the fuel cell stack current at time k+1 is shown. Solving the objective function at the time k and obtaining a control sequence u k Thereafter, the first element in the control sequence is +.>Acts on the system and then solves J N (u k+1 ,x 0 ) The method comprises the steps of carrying out a first treatment on the surface of the The above process is repeated, so that the real-time energy distribution between the fuel cell and the lithium battery can be realized.
The invention has the beneficial effects that:
1. the method of the invention achieves the relative optimization among the three of economy, state of charge and fuel cell efficiency. A multi-objective fuel cell hybrid electric vehicle energy management method is designed. On the premise of stably maintaining the SOC of the lithium battery, the efficiency of the fuel battery is greatly improved, and the hydrogen consumption is remarkably reduced;
2. the problem of future vehicle energy demand loss in the optimal control problem solving is solved by introducing a vehicle speed prediction method, so that the economical efficiency of the energy management method is further improved;
3. the road condition recognition method is introduced in the vehicle speed prediction, the running information database is divided into three types, and three transition probability matrixes are respectively established, so that the accuracy of the vehicle speed prediction is improved.
Drawings
FIG. 1 is a topological structure diagram of a hybrid powertrain;
FIG. 2 is a graph of transition probability matrices;
FIG. 3 is a road condition recognition accuracy test cycle chart;
FIG. 4 is a road condition recognition result diagram;
FIG. 5 is a graph of a vehicle speed prediction result;
FIG. 6 is a flow chart of a predictive energy management method implementation;
fig. 7 is a graph of hydrogen consumption during running of the vehicle;
FIG. 8 is a graph of SOC variation during vehicle travel;
fig. 9 is an operational efficiency map of a fuel cell in a conventional method;
fig. 10 is a graph of the operating efficiency of the fuel cell of the present invention.
Detailed Description
The invention aims to solve the problems of poor economical efficiency, low fuel cell efficiency and missing future vehicle speed information of the existing energy management method.
The existing energy management method for the fuel cell hybrid electric vehicle has a plurality of defects, and the invention aims to solve the problems in the traditional energy management method: 1. there is no consideration for the economy of the energy management method, which increases unnecessary hydrogen consumption; 2. ohmic polarization, activation polarization and concentration polarization phenomena exist in the fuel cell, and the energy-efficiency characteristic relationship exists. When the fuel cell operates in a low-efficiency region, the polarization phenomenon can cause larger voltage loss, so that the energy utilization rate is reduced; 3. in practical applications, future vehicle speed information is unpredictable. The traditional processing method aiming at the problem is to assume that the vehicle runs at a constant speed and then calculate, which obviously leads to poor optimization effect; 4. establishing the transition probability matrix is an important step in the process of establishing the vehicle speed prediction model. Because the running information of the vehicles under different road conditions has great difference, the transition probability matrix established by the traditional method is often high in divergence degree, so that the prediction accuracy is insufficient.
The invention is described in four parts: topology of the hybrid powertrain; establishing a fuel cell hybrid electric vehicle model; vehicle speed prediction and vehicle driving mode identification; the fuel cell hybrid electric vehicle on-line energy management method based on model predictive control is designed.
Topology of hybrid powertrain: and the fuel cell, the lithium battery and the bus are connected.
Fuel cell hybrid vehicle model building: and establishing an overall model of the fuel cell hybrid electric vehicle comprising a vehicle longitudinal dynamics model, a driving motor model, a fuel cell model and a lithium battery internal resistance model.
Vehicle speed prediction and vehicle driving mode identification: in order to solve the problem that the traditional energy management method lacks future vehicle speed, a transition probability matrix of vehicle speed and acceleration is established according to historical running information of the vehicle to predict the future vehicle speed. Considering that the difference of the vehicle running information under different road conditions is very large, the divergence degree of the transition probability matrix is too large, so the road conditions of the vehicle running are divided into three types: the method for identifying the running mode of the vehicle is designed according to the highway road conditions, the urban congestion road conditions and the urban unblocked road conditions, three transition probability matrixes corresponding to different road conditions are respectively established according to the three road conditions, and the accuracy of vehicle speed prediction is improved by adopting a mode of identifying before predicting.
The fuel cell hybrid electric vehicle online energy management method based on model predictive control is designed by the following steps: in order to solve the problem that the traditional energy management method does not consider the economical efficiency and the efficiency of the fuel cell, a multi-objective optimization method is designed. While keeping the lithium battery SOC around the reference value, the fuel cell is ensured to operate at high efficiency, and the consumption of hydrogen gas is minimized while satisfying the above conditions.
The technical scheme provided by the invention is further explained and illustrated by combining the accompanying drawings:
1. topology of hybrid power system
In a fuel cell hybrid vehicle, a fuel cell, a lithium battery, and a drive motor are connected by a bus. The topology of a fuel cell-lithium battery hybrid system can be divided into two types according to the connection mode of the fuel cell and the bus: the first is direct mixing and the second is indirect mixing. The invention adopts an indirect mixing mode, the topological structure of the mixing system is shown in figure 1, the fuel cell is connected with the bus through a unidirectional DC-DC converter, and the lithium battery is directly connected with the bus. Control of the fuel cell stack current may be achieved by controlling the duty cycle of the DC-DC converter.
2. Fuel cell hybrid vehicle model building
1. Longitudinal dynamics model of vehicle
The longitudinal dynamics model of the vehicle describes the relationship between the stress condition of the vehicle and the speed of the vehicle. The stress condition of the vehicle during running is that
F trac ＝F roll +F aero +F grade +F inertia (1)
Wherein F is trac For the driving force exerted on the vehicle, F roll For rolling resistance, F aero For air resistance, F grade For ramp resistance, F inertia For acceleration resistance, respectively calculated as
F roll ＝fmg cosθ (2)
F grade ＝mg sinθ (4)
Wherein m is the half-load mass of the vehicle, f is the coefficient of rolling resistance of the wheels, θ is the gradient of the running road surface, A f C is the windward area d The air resistance coefficient, u is the running speed, delta is the rotating mass conversion coefficient, g is the gravitational acceleration, and t is the time.
It follows that the driving force is a variable related to the vehicle speed, i.e
F trac ＝h(u) (6)
The moment T exerted on the wheels of the vehicle wheel And a rotational angular velocity omega wheel The relationship with the running driving force of the automobile and the vehicle speed u can be expressed as
T wheel ＝F trac ·r (7)
Where r is the wheel radius.
2. Driving motor model
The modeling method of the driving motor can be generally divided into a theoretical model built according to a driving principle and an efficiency model according to a table look-up method. The theoretical model of the motor is generally used for researching the working characteristics and control performance of the motor, the detailed parameter information of the motor needs to be known, and modeling is complex; the efficiency model according to the table lookup method is mainly based on Map diagrams of motors, has high simulation speed, and is suitable for researching the whole vehicle energy management method. Because the research focus of the invention is not the working principle of the driving motor, but the energy distribution of the fuel cell hybrid electric vehicle, the efficiency model based on table lookup is selected to build the motor model. Wherein, the Map table information of the motor is provided by a manufacturer or provided by a real vehicle experiment and can be regarded as a table look-up function of the output characteristics of the motor, namely
η MG ＝f MG (T MG ,ω MG ) (9)
Wherein T is MG For motor output torque, ω MG Angular velocity, eta MG The motor work efficiency is achieved. And according to the output torque and the output rotating speed of the motor at a certain moment, searching an efficiency MAP table to obtain the working efficiency of the motor at the moment. f (f) MG (. Cndot.) is an efficiency look-up table function.
From the dynamics of the whole vehicle, the motor torque and the motor rotation speed can be obtained by the following formula
ω MG ＝ω wheel i fd (11)
Wherein i is fd Is the main speed reduction ratio eta DL Is the transmission efficiency of the main speed reducing differential.
Mechanical power P of the motor MG And electric power P e Respectively are
Wherein P is MG And when the energy is less than 0, the energy flows from the motor to the storage battery for regenerative braking.
3. Power battery model
In the invention, an internal resistance model is adopted to model a power storage battery system. The storage battery system can be equivalently a series circuit of a power supply and an internal resistance so as to simulate the dynamic process of terminal voltage and SOC in the charging and discharging process.
According to the circuit principle, the model can be expressed as
V BP ＝V OC -I BP ·R (13)
Wherein V is OC For the open circuit voltage of the battery, I BP Is the current of the battery, R is the internal resistance of the battery, V BP For the voltage across the battery, P BP Is the power of the battery.
The battery SOC is used for representing the residual electric quantity condition of the battery and is expressed as
Wherein Q represents the remaining battery power, Q c Representing battery capacity.
In the internal resistance model, the estimation of SOC selects an ampere-hour integration method, which is formulated as
Wherein I is battery charge-discharge current, I is equal to or greater than 0, I is charging, and Q is less than 0 int For initial charge of battery, eta BP The coulombic efficiency of the battery was a value of 1 at the time of discharge.
Since there is a limit to the battery capacity, its maximum charge-discharge power can be expressed as
Wherein P is BP,max Is the maximum power which can be achieved when the battery is charged and discharged, V min Is the lowest value of the battery terminal voltage, V max Is the highest value of the battery voltage. The open-circuit voltage of the battery is a function related to the SOC of the battery, and the internal resistances of the battery at the time of charging and discharging are different and can be expressed as SOFunction of C.
4. Fuel cell model
Because the optimization objective of the energy management method in the invention comprises improving the efficiency of the fuel cell and reducing the hydrogen consumption, the fuel cell system model is established to mainly describe the corresponding relation between the hydrogen consumption rate and the power and efficiency of the fuel cell, wherein the hydrogen consumption rate is
Wherein eta fc For fuel cell efficiency, η is obtained from a fuel cell system efficiency-fuel cell system power output curve fc ＝f fc (P fcs )，P fcs The LHV is the low heating value of hydrogen, which is the power of the fuel cell system.
Output power P of DC-DC converter DCDC
P DCDC ＝η DCDC P fcs (19)
Wherein eta DCDC For DC-DC model conversion efficiency, table lookup calculation can be performed.
The power balance relationship between the components of the fuel cell-lithium battery hybrid system is
P e +P aux ＝P BP +P DCDC (20)
Wherein P is aux For accessory power, a fixed value is set during vehicle running.
3. Vehicle speed prediction and vehicle driving mode identification
1. Vehicle driving road condition identification
Generally, driving scenes of automobiles include expressways and urban roads. Vehicles on urban roads may encounter traffic jams or be clear all the way. Different driving scenes can generate different driving data, and the driving modes of the automobile can be classified according to the driving data. The classified driving data contains the characteristics of different driving scenes, so that the prediction of the vehicle speed is easier. The present invention classifies the driving modes of automobiles into three types: expressway road conditions, urban congestion road conditions and urban clear road conditions.
The support vector machine has the characteristics of high training speed, high recognition speed and high accuracy in the face of the classification problem of small samples with low dimensionality. The invention thus uses a support vector machine (Support Vector Machine, SVM) to solve the classification problem. It is worth mentioning that SVM can only solve the binary classification problem, whereas the classification problem herein is the ternary classification problem. Accordingly, the present invention solves this problem in a one-to-one approach. The one-to-one method is to train three support vector machines, classify any two of the three modes, and finally adopt a hand-held voting mode to obtain the mode of more tickets as a classification result.
In the present invention, the entire data set includes 600 standard cycle condition segments of 150 seconds length, which are divided into 10 packets, one packet being the test set and the remaining packets being the training set. 30 fragments are truncated in each training packet. Intercepting running condition section by adopting random number method
T 0 ＝τ(T-ΔT) (21)
Wherein T is 0 Is the start time of each sampling period, τ is a random number lying between 0 and 1, T is the length of each packet, Δt is the length of each sampling segment, and is 150s in the present invention.
Extracting feature vectors in each sample segment
l(k)＝[v ave ,ε,a max ,a min ] T (22)
Wherein v is ave Epsilon is the idle time ratio, a, for the average speed of each segment max At the maximum value of acceleration, a min Is the minimum value of the acceleration. Due to the orders of magnitude difference between each feature, the feature vectors are normalized
Wherein l * For normalized characteristic value, l is the original characteristic, l min And/l max The smallest and largest original features, respectively. Pre-preparationThe processed training set may be used to train a support vector machine. In order to maximize the use of the data set, the present invention uses a k-fold cross validation algorithm in support vector machine training. The SVM needs two parameters to be trained, namely penalty coefficient and kernel function width. The k-fold cross validation algorithm takes 10 data packets in the invention as a test set in sequence, trains 10 different classifiers, and takes the highest precision as a final training result.
2. Vehicle speed prediction
On the premise of knowing the running road conditions of the vehicle, the vehicle speed can be respectively predicted according to different road conditions. There are various speed prediction methods such as neural network prediction method, markov prediction method, deep learning method and multi-source information fusion-based method. Among these methods, the markov predictor has advantages of high accuracy and high efficiency. In order to improve the accuracy of speed prediction, the invention has designed a road condition recognition method. The part designs a Markov predictor aiming at three different driving road conditions (expressway, urban congestion and urban flow), and constructs a transition probability matrix by taking six standard driving conditions as a database. Discrete division of acceleration and velocity of a dataset into finite length sequences
Wherein the method comprises the steps ofRepresents N a Discrete acceleration data,/->Represents N r Discrete velocity data.
The transition probability matrix is
Wherein,indicating when the vehicle speed is equal to v i The acceleration is represented by a in n sampling periods i Becomes a j Is a probability of (2).
Computing using maximum likelihood estimation
Wherein,indicating when the vehicle speed is equal to v i At the time of acceleration a i Data number of->Indicating when the vehicle speed is equal to v i The acceleration is represented by a in n sampling periods i Becomes a j Is a data number of (a) in the data set.
Satisfy the following requirements
The established transition probability matrix is shown in fig. 2.
In order to verify the effectiveness of road condition identification, a custom driving cycle as shown in fig. 3 is designed, and the cycle is composed of three representative standard cycle conditions. As shown in FIG. 4, the vertical axis category 1 represents highway road conditions, 2 represents urban congestion road conditions, and 3 represents urban clear road conditions. The road condition recognition method provided by the invention has the advantages that the accuracy can reach more than 95%, the wrong fragments are recognized basically only when the mode is switched, and the matching degree of the recognition result and the real road condition is good when the mode is not switched. The accurate road condition recognition result provides a guarantee for vehicle speed prediction. Fig. 5 shows the result of predicting the vehicle speed under the highway condition. It can be seen that although the predicted vehicle speed has some error from the actual vehicle speed, the tendency of the predicted vehicle speed to be the same as the actual vehicle speed is approximately the same.
4. The specific design process of the energy management method is given in the section of the design of the fuel cell hybrid electric vehicle on-line energy management method based on model predictive control. Three optimization objectives in the present invention are first described in the form of formulas. In order to ensure that the battery does not run out of the electric quantity in the running process of the automobile, defining the performance index of the battery SOC
Wherein,is a binary norm of the matrix. The performance index characterizes the magnitude of error of the actual SOC from the reference SOC. Sometimes this performance index is also rewritten as an inequality constraint, but this has the disadvantage that the weight relationship between the SOC error index and other performance indexes cannot be adjusted. S (SOC (k)) is a penalty term in the cost function. The weight of the system can be adjusted according to the balance relation between the SOC and other performance indexes.
In view of the economy of the hybrid system, the hydrogen consumption should be as small as possible, thus defining a second performance index
Where N-1 is the length of the predicted time domain. Such performance indicators can ensure that the hydrogen consumption in each prediction domain is minimal.
In order to improve the efficiency of the fuel cell and prolong the service life of the electric pile, a third performance index is defined
Wherein eta fc (k) Is fuel cell efficiency. Since the solving process of the optimal control problem is a process of finding the minimum value, E (P fc (k) A negative sign to ensure maximum efficiency.
There is a certain physical constraint on the equipment in the power system of a fuel cell hybrid vehicle. In view of the safety and life of the components, it is necessary to incorporate these constraints in the design process of the optimal controller. The proton exchange membrane fuel cell system is composed of auxiliary equipment such as an air compressor, and in order to protect a motor of the air compressor, the power change rate of the fuel cell should be limited. In addition, in order to prevent frequent starting and shutdown of the air compressor, the limit of the fuel cell power should also be limited. According to the performance index and the constraint condition, the final objective function is that
Wherein x is 1 (k) Is SOC (k), x 2 (k) Is P fc (k) θ (k) is the vehicle speed at time k.
Solving the objective function to obtain a control sequence
Wherein,the fuel cell stack current at time k+1 is shown. Solving the objective function at the time k and obtaining a control sequence u k Thereafter, the first element in the control sequence is +.>Acts on the system and then solves J N (u k+1 ,x 0 ). The above process is repeated, so that the real-time energy distribution between the fuel cell and the lithium battery can be realized. Energy management in the present inventionThe implementation flow chart of the method is shown in fig. 6.
Simulation process and results
1. Vehicle driving mode identification:
the first step is to build a database required for SVM training. The data source is a standard circulation regime, where highway traffic data comes from circulation: US06_hwy, HWFET; urban clear road condition data comes from the circulation: UDDS, INDIA_URBAN_SAMPLE; urban congestion road condition data sources: MANHATTAN, NYCC. For the six above cycles, 100 pieces of driving data of 150s in length are intercepted by random number method for each cycle. The 600 segments are then feature extracted, and feature vectors have four dimensions, including average speed, maximum acceleration, minimum acceleration, idle time ratio. Since the dimensions of the features are not uniform, normalization processing is also performed on the features. This step is performed off-line.
The second step is SVM training. Because a single SVM can only solve the problem of two classifications, and the invention relates to the problem of three classifications, three SVMs are trained, any two of the three modes are identified in pairs, and the training process is optimized by using a k-fold cross validation method. This step is also performed off-line.
The third step is the identification process, which is performed online. Assuming that the current time is the kth second, firstly intercepting the driving fragments from the kth-150 to the kth-1 second, performing feature extraction and normalization processing on the intercepted driving fragments, and finally performing recognition.
The recognition results are shown in fig. 3 and 4 in the specification. Wherein fig. 3 is a speed profile of a custom driving cycle, and fig. 4 is a driving pattern recognition result of fig. 3. The vertical axis category 1 represents highway road conditions, 2 represents urban congestion road conditions, and 3 represents urban clear road conditions. The road condition recognition method provided by the invention has the advantages that the accuracy can reach more than 95%, the wrong fragments are recognized basically only when the mode is switched, and the matching degree of the recognition result and the real road condition is good when the mode is not switched. The accurate road condition recognition result provides a guarantee for vehicle speed prediction.
2. Vehicle speed prediction
The first step is to build a database required for vehicle speed prediction. The data source is a standard circulation regime, where highway traffic data comes from circulation: US06_hwy, HWFET; urban clear road condition data comes from the circulation: UDDS, INDIA_URBAN_SAMPLE; urban congestion road condition data sources: MANHATTAN, NYCC. For the six cycles above, the acceleration and velocity are discretized into integers. This step is performed off-line.
The second step is to build a transition probability matrix. According to the invention, the vehicle speed is predicted according to road conditions, three transition probability matrixes are established, and each road condition corresponds to one transition probability matrix. A transition probability matrix of 1s is established, and the establishment process is described as formulas (24) - (27). This step is also performed off-line.
The third step is the prediction of vehicle speed, which is done online. Assuming that the current time is the kth second, the vehicle acceleration and speed at the current time are calculated, and then the corresponding transition probability matrix is selected in combination with the driving pattern recognition result. And calculating a predicted vehicle speed sequence of t seconds in the future through searching and matching.
The result of the vehicle speed prediction is shown in fig. 5 in the specification. It can be seen that although the predicted vehicle speed has some error from the actual vehicle speed, the tendency of the predicted vehicle speed to be the same as the actual vehicle speed is approximately the same.
3. The simulation flow of the fuel cell hybrid electric vehicle on-line energy management method based on model predictive control is shown in fig. 6 in the specification. Assuming that the current time is the kth second, firstly intercepting the driving fragments from the kth-150 to the kth-1 second, performing feature extraction and normalization processing on the intercepted driving fragments, and finally performing driving mode recognition. And then calculating the acceleration and the speed of the vehicle at the current moment, and selecting a corresponding transition probability matrix by combining the driving mode identification result. And calculating a predicted vehicle speed sequence of t seconds in the future through searching and matching. Substituting the predicted vehicle speed sequence of the future t seconds into the model to obtain the vehicle energy demand sequence of the future t seconds. And substituting the vehicle energy demand sequence of t seconds into an objective function to perform optimal control problem solving, and solving a numerical solution sequence, wherein the first element in the sequence is the energy distribution scheme at the next moment.
The simulation results are shown in figures 7-10 in the specification. As shown in FIGS. 7 andFIG. 8 shows that λ is taken in equation (31) 1 ＝0.5,λ 2 ＝100，λ 3 The introduction of the vehicle speed prediction allows the consumption amount of hydrogen to be reduced by 2.5% or more, and at the same time, the error between SOC and the reference value of 0.6 is smaller than that of the energy management method without the vehicle speed prediction. Considering the efficiency aspect of the fuel cell, the conventional method as shown in fig. 9 cannot keep the fuel cell operating at a high efficiency, with an efficiency distribution of between 0 and 44%. As can be seen in fig. 10, the energy management method of the present invention can achieve an average operating efficiency of 38.4% for the fuel cell.
Claims (1)
1. A predictive energy management method for a fuel cell hybrid vehicle,
s1, topological structure of a hybrid power system;
s2, establishing a fuel cell hybrid electric vehicle model;
the method is characterized in that:
s3, vehicle speed prediction and vehicle driving mode identification
1. Vehicle driving road condition identification
Intercepting running condition section by adopting random number method
T 0 ＝τ(T-ΔT) (21)
Wherein T is 0 Is the start time of each sampling period, τ is a random number from 0 to 1, T is the length of each packet, Δt is the length of each sampling segment;
extracting feature vectors in each sample segment
l(k)＝[v ave ,ε,a max ,a min ] T (22)
Wherein v is ave Epsilon is the idle time ratio, a, for the average speed of each segment max At the maximum value of acceleration, a min Is the minimum value of acceleration;
normalizing feature vectors
Wherein l * For normalized characteristic value, l is the original characteristic, l min And/l max Original features which are minimum and maximum respectively;
2. vehicle speed prediction
Discrete division of acceleration and velocity of a dataset into finite length sequences
Wherein the method comprises the steps ofRepresents N a Discrete acceleration data,/->Represents N r A plurality of discrete velocity data;
the transition probability matrix is
Wherein,indicating when the vehicle speed is equal to v i The acceleration is represented by a in n sampling periods i Becomes a j Probability of (2);
computing using maximum likelihood estimation
Wherein,indicating when the vehicle speed is equal to v i At the time of acceleration a i Data number of->Indicating when the vehicle speed is equal to v i The acceleration is represented by a in n sampling periods i Becomes a j Data number of (2);
satisfy the following requirements
S4, designing an online energy management method of a fuel cell hybrid electric vehicle based on model predictive control
Defining performance index of battery SOC
Wherein,is a binary norm of the matrix; s (SOC (k)) is a penalty term in the cost function, and the weight of the penalty term can be adjusted according to the balance relation between the SOC and other performance indexes;
defining a second performance index
Wherein N-1 is the length of the predicted time domain;
defining a third performance index
Wherein eta fc (k) Is fuel cell efficiency;
according to the performance index and the constraint condition, the final objective function is that
Wherein x is 1 (k) Is SOC (k), x 2 (k) Is P fc (k) θ (k) is the vehicle speed at time k;
solving the objective function to obtain a control sequence
Wherein,representing the fuel cell stack current at time k+1, solving the objective function at time k and obtaining the control sequence u k Thereafter, the first element in the control sequence is +.>Acts on the system and then solves J N (u k+1 ,x 0 ) The method comprises the steps of carrying out a first treatment on the surface of the The above process is repeated, so that the real-time energy distribution between the fuel cell and the lithium battery can be realized.
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