CN110562239B - Variable-domain optimal energy management control method and device based on demand power prediction - Google Patents

Variable-domain optimal energy management control method and device based on demand power prediction Download PDF

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CN110562239B
CN110562239B CN201910801501.2A CN201910801501A CN110562239B CN 110562239 B CN110562239 B CN 110562239B CN 201910801501 A CN201910801501 A CN 201910801501A CN 110562239 B CN110562239 B CN 110562239B
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付翔
刘会康
向小龙
刘道远
吴森
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Wuhan University of Technology WUT
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
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    • B60W10/04—Conjoint control of vehicle sub-units of different type or different function including control of propulsion units
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W10/00—Conjoint control of vehicle sub-units of different type or different function
    • B60W10/04—Conjoint control of vehicle sub-units of different type or different function including control of propulsion units
    • B60W10/08—Conjoint control of vehicle sub-units of different type or different function including control of propulsion units including control of electric propulsion units, e.g. motors or generators
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W10/00—Conjoint control of vehicle sub-units of different type or different function
    • B60W10/24—Conjoint control of vehicle sub-units of different type or different function including control of energy storage means
    • B60W10/26—Conjoint control of vehicle sub-units of different type or different function including control of energy storage means for electrical energy, e.g. batteries or capacitors
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W20/00—Control systems specially adapted for hybrid vehicles
    • B60W20/10—Controlling the power contribution of each of the prime movers to meet required power demand
    • B60W20/15—Control strategies specially adapted for achieving a particular effect
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2510/00—Input parameters relating to a particular sub-units
    • B60W2510/06—Combustion engines, Gas turbines
    • B60W2510/0638—Engine speed
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2510/00—Input parameters relating to a particular sub-units
    • B60W2510/06—Combustion engines, Gas turbines
    • B60W2510/0657—Engine torque
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
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    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2510/00—Input parameters relating to a particular sub-units
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
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    • B60W2520/10—Longitudinal speed
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    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2520/00—Input parameters relating to overall vehicle dynamics
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    • B60W2520/105—Longitudinal acceleration
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    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
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Abstract

The invention discloses a variable-domain optimal energy management control method and device based on demand power prediction, belonging to the field of energy management of hybrid off-road vehicles, and the method comprises the following steps: through the design of a self-adaptive Markov chain prediction algorithm, longitudinal speed and longitudinal acceleration are selected as prediction variables, so that the indirect prediction of the required power is realized; the vehicle is controlled to work in two different driving modes through calculation and comparison of equivalent energy consumption of a real-time hybrid mode and a pure electric mode; the method is based on the equivalent fuel consumption minimum control strategy, considers the running condition characteristics and the demand characteristics of the off-road vehicle, carries out real-time solution of the optimization domain according to the variation of the demand power, and designs the variable domain equivalent fuel consumption minimum energy management control strategy. The invention can improve the prediction precision and the adaptability of the required power and the response characteristic of the power system, simultaneously reasonably switches the working modes, optimizes the overall energy consumption characteristic of the system, ensures the working stability of the power system and simultaneously improves the driving stability.

Description

Variable-domain optimal energy management control method and device based on demand power prediction
Technical Field
The invention belongs to the field of energy management of hybrid off-road vehicles, and particularly relates to a variable-domain optimal energy management control method and device based on demand power prediction.
Background
Although the pure electric automobile is developed more and more mature, key technologies such as a power battery and the like still need to be perfected. The hybrid electric vehicle is provided with two or three power sources such as an engine, a power battery or a super capacitor and the like, and can drive the vehicle to run by depending on one or two power sources, so that the working mode of the hybrid electric vehicle is more diversified, and the hybrid electric vehicle has great development potential in the aspects of energy conservation and emission reduction.
For the cross-country vehicle, the vehicle has large mass, complex driving road conditions, large power demand change and long power continuous output time. The traditional off-road vehicle adopts a scheme of being provided with a high-power engine and a four-wheel drive as a power system to solve the problem of high-power requirement of the off-road vehicle, but the problems of long transmission chain, low mechanical efficiency and poor fuel economy are always troublesome. The hybrid power configuration is applied to the off-road vehicle, so that the dynamic property of the vehicle can be improved, the combination of the double power sources improves the fuel economy of the vehicle, and the off-road vehicle has stronger adaptability on various complex roads.
The hybrid electric vehicle has two or more than two power sources, so that various performances of the vehicle are improved, and higher requirements on system integration and vehicle control are provided. The energy management strategy is a key technology for performing multi-energy management and exerting the advantages of the hybrid power system.
For an off-road hybrid vehicle, the required power has step-type variability, the change frequency is high, and the fluctuation amplitude is large. The traditional global optimal control method has the possibility of large difference between control variables at adjacent control moments, meanwhile, the response of an engine-generator system (EGU) has hysteresis, and a power system cannot respond to the global optimal control requirement under the conditions of high-frequency change and large-amplitude required power fluctuation in time.
In the aspect of model prediction, due to the non-aftereffect of the Markov prediction algorithm, the Markov prediction algorithm is applied more under the known road conditions. When the distribution probability of the vehicle speed state is stable, a prediction result with ideal precision can be obtained through a Markov prediction algorithm. However, when the running condition changes, the vehicle speed state transition rule also changes, and if the transition probability prediction matrix is not updated, the prediction result has larger error and even has wrong prediction results. Therefore, the predicted vehicle speed based on the Markov chain needs to be analyzed in depth.
Disclosure of Invention
Aiming at the defects or improvement requirements in the prior art, the invention provides a variable domain optimal energy management control method and device based on demand power prediction, so that the technical problems that the application working condition of a prediction algorithm is limited and the system responsiveness and stability under global optimal control are poor in the prior art are solved.
To achieve the above object, according to one aspect of the present invention, there is provided a variable domain optimal energy management control method based on demand power prediction, comprising the steps of:
(1) based on self-adaptive Markov chain prediction, taking longitudinal speed and longitudinal acceleration as prediction variables, performing real-time prediction on required power, and acquiring real-time required power;
(2) based on the real-time required power, determining a target driving mode used currently by comparing real-time hybrid mode equivalent energy consumption with real-time pure electric mode equivalent energy consumption;
(3) and under the condition that the target driving mode is the hybrid mode, on the basis of minimum equivalent fuel consumption, considering the driving condition characteristic and the demand characteristic of the vehicle, and performing real-time solution of the optimization domain by using the variable quantity of the real-time demand power to obtain the minimum variable domain equivalent fuel consumption.
Preferably, step (1) comprises:
(1.1) updating the vehicle speed state transition probability matrix and the longitudinal acceleration state transition probability matrix in real time according to the Markov chain state transition probability matrix based on the vehicle speed at the current moment and the longitudinal acceleration at the current moment;
(1.2) predicting the vehicle speed based on the updated vehicle speed state transition probability matrix to obtain the vehicle speed at the next moment, and predicting the longitudinal acceleration based on the updated longitudinal acceleration state transition probability matrix to obtain the longitudinal acceleration at the next moment;
and (1.3) establishing a running power balance model according to the vehicle speed at the next moment and the longitudinal acceleration at the next moment, and calculating the required power in real time based on the running power balance model to realize the real-time prediction of the required power.
Preferably, step (1.1) comprises:
acquiring a vehicle speed state Xt at the current moment and a vehicle speed state Xt-1 at the previous moment, and performing vehicle speed state processing by using Xt (Sj) and Xt-1 (Si); acquiring the longitudinal acceleration state Y of the current momenttAnd the longitudinal addition at the previous momentSpeed state Yt-1And is composed of Yt＝AjAnd Yt-1＝AiPerforming longitudinal acceleration state processing, wherein Sj represents the j state of the vehicle speed at the t moment, Si represents the i state of the vehicle speed at the t-1 moment, AjRepresenting the state j, A, of the longitudinal acceleration at time tiRepresenting the i state of the longitudinal acceleration at the time t-1;
updating the state transition frequency of the vehicle speed state by Fij ═ Fij +1, and updating the state transition frequency of the longitudinal acceleration state by Zij ═ Zij +1, wherein Fij represents the transition frequency of Si → Sj, and Zij represents Ai→AjThe frequency of transfer of (a);
updating a vehicle speed state transition probability matrix by n-n +1 and P (I, J) -F (I, J)/n, updating a longitudinal acceleration state transition probability matrix by m-m +1 and Q (I, J) -Z (I, J)/m, wherein n represents the total data number of state updates, F (I, J) represents the data number when all data in a Si state are transferred to a Sj state, P (I, J) represents a time sequence state transition probability matrix, I represents an I state at the time of t-1, J represents a J state at the time of t, m represents the total data number of state updates, and Z (I, J) represents an A state at the time of AiAll data of state are transferred to AjThe number of data in a state, Q (I, J), represents a time-series state transition probability matrix.
Preferably, step (1.2) comprises:
the vehicle speed state Xt +1 at the next time is predicted from Xt +1 ═ max (P (SJ): Yt+1Predicting the longitudinal acceleration state Y at the next time as max (Q (AJ:))t+1。
Preferably, the driving power balance model is:
Figure BDA0002182443810000031
wherein, PmRepresenting the power of travel, G the weight of the vehicle, f the coefficient of friction, p the road gradient, CDDenotes the coefficient of air resistance, A denotes the frontal area, uaRepresenting the vehicle speed, m representing the vehicle mass,
Figure BDA0002182443810000041
the acceleration of the whole vehicle is shown, and eta represents the mechanical transmission efficiency.
Preferably, in step (3), the optimizing domain is: [ T ]min,Tmax]And [ nmin,nmax]Wherein, Tmax＝min{Te(k)+ΔT,Temax}，Tmin＝max{Te(k)-ΔT,Temin}，nmax＝min{nemax,ne(k)+Δn}，nmin＝max{nemin,ne(k)-Δn}，Δn＝ΔP/Te(k)，ΔT＝ΔP/ne(k)，TmaxRepresents the upper limit of the torque of the optimization domain, TminRepresents the lower limit of the torque of the optimization domain, TemaxRepresenting the maximum output torque, T, of the engine at the current speedeminRepresenting the minimum output torque at the current speed, nmaxRepresents the upper limit of the rotation speed of the optimization domain, nminRepresents the lower limit of the rotation speed of the optimizing domain, nemaxIndicating the maximum operating speed of the engine, neminRepresenting the minimum working speed of the engine, deltaP representing the difference between the real-time power demands of adjacent control cycles, Te(k) Representing the operating point torque at the present moment, ne(k) The current operating point rotating speed is shown, the rotating speed domain width is shown by deltan, and the torque domain width is shown by deltaT.
Preferably, in step (3), the variable domain equivalent fuel consumption is:
Figure BDA0002182443810000042
wherein J represents the equivalent fuel consumption of variable domains, Pe(k) Representing the power of the engine at the current moment, delta t representing a discrete time step length, be representing a fuel consumption rate, beta (k) representing a fluctuation penalty term of the power battery SOC at the current moment, s (k) representing a power equivalent fuel consumption coefficient of the power battery at the current moment, (k) representing a working state value of the power battery at the current moment, PbatRepresenting the power of the power cell at the present moment, etadisRepresents the average discharge efficiency H of the high efficiency region of the power batteryμIndicating low calorific value of fuel, etachgThe average charging efficiency of the high-efficiency area of the power battery is shown.
Preferably, is prepared from
Figure BDA0002182443810000043
And determining a fluctuation penalty term of the SOC of the power battery at the current moment, wherein SOC (k) represents the percentage of the residual capacity of the battery at the current moment k, and Ref is a preset value.
According to another aspect of the present invention, there is provided a variable domain optimal energy management control apparatus based on demand power prediction, comprising:
the demand power prediction module is used for predicting the demand power in real time by taking the longitudinal speed and the longitudinal acceleration as prediction variables based on self-adaptive Markov chain prediction to obtain the real-time demand power;
the driving mode determining module is used for determining a target driving mode used currently by comparing real-time hybrid mode equivalent energy consumption with real-time pure electric mode equivalent energy consumption based on the real-time required power;
and the energy management module is used for performing real-time solution of an optimization domain according to the variable quantity of the real-time required power and acquiring the minimum variable domain equivalent fuel consumption by taking the characteristic of the running condition and the requirement characteristic of the vehicle as the basis of the minimum equivalent fuel consumption when the target driving mode is the hybrid mode.
According to another aspect of the present invention, there is provided a hybrid vehicle including the above-described domain-varying optimal energy management control apparatus based on the demanded power prediction.
In general, compared with the prior art, the above technical solution contemplated by the present invention can achieve the following beneficial effects:
1. the invention provides a real-time state transition probability matrix updating method based on the real-time state transition probability matrix updating of the current state variable and the historical state variable, which is applied to the prediction calculation of future variables, realizes the prediction of the self-adaptive Markov chain, and improves the prediction precision and the adaptability of working conditions.
2. The invention considers the existing problem of global optimal control and the response hysteresis problem of an EGU system, provides a method for rationalizing the optimization domain of the control variable and carrying out local optimization according to the minimum control strategy of equivalent fuel consumption, and improves the response characteristic of the system.
3. The invention sets the optimization domain range of the control variable and ensures that the output power has good following performance when the required power changes. Meanwhile, when the change of the required power is large, the optimization domain of the control variable is expanded to meet the power requirement; when the required power changes slightly, the optimization domain of the control variables is reduced, and the stability of the working state of the EGU system is enhanced.
4. The invention considers that for a series hybrid power configuration vehicle, the electric energy of a power battery cannot be supplemented through an external power grid, and the consumed electric energy can only be obtained through the fuel consumption at the present or a certain future moment. Meanwhile, frequent charging and discharging can bring problems of repeated energy conversion, low utilization rate and the like, and the SOC of the power battery is required to be kept in an efficient power output interval. Therefore, a power battery SOC fluctuation penalty term, namely a penalty coefficient beta, is introduced into the performance index function established by the method. When the SOC of the power battery is lower than a certain threshold value, multiplying the equivalent fuel consumption of the power battery by a coefficient which is larger than 1, so that the control strategy is inclined to be driven by an EGU system; when the SOC is higher than the threshold value, the equivalent fuel consumption of the power battery is multiplied by a coefficient smaller than 1, so that the control strategy is more inclined to drive by using the energy of the power battery, the SOC of the power battery is always kept in a high-efficiency area, the efficiency of the system is improved, the service life of the power battery is prolonged, and the running stability of the whole vehicle is ensured.
Drawings
FIG. 1 is a schematic diagram of a hybrid vehicle according to an embodiment of the present invention;
FIG. 2 is a logic diagram of a domain-change optimal energy management control method according to an embodiment of the present invention;
FIG. 3 is a flow chart illustrating vehicle speed prediction based on adaptive state transition probability matrix updating, according to an embodiment of the present invention;
FIG. 4 is a schematic structural diagram of a domain-change optimal energy management control apparatus according to an embodiment of the present invention;
FIG. 5 is a vehicle speed state transition probability matrix provided by an embodiment of the present invention;
fig. 6 is a diagram of an acceleration state transition probability distribution matrix according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. In addition, the technical features involved in the embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
The invention provides an energy management control method and device which can improve the accuracy and the self-adaptability of a prediction algorithm and effectively improve the response characteristic and the stability of an equivalent minimum fuel consumption strategy by taking the minimum equivalent fuel consumption as a strategy development basis and taking a Markov chain as the prediction algorithm.
As shown in fig. 1, a hybrid electric vehicle to which the present invention is applied includes a vehicle control unit, a driving motor controller, a driving motor system, a high-voltage power battery, a high-voltage generator, an engine, a driving mode selection unit, and an ECU;
the vehicle controller is in signal connection with the driving motor controller, the high-voltage power battery, the high-voltage generator and the driving mode selection unit, the vehicle is driven through the four-wheel hub motor, the driving motor controller is electrically connected with the driving motor system, the driving motor system is in mechanical transmission connection with the driving wheels, the high-voltage power battery is electrically connected with the driving motor controller and the high-voltage generator, and the high-voltage generator is in mechanical transmission connection with the engine.
As shown in fig. 2, the variable domain optimal energy management control method based on demand power prediction mainly includes three major parts: and (3) a minimum control strategy of self-adaptive Markov chain required power prediction, drive mode switching and variable-domain equivalent fuel consumption. These three main parts are discussed in detail below:
(1) demand power prediction based on adaptive Markov chain
The Markov prediction algorithm is established on the basis of a Markov random process and is based on an algorithm developed by Markov unreliability. The Markov process of discrete time is called as Markov chain, the Markov chain prediction only needs to analyze the recent state to obtain the transition rule among all the states in the recent data, and the obtained state transition rule is used for identifying and predicting the future state, so that the state at any moment after the current moment can be deduced after the initial state and the state transition probability matrix are obtained. The essence of the method is a prediction technology which applies a Markov chain theory method in probability theory to analyze the change of a time series and predict the change trend in a future period of time. For predicting state variables, if the vehicle always runs under the same working condition, the state distribution probability is stable, and a prediction result with ideal precision can be obtained according to the Markov chain prediction method. If the driving condition changes, the state transition rule changes, and if the transition probability prediction matrix is not updated, the error of the prediction result is larger, and even the wrong prediction result appears. Therefore, in the embodiment of the invention, an adaptive Markov chain vehicle speed prediction method based on real-time update of a state transition probability matrix is provided.
In the embodiment of the invention, the design of the prediction algorithm based on the Markov chain prediction theory mainly comprises three parts of prediction object division, state transition probability matrix calculation and state variable prediction. Longitudinal vehicle speed and acceleration prediction algorithm design is carried out according to the prediction algorithm design steps.
State division of longitudinal vehicle speed prediction object
The system state division is the first step of establishing the Markov prediction model and is the most important step, because the complexity and the prediction precision of establishing the prediction model are influenced by the condition of the system state division. In order to effectively predict the vehicle speed, it is necessary to divide the vehicle state, i.e., the vehicle speed, and if the number of divided states is too large, the system becomes too complicated, and if the number of divided states is too small, the vehicle speed state prediction error becomes too large.
For example, the vehicle speed state division may be performed in such a manner that if the maximum traveling vehicle speed of the vehicle is 125km/h, the vehicle speed state division may be performed in steps of 5km/h to obtain the following vehicle speed state sets.
Si＝{0,5,10,15,20,25,30...120,125}(i＝1,2,3...25,26)
Si represents a state space.
Longitudinal speed state transition probability matrix calculation
The state transition probability matrix is one of the important components of the markov chain model, and it is very important to correctly estimate the state transition probability matrix of the system. Common state transition probability matrix estimation algorithms include subjective probability estimation and statistical probability estimation. The subjective probability estimation method is a subjective estimation of the probability of each state transition of an event according to long-term accumulated experience and analysis of the internal rules of the event, and the method depends on experience and is often used in the absence of event statistical data. The statistical probability estimation method is to perform statistical calculation on transition probabilities among various states of an event by analyzing historical statistical information.
In the embodiment of the present invention, if the actual running speed of the vehicle is one [0,125 ]]In order to correspond to the vehicle speed states one by one, the random floating point type data distributed in the range correspond to the acquired vehicle speed according to the formula: v (t) ceil (Vreal/5) performs vehicle speed state preprocessing. And selecting a statistical state transition probability matrix calculation method according to the following state transition probability calculation formula:
Figure BDA0002182443810000091
and calculating the vehicle speed state transition probability.
In the above formula: v (t) represents a vehicle speed state at time t; vreal represents the actual vehicle speed at time t; ceil () represents an rounding-up function; pij represents the transition probability of Si → Sj; fij represents the transfer frequency of Si → Sj; fj represents the total frequency of transitions from other states to Sj.
In the embodiment of the invention, the state transition probability distribution map shown in fig. 5 is derived from the historical vehicle speed information.
Predicting based on current state and state transition probability matrix
When the vehicle speed state is in Si, the probability matrix P is transferred according to the stateijThe largest one of the vehicle speeds P (i,: is selected as the predicted vehicle speed for the future step. Similarly, according to the multi-step state transition probability matrix PkP in (1)k ijThe distribution probability can determine the vehicle speed state of k steps in the future.
For the state quantity of the vehicle speed, if the vehicle runs under the same working condition all the time, the distribution probability of the vehicle speed state is stable, and a prediction result with ideal precision can be obtained according to the Markov chain prediction method. If the running condition changes, the vehicle speed state transition rule changes, and if the transition probability prediction matrix is not updated, the prediction result error is large and even an error prediction result appears.
As shown in fig. 3, the state transition probability matrix real-time updating method is to update the state transition probability matrix in real time based on the current vehicle speed and the historical vehicle speed information, and apply the state transition probability matrix to the future vehicle speed prediction calculation, so as to realize the self-adaptive markov chain vehicle speed prediction. The state transition probability updating process is as follows:
firstly, acquiring a vehicle speed state Xt at the current moment and a vehicle speed state Xt-1 at the previous moment, and performing vehicle speed state processing:
Xt＝Sj、Xt-1＝Si
secondly, updating the state transition frequency:
J+＝1；
Figure BDA0002182443810000092
f (I, j) represents the transition frequency of each vehicle speed state at the next time obtained from the historical vehicle speed information.
And finally, updating the state transition probability:
P(I,J)＝F(I,J)/n
n represents the total update frequency of the current predicted state, I represents the vehicle speed state at the time t-1, and J represents the predicted vehicle speed state at the time t.
And finally, calculating the predicted vehicle speed according to the processed vehicle speed state and state transition probability updating information:
Xt+1＝max(P(SJ,:))
wherein, SJ represents the vehicle speed state at the current discrete sampling moment.
The same principle can be introduced into an adaptive Markov chain vehicle longitudinal acceleration prediction algorithm:
state partitioning of prediction objects
Acquiring information according to the running acceleration of the real vehicle, and setting the acceleration range to be [ -3.5,3.5 [ -3.5 [)]m/s2In the case of (1), discretization of the acceleration is performed at 0.5m/s2The acceleration state division for the step length results in the following set of acceleration states.
Ai＝{-3.5，-3.0，-2.5…2.5,3.0,3.5}(i＝1,2,3…14,15)
Calculation of State transition probability matrix
In order to unify the collected signal and the discretization state, the collected signal can be processed by adopting the following signal preprocessing method, and the processed signal is used for updating the state transition probability and predicting the future state.
Figure BDA0002182443810000101
In the above formula: a (t) represents the acceleration state at time t; area represents acceleration at time t; floor () represents a floor function.
According to
Figure BDA0002182443810000111
And calculating the longitudinal acceleration state transition probability.
In the above formula: qij denotes the transition probability of Ai → Aj; zij represents the transition frequency of Ai → Aj; zj represents the total frequency of transitions from other states to Aj.
In the embodiment of the present invention, a statistical state transition probability matrix calculation method is used to obtain an acceleration state transition probability distribution matrix diagram as shown in fig. 6.
Predicting based on current state and state transition probability matrix
And obtaining an acceleration prediction result at a future moment according to the current step acceleration state and the transition probability matrix.
The longitudinal acceleration state transition probability matrix is updated by m-m +1 and Q (I, J) -Z (I, J)/m, where m represents the total data number of state updates, Z (I, J) represents the data number when all data in the Ai state are transitioned to the Aj state, and Q (I, J) represents the time-series state transition probability matrix.
From Yt+1Predicting the longitudinal acceleration state Y at the next time as max (Q (AJ:))t+1
The driving power balance equation of the traditional automobile can be known from the automobile theory, and the driving power balance equation of the off-road vehicle is established by combining the structural form of a power system of the off-road vehicle as follows:
Figure BDA0002182443810000112
wherein, PmRepresenting the power of travel, G the weight of the vehicle, f the coefficient of friction, p the road gradient, CDDenotes the coefficient of air resistance, A denotes the frontal area, uaRepresenting the vehicle speed, m representing the vehicle mass,
Figure BDA0002182443810000113
the acceleration of the whole vehicle is shown, and eta represents the mechanical transmission efficiency.
The off-road vehicle running power balance model established above can find that the vehicle speed and the longitudinal acceleration are two important variables influencing the real-time required power of the vehicle in the vehicle running process. Therefore, the vehicle speed and the longitudinal acceleration are selected as prediction variables, and the required power of the whole vehicle is predicted in real time through the running power balance model.
(2) Drive mode switching
According to the real-time required power, when the vehicle works in the pure electric working mode, the equivalent fuel consumption of the hybrid driving mode is calculated in a comparison mode, and when the equivalent fuel consumption of the hybrid driving is smaller than that of the pure electric driving, the EGU system is started to control the vehicle to be switched to the hybrid driving mode. When the optimized output power of the EGU system is smaller, namely the consumption of the hybrid drive equivalent fuel is larger than that of the pure electric drive equivalent fuel, the EGU system is closed, and the vehicle is controlled to be switched to a pure electric mode.
(3) Variable-domain equivalent fuel consumption minimum control strategy design
Design of variation optimizing domain
For an off-road hybrid vehicle, the required power has high change frequency and large fluctuation amplitude, and if the system control is performed according to a traditional global optimal control method, large differences may exist between control variables at adjacent control moments. Meanwhile, due to the fact that the response of the EGU system is delayed, the power system cannot respond to the global optimization control requirement under the conditions of high-frequency change and large-amplitude required power fluctuation in time. Aiming at the optimization control problem, in the embodiment of the invention, a variable domain optimal control strategy is provided, and local optimization is carried out according to the equivalent fuel consumption minimum control strategy by rationalizing the optimization domain of the control variable so as to improve the response characteristic of the system.
The setting of the control variable optimizing domain has direct influence on the working state of the EGU system, and in order to ensure that the output power of the system has good follow-up property when the required power changes, the control variable optimizing domain is expanded to meet the power requirement when the required power changes greatly; when the required power changes slightly, the control variable optimizing domain should be reduced to keep the stability of the EGU system working state.
According to the control variable optimization domain design principle, the existing problem of global optimal control and the response hysteresis problem of an EGU system are considered, the optimization domain of the control variable is rationalized, local optimization is carried out according to the equivalent fuel consumption minimum control strategy, and the response characteristic of the system is improved. Specifically, the domain width is solved in real time according to the required power variation delta P, the torque domain width delta T is solved according to the rotating speed of the working point and the required power variation at the current moment, the rotating speed domain width delta n is solved according to the torque of the working point and the required power variation at the current moment, and if the current time is, the rotating speed domain width delta n is obtainedThe moment control variable is (T)e(k),ne(k) Then the two-domain width solution equation is as follows:
Δn＝ΔP/Te(k)
ΔT＝ΔP/ne(k)
in the above formula, Te(k) Representing the current operating point torque; n ise(k) Representing the rotating speed of the current working point; Δ P represents the difference between the required powers of the adjacent control periods.
Meanwhile, the optimization domain range of the control variable is set, and the output power of the system has good follow-up property when the required power changes. When the change of the required power is large, the optimization domain of the control variable is expanded to meet the power requirement; when the required power changes slightly, the optimization domain of the control variables is reduced, and the stability of the working state of the EGU system is enhanced. Specifically, the set optimization domain range should be within the engine external characteristic curve, so the optimization variable optimization domain range [ T [ [ T ]min,Tmax]，[nmin,nmax]The following restrictions apply.
Figure BDA0002182443810000131
In the above formula, Tmax、TminRepresenting the upper limit and the lower limit of the torque of the optimization domain; t isemaxRepresenting the maximum output torque at the current speed of the engine; t iseminRepresents the minimum output torque at the current rotation speed, and can be set to 0; n ismax、nminRepresenting the upper limit and the lower limit of the rotation speed of the optimizing domain; n isemax、neminIndicating the maximum and small operating speeds of the engine.
Design of equivalent fuel consumption minimum control strategy
Aiming at the hybrid power system researched by the invention, in a hybrid working mode, the SOC of a power battery is selected as a state variable, and the torque of a generator and the rotating speed of an engine are selected as control variables to carry out optimization control strategy design.
Establishing a system state equation
Figure BDA0002182443810000132
SOC(k)＝SOC(k-1)-(Pr(k)-Te(k)·ne(k)·9550/ηe(k))·Δt/Qbat
In the above formula: Δ t represents a discrete time step; qbatRepresenting the total capacity of the power battery; pbat(k) Representing power of the power battery; pr(k) Representing the required power; pe(k) Representing engine power; t ise(k) Representing engine operating torque; n ise(k) Representing the engine operating speed; etae(k) Indicating the current working efficiency of the engine; etabatThe average charge-discharge efficiency of the power battery is shown, SOC (k) shows the SOC value of the battery at the time k, and SOC (k-1) shows the SOC value of the battery at the time k-1.
Setting a target set and a control domain:
system state target set: SOCmin≤SOC(k)≤SOCmax
Variable control field:
Figure BDA0002182443810000141
in the above equation, SOCmin、SOCmaxRepresenting the optimal charge and discharge upper limit value and the optimal charge and discharge lower limit value of the power battery; t isemin、TemaxUpper and lower limit values representing an engine output torque; n isemin、nemaxAnd the upper and lower limit values of the rotating speed in the efficient working area of the engine are shown.
Establishing a system performance index function, namely the equivalent fuel consumption of the system:
J＝mf_eqv(k)＝mf_e(k)+mf_bat(k)
in the above formula, J represents the system equivalent fuel consumption, mf_eqv(k) Representing the instantaneous equivalent fuel consumption of the system in unit g; m isf_e(k) Representing the real-time fuel consumption of the engine in g; m isf_bat(k) Expresses the equivalent instantaneous fuel consumption of the power battery, unitgThe real-time fuel consumption calculation model of the engine is as follows:
Figure BDA0002182443810000142
wherein be represents the fuel consumption rate.
Meanwhile, the power battery has two working states of charging and discharging in the working process, so that a power battery charging and discharging equivalent fuel consumption calculation model is established as follows:
Figure BDA0002182443810000151
in the above formula, (k) represents the power battery operating state value ((k) ═ 1 represents that the power battery is in a discharging state, (k) ═ 0 represents that the power battery is in a charging state); etadisThe average discharge efficiency of the high-efficiency area of the power battery is represented; etachgThe average charging efficiency of the high-efficiency area of the power battery is represented; s (k) represents the power equivalent fuel consumption coefficient of the power battery; pbatRepresenting the real-time power of the power battery; hμAnd the fuel oil low heating value is expressed in kJ/g.
In addition, for a series hybrid vehicle, the power of the power battery cannot be supplemented by an external power grid, and the consumed electric energy can only be obtained through the fuel consumption at the present or a future moment. Meanwhile, frequent charging and discharging brings the problems of repeated energy conversion, low utilization rate and the like, and the SOC of the power battery is kept in an efficient power output interval, so that a power battery SOC fluctuation punishment item is introduced into a performance index function established in the embodiment of the invention, a high-efficiency charging and discharging working area of the power battery is set to be [40,80], and a punishment coefficient beta is solved. When the SOC of the power battery is lower than a preset value, 60% is taken out in the embodiment of the invention, the equivalent fuel consumption of the power battery is multiplied by a coefficient which is more than 1, so that the control strategy is inclined to be driven by an engine; when the SOC is higher than 60%, the equivalent fuel consumption of the power battery is multiplied by a coefficient smaller than 1, so that the control strategy is more inclined to drive by using the energy of the power battery, the SOC of the power battery is always kept in a high-efficiency area, the efficiency of the system is improved, the service life of the power battery is prolonged, and the running stability of the whole vehicle is ensured.
Figure BDA0002182443810000152
In summary, the established system performance indicator function is as follows:
Figure BDA0002182443810000153
as shown in fig. 4, in another embodiment of the present invention, there is also provided a variable domain optimal energy management control apparatus based on demand power prediction, including:
the demand power prediction module is used for predicting the demand power in real time by taking the longitudinal speed and the longitudinal acceleration as prediction variables based on self-adaptive Markov chain prediction to obtain the real-time demand power;
the driving mode determining module is used for determining a target driving mode used currently by comparing real-time hybrid mode equivalent energy consumption with real-time pure electric mode equivalent energy consumption based on the real-time required power;
and the energy management module is used for performing real-time solution of an optimization domain according to the variable quantity of the real-time required power and acquiring the minimum variable domain equivalent fuel consumption by taking the characteristic of the running condition and the requirement characteristic of the vehicle as the basis of the minimum equivalent fuel consumption when the target driving mode is the hybrid mode.
The specific implementation of each module may refer to the description in the above method embodiment, and the embodiment of the present invention will not be repeated.
In another embodiment of the invention, the hybrid electric vehicle comprises the variable-domain optimal energy management control device based on the demand power prediction.
The hybrid electric vehicle has various structures as shown in fig. 1, the variable domain optimal energy management control device based on demand power prediction may be integrated in a vehicle controller, or the vehicle controller may implement the variable domain optimal energy management control method based on demand power prediction, and the specific implementation manner may refer to the description of the method embodiment, which will not be repeated in the embodiments of the present invention.
It should be noted that, according to the implementation requirement, each step/component described in the present application can be divided into more steps/components, and two or more steps/components or partial operations of the steps/components can be combined into new steps/components to achieve the purpose of the present invention.
The above-described method according to the present invention can be implemented in hardware, firmware, or as software or computer code storable in a recording medium such as a CD ROM, a RAM, a floppy disk, a hard disk, or a magneto-optical disk, or as computer code originally stored in a remote recording medium or a non-transitory machine-readable medium and to be stored in a local recording medium downloaded through a network, so that the method described herein can be stored in such software processing on a recording medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware such as an ASIC or FPGA. It will be appreciated that the computer, processor, microprocessor controller or programmable hardware includes memory components (e.g., RAM, ROM, flash memory, etc.) that can store or receive software or computer code that, when accessed and executed by the computer, processor or hardware, implements the processing methods described herein. Further, when a general-purpose computer accesses code for implementing the processes shown herein, execution of the code transforms the general-purpose computer into a special-purpose computer for performing the processes shown herein.
It will be understood by those skilled in the art that the foregoing is only a preferred embodiment of the present invention, and is not intended to limit the invention, and that any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the present invention.

Claims (9)

1. A variable domain optimal energy management control method based on demand power prediction is characterized by comprising the following steps:
(1) based on self-adaptive Markov chain prediction, taking longitudinal speed and longitudinal acceleration as prediction variables, performing real-time prediction on required power, and acquiring real-time required power;
(2) based on the real-time required power, determining a target driving mode used currently by comparing real-time hybrid mode equivalent energy consumption with real-time pure electric mode equivalent energy consumption;
(3) under the condition that the target driving mode is the hybrid mode, on the basis of minimum equivalent fuel consumption, considering the driving condition characteristic and the demand characteristic of the vehicle, and carrying out real-time solution of the optimization domain according to the variable quantity of the real-time demand power to obtain minimum variable domain equivalent fuel consumption;
wherein, the equivalent fuel consumption of the variable domain is as follows:
Figure FDA0002550144020000011
wherein J represents the equivalent fuel consumption of variable domains, Pe(k) Representing the power of the engine at the current moment, delta t representing a discrete time step length, be representing a fuel consumption rate, beta (k) representing a fluctuation penalty term of the power battery SOC at the current moment, s (k) representing a power equivalent fuel consumption coefficient of the power battery at the current moment, (k) representing a working state value of the power battery at the current moment, PbatRepresenting the power of the power cell at the present moment, etadisRepresents the average discharge efficiency H of the high efficiency region of the power batteryμIndicating low calorific value of fuel, etachgThe average charging efficiency of the high-efficiency area of the power battery is shown.
2. The method of claim 1, wherein step (1) comprises:
(1.1) updating the vehicle speed state transition probability matrix and the longitudinal acceleration state transition probability matrix in real time according to the Markov chain state transition probability matrix based on the vehicle speed at the current moment and the longitudinal acceleration at the current moment;
(1.2) predicting the vehicle speed based on the updated vehicle speed state transition probability matrix to obtain the vehicle speed at the next moment, and predicting the longitudinal acceleration based on the updated longitudinal acceleration state transition probability matrix to obtain the longitudinal acceleration at the next moment;
and (1.3) establishing a running power balance model according to the vehicle speed at the next moment and the longitudinal acceleration at the next moment, and calculating the required power in real time based on the running power balance model to realize the real-time prediction of the required power.
3. The method of claim 2, wherein step (1.1) comprises:
acquiring a vehicle speed state Xt at the current moment and a vehicle speed state Xt-1 at the previous moment, and performing vehicle speed state processing by using Xt (Sj) and Xt-1 (Si); acquiring the longitudinal acceleration state Y of the current momenttAnd the longitudinal acceleration state Y at the previous momentt-1And is composed of Yt＝AjAnd Yt-1＝AiPerforming longitudinal acceleration state processing, wherein Sj represents the j state of the vehicle speed at the t moment, Si represents the i state of the vehicle speed at the t-1 moment, AjRepresenting the state j, A, of the longitudinal acceleration at time tiRepresenting the i state of the longitudinal acceleration at the time t-1;
updating the state transition frequency of the vehicle speed state by Fij ═ Fij +1, and updating the state transition frequency of the longitudinal acceleration state by Zij ═ Zij +1, wherein Fij represents the transition frequency of Si → Sj, and Zij represents Ai→AjThe frequency of transfer of (a);
updating a vehicle speed state transition probability matrix by n-n +1 and P (I, J) -F (I, J)/n, updating a longitudinal acceleration state transition probability matrix by m-m +1 and Q (I, J) -Z (I, J)/m, wherein n represents the total data number of state updates, F (I, J) represents the data number when all data in a Si state are transferred to a Sj state, P (I, J) represents a time sequence state transition probability matrix, I represents an I state at the time of t-1, J represents a J state at the time of t, m represents the total data number of state updates, and Z (I, J) represents an A state at the time of AiAll data of state are transferred to AjThe number of data in a state, Q (I, J), represents a time-series state transition probability matrix.
4. The method of claim 3, wherein step (1.2) comprises:
by Xt +1 ═ max (P)(SJ,:) predicting a vehicle speed state Xt +1 at the next time by Yt+1Predicting the longitudinal acceleration state Y at the next time as max (Q (AJ:))t+1。
5. The method of claim 4, wherein the driving power balance model is:
Figure FDA0002550144020000031
wherein, PmRepresenting the power of travel, G the weight of the vehicle, f the coefficient of friction, p the road gradient, CDDenotes the coefficient of air resistance, A denotes the frontal area, uaRepresenting the vehicle speed, m representing the vehicle mass,
Figure FDA0002550144020000032
the acceleration of the whole vehicle is shown, and eta represents the mechanical transmission efficiency.
6. The method according to any one of claims 1 to 5, wherein in step (3), the optimizing domain is: [ T ]min,Tmax]And [ nmin,nmax]Wherein, Tmax＝min{Te(k)+ΔT,Temax}，Tmin＝max{Te(k)-ΔT,Temin}，nmax＝min{nemax,ne(k)+Δn}，nmin＝max{nemin,ne(k)-Δn}，Δn＝ΔP/Te(k)，ΔT＝ΔP/ne(k)，TmaxRepresents the upper limit of the torque of the optimization domain, TminRepresents the lower limit of the torque of the optimization domain, TemaxRepresenting the maximum output torque, T, of the engine at the current speedeminRepresenting the minimum output torque at the current speed, nmaxRepresents the upper limit of the rotation speed of the optimization domain, nminRepresents the lower limit of the rotation speed of the optimizing domain, nemaxIndicating the maximum operating speed of the engine, neminRepresenting the minimum working speed of the engine, deltaP representing the difference between the real-time power demands of adjacent control cycles, Te(k) Indicating the operating point at the present momentMoment, ne(k) The current operating point rotating speed is shown, the rotating speed domain width is shown by deltan, and the torque domain width is shown by deltaT.
7. The method of claim 6, wherein the method is performed by
Figure FDA0002550144020000033
And determining a fluctuation penalty term of the SOC of the power battery at the current moment, wherein SOC (k) represents the percentage of the residual capacity of the battery at the current moment k, and Ref is a preset value.
8. A variable domain optimal energy management control apparatus based on demand power prediction, comprising:
the demand power prediction module is used for predicting the demand power in real time by taking the longitudinal speed and the longitudinal acceleration as prediction variables based on self-adaptive Markov chain prediction to obtain the real-time demand power;
the driving mode determining module is used for determining a target driving mode used currently by comparing real-time hybrid mode equivalent energy consumption with real-time pure electric mode equivalent energy consumption based on the real-time required power;
the energy management module is used for performing real-time solution of an optimization domain according to the variable quantity of the real-time required power and acquiring minimum variable domain equivalent fuel consumption by taking the characteristic of the running condition and the requirement characteristic of the vehicle into consideration on the basis of minimum equivalent fuel consumption when the target driving mode is the hybrid mode;
wherein, the equivalent fuel consumption of the variable domain is as follows:
Figure FDA0002550144020000041
wherein J represents the equivalent fuel consumption of variable domains, Pe(k) Representing the power of the engine at the current moment, delta t representing a discrete time step length, be representing a fuel consumption rate, beta (k) representing a fluctuation penalty term of the power battery SOC at the current moment, s (k) representing a power equivalent fuel consumption coefficient of the power battery at the current moment, and (k) representing power electricityWorking state value of pool at present time, PbatRepresenting the power of the power cell at the present moment, etadisRepresents the average discharge efficiency H of the high efficiency region of the power batteryμIndicating low calorific value of fuel, etachgThe average charging efficiency of the high-efficiency area of the power battery is shown.
9. A hybrid vehicle comprising the domain-varying optimal energy management control apparatus based on the demanded power prediction of claim 8.
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