CN109927711B - Automobile energy control method and device and terminal equipment - Google Patents
Automobile energy control method and device and terminal equipment Download PDFInfo
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Abstract
The invention is suitable for the technical field of automobile energy control, and provides an automobile energy control method, an automobile energy control device and terminal equipment, wherein the method comprises the following steps: predicting future time period [ k, k + p-1]]Power demand P of internal automobile gearboxreq(k + jj), wherein jj is 0,1, …, p-1, k is the current time, and p is a positive integer; calculating the future time period [ k, k + p-1]]The state vector X (k + jj) of the inner car; calculating a control vector U (k) of the automobile at the current moment k through a dynamic programming algorithm and an instant difference learning algorithm, and outputting the control vector U (k) to the automobile. According to the embodiment of the invention, the control vector is solved by organically combining the dynamic programming algorithm and the instant difference learning algorithm, so that the calculation amount of the algorithm is reduced, and the real-time property of vehicle control is improved.
Description
Technical Field
The invention belongs to the technical field of automobile energy control, and particularly relates to an automobile energy control method, an automobile energy control device and terminal equipment.
Background
In a hybrid vehicle, an energy control method seriously affects fuel economy and emissions of the vehicle, which is an important hybrid technology. In the existing energy control method, a random model prediction control method realizes the control of the energy of the vehicle by predicting the power demand of the vehicle in a period of time in the future. The random model prediction control method is an energy control method implemented by rolling optimization and rolling, and under the condition of unknown road conditions, the control action of the vehicle is obtained by combining power demand prediction in a period of time in the future and a vehicle model according to the current state of the vehicle, so that the change of the road conditions can be adapted, and online adjustment is realized.
However, in the process of obtaining the vehicle control action, the dynamic programming algorithm is used to solve the precise solution of the control vector at each decision step, so that a larger calculation amount is generated when the vehicle energy control is performed by using the stochastic model predictive control method, and the real-time performance of the vehicle control is poor.
Disclosure of Invention
In view of this, embodiments of the present invention provide an automobile energy control method, an automobile energy control device, and a terminal device, so as to solve the problem in the prior art that the real-time performance of vehicle control is poor due to a large calculation amount for solving a control vector.
The first aspect of the embodiment of the invention provides an automobile energy control method, which comprises the following steps:
predicting future time period [ k, k + p-1]]Power demand P of internal automobile gearboxreq(k + jj), wherein jj is 0,1, …, p-1, k is the current time, and p is a positive integer;
calculating a state vector X (k + jj) of the automobile in the future time period [ k, k + p-1 ];
calculating a control vector U (k) of the automobile at the current moment k through a dynamic programming algorithm and an instant difference learning algorithm, and outputting the control vector U (k) to the automobile.
A second aspect of an embodiment of the present invention provides an automobile energy control apparatus, including:
a prediction unit for predicting a future time period [ k, k + p-1]]Power demand P of internal automobile gearboxreq(k + jj), wherein jj is 0,1, …, p-1, k is the current time, and p is a positive integer;
the state vector calculation unit is used for calculating a state vector X (k + jj) of the automobile in the future time period [ k, k + p-1 ];
and the control vector calculation unit is used for calculating a control vector U (k) of the automobile at the current moment k through a dynamic programming algorithm and an instant difference learning algorithm and outputting the control vector U (k) to the automobile.
A third aspect of an embodiment of the present invention provides a terminal device for controlling vehicle energy, including: a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the method described above when executing the computer program.
A fourth aspect of embodiments of the present invention provides a computer-readable storage medium storing a computer program which, when executed by a processor, implements the steps of the method described above.
According to the embodiment of the invention, through the organic combination of a dynamic programming algorithm and an instant difference learning algorithm, a control vector is solved, and the control vector U (k) is output to the automobile, so that the torque output of an automobile engine and a motor is distributed, and the optimization of energy consumption is realized. Meanwhile, in the process of solving the control vector by combining the dynamic programming algorithm and the instant difference learning algorithm, the calculation amount of the algorithm is reduced, the real-time performance of vehicle control is improved, and the problem of poor real-time performance of vehicle control caused by large calculation amount of the solved control vector in the prior art is solved.
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In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
FIG. 1 is a schematic diagram of a power system of a parallel hybrid electric vehicle according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart illustrating an implementation of a method for controlling vehicle energy according to an embodiment of the present invention;
fig. 3 is a schematic flow chart illustrating an implementation flow of a vehicle energy control method S203 according to an embodiment of the present invention;
fig. 4 is a schematic diagram of an implementation flow of calculating a control vector u (k) by combining a dynamic programming algorithm and an instant difference learning algorithm according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of an implementation flow of a predictive control vector U1(k) using an instantaneous difference learning algorithm according to an embodiment of the present invention;
FIG. 6 is a schematic structural diagram of an energy control device for a vehicle according to an embodiment of the present invention;
fig. 7 is a schematic diagram of a terminal device for controlling vehicle energy according to an embodiment of the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.
With the continuous increase of automobile holding quantity all over the world, the energy shortage and air problem are increasingly highlighted, and new energy automobiles become a necessary trend for the development of the automobile industry. The hybrid electric vehicle has the advantages of low emission and long endurance of the traditional vehicle and the pure electric vehicle, can recover regenerative braking energy, and is easy to refit from the existing vehicle, so that the hybrid electric vehicle becomes the necessary choice in the transition period of the automobile industry.
Fig. 1 shows a schematic structural diagram of a power system of a parallel hybrid vehicle according to an embodiment of the present invention, in the power system of the parallel hybrid vehicle, an engine 10 and an electric machine 11 are coaxial, the engine 10 is connected with the electric machine 11 through a clutch 13, and when the vehicle is in operation, power is transmitted to wheels through a transmission system. When the clutch 13 is closed, the engine 10 rotates coaxially with the motor 11 and transmits power to the wheels through the transmission system. The hybrid automobile has four working modes of single motor driving, single engine driving, hybrid driving and braking energy recovery. The hybrid driving mode comprises two driving modes of driving by the aid of the motor and driving by the engine independently. The electric machine 11 can be used as a motor or a generator, and can be used as a generator to charge the battery 14 in the engine-only driving mode, for example.
In the hybrid vehicle, the energy control method seriously affects the fuel economy and the emission of the vehicle, and is one of the most critical hybrid technologies. In the existing energy control method, the control method based on the rules obtains better effect. However, this method is very dependent on the experience of the vehicle type and the vehicle manufacturer and cannot achieve the optimum. The recent stochastic model predictive control method realizes the control of the energy of the vehicle by predicting the power demand of the vehicle in a period of time in the future, and is an energy control method implemented by rolling optimization and rolling. The random model prediction control method is used for solving the vehicle control action by combining the power demand prediction in a period of time in the future and the vehicle model under the condition of unknown road conditions according to the existing state of the vehicle, can adapt to the change of the road conditions and realizes online adjustment.
However, in the process of solving the vehicle control action, the dynamic programming algorithm is used to solve the precise solution of the control vector at each decision step, so that when the vehicle energy control is carried out, a large calculation amount is generated, and the real-time performance of the vehicle control is poor.
In the invention, the control vector is solved through the organic combination of the dynamic programming algorithm and the instant difference learning algorithm, so that the problem of poor vehicle control real-time performance caused by large calculated amount of solving the control vector in the prior art is solved.
In order to explain the technical means of the present invention, the following description will be given by way of specific examples.
Fig. 2 shows a schematic implementation flow diagram of an automobile energy control method provided in an embodiment of the present invention, where the automobile energy control method includes steps S201 to S203.
In S201, a future time period [ k, k + p-1] is predicted]Power demand P of internal automobile gearboxreq(k + jj), where jj is 0,1, …, p-1, k is the current time, and p is a positive integer.
Wherein the required power P of the automobile gearboxreq(k + jj) is the output power of the vehicle when the user operates the vehicle with the gearbox, for example, when the vehicle accelerates, the required power becomes larger, and when the vehicle decelerates, the required power becomes smaller.
The required torque T of the automobile gearbox can be estimated by maximum likelihood according to historical driving data or some standard cycle conditions of the vehiclereqIs determined as a transition probability of
Wherein m isi,jIndicates the required torque from
Is transferred to
The number of times of (c); m isiIndicates the required torque from
The sum of the number of transitions to all other states, i.e.:
the transition probability matrix obtained by measurement statistics is:
in the embodiment of the invention, after the transition probability matrix is obtained, the future time period [ k, k + p-1] can be predicted according to the Markov chain]Required torque T of inner automobile gearboxreq(k + jj); for example, assume that the required torque at time k is Treq,iThe transition probability of the required torque from the time k to the future time k + jj is Pi,jThe required torque T at the future k + jj moment can be obtainedreq,jThe calculation formula of (2) is as follows:
in S202, a state vector X (k + jj) of the vehicle over the future time period [ k, k + p-1] is calculated.
The state vector X (k + jj) of the vehicle is used to indicate the vehicle state of the vehicle.
Optionally, said calculating said future time period [ k, k + p-1]]The state vector X (k + jj) of the inner car includes: according to the required power P of the automobile gearboxreq(k + jj) calculating the future time period [ k, k + p-1]]Required torque T of inner automobile gearboxreq(k + jj) and a vehicle speed v (k + jj), and the state of charge SOC (k + jj) is calculated to obtain a state vector X (k + jj) ═ Treq(k+jj),v(k+jj),SOC(k+jj))T。
Wherein, according to the required power P of the automobile gearboxreq(k + jj) calculating the future time period [ k, k + p-1]]Required torque T of inner automobile gearboxreq(k + jj) and the vehicle speed v (k + jj) include the steps of: a to D, wherein PreqThe required power of the automobile gearbox at the historical moment of automobile running is obtained.
A: the required power P of the automobile gearboxreqBy the formula
Conversion to the required torque Treq(ii) a Wherein n iseIs the engine speed;
b: will demand a torque TreqArray discretized into a finite number:
let Ti,jRepresenting the probability of a one-step transition of the system, i.e. the system being at time k
State at time k +1
The probability of the state is
Wherein the content of the first and second substances,
that is to say that the position of the first electrode,
and
indicates one of the state values 1,2, …, S, e.g., state value 1 or S.
C: under the standard circulating road condition, calculating the required power distribution:
Twheel＝Treqη+Tb＝(Te+Tm)η+Tb；
wherein, TwheelIs the required torque of the wheel, in units of N · m; m is the equipment mass of the automobile, the total mass of the automobile and unit kg; g is gravity acceleration in m/s2；CDIs the wind resistance coefficient; a is the windward area in m2(ii) a ρ is the air density, typically 1.2258 Ns2·m-4(ii) a The conversion coefficient of the rotating mass; θ is a gradient, and in the practice of the present invention, θ is 0; mu is rolling resistance coefficient; r is the wheel radius in m; v is vehicle speed in m/s; t ismIs the motor torque, in units of N · m; t iseIs the engine torque, in units of N · m; t isbThe braking torque of the friction brake on the wheel is in the unit of N.m; eta is the total transmission ratio; n iseIs the engine speed in r/min; n ismThe unit is the rotating speed of the motor and r/min; t is time.
According to the above formula
The required torque T of the user on the gearbox at each moment can be calculatedreq. Combined with discrete rear TreqSpace (A) of
The required torque can be quantized to be
D: is utilized atThe future time period [ k, k + p-1] predicted in step S201]Required torque T of inner automobile gearboxreq(k + jj), i.e., according to the above formula
And
calculating corresponding automobile speed v and engine/motor speed ne、nmAnd the required torque T of the motor vehicle gearboxreq. Further, only the motor torque T is determinedmDetermining the engine torque TeOn the contrary, as long as the engine torque T is determinedeTo determine the motor torque Tm。
In an embodiment of the invention, the motor torque T may be selectedmTo control vector U (k) ═ Tm(k) It should be noted that in other embodiments, the engine torque T may be selectedeTo control vector U (k) ═ Te(k) In that respect Demanded torque T of automobile gearboxreqWhen the vehicle speed v and the battery state of charge SOC are vehicle state variables of the vehicle and expressed as state vectors, there are: x (k) ═ Treq(k),v(k),SOC(k))T. Next, in step S203, the control vector u (k) is solved for Tm(k)。
In S203, a control vector u (k) of the vehicle at the current time k is calculated through a dynamic programming algorithm and an instantaneous difference learning algorithm, and the control vector u (k) is output to the vehicle.
In the embodiment of the present invention, the control vector u (k) ═ T is solvedm(k) Firstly, the required torque T is predicted through a dynamic programming algorithmreq(k + jj) to find the optimal control vector sequence in the control field C
[U0(k),…,UC-1(k+C-1)]。
Optionally, when the dynamic programming algorithm is used to solve the optimal control sequence in the control domain C corresponding to the demanded torque in the future p-period in the prediction domain, considering that the optimization target is to reduce fuel consumption, and at the same time, to ensure the use safety of the battery, therefore, the reward function is set as:
R(k)＝wfuelRfuel(k)+wemsRems(k)+wgsRgs(k)+wSOCRSOC(k)；
RSOC＝(SOC(k)-SOCref)2；
wherein SOC (k) is the state of charge of the battery at the moment k; rfuel、RSOC(k)、Rgs(k) And Rems(k) Respectively serving as a fuel consumption reward function, a battery charge state reward function, a gear shift reward function and an emission reward function at the moment k; w is afuel、wSOC、wemsAnd wgsAre respectively the corresponding weighting factor, SOCrefIs the desired SOC value at the end time, typically taken to be 0.6. Battery state of charge at time k +1
Wherein, VocAn open circuit voltage for the battery; rintIs the internal resistance of the battery; qmaxIs the maximum charge capacity; rtIs the termination impedance; etamThe motor efficiency; pm(k) And the battery provides power for the motor for the moment k.
In addition, the dynamic programming algorithm is used for solving the control vector U (k) ═ Tm(k) Then, the prediction domain [ k, k + p-1] needs to be defined]A value function J for the whole interval, the value function being:
where R (k) is the reward function at time k, γ is the discount factor, γ ∈ (0, 1).
Each state
The value function of (a) gives the accumulated value of the future reward function, the value function resulting from the optimal control quantity is called the optimal value function J*According to the bellman's optimal formula, the optimal value function of each state and the optimal value functions of its neighboring states have the following relationships:
that is, the optimal value function is found by minimizing the value function. Then, after the optimum value function is obtained, the state vector x (k) is obtained, and further, the state vector x (k) is (T)req(k),v(k),SOC(k))TAnd Twheel＝Treqη+Tb＝(Te+Tm)η+TbTherefore, the control vector u (k) T can be obtainedm(k) The optimal solution of (a) is that the dynamic programming algorithm obtains the control vector U (k) ═ Tm(k) The optimal solution of (1).
As shown in fig. 3, in S203, the control vector u (k) of the vehicle at the current time k is calculated through a dynamic programming algorithm and an instantaneous difference learning algorithm, and the control vector u (k) is reduced by introducing the instantaneous difference learning algorithm to Tm(k) The calculation amount of the solution specifically includes: steps S301 to S303.
In S301, determining whether an error index of the instant difference learning algorithm at the last time is greater than a preset threshold;
before determining whether the error index of the instantaneous difference learning algorithm at the previous time is greater than the preset threshold, the algorithm needs to be initialized, for example, k is 1, jj is 0, and the error index E (0) of the instantaneous difference learning algorithm is set to infinity.
Then predict [ k, k + p-1]]Required power P of time period automobile gearboxreq(k + jj), jj ═ 0,1, …, p-1, where k is the current sampling instant.
Then, according to the above formula
During initialization, the error index E (0) of the instant differential learning algorithm is set to be infinite, so that when the control vector U (k) is solved in each iteration, whether the error index of the instant differential learning algorithm at the last moment is greater than a preset threshold value needs to be judged; namely, judging whether E (k-1) is larger than a preset threshold value; the preset threshold may be threshold data set according to actual application or practical experience. If E (k-1) is greater than the preset threshold, executing step S302; otherwise, step S303 is executed.
In S302, if the error indicator of the instant difference learning algorithm at the previous time is greater than the preset threshold, the control vector u (k) of the vehicle at the current time k is calculated by combining the dynamic programming algorithm and the instant difference learning algorithm.
Optionally, as shown in fig. 4, the calculating a control vector u (k) of the vehicle at the current time k by combining the dynamic programming algorithm and the instant difference learning algorithm includes: step S401 to step S404.
In S401, an optimal control vector sequence [ U ] in the control domain C is calculated through a dynamic programming algorithm0(k),…,UC-1(k+C-1)]And calculating the first control vector U0(k) An assumed control vector U0(k) assumed to be at current time k; meanwhile, an instantaneous difference learning algorithm is adopted to predict a predicted control vector U1(k) of the current time k in a control domain C according to a state vector X (k) of the current time k of the automobile, wherein the control domain C is equal to p.
In the embodiment of the present invention, in the step S401, an optimal control vector sequence [ U ] in the control domain C is calculated through a dynamic programming algorithm0(k),…,UC-1(k+C-1)]The method comprises the following steps: by means of a dynamic programming algorithm, the torque T required in the forecastreq(k + jj) to find the optimal control vector sequence [ U ] in the control domain C0(k),…,UC-1(k+C-1)]。
Optionally, when the dynamic programming algorithm is used to solve the optimal control sequence in the control domain C corresponding to the demanded torque in the future p-period in the prediction domain, considering that the optimization target is to reduce fuel consumption, and at the same time, to ensure the use safety of the battery, therefore, the reward function is set as:
R(k)＝wfuelRfuel(k)+wemsRems(k)+wgsRgs(k)+wSOCRSOC(k)；
RSOC＝(SOC(k)-SOCref)2；
wherein SOC (k) is the state of charge of the battery at the moment k; rfuel、RSOC(k)、Rgs(k) And Rems(k) Respectively serving as a fuel consumption reward function, a battery charge state reward function, a gear shift reward function and an emission reward function at the moment k; w is afuel、wSOC、wemsAnd wgsAre respectively the corresponding weighting factor, SOCrefIs the desired SOC value at the end time, typically taken to be 0.6. Battery state of charge at time k +1
Wherein, VocAn open circuit voltage for the battery; rintIs the internal resistance of the battery; qmaxIs the maximum charge capacity; rtIs the termination impedance; etamThe motor efficiency; pm(k) And the battery provides power for the motor for the moment k.
In addition, the dynamic programming algorithm is used for solving the control vector U (k) ═ Tm(k) Then, the prediction domain [ k, k + p-1] needs to be defined]A value function J for the whole interval, the value function being:
where R (k) is the reward function at time k, γ is the discount factor, γ ∈ (0, 1).
Each state
The value function of (a) gives the accumulated value of the future reward function, the value function resulting from the optimal control quantity is called the optimal value function J*According to the bellman's optimal formula, the optimal value function of each state and the optimal value functions of its neighboring states have the following relationships:
that is, the optimal value function is found by minimizing the value function. Then, after the optimum value function is obtained, the state vector x (k) is obtained, and further, the state vector x (k) is (T)req(k),v(k),SOC(k))TAnd Twheel＝Treqη+Tb＝(Te+Tm)η+TbThus, the optimal control vector sequence [ U ] can be found0(k),…,UC-1(k+C-1)]I.e. the optimal control vector sequence [ U ] is obtained by the dynamic programming algorithm0(k),…,UC-1(k+C-1)]。
In the embodiment of the present invention, as shown in fig. 5, in step S401, predicting a predicted control vector U1(k) at a current time k in a control domain C according to a state vector x (k) at the current time k of an automobile by using an instantaneous difference learning algorithm includes: step S501 to step S508.
In S501, the just-in-time difference learning algorithm at the current time k is initialized, and the number of training times ii is initialized to 0;
after obtaining the vehicle state vector x (k) at the current time k of the vehicle, the algorithm needs to be initialized, for example, the training number ii is initialized to zero, i.e., ii is 0, and the weight vector is w (k) ═ w1(k),w2(k),w3(k)]The learning rate of the algorithm alpha belongs to [0,1.0 ]]And the algorithm attenuation factor lambda belongs to [0,1.0 ]]。
In S502, an instantaneous difference sequence at the current time k is calculated;
wherein the formula of the instant difference sequence is d(ii)(k)＝U1(ii+1)(k)-U1(ii)(k)；
In S503, the gradient of the predictive control vector U1(k) at the current time k to each component of the weight vector is calculated;
for example,
and
where kk is 1,2, …, k.
In S504, an increment of each component of the weight vector is calculated;
for example,
in S505, each component of the weight vector is updated;
for example,
in S506, it is determined whether the training frequency at the current time k is smaller than the control field C of the dynamic programming algorithm;
in S507, if yes, the training frequency is updated to ii +1, and the instantaneous difference sequence at the current time k is recalculated;
in S508, if not, the weight vector of the current time k is output
In S402, calculating an error index of the instant difference learning algorithm at the current moment k according to the assumed control vector U0(k) and a predicted control vector U1 (k);
optionally, the error indicator calculation formula of the instant difference learning algorithm is as follows:
In S403, if the error index at the current time k is greater than the preset threshold, using the assumed control vector U0(k) as the control vector U (k) of the automobile at the current time k;
in S404, if the error index at the current time k is less than or equal to the preset threshold, the predicted control vector U1(k) is used as the control vector U (k) of the vehicle at the current time k.
In S303, if the error index of the instantaneous difference learning algorithm at the previous time is less than or equal to the preset threshold, calculating a control vector u (k) of the vehicle at the current time k through the instantaneous difference learning algorithm.
It should be noted that, when the error index of the instant difference learning algorithm at the previous time is less than or equal to the preset threshold, it indicates that in step S302, the sample obtained by learning the state vector x (k) and the control vector u (k) by using the instant difference learning algorithm is large enough, that is, the error index is small enough to meet the actual requirement.
It should be noted that, the steps of the method for calculating the control vector U (k) of the vehicle at the current time k by using the instantaneous difference learning algorithm are the same as the steps of the method for predicting the predictive control vector U1(k) at the current time k in the control domain C according to the state vector x (k) at the current time k of the vehicle by using the instantaneous difference learning algorithm in step S401, that is, the methods in step S501 to step S508, and are not described herein again. Wherein, U1(k) ═ X (k) · W is obtainedT(k) Then, the predicted control vector U1(k) may be output to the vehicle as the control vector U (k) of the vehicle at the current time k.
Optionally, after the outputting the weight vector of the current time k in step S508, the method further includes: and setting the error index of the instant difference learning algorithm at the current moment to be equal to the preset threshold value.
That is to say, in the next control process, at the time k +1, in the step S202, when the control vector u (k) of the vehicle at the current time k is calculated through the dynamic programming algorithm and the instant difference learning algorithm, the control vector u (k) of the vehicle at the current time k can be calculated directly through the instant difference learning algorithm while the error index of the instant difference learning algorithm at the last time is judged to be not greater than the preset threshold (equal to the preset threshold), so that the calculation amount for calculating the control vector u (k) by using the dynamic programming algorithm is saved, the problem of poor vehicle control real-time performance due to large calculation amount for solving the control vector in the prior art is solved, and the real-time performance of the algorithm is improved.
In the embodiment of the invention, the required torque of the hybrid electric vehicle gearbox is converted into a discrete Markov chain, the Markov chain is established by combining the required power of a plurality of cyclic working conditions, the required power and the torque of the vehicle gearbox at the current moment are used for predicting the required power and the torque of the future cyclic working conditions, and the minimum value function is taken as an optimization target. Optimizing within the prediction time to obtain an optimal control vector sequence, and performing feedback correction and rolling optimization of the algorithm by applying the first step of the optimal control vector sequence, thereby realizing optimization of energy consumption by distributing torque output of the engine and the motor.
In addition, the control vector is obtained by combining a dynamic programming algorithm and an instant difference learning algorithm in the early stage of algorithm operation, and when the error index is less than or equal to a preset threshold value, the control vector is obtained by adopting the instant difference learning algorithm, so that the calculation amount of the obtained control vector is reduced, and the real-time performance of the algorithm is improved.
It should be noted that the embodiment of the present invention may also be used in other single-axle parallel hybrid electric vehicles and pure electric vehicles. In the control of the pure electric vehicle, the relevant parameters of the engine are set to be 0, such as: t ise＝0,Rfuel＝0,Rems＝0。
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
As shown in fig. 6, a schematic structural diagram of an automotive energy control apparatus provided in an embodiment of the present invention includes: a prediction unit 601, a state vector calculation unit 602, and a control vector calculation unit 603.
A prediction unit 601 for predicting a future time period [ k, k + p-1]]Power demand P of internal automobile gearboxreq(k + jj), wherein jj is 0,1, …, p-1, k is the current time, and p is a positive integer;
a state vector calculation unit 602, configured to calculate a state vector X (k + jj) of the vehicle in the future time period [ k, k + p-1 ];
and the control vector calculation unit 603 is configured to calculate a control vector u (k) of the vehicle at the current time k through a dynamic programming algorithm and an instant difference learning algorithm, and output the control vector u (k) to the vehicle.
Optionally, the state vector calculating unit 602 includes: according to the required power P of the automobile gearboxreq(k + jj) calculating the future time period [ k, k + p-1]]Required torque T of inner automobile gearboxreq(k + jj) and a vehicle speed v (k + jj), and calculating a battery state of charge SOC (k + jj) to obtain a stateVector quantity
X(k+jj)＝(Treq(k+jj),v(k+jj),SOC(k+jj))T。
Optionally, the control vector calculating unit 603 includes:
the judging module is used for judging whether the error index of the instant difference learning algorithm at the last moment is larger than a preset threshold value or not;
the control vector first calculation module is used for calculating a control vector U (k) of the automobile at the current moment k by combining a dynamic programming algorithm and an instant difference learning algorithm if the control vector U (k) is positive;
and the control vector second calculation module is used for calculating a control vector U (k) of the automobile at the current moment k through an instant difference learning algorithm if the control vector U (k) is not the same as the control vector U (k).
Optionally, the control vector first calculation module includes:
for calculating an optimal control vector sequence [ U ] in a control domain C by a dynamic programming algorithm0(k),…,UC-1(k+C-1)]And calculating the first control vector U0(k) An assumed control vector U0(k) assumed to be at current time k; predicting a predicted control vector U1(k) of the current time k in a control domain C according to a state vector X (k) of the current time k of the automobile by adopting an instant difference learning algorithm, wherein the control domain C is equal to p;
calculating an error index of the instant difference learning algorithm at the current moment k according to the assumed control vector U0(k) and a predicted control vector U1 (k);
if the error index of the current time k is larger than the preset threshold, taking the assumed control vector U0(k) as the control vector U (k) of the automobile at the current time k;
and if the error index at the current time k is less than or equal to the preset threshold, taking the predicted control vector U1(k) as the control vector U (k) of the automobile at the current time k.
Optionally, the control vector first calculation module further includes:
the instant difference learning algorithm is used for initializing the current moment k, and the initialization training time number ii is 0;
calculating an instant difference sequence of the current moment k;
calculating the gradient of the prediction control vector U1(k) at the current moment k to each component of the weight vector;
calculating the increment of each component of the weight vector;
updating each component of the weight vector;
judging whether the training times of the current moment k are smaller than a control domain C of the dynamic programming algorithm or not;
if so, updating the training times as ii +1, and recalculating the instant difference sequence of the current time k;
if not, outputting a weight vector of the current moment k; and calculating a prediction control vector U1(k) of the current time k according to the acquired state vector X (k) of the current time k of the automobile and the weight vector of the current time k.
Optionally, the control vector first calculation module further includes: and after the weight vector of the current moment k is output, setting the error index of the instant difference learning algorithm at the current moment to be equal to the preset threshold.
It should be noted that, for convenience and brevity of description, the specific working process of the vehicle energy control apparatus 600 described above may refer to the corresponding process of the method described in fig. 2 to fig. 5, and is not described herein again.
Fig. 7 is a schematic diagram of a terminal device for controlling vehicle energy according to an embodiment of the present invention. As shown in fig. 7, the terminal device 7 for vehicle energy control of this embodiment includes: a processor 70, a memory 71 and a computer program 72, such as a vehicle energy control program, stored in said memory 71 and operable on said processor 70. The processor 70, when executing the computer program 72, implements the steps in the various vehicle energy control method embodiments described above, such as the steps 201-203 shown in fig. 2. Alternatively, the processor 70, when executing the computer program 72, implements the functions of each module/unit in each device embodiment described above, for example, the functions of the units 601 to 603 shown in fig. 6.
Illustratively, the computer program 72 may be partitioned into one or more modules/units that are stored in the memory 71 and executed by the processor 70 to implement the present invention. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution process of the computer program 72 in the vehicle energy control terminal device 7. For example, the computer program 72 may be divided into a prediction unit, a state vector calculation unit, and a control vector calculation unit (a module in a virtual device), and each unit specifically functions as follows:
a prediction unit for predicting a future time period [ k, k + p-1]]Power demand P of internal automobile gearboxreq(k + jj), wherein jj is 0,1, …, p-1, k is the current time, and p is a positive integer;
the state vector calculation unit is used for calculating a state vector X (k + jj) of the automobile in the future time period [ k, k + p-1 ];
and the control vector calculation unit is used for calculating a control vector U (k) of the automobile at the current moment k through a dynamic programming algorithm and an instant difference learning algorithm and outputting the control vector U (k) to the automobile.
The terminal device 7 for controlling the automobile energy can be a desktop computer, a notebook, a palm computer, a cloud server and other computing devices. The terminal device for vehicle energy control may include, but is not limited to, a processor 70 and a memory 71. It will be understood by those skilled in the art that fig. 7 is merely an example of the terminal device 7 for vehicle energy control and does not constitute a limitation of the terminal device 7 for vehicle energy control, and may include more or less components than those shown, or combine some components, or different components, for example, the terminal device for vehicle energy control may further include input-output devices, network access devices, buses, etc.
The Processor 70 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 71 may be an internal memory unit of the vehicle energy control terminal 7, such as a hard disk or a memory of the vehicle energy control terminal 7. The memory 71 may also be an external storage device of the vehicle energy control terminal device 7, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are equipped on the vehicle energy control terminal device 7. Further, the memory 71 may also include both an internal memory unit and an external memory device of the vehicle energy control terminal device 7. The memory 71 is used to store the computer program and other programs and data required by the vehicle energy control terminal. The memory 71 may also be used to temporarily store data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus/terminal device and method may be implemented in other ways. For example, the above-described embodiments of the apparatus/terminal device are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present invention, and are intended to be included within the scope of the present invention.
Claims (9)
1. An automotive energy control method, characterized by comprising:
predicting future time period [ k, k + p-1]]Power demand P of internal automobile gearboxreq(k + jj), wherein jj is 0,1, …, p-1, k is the current time, and p is a positive integer;
calculating a state vector X (k + jj) of the automobile in the future time period [ k, k + p-1 ];
calculating a control vector U (k) of the automobile at the current moment k through a dynamic programming algorithm and an instant difference learning algorithm, and outputting the control vector U (k) to the automobile;
the method for calculating the control vector U (k) of the automobile at the current moment k through the dynamic programming algorithm and the instant difference learning algorithm comprises the following steps:
judging whether the error index of the instant difference learning algorithm at the last moment is greater than a preset threshold value or not;
if yes, combining a dynamic planning algorithm and an instant difference learning algorithm to calculate a control vector U (k) of the automobile at the current moment k;
if not, calculating a control vector U (k) of the automobile at the current time k through an instant difference learning algorithm.
2. The vehicle energy control method of claim 1, wherein said calculating a state vector X (k + jj) of the vehicle over the future time period [ k, k + p-1] comprises:
according to the required power P of the automobile gearboxreq(k + jj) calculating the future time period [ k, k + p-1]]Required torque T of inner automobile gearboxreq(k + jj) and a vehicle speed v (k + jj), and the state of charge SOC (k + jj) is calculated to obtain a state vector X (k + jj) ═ Treq(k+jj),v(k+jj),SOC(k+jj))T。
3. The vehicle energy control method according to claim 1 or 2, wherein the calculating a control vector u (k) of the vehicle at the current time k by combining a dynamic programming algorithm and an instantaneous difference learning algorithm comprises:
calculating an optimal control vector sequence [ U ] in the control domain C through a dynamic programming algorithm0(k),…,UC-1(k+C-1)]And calculating the first control vector U0(k) An assumed control vector U0(k) assumed to be at current time k; predicting a predicted control vector U1(k) of the current time k in a control domain C according to a state vector X (k) of the current time k of the automobile by adopting an instant difference learning algorithm, wherein the control domain C is equal to p;
calculating an error index of the instant difference learning algorithm at the current moment k according to the assumed control vector U0(k) and a predicted control vector U1 (k);
if the error index of the current time k is larger than the preset threshold, taking the assumed control vector U0(k) as the control vector U (k) of the automobile at the current time k;
and if the error index at the current time k is less than or equal to the preset threshold, taking the predicted control vector U1(k) as the control vector U (k) of the automobile at the current time k.
4. The vehicle energy control method according to claim 3, wherein predicting the predicted control vector U1(k) at the current time k in the control domain C according to the state vector X (k) at the current time k of the vehicle by using an instantaneous difference learning algorithm comprises:
initializing the instant difference learning algorithm at the current moment k, and initializing the training times ii as 0;
calculating an instant difference sequence of the current moment k;
calculating the gradient of the prediction control vector U1(k) at the current moment k to each component of the weight vector;
calculating the increment of each component of the weight vector;
updating each component of the weight vector;
judging whether the training times of the current moment k are smaller than a control domain C of the dynamic programming algorithm or not;
if so, updating the training times as ii +1, and recalculating the instant difference sequence of the current time k;
if not, outputting a weight vector of the current moment k; and calculating a prediction control vector U1(k) of the current time k according to the acquired state vector X (k) of the current time k of the automobile and the weight vector of the current time k.
5. The vehicle energy control method according to claim 4, wherein after outputting the weight vector of the current time k, the method further comprises: and if the error index of the current moment k is less than or equal to the preset threshold, setting the error index of the instant difference learning algorithm at the current moment to be equal to the preset threshold.
6. An automotive energy control device, characterized by comprising:
a prediction unit for predicting a future time period [ k, k + p-1]]Power demand P of internal automobile gearboxreq(k + jj), wherein jj is 0,1, …, p-1, k is the current time, and p is a positive integer;
the state vector calculation unit is used for calculating a state vector X (k + jj) of the automobile in the future time period [ k, k + p-1 ];
the control vector calculation unit is used for calculating a control vector U (k) of the automobile at the current moment k through a dynamic programming algorithm and an instant difference learning algorithm and outputting the control vector U (k) to the automobile;
the control vector calculation unit comprises a judgment module, a first control vector calculation module and a second control vector calculation module:
the judging module is used for judging whether the error index of the instant difference learning algorithm at the last moment is greater than a preset threshold value or not;
the control vector first calculation module is used for calculating a control vector U (k) of the automobile at the current moment k by combining a dynamic programming algorithm and an instant difference learning algorithm if the control vector is positive;
and the control vector second calculation module is used for calculating a control vector U (k) of the automobile at the current moment k through an instant difference learning algorithm if the control vector U (k) is not the same as the control vector U (k).
7. The automotive energy control device of claim 6, characterized in that the control vector calculation unit includes:
the judging module is used for judging whether the error index of the instant difference learning algorithm at the last moment is larger than a preset threshold value or not;
the control vector first calculation module is used for calculating a control vector U (k) of the automobile at the current moment k by combining a dynamic programming algorithm and an instant difference learning algorithm if the control vector U (k) is positive;
and the control vector second calculation module is used for calculating a control vector U (k) of the automobile at the current moment k through an instant difference learning algorithm if the control vector U (k) is not the same as the control vector U (k).
8. A vehicle energy control terminal, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to carry out the steps of the method according to one of claims 1 to 5.
9. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 5.
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Effective date of registration: 20221111 Address after: Room 306, building 4, future pioneer Plaza, high tech Zone, Jinan City, Shandong Province Patentee after: Shandong Zhongke advanced technology Co.,Ltd. Address before: 518000 No. 1068, Xue Yuan Avenue, Shenzhen University Town, Nanshan District, Shenzhen, Guangdong. Patentee before: SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY CHINESE ACADEMY OF SCIENCES |