CN108313057B - Pure electric automobile self-adapting cruise control method based on MPC and convex optimized algorithm - Google Patents
Pure electric automobile self-adapting cruise control method based on MPC and convex optimized algorithm Download PDFInfo
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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
- B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
- B60W30/14—Adaptive cruise control
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Abstract
The present invention relates to a kind of pure electric automobile self-adapting cruise control method based on MPC and convex optimized algorithm, belongs to technical field of new energy.This method specifically includes: S1: being required according to follow the bus control performance, establishes prediction model；S2: the model established according to S1 predicts the speed output of vehicle future time instance by MPC algorithm；S3: according to the speed output found out in MPC algorithm in S2 and the model established in S1, the power demand of vehicle future time instance is found out；S4: the power demand obtained according to S3 solves optimal torque with convex optimized algorithm and distributes, so that two motor work are in high efficiency region, it is minimum that battery exports electricity.The present invention by follow the bus control with it is energy-optimised, while two vehicle of front and back is maintained at the distance range of a safety, obtain optimal energy management strategies without influence Model Predictive Control real-time utilization, not only alleviate traffic pressure, moreover it is possible to reduce energy consumption.
Description
Technical Field
The invention belongs to the technical field of new energy automobiles, and relates to a pure electric vehicle following control and whole vehicle energy management method based on model prediction and convex optimization algorithm.
Background
Since hundreds of years of automobile development, automobile technology has brought about a tremendous change in people's daily life. However, with the increase of automobile output in recent years, the exhaust emission reaches the range which can be borne by the environment, and causes great pollution to the environment, so that governments in various countries have vigorously developed automobile energy-saving technologies to reduce the pollution of automobiles to the environment.
With the annual increase of the number of automobiles in China, the requirement of the quick and safe running of the automobiles on a traffic system is higher and higher, and the countries begin to focus on the fleet queue control under intelligent traffic. If two electric vehicles are in road running and are not subjected to queue coordination control, the electric quantity consumption of the two electric vehicles is large. For fleet control, the current control algorithm is mostly based on the principle of trajectory tracking, i.e. tracking is based on a reference path curve, which is related to both time and space, and requires that the vehicle reach a predetermined reference path point within a specified time. Due to the characteristic that the dynamic characteristic of the speed of the automobile is changed fast in the running process, real-time control still needs to be solved urgently, with the maturity of the technology of the vehicle-mounted sensor, the output of the next moment can be predicted by combining the historical information of the automobile through the on-board distance sensor and the expected value in model prediction control, and the model prediction control algorithm is related to the prediction duration and the prediction step number. When the driving speed of the automobile at the future moment is predicted by the model predictive control algorithm, the power demand of the automobile can be calculated by the automobile longitudinal dynamics formula.
The vehicle model adopted by the invention is two pure electric vehicles with a double-motor arrangement, the transmission system of the pure electric vehicles comprises two motors with different sizes (the maximum output power and the torque are different), a power lithium battery, a clutch and the like, and the transmission systems of the two vehicles are the same. The clutch is arranged between the large motor (arranged at the rear) and the rear driving shaft, when the required torque is smaller, the clutch is disconnected, the large motor does not work, and the small motor (arranged at the front) provides driving torque; and driving the automobile to run, and distributing the required torque according to the minimum electric quantity consumption by using an optimization algorithm when the required torque is large. For the selection of the optimization method, if only the global optimum requirement is required to be ensured, a Dynamic Programming (DP) algorithm can meet the requirement, but the time of the optimization process of the Dynamic Programming is increased in an exponential form along with the increase of the control variable, conflicts with the guarantee of real-time control during model prediction control, and the effectiveness of trajectory tracking cannot be met.
Disclosure of Invention
In view of the above, the invention aims to provide an intelligent vehicle following control combined algorithm based on a pure electric vehicle, which optimizes the energy consumption of the electric vehicle under the condition of ensuring the vehicle following running safety, stability and dynamic property.
In order to achieve the purpose, the invention provides the following technical scheme:
a pure electric vehicle self-adaptive cruise control method based on an MPC and convex optimization algorithm specifically comprises the following steps:
s1: establishing a prediction model according to the following vehicle control performance requirement;
s2: predicting a speed output v of the vehicle at a future time by a Model Predictive Control (MPC) algorithm based on the prediction Model established at S1c(k)；
S3: from the speed output v found in the MPC algorithm in S2c(k) And the automobile longitudinal dynamic model established in S1 is used for solving the power demand P of the automobile at the future momentdem(k)；
S4: and solving the optimal torque distribution by using a convex optimization algorithm according to the power requirement obtained in the step S3, so that the two motors work in a high-efficiency area and the output electric quantity of the battery is minimum.
Further, the step S2 specifically includes the following steps:
s21: establishing a cost function J of a model predictive control algorithm for meeting the following performance by taking the driving safety and stability of the automobile as targetsmpc,cost：
Jmpc,cost＝min(α1ds+α2v′c)
α therein1,α2For the weighting factor, the larger the value, the higher the corresponding performance requirement, such as α1The larger the value, the stricter the limit on the distance between the two vehicles is; ds is the distance, v ', of the two vehicles during driving'cThe speed difference of two vehicles during running;
s22: according to the constraint in step S1, v is obtainedc(k)。
Further, the model predictive control algorithm described in step S21 specifically includes the following steps:
1): through rolling optimization, the optimal rear vehicle running acceleration and speed value a are obtainedc(k)，vc(k) Selecting proper prediction interval length, control length and prediction step number during rolling optimization; the invention selects a prediction interval as 1;
2): comparing the state value optimized by rolling in the step 1) with the expected output, correcting the output a after correction through error feedbackc(k)，vc(k) (ii) a The formula is as follows:
wherein,to predict the output quantity, x0(k +1) represents the output quantity at which the control variable is constant at time k, a1Δ u (k) the increased output quantity of the control variable, e (k +1) is the error quantity, and x (k +1) is the actual output quantity.
Further, the power demand P of the vehicle at the future time is described in step S3dem(k) The calculation formula is as follows:
Ft(k)＝Fair(k)+Froll(k)+Fslope(k)+Fa(k)
Pdem(k)＝Ft(k)*v(k)
wherein, Ft(k) Traction of the vehicle at time k, Fair(k) Is the air resistance of the automobile during running, Froll(k) Rolling resistance when the vehicle is running, Fa(k) V (k) is the acceleration resistance of the vehicle during driving, and v (k) is the speed of the vehicle at time k.
Further, the step S4 specifically includes: the cost function (for solving the minimum energy consumption of the rear vehicle) in the energy management strategy is as follows:
Jopt,cost＝min∫Pbat dt
solving the optimal torque distribution by adopting a numerical method and converting a continuous variable PbatConverting into discrete variables, and modifying the cost function into:
where N is the number of sampling points, Δ t is the sampling interval, PbatIndicating the rear vehicle running energy consumption.
The invention has the beneficial effects that: according to the invention, through vehicle following control and energy optimization, the front vehicle and the rear vehicle are kept in a safe distance range, and meanwhile, an optimal energy management strategy is obtained without influencing the real-time application of model prediction control, so that traffic pressure can be relieved, and energy consumption can be reduced. The method comprises the following specific steps:
1) by following control, traffic jam can be effectively reduced;
2) the vehicle runs in a queue mode in the running process, so that the running safety can be improved to a greater extent;
3) the combination of model predictive control and convex optimization algorithm can provide possibility for real-time application:
4) the convex optimization algorithm can quickly optimize power distribution and reduce the energy consumption of the whole vehicle;
5) and the control of the combined algorithm can save the energy consumption of the motorcade in the driving process.
Drawings
In order to make the object, technical scheme and beneficial effect of the invention more clear, the invention provides the following drawings for explanation:
FIG. 1 is a schematic view of a car following control;
FIG. 2 is a schematic diagram of the following coordination control of two vehicle fleets;
FIG. 3 is a control diagram of a joint algorithm;
fig. 4 is a configuration of an electric vehicle used in the present invention.
Detailed Description
Preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
The implementation of the invention can be realized by a pure electric car model (as shown in fig. 4), wherein a car following control structure chart is shown in fig. 1, under the condition that the safety of the car following process is ensured to meet the conditions, a rear car obtains the driving information of a front car through a distance sensor and a speed sensor to obtain an expected output value, then through model prediction control, under the condition that the dynamic property and the stability are met, a corresponding output value is calculated, the output of the model prediction control is used as the input of energy management control, and the torque distribution rate with the minimum energy consumption is calculated through a convex optimization algorithm, wherein the specific algorithm process is shown in fig. 2; FIG. 3 is an optimized graph of FIG. 2, and FIG. 3 shows the powertrain efficiency of the overall powertrain architecture diagram.
The method comprises the following specific steps:
s1: and establishing a prediction model according to the following performance requirements. During the following control at the moment k, the following control method firstly satisfies the requirement that the distance between two vehicles is kept within a safe range, and the driving process of a rear vehicle is stable:
xk+1＝f(xk,uk)
ds∈[dsmax,dsmin]；
vc∈[vcmin,vcmax]；
wherein xk+1The state of the rear vehicle at the time k +1, xk,ukRespectively the state of the rear vehicle at the moment k and the control variable value, namely the state of the vehicle at the future moment is a function of the state at the current moment, ds is the distance between the two vehicles during the driving process, vcThe speed of the rear vehicle in the driving process. The state of the automobile only comprises the speed, the position and the output torque when the automobile runs, and does not comprise the transverse dynamic content of the automobile.
S2: predicting the speed output v of the vehicle at the future time by a model predictive control algorithm based on the prediction model established at S1c(k)：
Firstly, establishing a cost function J of a model predictive control algorithmmpc,costThe cost function of the control algorithm takes the driving safety and the stability of the automobile as targets:
Jmpc,cost＝min(α1ds+α2v′c)
α therein1,α2For the weighting factor, the larger the value, the higher the corresponding performance requirement, such as α1The larger the value, the more severe the restriction on the distance between the two vehicles. Then, v is obtained according to the constraint in step S1c(k)。
S3: from the output v found in the model predictive control algorithm in S2c(k) Calculating the power demand P of the vehicle at the future time with the vehicle longitudinal dynamics model established in S1dem(k)：
Ft(k)＝Fair(k)+Froll(k)+Fslope(k)+Fa(k)
Pdem(k)＝Ft(k)*v(k)
Wherein, Ft(k) Traction of the vehicle at time k, Fair(k) Is the air resistance of the automobile during running, Froll(k) Rolling resistance when the vehicle is running, Fa(k) V (k) is the acceleration resistance of the vehicle during driving, and v (k) is the speed of the vehicle at time k.
S4: according to the power requirement obtained by S3, solving the optimal torque distribution by using a convex optimization algorithm, so that the two motors work in a high-efficiency area, the output electric quantity of the battery is minimum, and the cost function in the energy management strategy is established as follows:
Jopt,cost＝min∫Pbatdt
the present invention adopts a numerical method to solve the optimal solution in S4, and therefore, the continuous variable P needs to be usedbatConverted to a discrete variable, so the cost function is modified to:
in the following control of the present invention, the following control based on the model prediction algorithm, as shown in fig. 2, specifically includes the following steps:
s21 obtaining the optimal control variable a through rolling optimizationc(k)’，vc(k) ' when rolling optimization, selecting proper prediction interval length, control length and prediction step number, the invention selects the prediction interval as 1,
s22: comparing the state value obtained by the roll optimization in step S21 with the expected output, correcting the output a by error feedbackc(k)，vc(k) In that respect The formula is as follows:
whereinTo predict the output quantity, x0(k +1) represents the output quantity at which the control variable is constant at time k, a1Δ u (k) the increased output quantity of the control variable, e (k +1) is the error quantity, and x (k +1) is the actual output quantity.
Finally, it is noted that the above-mentioned preferred embodiments illustrate rather than limit the invention, and that, although the invention has been described in detail with reference to the above-mentioned preferred embodiments, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the scope of the invention as defined by the appended claims.
Claims (3)
1. A pure electric vehicle self-adaptive cruise control method based on an MPC and a convex optimization algorithm is characterized by specifically comprising the following steps:
s1: establishing a prediction model according to the following vehicle control performance requirement;
s2: predicting a speed output v of the vehicle at a future time by a Model Predictive Control (MPC) algorithm based on the prediction Model established at S1c(k) And acceleration value ac(k)；
S3: according toVelocity output v found in MPC algorithm in S2c(k) And ac(k) And the automobile longitudinal dynamic model established in the S1 is used for solving the power demand P of the automobile at the future momentdem(k)；
S4: according to the power requirement obtained in the S3, the optimal torque distribution is solved by using a convex optimization algorithm, so that the two motors work in a high-efficiency area, and the output electric quantity of the battery is minimum;
the step S2 specifically includes the following steps:
s21: establishing a cost function J of a model predictive control algorithm by taking the driving safety and the stability of the automobile as targetsmpc,cost：
Jmpc,cost＝min(α1ds+α2v′c)
α therein1,α2Ds is a weight factor and is the distance between two vehicles during driving, v'cThe speed difference of the two vehicles;
s22: according to the constraint in step S1, v is obtainedc(k) And ac(k)。
2. The adaptive cruise control method for pure electric vehicles based on MPC and convex optimization algorithm as claimed in claim 1, wherein the power demand P of the vehicle at the future time of step S3dem(k) The calculation formula is as follows:
Ft(k)＝Fair(k)+Froll(k)+Fslope(k)+Fa(k)
Pdem(k)＝Ft(k)*v(k)
wherein, Ft(k) Traction of the vehicle at time k, Fair(k) Is the air resistance of the automobile during running, Froll(k) Rolling resistance when the vehicle is running, Fa(k) V (k) is the acceleration resistance of the vehicle during driving, and v (k) is the speed of the vehicle at time k.
3. The pure electric vehicle adaptive cruise control method based on MPC and convex optimization algorithm as claimed in claim 1, wherein said step S4 specifically comprises: the cost function in the energy management strategy is as follows:
Jopt,cost＝min∫Pbatdt
solving the optimal torque distribution by adopting a numerical discrete method and enabling a continuous variable P to be obtainedbatConverting into discrete variables, and modifying the cost function into:
where N is the number of sampling points, Δ t is the sampling interval, PbatIndicating the rear vehicle running energy consumption.
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| CN111231930B (en) * | 2020-01-09 | 2021-06-29 | 重庆大学 | Multi-target energy management method in HEV adaptive cruise based on MPC |
| CN112435504B (en) * | 2020-11-11 | 2022-07-08 | 清华大学 | Centralized collaborative track planning method and device under vehicle-road collaborative environment |
| CN112685960B (en) * | 2021-01-04 | 2022-08-19 | 北京理工大学 | Energy management method of pure electric sweeping vehicle |
| CN113085860B (en) * | 2021-05-07 | 2022-05-17 | 河南科技大学 | Energy management method of fuel cell hybrid electric vehicle in following environment |
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