CN116811601A - Distributed driving electric automobile torque control method based on model predictive control - Google Patents
Distributed driving electric automobile torque control method based on model predictive control Download PDFInfo
- Publication number
- CN116811601A CN116811601A CN202310814861.2A CN202310814861A CN116811601A CN 116811601 A CN116811601 A CN 116811601A CN 202310814861 A CN202310814861 A CN 202310814861A CN 116811601 A CN116811601 A CN 116811601A
- Authority
- CN
- China
- Prior art keywords
- representing
- vehicle
- torque
- tire
- model
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 21
- 238000005457 optimization Methods 0.000 claims description 9
- 238000005070 sampling Methods 0.000 claims description 3
- 238000010276 construction Methods 0.000 claims description 2
- 230000005251 gamma ray Effects 0.000 claims description 2
- 238000005096 rolling process Methods 0.000 claims description 2
- 230000006399 behavior Effects 0.000 abstract description 4
- 238000004134 energy conservation Methods 0.000 abstract description 2
- 238000013461 design Methods 0.000 description 5
- 238000005516 engineering process Methods 0.000 description 2
- 230000004044 response Effects 0.000 description 2
- 230000001133 acceleration Effects 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005265 energy consumption Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 239000000758 substrate Substances 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/72—Electric energy management in electromobility
Landscapes
- Steering Control In Accordance With Driving Conditions (AREA)
Abstract
The application discloses a distributed driving electric automobile torque control method based on model predictive control, relates to the technical field of automatic steering control, solves the technical problem that the stability and energy conservation of vehicle operation are insufficient, and adopts the technical scheme that a torque vector distribution frame is designed, the speed control requirement of an upper driver generates total torque, a proportional integral control algorithm is adopted, and steering behaviors are obtained through a driving simulator. The optimized torque vector is then distributed to the front/rear axles. The lower layer generates a direct yaw moment control input by the longitudinal differential force of the left and right wheels to ensure steering stability of the vehicle.
Description
Technical Field
The application relates to the technical field of automatic driving automobiles, in particular to an automatic driving steering control technology, and especially relates to a distributed driving electric automobile torque control method based on model predictive control.
Background
Electric vehicles are regarded as one of effective green vehicles in the field of environmental protection due to their zero emission characteristics. Distributed electric vehicles employing multiple power systems may provide more control schemes through different torque vector distribution methods. However, limited range becomes an important factor limiting the development of the electric vehicle industry. How to develop advanced battery technologies such as higher energy density and ultra-fast charging methods has become the focus of extensive research. In addition, improving the work efficiency of the hub motor is also an effective way of reducing energy consumption. Since the motors of the distributed electric vehicle are independently controllable, the distributed electric vehicle can be realized through reasonable torque vector distribution. It should be noted that yaw motion control resulting from a differential torque input between the left and right wheels may cause instability of the vehicle. Therefore, how to design a torque vector distribution frame, and achieve energy saving of a distributed electric vehicle while improving vehicle steering stability, would be an important problem to be solved.
Disclosure of Invention
The application provides a distributed driving electric vehicle torque control method based on model predictive control, which aims to optimally set a torque vector distribution frame of a distributed driving electric vehicle so as to ensure the energy-saving performance and the stability of the distributed electric vehicle.
The technical aim of the application is realized by the following technical scheme:
a distributed driving electric vehicle torque control method based on model predictive control comprises the following steps:
s1: building a tire dynamics model;
s2: constructing a model predictive controller according to the tire dynamics model, and constructing an optimization objective function of the model predictive controller;
s3: acquiring a reference centroid side deviation angle and a reference yaw rate through a vehicle two-degree-of-freedom model, and designing a stability cost function aiming at improving the vehicle stability according to the reference centroid side deviation angle and the reference yaw rate;
s4: constructing an optimal control problem according to an optimal objective function and a stability cost function of the model predictive controller, solving the optimal control problem to obtain a required yaw moment, and distributing required driving torques for four wheels according to the yaw moment;
s5: and controlling the torque of the distributed driving electric automobile according to the required driving torques of the four wheels.
The application has the beneficial effects that: according to the distributed driving electric vehicle torque control method based on model predictive control, torque vector control of different control layers is decoupled, and motor efficiency optimization and vehicle lateral movement stability control are realized. In order to achieve energy conservation of the distributed electric vehicle while improving vehicle steering stability, a torque vector distribution frame is designed, an upper driver speed control requirement generates total torque, a Proportional Integral (PI) control algorithm design is adopted, and steering behaviors are obtained through a driving simulator. And taking a hub motor energy chart based on a dynamometer test as a standard for optimizing a torque distribution strategy. The optimized torque vector is then distributed to the front/rear axles. The lower layer generates a direct yaw moment control input by the longitudinal differential force of the left and right wheels to ensure steering stability of the vehicle.
Drawings
FIG. 1 is a flow chart of a method for controlling the torque of a distributed driving electric vehicle based on model predictive control;
FIG. 2 is a distributed drive motor efficiency map;
FIG. 3 is a schematic diagram of a two-degree-of-freedom dynamics model of a vehicle.
Detailed Description
The technical scheme of the application will be described in detail with reference to the accompanying drawings.
Fig. 1 is a flow chart of a method for controlling torque of a distributed driving electric vehicle based on model predictive control, based on fig. 1, the method specifically includes:
s1: and constructing a tire dynamics model.
In the embodiment of the application, the construction process of the tire dynamics model is as follows:
s11: the wheel drive dynamics model taking into account tire slip is expressed as:
wherein ,Fxi Representing the longitudinal stress of tyre i, J wi Representing the moment of inertia of tyre i about the z-axis, T wi Motor torque input, R, representing tire i e Representing the rolling radius, w, of the tyre i and Vxi,w Respectively the angular velocity and the longitudinal velocity, k, of the tyre i i The longitudinal stiffness of tire i, i=f for front wheel and i=r for rear wheel; lambda (lambda) wi The longitudinal slip ratio of tire i is indicated.
S12: the rotational motion of the tire from the wheel driving force model is expressed as:
wherein the longitudinal slip rate lambda of tyre i wi Determines the longitudinal stress F of the tire i xi 。
Considering the nonlinearity of the tire, the longitudinal rigidity and cornering rigidity of the tire are not constant during actual running, the longitudinal rigidity k 'of the improved tire i' i And turnFlexural rigidity C' i Obtained by a magic formula expressed as:
wherein ,Fxi Representing the longitudinal stress of tyre i; f (F) yi Representing the lateral stress of tyre i; μ represents the road-tire friction coefficient; f (F) zi Representing the vertical force of tire i; b (B) x 、B y 、C x 、C y 、D x and Dy All represent fitting parameters; k (k) i Represents the longitudinal stiffness of tyre i; alpha i The slip angle of tire i is shown.
Due to the attached ellipse, the tire stress is rewritten as:
wherein ,representing the longitudinal stress of the corrected tyre i; />Indicating the lateral force of the corrected tyre i.
S13: obtaining the longitudinal rigidity k 'of the corrected tire in real time according to the stress condition of the corrected tire' i And cornering stiffness C' i Expressed as:
to sum up, the state space equation of the wheel motion, i.e. the tire dynamics model, is derived as:
s2: and constructing a model prediction controller according to the tire dynamics model, and constructing an optimization objective function of the model prediction controller.
Specifically, step S2 includes:
s21: taking the uncertainty of the longitudinal speed and the longitudinal rigidity of the tire into consideration, carrying out nonlinear processing on the model by adopting model predictive control. In model predictive controller design, equation (7) is discrete. The tire dynamics model is discretized, and according to the Euler method, the discretization equation is rewritten as:
x(k+1)＝A′x(k)+B′u(k)
wherein x (k) represents the vehicle state at time k; u (k) represents a motor torque input at time k; Δt represents the sampling time.
S22: notably, in MPC (model predictive controller) design, V xi,w and k′i Updates are made at different sampling times. Thus, the accuracy of the model predictive controller can be ensured, and improper control input is avoided. At the heart of the model predictive controller is a prediction of the future state of the system. By minimizing the cost function constructed from the system tracking error, the optimal control input can be calculated. With vision distance predicted by the presentation system being equal to the control vision distance N p 。
Then based on the model predictive controller, if the current time is assumed to be k, the system predictive control input is expressed as: [ u (k), u (k+1),. The term, x (k+n) p -1)]Then the system predicts the state [ x (k+1), x (k+2),.. p )]Expressed as:
x(k+1)＝A′x(k)+B′u(k)
wherein ,Np Representing control stadia;
s23: and constructing an optimization objective function of the model predictive controller according to the system predictive state.
First, during the front and rear axle torque distribution, the driver's torque input demand is first satisfied. The PI controller is designed to simulate the driving/braking behavior of the driver while tracking the target vehicle speed. The total torque input can be expressed as:
T wd ＝K P e v +K I ∫e v dt； (10)
wherein ,KP and KI Respectively representing a proportional coefficient and an integral coefficient e v Representing the deviation between the actual speed and the target speed.
The cost function reflecting the total torque input is expressed as:
T w,min ≤T wi ≤T w,max
wherein ,Tw,min Representing minimum allowable torque input, T w,max Representing a maximum allowable torque input; ρ represents a weight coefficient; t (T) wf and Twr Torque inputs for the front and rear wheels, respectively; t represents the current time.
Then, the energy efficiency is mainly considered in the upper layer design, and the basic method is to provide an efficient working area for the hub motor. Thus, a wheel speed efficiency map function is constructed. According to the motor efficiency map shown in FIG. 2, the most effective torque input of the front and rear wheels at the current wheel speed is selected as the reference torque T wi,r (i=f, r) to optimize the torque distribution strategy. The average wheel speeds of the left and right wheels are used to match the most efficient torque. And uniformly distributing the calculated optimized front and rear axle torque to the left and right wheels. In addition, the small tire slip ratio is also used as an optimization index for improving the vehicle stability. The tire effective torque and longitudinal slip are consideredThe cost function of the shift rate is expressed as:
wherein ,Twf,r Representing a front wheel reference torque, i.e., the most efficient torque input to the front wheel at the current wheel speed; t (T) wr,r Representing a rear wheel reference torque, i.e., the most efficient torque input of the rear wheel at the current wheel speed; lambda (lambda) wf Representing the longitudinal slip rate of the front wheels; lambda (lambda) wr Indicating the longitudinal slip rate of the rear wheel;α 1 and α2 All represent weight coefficients.
For the cost function (12), the front wheels are preferentially matched to the reference torque in view of vehicle longitudinal stability control. As the front axle of the vehicle approaches the tire-road attachment limit, rapid acceleration or deceleration may cause the vehicle to sideslip. Thus, the first and second substrates are bonded together,with a larger value. In addition, the differential torque input from yaw motion control and steering behavior can result in a greater tire slip rate on the axle side. Distributed drive electric vehicles require an effective control scheme to avoid wheel tire slip. Thus, in the cost function->In (1) state variable lambda wf and λwr The larger left-right wheel slip ratio is selected to suppress excessive rotation. Meanwhile, in order to avoid the conflict between the torque optimization result and the intention of the driver, logic judgment is added. If the rear wheel optimized torque input is opposite the driver total torque input, the rear wheel torque input is zero.
The optimized objective function of the model predictive controller is expressed as:
s3: and acquiring a reference centroid side deviation angle and a reference yaw rate through a two-degree-of-freedom model of the vehicle, and designing a stability cost function aiming at improving the stability of the vehicle according to the reference centroid side deviation angle and the reference yaw rate.
Specifically, the lower controller generates a direct yaw moment control input by a longitudinal differential force of left and right wheels to ensure steering stability of the vehicle, comprising:
s31: a vehicle two-degree-of-freedom model is constructed, as shown in fig. 3, and is expressed as:
wherein m represents the mass of the whole vehicle, V represents the longitudinal speed of the vehicle, beta represents the centroid slip angle, gamma represents the yaw rate, and l f Representing the distance from the centroid of the vehicle to the front axle, l r Representing the distance from the center of mass of the vehicle to the rear axle, C f Representing the deflection stiffness of the front axle tire sidewall C r Indicating the deflection rigidity of the rear axle tyre side I z Representing the moment of inertia of the whole vehicle, M c Indicating yaw moment experienced by the vehicle; delta f Indicating the front wheel rotation angle.
S32: and taking the yaw rate and the centroid side offset angle as yaw references, and obtaining the reference yaw rate and the reference centroid side offset angle through stable yaw motion response. The reference centroid slip angle is selected to be zero to ensure vehicle stability, considering that the yaw torque control input will have an effect on the static response of the centroid slip angle. The reference centroid slip angle and the reference yaw rate are expressed as:
wherein ,βref Representing a reference centroid slip angle, the value of which is 0, to ensure the stability of the vehicle; gamma ray ref Indicating the reference yaw rate.
S33: designing a stability cost function aiming at improving the stability of the vehicle according to the reference centroid slip angle and the reference yaw rate, wherein the stability cost function is expressed as follows:
M min ≤M c ≤M max
wherein ,λ1 、λ 2 and λ3 All represent weight coefficients; m is M min Representing the minimum value of yaw moment input, M max Representing the maximum value of the yaw moment input.
S4: and constructing an optimal control problem according to the optimal objective function and the stability cost function of the model predictive controller, solving the optimal control problem to obtain a required yaw moment, and distributing the required driving torque for the four wheels according to the yaw moment.
Specifically, step S4 includes:
s41: and (3) integrating cost functions (11), (12) and (15), and constructing an optimal control problem, wherein the optimal control problem is expressed as follows:
s42: solving the optimal control problem according to a nonlinear programming solver to obtain a required yaw torque, and distributing the required driving torque to four wheels according to the required yaw torque, wherein the required driving torque is expressed as:
wherein ,Tw,fl Representing the driving torque of the left front wheel of the vehicle,T w,fr representing the driving torque of the right front wheel of the vehicle, T w,rl Representing the driving torque of the left rear wheel of the vehicle, T w,rr Indicating the driving torque of the right rear wheel of the vehicle, t w Representing the track width of the vehicle.
S5: and controlling the torque of the distributed driving electric automobile according to the required driving torques of the four wheels.
The foregoing is an exemplary embodiment of the application, the scope of which is defined by the claims and their equivalents.
Claims (5)
1. The distributed driving electric vehicle torque control method based on model predictive control is characterized by comprising the following steps of:
s1: building a tire dynamics model;
s2: constructing a model predictive controller according to the tire dynamics model, and constructing an optimization objective function of the model predictive controller;
s3: acquiring a reference centroid side deviation angle and a reference yaw rate through a vehicle two-degree-of-freedom model, and designing a stability cost function aiming at improving the vehicle stability according to the reference centroid side deviation angle and the reference yaw rate;
s4: constructing an optimal control problem according to an optimal objective function and a stability cost function of the model predictive controller, solving the optimal control problem to obtain a required yaw moment, and distributing required driving torques for four wheels according to the yaw moment;
s5: and controlling the torque of the distributed driving electric automobile according to the required driving torques of the four wheels.
2. The method according to claim 1, wherein in step S1, the construction of the tire dynamics model is represented as:
wherein ,λwi The longitudinal slip ratio of tire i; t (T) wi A motor torque input representing tire i; r is R e Representing the rolling radius of the wheel; v (V) xi,w Representing the longitudinal speed of tyre i; j (J) wi Representing the moment of inertia of tyre i about the z-axis; k' i Representing the corrected longitudinal stiffness of tyre i; i=f, r, i=f represents a front wheel, and i=r represents a rear wheel;
wherein ,representing the longitudinal stress of the corrected tyre i; f (F) xi Representing the longitudinal stress of tyre i; μ represents the road-tire friction coefficient; f (F) zi Representing the vertical force of tire i; b (B) x 、C x and Dx All represent fitting parameters; k (k) i Represents the longitudinal stiffness of tyre i; alpha i The slip angle of tire i is shown.
3. The method according to claim 2, wherein the step S2 includes:
s21: discretizing the tire dynamics model, and obtaining a model prediction controller by using an Euler method, wherein the model prediction controller is expressed as follows:
wherein x (k) represents the vehicle state at time k; u (k) represents a motor torque input at time k; Δt represents the sampling time;
s22: according to the model predictive controller, if the current time is k, the system predictive control input is expressed as: [ u (k), u (k+1),. The term, x (k+n) p -1)]Then the system predicts the state [ x (k+1), x (k+2),.. p )]Expressed as:
wherein ,Np Representing control stadia;
s23: constructing an optimization objective function of the model predictive controller according to the system predictive state, wherein the optimization objective function is expressed as:
wherein ,J1 Representing total torque input T wd Cost function, T wd ＝K P e v +K I ∫e v dt，K P and KI Respectively representing a proportional coefficient and an integral coefficient e v Representing a deviation between the actual speed and the target speed; t (T) w,min Representing minimum allowable torque input, T w,max Representing a maximum allowable torque input; ρ represents a weight coefficient; t (T) wf and Twr Torque inputs for the front and rear wheels, respectively; t represents the current time; j (J) 2 A cost function that considers the tire effective torque and the longitudinal slip rate; t (T) wf,r Representing a front wheel reference torque, i.e., the most efficient torque input to the front wheel at the current wheel speed; t (T) wr,r Representing a rear wheel reference torque, i.e., the most efficient torque input of the rear wheel at the current wheel speed; lambda (lambda) wf Representing the longitudinal slip rate of the front wheels; lambda (lambda) wr Indicating the longitudinal slip rate of the rear wheel; α 1 and α2 All represent weight coefficients.
4. A method according to claim 3, wherein said step S3 comprises:
s31: constructing a vehicle two-degree-of-freedom model, wherein the vehicle two-degree-of-freedom model is expressed as:
wherein m represents the mass of the whole vehicle, V represents the longitudinal speed of the vehicle, beta represents the centroid slip angle, gamma represents the yaw rate, and l f Representing the distance from the centroid of the vehicle to the front axle, l r Representing the distance from the center of mass of the vehicle to the rear axle, C f Representing the deflection stiffness of the front axle tire sidewall C r Indicating the deflection rigidity of the rear axle tyre side I z Representing the moment of inertia of the whole vehicle, M c Indicating yaw moment experienced by the vehicle; delta f Indicating the front wheel rotation angle;
s32: obtaining a reference centroid slip angle and a reference yaw rate through a vehicle two-degree-of-freedom model, wherein the reference centroid slip angle and the reference yaw rate are expressed as follows:
wherein ,βref Representing a reference centroid slip angle, the value of which is 0, to ensure the stability of the vehicle; gamma ray ref Representing a reference yaw rate;
s33: designing a stability cost function aiming at improving the stability of the vehicle according to the reference centroid slip angle and the reference yaw rate, wherein the stability cost function is expressed as follows:
wherein ,λ1 、λ 2 and λ3 All represent weight coefficients; m is M min Representing the minimum value of yaw moment input, M max Representing the maximum value of the yaw moment input.
5. The method of claim 4, wherein said step S4 comprises:
s41: the optimal control problem is expressed as:
s42: solving the optimal control problem according to a nonlinear programming solver to obtain a required yaw torque, and distributing the required driving torque to four wheels according to the yaw torque, wherein the required driving torque is expressed as:
wherein ,Tw,fl Representing the driving torque of the left front wheel of the vehicle, T w,fr Representing the driving torque of the right front wheel of the vehicle, T w,rl Representing the driving torque of the left rear wheel of the vehicle, T w,rr Indicating the driving torque of the right rear wheel of the vehicle, t w Representing the track width of the vehicle.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310814861.2A CN116811601A (en) | 2023-07-05 | 2023-07-05 | Distributed driving electric automobile torque control method based on model predictive control |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310814861.2A CN116811601A (en) | 2023-07-05 | 2023-07-05 | Distributed driving electric automobile torque control method based on model predictive control |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116811601A true CN116811601A (en) | 2023-09-29 |
Family
ID=88114394
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310814861.2A Pending CN116811601A (en) | 2023-07-05 | 2023-07-05 | Distributed driving electric automobile torque control method based on model predictive control |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116811601A (en) |
-
2023
- 2023-07-05 CN CN202310814861.2A patent/CN116811601A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111890951B (en) | Intelligent electric automobile trajectory tracking and motion control method | |
| CN109795502B (en) | Intelligent electric vehicle path tracking model prediction control method | |
| CN104443022B (en) | A kind of four motorized wheels electric car stability control method and system | |
| CN109552312A (en) | Intact stability model predictive control method | |
| Ma et al. | Lateral stability integrated with energy efficiency control for electric vehicles | |
| CN105691381A (en) | Stability control method and system for electric automobile with four independently driven wheels | |
| CN111959500B (en) | Automobile path tracking performance improving method based on tire force distribution | |
| CN112373459B (en) | Method for controlling upper-layer motion state of four-hub motor-driven vehicle | |
| Tian et al. | Integrated control with DYC and DSS for 4WID electric vehicles | |
| CN112644455B (en) | Distributed driving vehicle running stability control method | |
| CN112829766B (en) | Adaptive path tracking method based on distributed driving electric vehicle | |
| CN112026533B (en) | Traction control method for four-wheel independent drive electric automobile under limit working condition | |
| CN112849127A (en) | Method, device, storage medium and equipment for controlling steering of vehicle | |
| CN114771530A (en) | Vehicle steering control method and device, vehicle and storage medium | |
| CN108694283A (en) | A kind of forecast Control Algorithm and system for improving electric vehicle lateral stability | |
| CN111731267B (en) | Distributed electric vehicle stability control system and method equipped with non-inflatable elastic wheels | |
| WO2024012089A1 (en) | Control method and apparatus for distributed three-motor vehicle, electric vehicle and medium | |
| Li et al. | Adaptive sliding mode control of lateral stability of four wheel hub electric vehicles | |
| Hu et al. | Integrated control of AFS and DYC for in-wheel-motor electric vehicles based on operation region division | |
| CN108357485B (en) | A kind of lateral redundancy control method and system of automatic driving vehicle | |
| CN112346337A (en) | Vehicle stability control method based on active steering of rear wheels under limit working condition | |
| CN114889448B (en) | In-wheel motor driven vehicle self-adaptive torque control method, system and storage medium | |
| CN116811601A (en) | Distributed driving electric automobile torque control method based on model predictive control | |
| CN114734983A (en) | Distributed electric vehicle stability control method based on stable domain | |
| JP4949319B2 (en) | Vehicle driving force control device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |