CN116001589A - Multi-system coupling integrated control system based on electric automobile - Google Patents
Multi-system coupling integrated control system based on electric automobile Download PDFInfo
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Abstract
The invention discloses a multi-system coupling integrated control system based on an electric automobile in the field of electric automobiles, which comprises a layered integrated control structure, wherein the layered integrated control structure comprises an upper layer, a middle layer and a lower layer; the upper layer is an Eco-CACC controller, the Eco-CACC controller comprises a following mode, a cruising mode and a lane changing mode, and the Eco-CACC controller switches the driving mode through a finite state machine controller based on the minimum safe vehicle distance; the Eco-CACC controller is used for providing a control requirement of a target vehicle speed and a target path for the middle layer based on the safety index, the economical index, the comfort index, the following index and the constraint condition of the actuator; the middle layer is a driving/steering integrated controller which is used for providing control instructions of wheel rotation angles, total driving torque and additional yaw moment for the lower layer by taking track tracking as a main target; the lower layer is a torque energy efficiency optimizing controller which is used for controlling and distributing motor energy and tire slip loss according to the torque requirement.
Description
Technical Field
The invention belongs to the field of electric automobiles, and particularly relates to a multi-system coupling integrated control system based on an electric automobile.
Background
In recent years, the application of intelligent traffic systems has been rapidly developed, so various information can now be transmitted into vehicles through internet of vehicles, global positioning systems and geographic information systems. The electric automobile can better understand the surrounding traffic environment through the rich information, and meanwhile, the information can also be used for developing a real-time energy-saving control method to improve the energy efficiency, so that the driving distance of the automobile is improved. The electric automobile is connected with each electronic control system through a vehicle-mounted communication network, and integrates the functions of environment sensing, information fusion, vehicle dynamics control, energy management and the like. With the increase of automobile anti-lock systems (ABS), automobile electronic stability control systems (ESC), adaptive cruise control systems (ACC), emergency braking systems (AEB) and other automobile electronic control systems, but the subsystems are relatively closed, when a plurality of control systems are simultaneously operated, collision and interference are easy to occur due to the coupling characteristics of vehicles, so that integrated control provides a solution for the nonlinear control system of the multi-system coupling.
In order to solve the above problems, the invention provides a multi-drive coupling integrated control system proposed in chinese patent publication No. CN104865936a, which includes two dc buses, a set of precharge circuits, a set of support capacitors, a set of absorption capacitors, a set of signal detection systems, a set of control power supply systems, and at least two motor controllers. The output of the system driving loop is independent, and the system driving loop does not affect the work of the system driving loop, even if one or more of the system driving loops temporarily do not work or fail, the rest loops can continue to work.
However, the above technical solutions do not consider that in the prior art, the energy efficiency optimization control on vehicles in traffic flows is mostly concentrated on energy efficiency optimization in a single field, such as energy efficiency optimization based on traffic information or torque energy efficiency optimization, but driving or steering systems are omitted, so that the stability performance and energy utilization efficiency of the whole electric vehicle are not high; therefore, it is necessary to provide a multi-system coupling integrated control system based on an electric automobile.
Disclosure of Invention
In order to solve the problems, the invention aims to provide a multi-system coupling integrated control system capable of improving the stability performance and the energy utilization efficiency of the whole vehicle.
In order to achieve the above object, the technical scheme of the present invention is as follows: the multi-system coupling integrated control system based on the electric automobile comprises a layered integrated control structure, wherein the layered integrated control structure comprises an upper layer, a middle layer and a lower layer; the upper layer is an Eco-CACC controller, the Eco-CACC controller comprises a following mode, a cruising mode and a lane changing mode, and the Eco-CACC controller switches the driving mode through a finite state machine controller based on the minimum safe vehicle distance; the Eco-CACC controller is used for providing a control requirement of a target vehicle speed and a target path for the middle layer based on the safety index, the economical index, the comfort index, the following index and the constraint condition of the actuator; the middle layer is a driving/steering integrated controller which is used for providing control instructions of wheel rotation angles, total driving torque and additional yaw moment for the lower layer by taking track tracking as a main target; the lower layer is a torque energy efficiency optimizing controller which is used for controlling and distributing motor energy and tire slip loss according to the torque requirement and sending motor torque and wheel rotation angle signals to an actuator.
Further, the expression of the minimum safe distance is as follows:
wherein SV is a target vehicle, LV is a vehicle following SV on a current lane, and FV is a vehicle following SV on the current lane; PLV is the vehicle that SV is about to follow on the target lane, PFV is the vehicle behind SV on the target lane; d, d LV 、d FV 、d PLV 、d PFV Actual distance from SV to LV, FV, PLV, PFV, τ, respectively THWLV 、τ THWFV 、τ THWPLV 、τ THWPFV Safe inter-vehicle hours, τ, of SV to LV, FV, PLV, PFV respectively TTCLV 、τ TTCPV 、τ TTCPLV 、τ TTCPFV Collision times, v, of SV to LV, FV, PLV, PFV respectively SV 、v LV 、v FV 、v FLV 、v PFV And a running speed of each SV, LV, FV, PLV, PFV.
Further, the calculation formula of the safety index is as follows:
wherein e s_min And e s_max E is the minimum value and the maximum value of the vehicle distance error respectively v_min And e v_max Respectively the minimum value and the maximum value of the vehicle speed error;
the calculation formula of the economic index is as follows:
wherein C is D (Δs i ) Is the air resistance coefficient;
the comfort index is calculated as follows:
wherein the method comprises the steps of，J com To predict comfort index in time domain, w com Is a comfort index weight coefficient;
the following index is calculated as follows:
wherein J is tra To predict the vehicle following index in the time domain, w s Is the weight coefficient of the vehicle distance error, w v Is the vehicle speed error weight coefficient.
Further, the design of the drive/steering integrated controller includes the steps of:
s1, determining a layered driving/steering integrated control frame;
s2, designing a driving/steering integrated control algorithm based on an MPC theory.
Further, the design of the torque energy efficiency optimization controller comprises the following steps:
s1, building a motor energy consumption model and a tire energy consumption model;
s2, establishing a moment distribution model, and designing a moment control distribution method based on constraint optimization;
further, the formula of the motor energy model is as follows:
wherein P is drive,i And P regen,i (i=fl, fr, rl, rr) is the drive output power and the brake energy feedback power of the in-wheel motor, respectively; η (eta) drive,i And eta regen,i The driving output efficiency and the braking energy feedback efficiency of the hub motor are respectively.
Further, the formula of the tire energy consumption model is as follows:
P tyre ＝P tyre_lon +P tyre_lat (9)
wherein X is si And Y si Longitudinal and lateral sliding forces of the sliding region at the tire grounding mark, u slide_xi And u slide_yi Respectively the longitudinal and lateral sliding speeds, X of the tyre Ti And Y Ti Longitudinal and lateral forces calculated by the tire model, respectively.
Further, the moment distribution model formula is as follows:
ΔF＝[ΔF tfl ΔF tfr ΔF trl ΔF trr ΔF sfl ΔF sfr ΔF srl ΔF srr ] T (11)
B M ＝[00ΔM z ] T (12)
wherein m is the vehicle servicing mass, v x V is the longitudinal speed of the vehicle y For the lateral velocity at the centroid, r is the yaw rate of the vehicle about the z-axis, F xi For the wheel-to-ground longitudinal force of the vehicle, F yi I=fl, fr, rl, rr denote the left front wheel, right front wheel, left rear wheel and right rear wheel, respectively, I for the wheel-ground side force of the vehicle z For yaw moment of inertia of the vehicle, l f For the wheelbase of the front axle of the vehicle, l r B, for the rear axle distance of the vehicle f B, for the track of the front axle of the vehicle r Delta for the rear wheelbase of the vehicle i Is the steering wheel angle.
Further, the torque control distribution method based on constraint optimization has the following formula:
wherein alpha, beta and gamma are weight factors of motor energy consumption, target torque tracking and tire energy consumption penalty functions respectively, B opt 、U opt 、U des 、a i 、b i And are each represented by the following formula:
U opt ＝[F tfl F tfr F trl F trr ] T (15)
U des ＝[T t_total ΔM z ] T (16)
after the scheme is adopted, the following beneficial effects are realized:
(1) The invention provides a multi-system coupling integrated control system based on an electric automobile, which designs a layered integrated control structure, realizes multi-objective dynamic decoupling and coordination distribution of safety-power-energy conservation of the electric automobile, and improves the stability performance and the energy utilization efficiency of the whole automobile.
(2) In order to solve the coupling and restriction problems of a driving system and a steering system in an electric automobile, a driving/steering integrated control algorithm based on an MPC theory is provided, the integrated control of yaw stability and motor torque distribution of the whole automobile is realized, and the track tracking capacity and stability performance of the whole automobile are improved.
(3) On the basis of guaranteeing the safety of the vehicle, in order to improve the running economy of the electric vehicle, a motor energy consumption model and a tire energy consumption model are built based on data and mechanisms, driving saturation and safety constraint conditions are considered, a torque energy efficiency optimization controller based on control distribution is designed, torque commands of a bottom execution motor are optimized and output, electricity consumption is reduced, and the energy utilization rate of the whole vehicle is improved.
Drawings
Fig. 1 (a) is a graph of vehicle speed in accordance with an embodiment of the present invention.
Fig. 1 (b) is an acceleration graph of an embodiment of the present invention.
Fig. 1 (c) is a graph of inter-vehicle spacing according to an embodiment of the present invention.
Fig. 2 (a) is a graph of vehicle speed error during a cruise condition according to an embodiment of the present invention US 06.
Fig. 2 (b) is a graph of the inter-vehicle distance error under the cruise condition of the embodiment US06 of the present invention.
FIG. 3 is a graph showing the variation of the air resistance coefficient of vehicles LV, SV and FV under the circulation condition of US06 according to the embodiment of the invention.
FIG. 4 is a graph showing the change in air resistance energy saving power number of vehicles LV, SV and FV under the cycle condition of US06 in accordance with the embodiment of the present invention.
Fig. 5 (a) is a vehicle track condition diagram according to an embodiment of the present invention.
Fig. 5 (b) is a graph of the lateral acceleration response of a vehicle in accordance with an embodiment of the present invention.
Fig. 5 (c) is a response chart of the centroid slip angle of the vehicle according to the embodiment of the present invention.
Fig. 5 (d) is a graph showing the yaw rate response of the vehicle according to the embodiment of the present invention.
Fig. 5 (e) is a state diagram of the front wheel steering angle with time calculated by the driving/steering integrated controller according to the embodiment of the present invention.
Fig. 5 (f) is a state diagram of the rear wheel steering angle with time calculated by the driving/steering integrated controller according to the embodiment of the present invention.
FIG. 6 (a) is a diagram illustrating a vehicle trajectory under the control of a drive/steering integrated controller under four simulated conditions according to an embodiment of the present invention.
Fig. 6 (b) is a graph of lateral acceleration under the control of the integrated drive/steering controller according to an embodiment of the present invention.
Fig. 6 (c) is a centroid slip angle map under the control of the drive/steering integrated controller according to an embodiment of the present invention.
Fig. 6 (d) is a yaw rate map under the control of the drive/steering integrated controller according to the embodiment of the present invention.
Fig. 6 (e) is a graph showing the change of the front wheel steering angle under the control of the driving/steering integrated controller under four simulated conditions according to the embodiment of the present invention.
Fig. 6 (f) is a graph showing the change of the rear wheel steering angle under the control of the driving/steering integrated controller under four simulated conditions according to the embodiment of the present invention.
FIG. 7 is a graph showing the response of vehicle speed under NEDC cycle conditions in accordance with an embodiment of the present invention.
FIG. 8 is a graph showing the response of the vehicle speed under the FTP75 cycle condition according to an embodiment of the present invention.
Fig. 9 (a) is a graph showing a time change in the left front wheel torque according to the embodiment of the present invention.
Fig. 9 (b) is a graph showing a time change in the torque of the right front wheel according to the embodiment of the present invention.
Fig. 10 (a) is an energy consumption curve of the output power of the motor according to the embodiment of the present invention.
Fig. 10 (b) is a tire energy consumption power curve according to an embodiment of the present invention.
Fig. 11 is a schematic diagram of the operation of the finite state machine controller.
Fig. 12 is a layered integrated control structure diagram.
Detailed Description
The following is a further detailed description of the embodiments:
an example is substantially as shown in figures 1-12:
the multi-system coupling integrated control system based on the electric automobile comprises a layered integrated control structure, wherein the layered integrated control structure comprises an upper layer, a middle layer and a lower layer; the upper layer is an Eco-CACC controller, the Eco-CACC controller comprises a following mode, a cruising mode and a lane changing mode, and the Eco-CACC controller switches the driving mode through a finite state machine controller based on the minimum safe vehicle distance; the Eco-CACC controller is used for providing a control requirement of a target vehicle speed and a target path for the middle layer based on the safety index, the economical index, the comfort index, the following index and the constraint condition of the actuator; the middle layer is a driving/steering integrated controller which is used for providing control instructions of wheel rotation angles, total driving torque and additional yaw moment for the lower layer by taking track tracking as a main target; the lower layer is a torque energy efficiency optimizing controller which is used for controlling and distributing motor energy and tire slip loss according to the torque requirement and sending motor torque and wheel rotation angle signals to an actuator.
The expression of the minimum safe vehicle distance is as follows:
wherein SV is a target vehicle, LV is a vehicle following SV on a current lane, and FV is a vehicle following SV on the current lane; PLV is the vehicle that SV is about to follow on the target lane, PFV is the vehicle behind SV on the target lane; d, d LV 、d FV 、d PLV 、d PFV Actual distance from SV to LV, FV, PLV, PFV, τ, respectively THWLV 、τ THWFV 、τ THWPLV 、τ THWPFV Safe inter-vehicle hours, τ, of SV to LV, FV, PLV, PFV respectively TTCLV 、τ TTCPV 、τ TTCPLV 、τ TTCPFV Collision times, v, of SV to LV, FV, PLV, PFV respectively SV 、v LV 、v FV 、v FLV 、v PFV And a running speed of each SV, LV, FV, PLV, PFV.
The calculation formula of the safety index is as follows:
wherein e s_m i n And e s_max E is the minimum value and the maximum value of the vehicle distance error respectively v_m i n And e v_max Respectively the minimum value and the maximum value of the vehicle speed error;
the calculation formula of the economic index is as follows:
wherein C is D (Δs i ) Is the air resistance coefficient;
the comfort index is calculated as follows:
wherein J is com To predict comfort index in time domain, w com Is a comfort index weight coefficient;
the following index is calculated as follows:
J tra w s w v
the vehicle following performance index in the prediction time domain is a vehicle distance error weight coefficient and a vehicle speed error weight coefficient.
The design of the drive/steering integrated controller comprises the following steps:
s1, determining a layered driving/steering integrated control frame;
s2, designing a driving/steering integrated control algorithm based on an MPC theory.
Further, the design of the torque energy efficiency optimization controller comprises the following steps:
s1, building a motor energy consumption model and a tire energy consumption model;
s2, establishing a moment distribution model, and designing a moment control distribution method based on constraint optimization;
the formula of the energy consumption model is as follows:
wherein P is drive,i And P regen,i (i=fl, fr, rl, rr) is the drive output power and the brake energy feedback power of the in-wheel motor, respectively;η drive,i and eta regen,i The driving output efficiency and the braking energy feedback efficiency of the hub motor are respectively.
The formula of the tire energy consumption model is as follows:
P tyre ＝P tyre_lon +P tyre_lat (9)
wherein X is si And Y si Longitudinal and lateral sliding forces of the sliding region at the tire grounding mark, u slide_xi And u slide_yi Respectively the longitudinal and lateral sliding speeds, X of the tyre Ti And Y Ti Longitudinal and lateral forces calculated by the tire model, respectively.
Further, the moment distribution model formula is as follows:
ΔF＝[ΔF tfl ΔF tfr ΔF trl ΔF trr ΔF sfl ΔF sfr ΔF srl ΔF srr ] T (11)
B M ＝[0 0 ΔM z ] T (12)
wherein m is the vehicle servicing mass, v x V is the longitudinal speed of the vehicle y For the lateral velocity at the centroid, r is the yaw rate of the vehicle about the z-axis, F xi For the wheel-to-ground longitudinal force of the vehicle, F yi I=fl, fr, rl, rr denote the left front wheel, right front wheel, left rear wheel and right rear wheel, respectively, I for the wheel-ground side force of the vehicle z For yaw moment of inertia of the vehicle, l f For the wheelbase of the front axle of the vehicle, l r Is the rear of the vehicleShaft-to-shaft distance, b f B, for the track of the front axle of the vehicle r Delta for the rear wheelbase of the vehicle i Is the steering wheel angle.
The torque control distribution method based on constraint optimization has the following formula:
wherein alpha, beta and gamma are weight factors of motor energy consumption, target torque tracking and tire energy consumption penalty functions respectively, B opt 、U opt 、U des 、a i 、b i And are each represented by the following formula:
U opt ＝[F tfl F tfr F trl F trr ] T (15)
U des ＝[T t_total ΔM z ] T (16)
the specific implementation process is as follows:
simulation verification is carried out on the Eco-CACC controller:
a collaborative driving system simulation platform comprising a car following system model and an electric car model is created in MATLAB simulation software, and each car in the system can exchange information with surrounding cars through a wireless communication technology; in order to simulate the state of the vehicle queue during high-speed running, the US06 circulation working condition is selected as the verification working condition, and the vehicle queue has the characteristics of high speed, high acceleration, frequent vehicle speed fluctuation, multiple starting and the like.
During simulation verification, the vehicle runs on a straight and flat road, the speed change of LV in the queue is set as US06 speed time course, and the parameters of the cooperative driving control system are shown in Table 1:
TABLE 1
The Eco-CACC controller first meets the requirements of the following performance and safety of the cruise control, as shown in fig. 1 (a) and fig. 1 (b), the vehicles follow each other at reasonable speed and acceleration, and the speed and acceleration curves of the following vehicles SV and LV are smoother compared to the preceding vehicle LV. The smooth speed and acceleration track can reduce unnecessary driving and braking actions of vehicles in the train, so that the economy and comfort of the vehicles are improved. As shown in fig. 1 (c), it can be seen that realizing the safe distance control can adapt to the actual driving conditions such as frequent driving, braking, etc.
As shown in fig. 3 and 4, when the vehicle is traveling at a high speed, the vehicle air resistance coefficient increases as the inter-vehicle distance increases to ensure the safety of the vehicle. The invention assumes that the original air resistance coefficient of the vehicle is 0.35, the air resistance coefficients of three vehicles in the queue are all reduced, and the air resistance coefficient of the vehicle at the tail of the queue is changed the largest.
As the air resistance coefficient decreases, the energy loss of the vehicle due to air resistance decreases, and as shown in fig. 4, the energy saving efficiency of FV is highest, SV times, LV is smallest; the energy savings from reduced air resistance for LV, SV and FV were 58.6kJ, 457.9kJ and 799.6kJ, respectively, throughout the US06 cycle.
Simulation verification is carried out on the driving/steering integrated controller:
the simulation selects the lane change steering movement as a verification working condition, and the output response of the control system is matched with an AFS systemComparing the output responses of the systems; wherein the simulation run time is set to t=10s, and the sampling step size is set to T s =1 ms, weighting parameters of the controller, prediction time domain, control time domain, etc., as shown in table 2:
TABLE 2
Under the double-lane-change working condition, the electric automobile runs at an initial speed of 20m/s, a driver starts to control the steering lane change of the automobile after 2 seconds, and the adhesion coefficient between wheels and the ground is 0.65 on the assumption that the road surface is flat and has no gradient.
As shown in fig. 5 (a), the driving/steering integrated controller provided by the invention has better track tracking performance than the AFS controller; as shown in fig. 5 (b), 5 (c) and 5 (d), it can be seen that the lateral acceleration peak value, the centroid side deviation angle and the yaw rate of the AFS controller are all greater than those of the driving/steering integrated controller, which indicates that the driving/steering integrated controller can better reduce overshoot and jitter in severe steering, effectively reduce the yaw rate and centroid side deviation angle, and improve the stability in emergency steering. As shown in fig. 5 (e) and 5 (f), it can be seen that the driving/steering integrated controller cooperates with the front wheel steering by the rear wheel active steering, thereby reducing the front wheel steering angle value.
In order to verify the robustness of the proposed MPC-based integrated controller, electric vehicle simulation verification is performed under different longitudinal speeds and road conditions, as shown in fig. 6 (a), the proposed driving/steering integrated controller can better track the target track under four working conditions; further, fig. 6 (e) and 6 (f) depict the front wheel rotation angle and the rear wheel rotation angle under four conditions, and it can be seen from the figures that both the front and rear steering angles increase with an increase in the longitudinal speed and decrease with an increase in the road friction coefficient. Under low friction coefficient and high speed conditions, the vehicle must have a large steering angle to obtain a larger lateral tire force to ensure steering stability of the vehicle. In addition, under the low-speed working condition, the directions of the front wheel steering angle and the rear wheel steering angle of the vehicle are opposite; under the high-speed working condition, the directions of the front wheel steering angle and the rear wheel steering angle of the vehicle are the same, so that the vehicle has smaller turning radius at low speed, and the stability of the vehicle can be ensured at high speed. The above results indicate that the proposed drive/steering integrated controller has a very strong robustness.
Simulation verification is carried out on the torque energy efficiency optimization controller:
a high-precision electric automobile simulation platform is built in MATLAB simulation software, and NEDC (New electric vehicle) circulation working condition and FTP75 circulation working condition are selected to serve as verification working conditions of a torque optimization controller, wherein the NEDC circulation working condition and the FTP75 circulation working condition can reflect acceleration, deceleration and constant-speed operation conditions in the driving process of a vehicle. And selecting a US06 circulation working condition and an overtaking working condition to verify the proposed multi-system coupling integrated control strategy, wherein the US06 circulation working condition and the overtaking working condition are respectively used for verifying the effectiveness of the algorithm when the vehicle runs at a high speed and turns, and the vehicle runs on a straight flat road surface under the verification working condition, wherein the ground attachment coefficient is 0.85, and the gradient is 0.
As shown in fig. 7 and 8, the speed tracking error of both control strategies is very small, the standard deviation of the average distributed speed tracking error is 0.0136m/s, and the standard deviation of the optimal distributed speed tracking error is 0.0137m/s, which shows that the effect of different distribution strategies on the tracking control of the vehicle speed is very small.
As shown in fig. 9 (a) and 9 (b), during acceleration, the optimal allocation optimization strategy allocates most or even all of the torque demand to several but not all of the wheels, ensuring that most of the electric machine operates in a high efficiency zone; in the deceleration process, the front wheel motor participates in wheel braking, so that the rear wheel obtains more braking torque requirements to realize energy recovery maximization; when the total torque demand is low, the energy efficiency of the whole vehicle system can be improved by distributing most or even all of the torque demand to the wheel motor bearing large vertical load.
As can be seen from fig. 10 (a) and 10 (b), the motor output power of the two dispensing strategies is nearly identical during the constant speed phase; in the acceleration stage, the optimal allocation optimization strategy enables most of driving motors to work in a high-efficiency area, so that the output power of the motors is smaller; in the deceleration phase, the energy consumption curve drops significantly due to the participation of the motor in the regenerative braking. From fig. 10 (b), it can be seen that the optimal allocation optimization strategy reduces the tire slip loss compared with the average allocation, and improves the energy utilization efficiency of the whole vehicle. These results clearly demonstrate that the proposed optimal allocation strategy can effectively enable the drive system to operate in a high efficiency area to improve the overall energy efficiency of the vehicle.
The foregoing is merely an embodiment of the present invention, and a specific structure and characteristics of common knowledge in the art, which are well known in the scheme, are not described herein, so that a person of ordinary skill in the art knows all the prior art in the application day or before the priority date of the present invention, and can know all the prior art in the field, and have the capability of applying the conventional experimental means before the date, so that a person of ordinary skill in the art can complete and implement the present embodiment in combination with his own capability in the light of the present application, and some typical known structures or known methods should not be an obstacle for a person of ordinary skill in the art to implement the present application. It should be noted that modifications and improvements can be made by those skilled in the art without departing from the structure of the present invention, and these should also be considered as the scope of the present invention, which does not affect the effect of the implementation of the present invention and the utility of the patent. The protection scope of the present application shall be subject to the content of the claims, and the description of the specific embodiments and the like in the specification can be used for explaining the content of the claims.
Claims (9)
1. Multisystem coupling integrated control system based on electric automobile, its characterized in that: the integrated control structure comprises an upper layer, a middle layer and a lower layer;
the upper layer is an Eco-CACC controller, the Eco-CACC controller comprises a following mode, a cruising mode and a lane changing mode, and the Eco-CACC controller switches the driving mode through a finite state machine controller based on the minimum safe vehicle distance; the Eco-CACC controller is used for providing a control requirement of a target vehicle speed and a target path for the middle layer based on the safety index, the economical index, the comfort index, the following index and the constraint condition of the actuator;
the middle layer is a driving/steering integrated controller which is used for providing control instructions of wheel rotation angles, total driving torque and additional yaw moment for the lower layer by taking track tracking as a main target;
the lower layer is a torque energy efficiency optimizing controller which is used for controlling and distributing motor energy and tire slip loss according to the torque requirement and sending motor torque and wheel rotation angle signals to an actuator.
2. The electric-vehicle-based multi-system coupling integrated-control system of claim 1, wherein: the expression of the minimum safe vehicle distance is as follows:
wherein SV is a target vehicle, LV is a vehicle following SV on a current lane, and FV is a vehicle following SV on the current lane; PLV is the vehicle that SV is about to follow on the target lane, PFV is the vehicle behind SV on the target lane; d, d LV 、d FV 、d PLV 、d PFV Actual distance from SV to LV, FV, PLV, PFV, τ, respectively THWLV 、τ THWFV 、τ THWPLV 、τ THWPFV Safe inter-vehicle hours, τ, of SV to LV, FV, PLV, PFV respectively TTCLV 、τ TTCPV 、τ TTCPLV 、τ TTCPFV Collision times, v, of SV to LV, FV, PLV, PFV respectively SV 、v LV 、v FV 、v FLV 、v PFV And a running speed of each SV, LV, FV, PLV, PFV.
3. The electric-vehicle-based multisystem coupling integrated-control system of claim 2, wherein: the calculation formula of the safety index is as follows:
wherein e s_min And e s_max E is the minimum value and the maximum value of the vehicle distance error respectively v_min And e v_max Respectively the minimum value and the maximum value of the vehicle speed error;
the calculation formula of the economic index is as follows:
wherein C is D (Δs i ) Is the air resistance coefficient;
the comfort index is calculated as follows:
wherein J is com To predict comfort index in time domain, w com Is a comfort index weight coefficient;
the following index is calculated as follows:
J tra w s w v
the vehicle following performance index in the prediction time domain is a vehicle distance error weight coefficient and a vehicle speed error weight coefficient.
4. The electric-vehicle-based multisystem coupling integrated-control system of claim 3, wherein: the design of the drive/steering integrated controller comprises the following steps:
s1, determining a layered driving/steering integrated control frame;
s2, designing a driving/steering integrated control algorithm based on an MPC theory.
5. The electric-vehicle-based multi-system coupling integrated-control system of claim 4, wherein: the design of the torque energy efficiency optimization controller comprises the following steps:
s1, building a motor energy consumption model and a tire energy consumption model;
s2, a moment distribution model is established, and a moment control distribution method based on constraint optimization is designed.
6. The electric-vehicle-based multi-system coupling integrated-control system of claim 5, wherein: the formula of the energy consumption model is as follows:
wherein P is drive,i And P regen,i (i=fl, fr, rl, rr) is the drive output power and the brake energy feedback power of the in-wheel motor, respectively; η (eta) drive,i And eta regen,i The driving output efficiency and the braking energy feedback efficiency of the hub motor are respectively.
7. The electric-vehicle-based multi-system coupling integrated-control system of claim 6, wherein: the formula of the tire energy consumption model is as follows:
P tyre ＝P tyre_lon +P tyre_lat (9)
wherein X is si And Y si Longitudinal and lateral sliding forces of the sliding region at the tire grounding mark, u slide_xi And u slide_yi Respectively the longitudinal and lateral sliding speeds, X of the tyre Ti And Y Ti Longitudinal and lateral forces calculated by the tire model, respectively.
8. The electric-vehicle-based multisystem coupling integrated-control system of claim 7, wherein: the moment distribution model formula is as follows:
A M ·ΔF＝B M (10)
ΔF＝[ΔF tfl ΔF tfr ΔF trl ΔF trr ΔF sfl ΔF sfr ΔF srl ΔF srr ] T (11)
B M ＝[0 0 ΔM z ] T (12)
wherein m is the vehicle servicing mass, v x V is the longitudinal speed of the vehicle y For the lateral velocity at the centroid, r is the yaw rate of the vehicle about the z-axis, F xi For the wheel-to-ground longitudinal force of the vehicle, F yi I=fl, fr, rl, rr denote the left front wheel, right front wheel, left rear wheel and right rear wheel, respectively, I for the wheel-ground side force of the vehicle z For yaw moment of inertia of the vehicle, l f For the wheelbase of the front axle of the vehicle, l r B, for the rear axle distance of the vehicle f B, for the track of the front axle of the vehicle r Delta for the rear wheelbase of the vehicle i Is the steering wheel angle.
9. The electric-vehicle-based multisystem coupling integrated-control system of claim 8, wherein: the torque control distribution method based on constraint optimization has the following formula:
wherein, alpha, beta and gamma are motors respectivelyWeight factors of energy consumption, target torque tracking and tire energy consumption penalty functions, B opt 、U opt 、U des 、a i 、b i And are each represented by the following formula:
U opt ＝[F tfl F tfr F trl F trr ] T (15)
U des ＝[T t_total ΔM z ] T (16)
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