Electric articulated vehicle energy recovery control method and system considering drifting risk

Abstract

The invention discloses an electric articulated vehicle energy recovery control method and system considering a drifting risk. The method comprises the following steps that an energy recovery nonlinear predictive control model of an electric articulated vehicle is built based on a symmetric simplification strategy; the balance safety, the energy recovery rate and the braking performance serve as consideration factors to construct a multi-dimensional optimization objective function containing drifting suppression optimization and energy recovery optimization; calling an energy recovery nonlinear predictive control model based on the multi-dimensional optimization objective function to solve an optimal control sequence, and calling a control element of the electric articulated vehicle to perform vehicle control according to a first element of the optimal control sequence; an energy recovery nonlinear predictive control model is called along with state updating of the electric articulated vehicle to conduct cyclic predictive control; according to the method, dynamic changes in the braking process can be accurately captured, potential safety hazards such as drifting can be effectively estimated and avoided, and the stability and safety of the braking process are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of dynamic electric energy recovery braking, and particularly to an energy recovery control method and system for an electric articulated vehicle considering the risk of jackknifing. Background Art

[0002] For articulated vehicles, especially electric articulated vehicles, the braking force distribution strategy during braking is crucial; an articulated vehicle consists of a tractor and a trailer, which are connected by an articulation device and travel as a whole; during braking, the braking force distribution between the tractor and the trailer, as well as the coordination between electric braking and mechanical braking, are all key factors affecting the braking performance and stability of the vehicle; if the braking force is not properly distributed, a huge internal force will be borne at the articulation between the front and rear vehicles, which will not only accelerate the wear of the vehicle, but also generate parasitic power, reducing the braking efficiency and energy recovery rate of the vehicle; more seriously, when the braking force distribution effect is extremely poor, due to the inertia of the trailer and the action of ground disturbances, the trailer is extremely prone to jackknifing, which will not only endanger driving safety, but also pose a potential threat to the road and other vehicles.

[0003] Especially in electric articulated vehicles, due to the presence of the trailer, the energy recovery problem becomes more complex; during braking, it is necessary to ensure that the vehicle speed decreases rapidly according to the braking demand, improve the energy recovery rate as much as possible, and at the same time ensure the balance of each axle and the front and rear vehicle bodies to prevent the occurrence of adverse phenomena such as jackknifing; this is a typical multi-objective optimization problem that requires comprehensive consideration of multiple factors to formulate a reasonable braking force distribution strategy; however, in previous energy recovery control research, researchers often started from a single perspective, such as the energy recovery rate or anti-lock performance, and ignored the influence of steering motion on the braking performance of the vehicle; they usually assumed that the driving directions of the front and rear vehicles were the same, thus simplifying the lateral model, which made it impossible for them to quantitatively and accurately evaluate the risk of jackknifing, nor could they effectively suppress the risk of jackknifing while recovering energy; although some studies have explored the suppression of braking jackknifing, these studies mainly focus on the classification and control optimization of the folding jackknifing state in the case of wheel lock-up, and do not deeply explore how to improve the energy recovery rate while preventing jackknifing; in addition, for the braking control of the tractor and trailer under steering conditions, the existing research is also not deep and comprehensive enough, specifically as follows: Chinese Patent CN113978451B discloses a vehicle swing warning method, device and computer-readable storage medium, which can classify and warn the swing risk of the trailer, but cannot give a specific control method.

[0004] Chinese patent CN116118518A discloses a method for controlling the lateral stability of a distributed electric drive semi-trailer vehicle train. The patent uses an advanced control algorithm to control the lateral stability of the vehicle, but still uses a proportional distribution method when distributing the braking force, which is unable to prevent tailspin while taking into account the improvement of energy recovery rate.

[0005] Chinese patent CN118387063A discloses a method and system for controlling the braking consistency of a main vehicle and a trailer in a tractor. The patent realizes the braking consistency control of the tractor and the trailer, but does not discuss how to optimize the energy recovery rate.

[0006] Chinese patent CN117901818A discloses a braking control method, device, equipment and storage medium for a traction train. The patent calculates the braking force sequence and corrects the forward amount by collecting braking status information; however, the solution is only applicable to mechanical braking, not to energy recovery of electric semi-trailers, and does not consider the energy recovery problem during steering.

[0007] In addition, some papers in this field often greatly simplify the vehicle dynamics model when proposing trailer lateral stability control methods, ignore the coupling relationship between various components and various spatial degrees of freedom, or ignore the influence of longitudinal motion on lateral motion; this makes their control methods may not achieve the expected results in practical applications, especially during braking when the vehicle's longitudinal speed changes greatly.

[0008] In summary, in the research on energy recovery and tail-swing suppression of electric articulated vehicles in this field, although the existing technical solutions have proposed some early warning methods and control strategies, most of them have limitations. Summary of the invention

[0009] The object of the present invention is to provide an energy recovery control method and system for an electric articulated vehicle taking into account the risk of drifting, thereby solving all or one of the above-mentioned problems existing in the prior art.

[0010] In order to solve the above technical problems, the specific technical solutions of the present invention are as follows: In one aspect, the present invention provides an energy recovery control method for an electric articulated vehicle considering the risk of drifting, comprising the following steps: A nonlinear predictive control model for energy recovery of electric articulated vehicles is built based on a symmetric simplification strategy; Taking the balance safety, energy recovery rate and braking performance into consideration, a multi-dimensional optimization objective function including drift suppression optimization and energy recovery optimization is constructed; Call the energy recovery non - linear predictive control model based on the multi - dimensional optimization objective function to solve the optimal control sequence, and call the control elements of the electric articulated vehicle to control the vehicle according to the first element of the optimal control sequence; along with the update of the state of the electric articulated vehicle, call the energy recovery non - linear predictive control model for cyclic predictive control.

[0011] As an improved scheme, the symmetric simplification strategy includes: Take the vehicle steering process and the vehicle lateral movement as consideration factors, and establish a braking dynamics model of the electric articulated vehicle; Simplify the braking dynamics model into a single - track model on the longitudinal symmetry plane of the vehicle body.

[0012] As an improved scheme, the building of the energy recovery non - linear predictive control model of the electric articulated vehicle based on the symmetric simplification strategy further includes: Determine the kinematic and mechanical constraint relationships of the longitudinal movement, lateral movement, and yaw movement between the tractor and the trailer in the electric articulated vehicle at the articulation point of the electric articulated vehicle; Based on the kinematic and mechanical constraint relationships, transform the symmetrically simplified braking dynamics model into a non - linear state - space equation, and use the non - linear state - space equation as the energy recovery non - linear predictive control model.

[0013] As an improved scheme, the construction of the multi - dimensional optimization objective function that takes balance safety, energy recovery rate, and braking performance as consideration factors and includes over - steer suppression optimization and energy recovery optimization further includes: Define the expected braking vehicle speed optimization objective, and set a first objective function for suppressing the lateral movement between the tractor and the trailer based on the expected braking vehicle speed optimization objective; Define the change quantity fluctuation minimization optimization objective, and set a second objective function for suppressing the high input fluctuation span under steady - state conditions based on the change quantity fluctuation minimization optimization objective; Define the energy recovery rate optimization objective, and set a third objective function for improving the energy recovery rate while suppressing the over - steer risk based on the energy recovery rate optimization objective; Define the braking over - steer suppression optimization objective, and set a fourth objective function for suppressing vehicle braking over - steer based on the braking over - steer suppression optimization objective; Define the calculation process optimization objective, and set a terminal cost function for optimizing the convergence of the calculation process based on the calculation process optimization objective; Integrate the first objective function, the second objective function, the third objective function, the fourth objective function, and the terminal cost function to obtain the multi - dimensional optimization objective function.

[0014] As an improved solution, the expected braking speed optimization objective includes: Controlling the braking speeds of the tractor and the trailer to be consistent with the expected braking speed; The variation fluctuation minimization optimization objective includes: Reducing the fluctuation of the input variation of the control system of the electric articulated vehicle; The energy recovery rate optimization objective includes: Controlling the tractor to optimize the distribution of electric braking force and mechanical braking force; The braking fishtail suppression optimization objective includes: Controlling the articulation angle between the tractor and the trailer to be close to and not exceed the steady-state articulation angle.

[0015] As an improved solution, the calculation process optimization objective includes: Aiming at improving the convergence speed, solving constraints are imposed on the weighted summation process of the first objective function, the second objective function, the third objective function, and the fourth objective function.

[0016] As an improved solution, when calling the energy recovery nonlinear predictive control model based on the multi-dimensional optimization objective function to solve the optimal control sequence, it further includes: Optimizing the solution process based on a numerical optimization method.

[0017] As an improved solution, the numerical optimization method includes: sequential quadratic programming method, interior point method, numerical continuation method, and forward difference generalized minimum residual method.

[0018] As an improved solution, when the state update of the electric articulated vehicle is accompanied by calling the energy recovery nonlinear predictive control model for cyclic predictive control, it further includes: Cyclically executing: in response to the state of the electric articulated vehicle being updated to the next moment, using the updated state as the new initial input of the energy recovery nonlinear predictive control model to solve the optimization problem of the next moment.

[0019] On the other hand, the present invention also provides an energy recovery control system for an electric articulated vehicle considering fishtail risk, including: A model optimization module for: building an energy recovery nonlinear predictive control model of the electric articulated vehicle based on a symmetric simplification strategy; A function calculation module for: constructing a multi-dimensional optimization objective function including fishtail suppression optimization and energy recovery optimization by taking balance of safety, energy recovery rate, and braking performance as consideration factors; An optimization control module, configured to: call the energy recovery nonlinear predictive control model based on the multi-dimensional optimization objective function to solve the optimal control sequence, and call the control elements of the electric articulated vehicle to perform vehicle control according to the first element of the optimal control sequence; and call the energy recovery nonlinear predictive control model for cyclic predictive control along with the update of the state of the electric articulated vehicle.

[0020] The beneficial effects of the technical solution of the present invention are as follows: 1. The energy recovery control method for an electric articulated vehicle considering the risk of fishtailing in the present invention accurately captures the dynamic changes during the braking process by constructing a dynamic prediction model for the braking process of the electric articulated vehicle, effectively estimates and avoids potential safety hazards such as fishtailing, and ensures the stability and safety of the braking process.

[0021] 2. The energy recovery control method for an electric articulated vehicle considering the risk of fishtailing in the present invention rationally distributes the braking force of each braking element based on the nonlinear model predictive control algorithm with terminal state constraints, improves the proportion of electric braking power, maximizes the braking energy recovery rate, and improves the overall vehicle energy utilization efficiency.

[0022] 3. The energy recovery control method for an electric articulated vehicle considering the risk of fishtailing in the present invention comprehensively considers the relationship among braking safety, energy recovery rate, and braking performance by designing a multi-dimensional optimization objective function, and achieves dynamic balance under different braking conditions through a multi-objective optimization method, which not only meets the safety performance requirements but also achieves the braking target while maximizing energy recovery.

[0023] 4. The energy recovery control method for an electric articulated vehicle considering the risk of fishtailing in the present invention ensures that the nonlinear model predictive control algorithm is always solved under complex nonlinear constraints by introducing a terminal state cost function, guarantees the engineering feasibility of the control theory, and makes the algorithm operable in practical applications.

[0024] 5. The energy recovery control system for an electric articulated vehicle considering the risk of fishtailing in the present invention can realize the energy recovery control method for an electric articulated vehicle considering the risk of fishtailing in the present invention through the mutual cooperation of system modules. Description of the Drawings

[0025] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the specific embodiments or the prior art. Obviously, the following drawings are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0026] Figure 1 It is a schematic flow chart of the energy recovery control method for an electric articulated vehicle considering the risk of fishtailing described in Embodiment 1 of the present invention; Figure 2 It is a schematic configuration diagram of the power and braking systems of a typical articulated vehicle in the energy recovery control method for an electric articulated vehicle considering the risk of fishtailing described in Embodiment 1 of the present invention; Figure 3 It is a schematic diagram of the basic motion relationship between the tractor and the trailer in the energy recovery control method for an electric articulated vehicle considering the risk of fishtailing described in Embodiment 1 of the present invention; Figure 4 It is a schematic diagram of the architecture expression of the dynamic model of an electric articulated vehicle in the energy recovery control method for an electric articulated vehicle considering the risk of fishtailing described in Embodiment 1 of the present invention; Figure 5 It is a schematic logic diagram of the energy recovery control method for an electric articulated vehicle considering the risk of fishtailing described in Embodiment 1 of the present invention; Figure 6 It is a schematic architecture diagram of the energy recovery control system for an electric articulated vehicle considering the risk of fishtailing described in Embodiment 2 of the present invention. Detailed implementation manners

[0027] The following elaborates on the preferred embodiments of the present invention in conjunction with the accompanying drawings, so that the advantages and features of the present invention can be more easily understood by those skilled in the art, thereby making a clearer and more definite definition of the protection scope of the present invention.

[0028] In the description of the present invention, it should be noted that the embodiments described in the present invention are some embodiments of the present invention, rather than all embodiments; all other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present invention without creative efforts belong to the protection scope of the present invention.

[0029] The terms "first", "second", etc. in the specification and claims of this article and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily need to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances, so that the embodiments described in this article can be implemented in an order other than those illustrated or described here. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, device, product, or equipment that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or are inherent to these processes, methods, products, or equipment.

[0030] In the description of the present invention, it should be noted that an electric articulated vehicle is a heavy vehicle with a novel configuration, consisting of a tractor and a trailer; a configuration diagram of the power and braking systems of a typical electric articulated vehicle is as shown in Figure 2 ; generally, its power system is located in the tractor part, and there are multiple drive motors, usually located in the middle axle and rear axle of the tractor. When the drive motors operate in the regenerative braking mode, mechanical kinetic energy can be converted into electrical energy, and this part of the braking energy can be recycled; generally, there are no drive components in the trailer part. Usually, both the tractor and the trailer are equipped with pneumatic braking systems, which convert mechanical kinetic energy into heat potential energy of friction discs, etc., to reduce the vehicle speed, and this part of the braking energy cannot be recycled; in addition, the braking components of the tractor and the trailer are uniformly controlled by the vehicle control unit; for such configured vehicles, optimizing the design of the braking energy recovery control strategy can effectively improve the energy-saving effect of the whole vehicle.

[0031] Embodiment 1. This embodiment provides an energy recovery control method for an electric articulated vehicle considering the risk of fishtailing, as shown in Figures 1 - 5 ; it includes the following steps: S100. Build a non-linear predictive control model for the energy recovery of an electric articulated vehicle based on the symmetric simplification strategy, specifically as follows: S101. To ensure that the energy recovery rate and braking safety can be effectively improved, not too much model simplification is done in this step. However, to consider the fishtailing problem in the case of energy recovery (i.e., the situation that is likely to occur when the speed directions of the tractor and the trailer are inconsistent), the vehicle steering process and the lateral movement of the vehicle are considered when establishing the braking dynamics model; finally, the semi-trailer dynamics model is simplified to a single-track model on the longitudinal symmetry plane of the vehicle body, and the three axles of the trailer are equivalent to its middle axle, equivalent to a single-wheel trailer located on the middle axle, thus completing the symmetric simplification; based on the above symmetric simplification process, the final braking dynamics model of the articulated vehicle is as follows: ; Where: is the total mass of the tractor; , are respectively the longitudinal force and lateral force received by the th equivalent wheel; is the front wheel steering angle; is the total moment of inertia of the tractor; , , are respectively the distances from each axle of the tractor to the center of mass of the tractor; is the distance from the center of mass of the tractor to the articulation point; , are respectively the longitudinal and lateral components of the hinge force of the trailer on the tractor; is the total mass of the trailer; is the total moment of inertia of the trailer; is the distance from the equivalent wheel of the trailer to the center of mass of the trailer; is the distance from the center of mass of the trailer to the hinge point; , are respectively the longitudinal and lateral components of the hinge force of the tractor on the trailer; is the ground resistance coefficient; is the air resistance coefficient; is the frontal area of the tractor; is the frontal area of the trailer; is the air density.

[0032] It should be noted that: (1) In the initial stage of kinematic analysis in this step, the mutual relationship between the tractor and the trailer is established, as Figure 3 shown; where the tractor and the semi-trailer are regarded as two mutually coupled independent rigid bodies, and the two are coordinated through hinge constraints; among them, , are respectively the centers of mass of the tractor and the trailer; is the hinge point between the tractor and the trailer; , , are respectively the longitudinal speed, lateral speed and yaw angular velocity of the tractor; , , are respectively the longitudinal speed, lateral speed and yaw angular velocity of the trailer.

[0033] (2) The symmetric simplification strategy can not only retain the hinge relationship between the tractor and the trailer, meet the prediction requirements of lateral movement, but also reflect the influence of ground reaction force on vehicle speed through the expression of longitudinal and lateral forces on each axis; through engineering practice, it is found that compared with the complete complex prediction model, the symmetric simplification model of this patent has high enough accuracy, short calculation time and high calculation efficiency.

[0034] S102. Based on the above model, comprehensively considering the constraint characteristics of the articulation between the tractor and the trailer, the kinematic and mechanical constraint relationships existing in the longitudinal movement, lateral movement, and yaw movement between the two at the articulation point are expressed as follows: ; Among them, is the articulation angle between the tractor and the trailer.

[0035] S103. Based on the above constraint relationships, substituting the articulated vehicle braking dynamics model and eliminating variables, it is finally transformed into a nonlinear state-space equation, which is used as a prediction model that can calculate the state at the next moment based on the current state and input, as follows: x(k + 1) = f(x(k), u(k)); Among them, the state vector x is: x = { , , , , }; Among them, the input vector (control vector) u is: u = { }; Among them, are the braking forces of the mechanical braking elements respectively; are the braking forces of the electric braking elements respectively; however, the braking forces of the mechanical braking elements and the electric braking elements are the target objects actually controlled by this method.

[0036] It should be noted that: (1) The electric braking force and the mechanical braking force output by the prediction model should always satisfy their constraint conditions (that is, their minimum values, maximum values, and dynamic change rates are all restricted, and the synthesis of the electric braking force and the mechanical braking force on each axle is also restricted by the ground adhesion conditions).

[0037] (2) During the establishment process of the above prediction model, if the internal articulation force is not easy to eliminate, after iteratively solving the internal articulation force, x(k + 1) is calculated based on x(k) and u(k); in other methods except elimination, implicit expression models such as the TruckSim model or neural network can be used to replace the prediction model to achieve recursive calculation.

[0038] (3) When the subsequent optimization method is selected appropriately, the prediction model does not need to be simplified. A braking process dynamics model can be established according to all drive elements, all axles, and all wheels of the vehicle, and transformed into a prediction model that predicts the state at the next moment from the current state and the current input. The complete method of this patent also supports a complete and complex prediction model.

[0039] S200. Based on the optimization objective of the complex relationship among balance safety, energy recovery rate, and braking performance, construct a multi-dimensional optimization objective function that includes oversteer suppression optimization and energy recovery optimization, as follows: S201. According to the basic principle of model predictive control, define the prediction horizon and control horizon of the braking MPC controller as N (i.e., the maximum number of iterations of the prediction model is N times); S202. Define the optimization objective of the desired braking vehicle speed: that is, control the braking vehicle speed of the tractor and the trailer to be consistent with the desired braking vehicle speed, so that the tractor and the trailer can brake in time according to the desired vehicle speed, and make the gap between the actual vehicle speed of the tractor and the trailer and the desired vehicle speed as small as possible, avoiding lateral movement between the tractor and the trailer. The relevant first objective function is as follows: ; Among them, , are the reference vehicle speeds of the tractor and the trailer respectively, both of which are sequence values and are generated by the driving program or the manipulation signal.

[0040] S203. Define the optimization objective of minimizing the change amount fluctuation: reduce the fluctuation of the input change amount of the vehicle control system (the input of the vehicle system includes the front wheel angle and the longitudinal force of 4 groups of axles, so this change amount is the change amount of the front wheel angle and the braking force of the vehicle), thereby weakening the longitudinal impact degree of the whole vehicle, improving the braking safety to a certain extent, and avoiding the adverse impact of excessive input fluctuation on the braking performance under the steady-state condition. The relevant second objective function is as follows: ; S204. Define the optimization objective of the energy recovery rate: Since the trailer is only equipped with a traditional mechanical braking system and cannot perform energy recovery, this objective only controls the reasonable optimization distribution of the electric braking and mechanical braking of the tractor, thereby improving the energy recovery rate while preventing the trailer from oversteering. The relevant third objective function is as follows: ; Among them, is the total electric braking power, and its calculation formula is: ; Among them, is the total mechanical braking power, and its calculation formula is: ; It should be noted that due to considering the optimization of the braking power, when the value of F is less than 0 (i.e., under the braking condition), F is included in the optimization calculation of the braking energy recovery rate.

[0041] S205, defining a brake-swing suppression optimization objective: controlling the articulation angle to be close to but not exceeding the steady-state articulation angle during steering, thereby preventing the trailer from uncontrollably swinging and folding when the articulation angle is too large. In this objective, the actual articulation angle is made as close as possible to the steady-state articulation angle, thereby preventing the semitrailer from braking and swinging. The related fourth objective function is as follows: ; It should be noted that, given a steering wheel angle, the steady-state steering angle can be calculated based on vehicle kinematics, with the steering centers of the tractor and trailer coinciding. At the steady-state steering angle, the slip wear of the rear wheels of the vehicle is minimal. Although the above optimization objectives do not explicitly specify the yaw angular velocity of the vehicle, , Constraints are performed, but their constraints are implicit in the hinge angle constraints.

[0042] It should be noted that the above four objectives are all process objectives. In each forecast period, the state change effects of the above optimization objectives will be superimposed together for weighted summation calculation, namely: ; in, , , , In order to adjust the weight parameters of the influencing weights of the above four optimization objectives, they are optimized according to the actual vehicle conditions.

[0043] S206, define the optimization goal of the calculation process: set the terminal cost function, use this function as the fifth objective function, and then constrain the solution process of minJ to help the optimization calculation process converge quickly and achieve a stable solution. The terminal cost function is as follows: ; in, Refers to is the initial state, and the x state value after N iterations. This representation method will continue to be used in the future to satisfy , ; is a K-type function, [ : ( is a positive real number), continuous and strictly increasing, satisfying =0]; It should be noted that the terminal cost function is the most critical, because the recursive calculation process of the prediction model of the braking process of articulated vehicles is the process of inferring the state of the next moment from the current state. It is a typical nonlinear complex process. In addition, there are many input and state quantities for articulated vehicles, which makes the optimization solution of minJ extremely difficult. Therefore, an additional terminal cost function is needed to constrain the solution process.

[0044] S207. Optimization objective integration: Based on the above five optimization objectives, the following optimization function for the energy recovery control method of an electric articulated vehicle considering the fishtailing risk is obtained through integration: ; Based on the function q representing the first to fifth objective functions, the above optimization function is expressed as: ; where U is the reachable set of the input vector.

[0045] S300. Further optimize the solution of the discrete optimization problem based on the numerical optimization method, and use the first element in the obtained optimal control sequence as the target control command for each control element of the electric articulated vehicle for control implementation, as follows: S301. The numerical optimization method includes, but is not limited to: sequential quadratic programming (SQP), interior point method (IPM), numerical continuation method, and forward difference generalized minimum residual method; S302. In response to the state of the electric articulated vehicle being updated to the next moment, use the updated state as the new initial input to continue solving the optimization problem at the next moment, and loop in this way to achieve continuous control output during the braking process of the electric articulated vehicle.

[0046] In addition, it should be noted that since the solution result of the optimization problem will be used in the actual control system, the solvability of the optimization problem is equivalent to the stability of the closed-loop system, and the key to the non-linear model predictive control of this patent also lies in its stability. Therefore, the stability is proved through the following steps: For the model predictive control of the electric articulated vehicle, the attraction domain is defined as the set of state points that satisfy the optimization function of all optimization objectives and whose optimization terminal state will automatically enter a certain neighborhood of the origin ; this attraction domain must satisfy the invariant set property, that is, for any point in the attraction domain, its optimization terminal state will automatically enter , and the optimization terminal state of the next moment state will also automatically enter ; therefore, to ensure stability, it is only necessary to satisfy the following conditions: (i) For x ∈ , all Let \(U\) make (x, ) + hold, and there is ∈ ; (ii) At this time, define the minimum value of \(J(u,\) ) as . Assume that the optimal input sequence (belonging to the ideal sequence) is = { ,..., }, and at this time the optimal state prediction trajectory is: = { ,..., }; (iii) If the aforementioned optimization function has a solution and the optimized end state automatically enters , that is, ∈ , then when the system transitions to the next state , take the following feasible control law: ; where is the control law that satisfies ( , ) + , and ; Obviously, the performance index corresponding to this feasible control law satisfies: ; (iii) Therefore, there exists: ; Thus, it can be seen that is a Lyapunov function; therefore, when the rolling control law is adopted, the system is stable.

[0047] It should be noted that the above examples are only for explaining the present invention and should not limit the protection scope of the present invention.

[0048] Example 2. This example provides an energy recovery control system for an electric articulated vehicle considering the risk of fishtailing, based on the same inventive concept as the energy recovery control method for an electric articulated vehicle considering the risk of fishtailing described in Example 1. As shown in Figure 6 , it includes: A model optimization module for: building a non-linear predictive control model for energy recovery of an electric articulated vehicle based on a symmetric simplification strategy; A function calculation module, configured to: construct a multi-dimensional optimization objective function including oversteer suppression optimization and energy recovery optimization by taking balance of safety, energy recovery rate, and braking performance as consideration factors; An optimization control module, configured to: call the energy recovery non-linear predictive control model based on the multi-dimensional optimization objective function to solve an optimal control sequence, and call a control element of an electric articulated vehicle to perform vehicle control according to a first element of the optimal control sequence; and perform loop predictive control by calling the energy recovery non-linear predictive control model along with the update of the state of the electric articulated vehicle.

[0049] Different from the prior art, by adopting the energy recovery control method and system for an electric articulated vehicle considering oversteer risk in this application, a dynamic prediction model for the braking process of the electric articulated vehicle is constructed to accurately capture dynamic changes during the braking process, effectively estimate and avoid potential safety hazards such as oversteer, and ensure the stability and safety of the braking process.

[0050] It should be understood that in various embodiments of this article, the magnitudes of the serial numbers of the above processes do not mean the order of execution. The order of execution of each process should be determined according to its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of this article.

[0051] It should also be understood that in the embodiments of this article, the term "and / or" is only a description of the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B may represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this article generally represents an "or" relationship between the associated objects before and after.

[0052] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in this article can be implemented by electronic hardware, computer software, or a combination of the two. To clearly illustrate the interchangeability of hardware and software, the components and steps of each example have been generally described according to their functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this article.

[0053] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments, and will not be described in detail here.

[0054] In several embodiments provided in this document, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Additionally, the displayed or discussed couplings or direct couplings or communication connections to each other can be indirect couplings or communication connections through some interfaces, devices, or units, and can also be in the form of electrical, mechanical, or other connections.

[0055] The units described as separate components may or may not be physically separated. The components shown as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the objectives of the solution of the embodiments in this document.

[0056] Furthermore, in each embodiment of this document, the functional units can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

[0057] If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the essence of the technical solution in this document, or the part that contributes to the prior art, or all or part of this technical solution can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of this document. The aforementioned storage medium includes: USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs, etc., which can store program codes.

[0058] The above are only the embodiments of the present invention and do not limit the patent scope of the present invention. Any equivalent structural or equivalent process transformations made using the content of the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, are equally included in the patent protection scope of the present invention.

Claims

1. An energy recovery control method for an electric articulated vehicle considering the risk of tail swing, characterized in that: The following steps are involved: A nonlinear predictive control model for energy recovery of electric articulated vehicles is built based on a symmetric simplification strategy; Taking the balance safety, energy recovery rate and braking performance into consideration, a multi-dimensional optimization objective function including drift suppression optimization and energy recovery optimization is constructed; Based on the multi-dimensional optimization objective function, the energy recovery nonlinear predictive control model is called to solve the optimal control sequence, and the control element of the electric articulated vehicle is called to control the vehicle according to the first element of the optimal control sequence; along with the state update of the electric articulated vehicle, the energy recovery nonlinear predictive control model is called to perform cyclic predictive control.

2. The energy recovery control method for electric articulated vehicles considering the risk of tail-swinging according to claim 1, characterized in that: The symmetric simplification strategy includes: The braking dynamics model of electric articulated vehicles is established by taking vehicle steering process and vehicle lateral motion into consideration. The braking dynamics model is simplified to a single-track model located on the longitudinal symmetry plane of the vehicle body.

3. The energy recovery control method for an electric articulated vehicle considering the risk of drifting according to claim 2, characterized in that: The energy recovery nonlinear predictive control model for electric articulated vehicles based on the symmetric simplification strategy further includes: Determine the kinematic and mechanical constraints of the longitudinal, lateral and yaw motions of the tractor and trailer in the electric articulated vehicle at the articulation point of the electric articulated vehicle; Based on the kinematic and mechanical constraint relationships, the symmetrically simplified braking dynamics model is transformed into a nonlinear state space equation, and the nonlinear state space equation is used as the energy recovery nonlinear predictive control model.

4. The energy recovery control method for electric articulated vehicles considering the risk of drifting according to claim 1, characterized in that: The multi-dimensional optimization objective function including drift suppression optimization and energy recovery optimization is constructed by taking into account the balance safety, energy recovery rate and braking performance, further comprising: defining an expected braking speed optimization target, and setting a first objective function for suppressing lateral movement between the tractor and the trailer based on the expected braking speed optimization target; defining a variation fluctuation minimization optimization objective, and setting a second objective function for suppressing a high input fluctuation span under a steady-state condition based on the variation fluctuation minimization optimization objective; defining an energy recovery rate optimization target, and setting a third objective function for improving the energy recovery rate while suppressing the risk of tail swing based on the energy recovery rate optimization target; defining a brake drift suppression optimization target, and setting a fourth objective function for suppressing vehicle brake drift based on the brake drift suppression optimization target; Defining a calculation process optimization goal, and setting a terminal cost function for optimizing the convergence of the calculation process based on the calculation process optimization goal; The first objective function, the second objective function, the third objective function, the fourth objective function and the terminal cost function are integrated to obtain the multi-dimensional optimization objective function.

5. The energy recovery control method for electric articulated vehicles considering the risk of drifting according to claim 4, characterized in that: The desired braking speed optimization target includes: Control the braking speed of the tractor and trailer to be consistent with the expected braking speed; The optimization objective of minimizing the fluctuation of the variation includes: Reducing fluctuations in control system input variation of electric articulated vehicles; The energy recovery rate optimization objectives include: Control the tractor to optimize the distribution of electric braking force and mechanical braking force; The braking tail-swing suppression optimization objectives include: The articulation angle between the tractor and the trailer is controlled to be close to but not exceed the steady-state articulation angle.

6. The energy recovery control method for electric articulated vehicles considering the risk of drifting according to claim 4, characterized in that: The optimization objectives of the calculation process include: With the goal of improving the convergence speed, constraints are solved for the weighted summation process of the first objective function, the second objective function, the third objective function and the fourth objective function.

7. The energy recovery control method for electric articulated vehicles considering the risk of drifting according to claim 1, characterized in that: The method of calling the energy recovery nonlinear predictive control model based on the multidimensional optimization objective function to solve the optimal control sequence further includes: The solution process is optimized based on numerical optimization methods.

8. The energy recovery control method for electric articulated vehicles considering the risk of drifting according to claim 7, characterized in that: The numerical optimization method includes: sequential quadratic programming method, interior point method, numerical extension method and forward difference generalized minimum residual method.

9. The energy recovery control method for electric articulated vehicles considering the risk of tail-swinging according to claim 1, characterized in that: The state update of the electric articulated vehicle calling the energy recovery nonlinear predictive control model to perform cyclic predictive control further includes: Loop execution: in response to the state of the electric articulated vehicle being updated to the next moment, the updated state is used as a new initial input of the energy recovery nonlinear predictive control model to solve the optimization problem at the next moment.

10. An energy recovery control system for an electric articulated vehicle taking into account the risk of drifting, characterized in that: include: Model optimization module, used to: build a nonlinear predictive control model of energy recovery for electric articulated vehicles based on a symmetric simplification strategy; Function calculation module, used to: consider balance safety, energy recovery rate and braking performance as factors to construct a multi-dimensional optimization objective function including drift suppression optimization and energy recovery optimization; The optimization control module is used to: call the energy recovery nonlinear predictive control model to solve the optimal control sequence based on the multidimensional optimization objective function, call the control element of the electric articulated vehicle to control the vehicle according to the first element of the optimal control sequence; and call the energy recovery nonlinear predictive control model to perform cyclic predictive control along with the state update of the electric articulated vehicle.

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