Energy Recovery Control Method and System for Electric Articulated Vehicles Considering Fishtailing Risk

Abstract

The present invention discloses an energy recovery control method and system for an electric articulated vehicle considering the risk of fishtailing. The method includes the following steps: building a non-linear predictive control model for energy recovery of the electric articulated vehicle based on a symmetric simplification strategy; 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; calling the non-linear predictive control model for energy recovery to solve the optimal control sequence based on the multi-dimensional optimization objective function, and calling the control elements of the electric articulated vehicle to control the vehicle according to the first element of the optimal control sequence; and calling the non-linear predictive control model for energy recovery for cyclic predictive control along with the update of the state of the electric articulated vehicle. The present invention can effectively estimate and avoid potential safety hazards such as fishtailing by accurately capturing the dynamic changes during the braking process, and ensure the stability and safety of the braking process.

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 a hinge device to form an integral unit for driving; 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 hinge 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, while ignoring 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 to effectively suppress the risk of jackknifing while recovering energy; although some studies have discussed the suppression of braking jackknifing, these studies mainly focus on the classification and control optimization of the folding jackknifing state under the condition of wheel locking, 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:

[0004] 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.

[0005] 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.

[0006] 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.

[0007] 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.

[0008] 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.

[0009] 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

[0010] 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.

[0011] In order to solve the above technical problems, the specific technical solutions of the present invention are as follows:

[0012] 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:

[0013] A nonlinear predictive control model for energy recovery of electric articulated vehicles is built based on a symmetric simplification strategy;

[0014] 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;

[0015] 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.

[0016] As an improved scheme, the symmetric simplification strategy includes:

[0017] Take the vehicle steering process and the vehicle lateral movement as consideration factors, and establish the braking dynamics model of the electric articulated vehicle;

[0018] Simplify the braking dynamics model into a single - track model on the longitudinal symmetry plane of the vehicle body.

[0019] 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:

[0020] 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;

[0021] 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.

[0022] 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 yaw - swing suppression optimization and energy recovery optimization further includes:

[0023] Define the expected braking vehicle speed optimization objective, and set the first objective function for suppressing the lateral movement between the tractor and the trailer based on the expected braking vehicle speed optimization objective;

[0024] Define the change quantity fluctuation minimization optimization objective, and set the second objective function for suppressing the high input fluctuation span under steady - state conditions based on the change quantity fluctuation minimization optimization objective;

[0025] Define the energy recovery rate optimization objective, and set the third objective function for improving the energy recovery rate while suppressing the yaw - swing risk based on the energy recovery rate optimization objective;

[0026] Define the braking yaw - swing suppression optimization objective, and set the fourth objective function for suppressing the vehicle braking yaw - swing based on the braking yaw - swing suppression optimization objective;

[0027] Define the optimization objective of the calculation process, and set a terminal cost function for optimizing the convergence of the calculation process based on the optimization objective of the calculation process;

[0028] 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.

[0029] As an improved solution, the expected braking speed optimization objective includes:

[0030] Control the braking speeds of the tractor and the trailer to be consistent with the expected braking speed;

[0031] The optimization objective of minimizing the change amount fluctuation includes:

[0032] Reduce the fluctuation of the input change amount of the control system of the electric articulated vehicle;

[0033] The optimization objective of the energy recovery rate includes:

[0034] Control the tractor to optimize the distribution of electric braking force and mechanical braking force;

[0035] The optimization objective of suppressing braking fishtailing includes:

[0036] Control the articulation angle between the tractor and the trailer to be close to and not exceed the steady-state articulation angle.

[0037] As an improved solution, the optimization objective of the calculation process includes:

[0038] Aiming at improving the convergence speed, solve the constraint of the weighted summation process of the first objective function, the second objective function, the third objective function and the fourth objective function.

[0039] As an improved solution, further comprising: based on the multi-dimensional optimization objective function, calling the energy recovery non-linear predictive control model to solve the optimal control sequence:

[0040] Optimize the solution process based on numerical optimization methods.

[0041] As an improved solution, the numerical optimization methods include: sequential quadratic programming method, interior point method, numerical continuation method, and forward difference generalized minimum residual method.

[0042] As an improved solution, further comprising: calling the energy recovery non-linear predictive control model for cyclic predictive control along with the state update of the electric articulated vehicle:

[0043] Loop execution: In response to the state of the electric articulated vehicle being updated to the next moment, the updated state is used as the new initial input of the energy recovery non-linear predictive control model to solve the optimization problem for the next moment.

[0044] On the other hand, the present invention also provides an energy recovery control system for an electric articulated vehicle considering the risk of fishtailing, including:

[0045] A model optimization module for: building an energy recovery non-linear predictive control model of an electric articulated vehicle based on a symmetric simplification strategy;

[0046] A function calculation module for: constructing a multi-dimensional optimization objective function including fishtailing suppression optimization and energy recovery optimization by taking balance of safety, energy recovery rate, and braking performance as consideration factors;

[0047] An optimization control module for: calling the energy recovery non-linear predictive control model based on the multi-dimensional optimization objective function to solve the optimal control sequence, and calling the control elements of the electric articulated vehicle to control the vehicle according to the first element of the optimal control sequence; and performing 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.

[0048] The beneficial effects of the technical solution of the present invention are:

[0049] 1. The energy recovery control method for an electric articulated vehicle considering the risk of fishtailing according to 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.

[0050] 2. The energy recovery control method for an electric articulated vehicle considering the risk of fishtailing according to the present invention rationally distributes the braking force of each braking element based on a non-linear 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.

[0051] 3. The energy recovery control method for an electric articulated vehicle considering the risk of fishtailing according to 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 realizes 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.

[0052] 4. The energy recovery control method for an electric articulated vehicle considering the risk of fishtailing according to the present invention ensures the solution of the non-linear model predictive control algorithm under complex non-linear constraints by introducing a terminal state cost function, guarantees the engineering feasibility of the control theory, and makes the algorithm operable in practical applications.

[0053] 5. The energy recovery control system for an electric articulated vehicle considering the risk of fishtailing according to the present invention can realize the energy recovery control method for an electric articulated vehicle considering the risk of fishtailing according to the present invention through the mutual cooperation of system modules. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] 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 drawings in the following description 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.

[0055] Figure 1 is a schematic flow chart of the energy recovery control method for an electric articulated vehicle considering the risk of fishtailing according to Embodiment 1 of the present invention;

[0056] Figure 2 is a schematic configuration diagram of a typical articulated vehicle power and braking system in the energy recovery control method for an electric articulated vehicle considering the risk of fishtailing according to Embodiment 1 of the present invention;

[0057] Figure 3 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 according to Embodiment 1 of the present invention;

[0058] Figure 4 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 according to Embodiment 1 of the present invention;

[0059] Figure 5 is a schematic brief logic diagram of the energy recovery control method for an electric articulated vehicle considering the risk of fishtailing according to Embodiment 1 of the present invention;

[0060] Figure 6 is a schematic architecture diagram of the energy recovery control system for an electric articulated vehicle considering the risk of fishtailing according to Embodiment 2 of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0061] 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 the scope of protection of the present invention more clearly defined.

[0062] 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; based on the embodiments in the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0063] The terms "first", "second", etc. in the specification, claims and drawings of this article 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 herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" 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 not clearly listed or inherent to these processes, methods, products or equipment.

[0064] 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 Figure 2 shown; usually its power system is located in the tractor part, and there are multiple drive motors, generally located in the middle bridge and rear bridge 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; there are usually no drive elements 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 such as friction discs to reduce the vehicle speed, and this part of the braking energy cannot be recycled; in addition, the braking elements of the tractor and the trailer are comprehensively controlled by the vehicle control unit; for vehicles with such configurations, optimizing the design of the braking energy recovery control strategy can effectively improve the energy-saving effect of the whole vehicle.

[0065] Embodiment 1. This embodiment provides an energy recovery control method for an electric articulated vehicle considering the risk of fishtailing, as Figures 1 to 5 shown, including the following steps:

[0066] 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:

[0067] S101. To ensure effective improvement of energy recovery rate and braking safety, not too much model simplification is done in this step. However, to consider the problem of fishtailing under energy recovery conditions (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 vehicle lateral movement 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 symmetry simplification. Based on the above symmetry simplification process, the final braking dynamics model of the articulated vehicle is as follows:

[0068] ;

[0069] where:

[0070] is the total mass of the tractor;

[0071] , are respectively the longitudinal force and the lateral force received by the th equivalent wheel;

[0072] is the front wheel steering angle;

[0073] is the total moment of inertia of the tractor;

[0074] , , are respectively the distances from each axle of the tractor to the center of mass of the tractor;

[0075] is the distance from the center of mass of the tractor to the articulation point;

[0076] , are respectively the longitudinal component force and the lateral component force of the hinge force of the trailer on the tractor;

[0077] is the total mass of the trailer;

[0078] is the total moment of inertia of the trailer;

[0079] is the distance from the equivalent wheel of the trailer to the center of mass of the trailer;

[0080] is the distance from the center of mass of the trailer to the articulation point;

[0081] , are respectively the longitudinal component force and the lateral component force of the hinge force of the tractor on the trailer;

[0082] is the ground resistance coefficient;

[0083] is the air resistance coefficient;

[0084] is the frontal area of the tractor;

[0085] is the frontal area of the trailer;

[0086] is the air density.

[0087] It should be noted that:

[0088] (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 and move together; where, , are the centers of mass of the tractor and the trailer respectively; is the hinge point between the tractor and the trailer; , , are the longitudinal speed, lateral speed and yaw angular velocity of the tractor respectively; , , are the longitudinal speed, lateral speed and yaw angular velocity of the trailer respectively.

[0089] (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.

[0090] S102. Based on the above model, comprehensively considering the constraint characteristics of the hinge 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 hinge point are expressed as follows:

[0091] ;

[0092] Among them, is the hinge angle between the tractor and the trailer.

[0093] S103. Based on the above constraint relationships, substitute the articulated vehicle braking dynamics model and eliminate variables. Finally, it is transformed into a non - linear state - space equation, which serves 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));

[0094] Among them, the state vector x is: x={ , , , , };

[0095] Among them, the input vector (control vector) u is: u={ };

[0096] 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.

[0097] It should be noted that:

[0098] (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 axis is also restricted by the ground adhesion conditions).

[0099] (2) In the process of establishing the above - mentioned prediction model, if the articulated internal force is not easy to eliminate, after iterative solution of the articulated internal force, based on x(k) and u(k), calculate x(k + 1); 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.

[0100] (3) When the subsequent optimization method is selected appropriately, the prediction model does not need to be simplified. Establish a braking process dynamics model according to all drive elements, all axles, and all wheels of the vehicle, and transform it 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.

[0101] S200. Based on the optimization goal of balancing the complex relationships among safety, energy recovery rate, and braking performance, construct a multi - dimensional optimization objective function that includes over - steer suppression optimization and energy recovery optimization, as follows:

[0102] S201. According to the basic principle of model predictive control, define that both the prediction horizon and the control horizon of the braking MPC controller are N (that is, the maximum number of iterations of the prediction model is N times);

[0103] S202. Define the optimization objective for the desired braking speed: that is, control the braking speeds of the tractor and the trailer to be consistent with the desired braking speed, so that the tractor and the trailer can brake in a timely manner according to the desired speed, and make the difference between the actual speeds of the tractor and the trailer and the desired speed as small as possible to avoid lateral movement between the tractor and the trailer. The relevant first objective function is as follows:

[0104] ;

[0105] Among them, , are the reference speeds of the tractor and the trailer respectively, both of which are sequence values and are generated by the driving program or the control signal.

[0106] S203. Define the optimization objective for 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 steering angle and the longitudinal forces of 4 groups of axles, so this change amount is the change amount of the front wheel steering angle and the braking force of the vehicle), and then weaken the longitudinal impact degree of the whole vehicle, improve the braking safety to a certain extent, and avoid the adverse impact of excessive input fluctuation on the braking performance under the steady state condition. The relevant second objective function is as follows:

[0107] ;

[0108] S204. Define the optimization objective for the energy recovery rate: since the trailer is only equipped with a traditional mechanical braking system and cannot recover energy, this objective only controls the reasonable optimization distribution of the electric braking and the mechanical braking of the tractor, and then improves the energy recovery rate while preventing the trailer from whipping. The relevant third objective function is as follows:

[0109] ;

[0110] Among them, is the total electric braking power, and its calculation formula is:

[0111] ;

[0112] Among them, is the total mechanical braking power, and its calculation formula is:

[0113] ;

[0114] It should be noted that due to considering the optimization of the braking power, when the value of F is less than 0 (that is, under the braking condition), F is included in the optimization calculation of the braking energy recovery rate.

[0115] 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:

[0116] ;

[0117] 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.

[0118] 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:

[0119] ;

[0120] 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.

[0121] 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:

[0122] ;

[0123] 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];

[0124] It should be noted that the terminal cost function is the most crucial. Since the recursive calculation process of the articulated vehicle braking process prediction model is a process of inferring the next moment's state from the current state, which belongs to a typical non-linear complex process, and there are many input and state variables for the articulated vehicle, resulting in extremely difficult optimization problems for minJ. Therefore, an additional terminal cost function is required to constrain the solution process.

[0125] 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 fishtail risk is integrated:

[0126] ;

[0127] Based on the function q representing the first to fifth objective functions, the above optimization function is expressed as:

[0128] ;

[0129] where U is the reachable set of the input vector.

[0130] 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:

[0131] 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;

[0132] 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 for the next moment, and loop in this way to achieve continuous control output during the braking process of the electric articulated vehicle.

[0133] 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:

[0134] For the model predictive control of an 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 optimized terminal state will automatically enter a certain neighborhood of the origin ; this attraction domain The invariant set property must be satisfied, i.e., for any point within the region of attraction , its optimized terminal state will automatically enter , and the optimized terminal state of the state at the next moment will also automatically enter ; therefore, to ensure stability, it is only necessary to satisfy the following conditions:

[0135] (i) For x ∈ , there is always U such that (x, ) + holds, and there is ∈ ;

[0136] (ii) At this time, define the minimum value of J(u, ) as , and assume that the optimal input sequence (belonging to the ideal sequence) is ={ ,..., }; at this time, the optimal state prediction trajectory is: ={ ,..., };

[0137] (iii) If the aforementioned optimization function has a solution and the optimized terminal state automatically enters , i.e., ∈ , then when the system transitions to the next state , take the following feasible control law:

[0138] ;

[0139] where is the control law that satisfies ( , ) + , and ;

[0140] Obviously, the performance index corresponding to this feasible control law satisfies:

[0141] ;

[0142] (iii) Therefore, there exists:

[0143] ;

[0144] It can be seen that is a Lyapunov function; therefore, when the rolling control law is adopted, the system is stable.

[0145] 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 accordingly.

[0146] Embodiment 2. Based on the same inventive concept as the energy recovery control method for an electric articulated vehicle considering the risk of fishtailing described in Embodiment 1, this embodiment provides an energy recovery control system for an electric articulated vehicle considering the risk of fishtailing, as Figure 6 shown, including:

[0147] 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;

[0148] A function calculation module for: constructing a multi-dimensional optimization objective function including fishtail suppression optimization and energy recovery optimization by taking balance safety, energy recovery rate, and braking performance as consideration factors;

[0149] An optimization control module for: calling the non-linear predictive control model for energy recovery to solve the optimal control sequence based on the multi-dimensional optimization objective function, calling the control elements of the electric articulated vehicle to control the vehicle according to the first element of the optimal control sequence; and cyclically predicting and controlling by calling the non-linear predictive control model for energy recovery along with the update of the state of the electric articulated vehicle.

[0150] Different from the prior art, by adopting the energy recovery control method and system for an electric articulated vehicle considering the risk of fishtailing in this application, a dynamic prediction model for the braking process of the electric articulated vehicle is constructed to accurately capture the dynamic changes during the braking process, effectively estimate and avoid potential safety hazards such as fishtailing, and ensure the stability and safety of the braking process.

[0151] It should be understood that in various embodiments herein, the magnitudes of the sequence 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 herein.

[0152] It should also be understood that in the embodiments herein, 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.

[0153] Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of the two. To clearly illustrate the interchangeability of hardware and software, the composition and steps of the examples have been generally described according to 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.

[0154] 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 elaborated herein.

[0155] In the several embodiments provided in this article, 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 may 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. In addition, 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 electrical, mechanical, or other forms of connection.

[0156] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place, or can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiments in this article.

[0157] In addition, the functional units in the various embodiments of this article 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 integrated units can be implemented in the form of hardware or in the form of software functional units.

[0158] When the 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 technical solution herein, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments herein. The foregoing storage medium includes: various media that can store program codes such as USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs.

[0159] The above are only the embodiments of the present invention, and thus do not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall be 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 fishtailing, characterized in that, It includes the following steps: Build a non-linear predictive control model for energy recovery of an electric articulated vehicle based on a symmetry simplification strategy; Define an optimization target for the desired braking vehicle speed, and set a first objective function for suppressing lateral movement between the tractor and the trailer based on the optimization target for the desired braking vehicle speed; Define an optimization target for minimizing the fluctuation of the change amount, and set a second objective function for suppressing a high input fluctuation span under steady-state conditions based on the optimization target for minimizing the fluctuation of the change amount; Define an optimization target for the energy recovery rate, and set a third objective function for increasing the energy recovery rate while suppressing the risk of fishtailing based on the optimization target for the energy recovery rate; define an optimization target for suppressing braking fishtailing, and set a fourth objective function for suppressing vehicle braking fishtailing based on the optimization target for suppressing braking fishtailing; Define an optimization target for the calculation process, and set a terminal cost function for optimizing the convergence of the calculation process based on the optimization target for the calculation process; integrate the first objective function, the second objective function, the third objective function, the fourth objective function and the terminal cost function to obtain a multi-dimensional optimization objective function; Call the non-linear predictive control model for energy recovery to solve the optimal control sequence based on the multi-dimensional optimization objective function, and call the control element of the electric articulated vehicle to control the vehicle according to the first element of the optimal control sequence; call the non-linear predictive control model for energy recovery for cyclic predictive control along with the update of the state of the electric articulated vehicle.

2. The energy recovery control method for an electric articulated vehicle considering the risk of fishtailing according to claim 1, wherein: The symmetry simplification strategy includes: Regarding the vehicle steering process and the vehicle lateral movement as consideration factors, 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.

3. The energy recovery control method for an electric articulated vehicle considering the risk of fishtailing according to claim 2, wherein: Building the non-linear predictive control model for energy recovery of the electric articulated vehicle based on the symmetry 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 non-linear predictive control model for energy recovery.

4. The energy recovery control method for an electric articulated vehicle considering the risk of fishtailing according to claim 1, wherein: The optimization target for the desired braking vehicle speed includes: Control the braking vehicle speeds of both the tractor and the trailer to be consistent with the desired braking vehicle speed; The optimization target for minimizing the fluctuation of the change amount includes: Reduce the fluctuation of the input change amount of the control system of the electric articulated vehicle; The optimization target for the energy recovery rate includes: Control the tractor to optimize the distribution of electric braking force and mechanical braking force; The optimization target for suppressing braking fishtailing includes: Control the articulation angle between the towing vehicle and the trailer to be close to and not exceed the steady-state articulation angle.

5. The energy recovery control method for an electric articulated vehicle considering the risk of fishtailing according to claim 1, characterized in that: The optimization objectives of the calculation process include: Aiming to improve the convergence speed, solve the constraints for the weighted summation process of the first objective function, the second objective function, the third objective function, and the fourth objective function.

6. The energy recovery control method for an electric articulated vehicle considering the risk of fishtailing according to claim 1, characterized in that: Based on the multi-dimensional optimization objective function, calling the energy recovery non-linear predictive control model to solve the optimal control sequence, further including: Optimizing the solution process based on numerical optimization methods.

7. The energy recovery control method for an electric articulated vehicle considering the risk of fishtailing according to claim 6, characterized in that: The numerical optimization methods include: sequential quadratic programming method, interior point method, numerical continuation method, and forward difference generalized minimum residual method.

8. The energy recovery control method for an electric articulated vehicle considering the risk of fishtailing according to claim 1, characterized in that: Based on the update of the state of the electric articulated vehicle, calling the energy recovery non-linear predictive control model for cyclic predictive control, further including: Cyclically execute: in response to the update of the state of the electric articulated vehicle to the next moment, use the updated state as the new initial input of the energy recovery non-linear predictive control model to solve the optimization problem at the next moment.

9. An energy recovery control system for an electric articulated vehicle considering the risk of fishtailing, characterized in that, Including: A model optimization module, used for: building an energy recovery non-linear predictive control model for an electric articulated vehicle based on a symmetric simplification strategy; A function calculation module, used for: defining an optimization objective for the desired braking vehicle speed, and setting a first objective function for suppressing lateral movement between the towing vehicle and the trailer based on the optimization objective for the desired braking vehicle speed; Defining an optimization objective for minimizing the change amount fluctuation, and setting a second objective function for suppressing a high input fluctuation span under steady-state conditions based on the optimization objective for minimizing the change amount fluctuation; Defining an optimization objective for the energy recovery rate, and setting a third objective function for increasing the energy recovery rate while suppressing the risk of fishtailing based on the optimization objective for the energy recovery rate; defining an optimization objective for suppressing braking fishtailing, and setting a fourth objective function for suppressing vehicle braking fishtailing based on the optimization objective for suppressing braking fishtailing; Defining an optimization objective for the calculation process, and setting a terminal cost function for optimizing the convergence of the calculation process based on the optimization objective for the calculation process; integrating the first objective function, the second objective function, the third objective function, the fourth objective function, and the terminal cost function to obtain a multi-dimensional optimization objective function; An optimization control module, used for: based on the multi-dimensional optimization objective function, calling the energy recovery non-linear predictive control model to solve the optimal control sequence, and calling the control elements of the electric articulated vehicle to control the vehicle according to the first element of the optimal control sequence; based on the update of the state of the electric articulated vehicle, calling the energy recovery non-linear predictive control model for cyclic predictive control.

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