Multi-time-scale energy management method and system for birotor hybrid electric vehicle

Abstract

The invention discloses a multi-time-scale energy management method and system for a double-rotor hybrid electric vehicle, and belongs to the technical field of hybrid electric vehicle energy control. According to the method, firstly, a multi-time scale frame is constructed, the automobile fuel economy is optimized under the long time scale, and the dynamic performance of the automobile is optimized under the short time scale; according to the long-time scale energy management strategy, the nonlinear optimization problem is solved through an improved alternating direction multiplier method, the optimal power distribution within the prediction range is obtained, and the calculation efficiency is improved; the short-time scale energy management strategy ensures the stability of the torque of an output shaft by estimating the output torque of the internal combustion engine, observing the coupling torque of the double-rotor motor and compensating extra power brought by speed regulation of the internal combustion engine through an outer motor.

Description

Technical Field

[0001] The present invention belongs to the technical field of hybrid electric vehicle energy control, and in particular relates to a multi-time scale energy management method and system for a dual-rotor hybrid electric vehicle. Background Art

[0002] Dual-rotor hybrid systems have the potential to improve the efficiency, reliability and compactness of powertrain operation. Compared with other dual-rotor motors, permanent magnet reluctance dual-rotor motors have better thermal and mechanical stability, high power density, and are suitable for applications that require frequent starting and disconnection. However, the complex electromagnetic coupling between the inner and outer motors increases electromagnetic losses and weakens the output torque. In addition, the lower moment of inertia of the dual-rotor motor may cause changes in the operating point of the internal combustion engine. These transient characteristics not only affect the dynamic performance of the vehicle, but also have an impact on the power allocation of the energy management strategy. Existing energy management strategies are usually based on steady-state conditions and belong to long-term optimization, ignoring the above-mentioned transient response characteristics, which will lead to real-time energy imbalance.

[0003] To solve the above problems, there are currently two main technical directions: multi-objective optimization and combination with coordinated control. However, these two solutions still have some problems. The solution of multi-objective optimization problems and additional control degrees of freedom may increase the computational burden of the energy management system, making its real-time application difficult. At the same time, fuel economy and dynamic performance are optimization objectives at different time scales, and there is a trade-off between the two optimization results. When optimizing dynamic performance on a long time scale, it is difficult to accurately simulate the transient response of the powertrain. Correspondingly, optimizing fuel economy on a short time scale will bring additional computational pressure. The multi-time scale framework can optimize the necessary combination of fast and slow system dynamics with reasonable computing time while maintaining high accuracy, and is widely used in energy management of integrated energy systems. In a dual-rotor hybrid system, the dynamic characteristics of the internal combustion engine and the dual-rotor motor are very different, and their dynamic characteristics cannot be accurately described on a long time scale, especially the electromagnetic coupling relationship between the inner motor and the outer motor in the dual-rotor motor. In addition, in order to achieve the optimal power distribution within the prediction range, a long fixed step optimization window must be used, which may result in a long computing time. On the other hand, although the superiority of the alternating direction multiplier method embedded in the model predictive control framework has been demonstrated in fuel cell hybrid vehicles, parallel hybrid vehicles and power split hybrid vehicles, there are relatively few studies on the convex model of dual-rotor hybrid vehicles.

[0004] After searching the prior art, it was found that Chinese patent document No. CN114954422A, published on August 30, 2022, discloses a multi-time-scale multi-energy hybrid power system management and multi-controller coordination method, and proposes an upper energy management layer and a lower actuator control subsystem for a multi-energy hybrid power system. Among them, the energy management layer performs longitudinal control at different time scales according to the different dynamic characteristics of fuel cells, supercapacitors and batteries; at the same time, the upper energy management layer and the lower actuator subsystem perform lateral multi-time-scale control. Chinese patent document No. CN116154749A, published on May 23, 2023, discloses a hierarchical control method for a high average-peak ratio hybrid energy system of a multi-electric aircraft, and proposes a hierarchical optimization method for the operation planning problem of multi-electric aircraft. Different optimization objectives are optimized at different time scales, and then the optimization objectives are coordinated to achieve the optimal operation control of the multi-electric aircraft system. However, the above method is mainly aimed at the energy storage and power generation systems in the all-electric system. During the operation of the dual-rotor hybrid system, there is a problem of electromagnetic coupling inside the dual-rotor motor, and the coupling situation is different under different operating conditions; in addition, while the internal combustion engine performs the power generation task, it also transmits its output torque through the internal motor magnetic field. When the internal motor adjusts the speed of the internal combustion engine, it will also bring additional power requirements. These problems cannot be taken into account by the energy management layer. Summary of the invention

[0005] The purpose of the present invention is to overcome the shortcomings of the above-mentioned prior art and provide a multi-time-scale energy management method and system for a dual-rotor hybrid vehicle to solve the shortcomings of the existing energy management methods of dual-rotor hybrid systems in the prior art, which cannot achieve coordinated optimization of fuel economy and dynamic performance and ignore the influence of dual-rotor electromagnetic coupling on the energy management strategy.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions: A multi-time scale energy management method for a dual-rotor hybrid electric vehicle comprises the following steps: S1, for dual-rotor hybrid vehicles, a dual-rotor motor power model based on steady state and a dual-rotor motor torque model based on transient state are constructed, and a plug-in hybrid vehicle system model of the dual-rotor motor is further obtained; S2, according to the current vehicle operation status, the vehicle speed is predicted by the back propagation neural network optimized by the improved particle swarm algorithm. Within the predicted vehicle speed range, the plug-in hybrid vehicle system model of the dual-rotor motor is convexified, and the convexified plug-in hybrid vehicle system model is solved by the improved alternating direction multiplier method to obtain the optimal internal combustion engine power and the optimal working curve of the internal combustion engine, and further obtain the internal combustion engine given torque and the dual-rotor motor given torque of the twin hybrid vehicle; S3, constructing an internal combustion engine transient model and a dual-rotor motor coupling torque model of a plug-in hybrid vehicle system model with a dual-rotor motor; monitoring and estimating the state of the plug-in hybrid vehicle system in real time through the internal combustion engine transient model, taking the given torque of the internal combustion engine of the twin hybrid vehicle as the target, and combining the dual-rotor motor coupling torque model to obtain the internal combustion engine speed and torque, as well as the dual-rotor motor torque.

[0007] A further improvement of the present invention is: Preferably, in S1, the steady-state-based dual-rotor motor power model is: (2) In the formula, and They are the internal and external motor powers, and are the average coupling torques of the inner and outer motors on a long time scale, and are the outer rotor speed and the internal combustion engine speed respectively, is the character extraction function, and are the internal and external motor efficiencies respectively.

[0008] Preferably, in S1, the transient-based dual-rotor motor torque model is: (1) In the formula, is a table lookup function, is the dual-rotor motor torque given by the long-time-scale energy management strategy, and They are the inner rotor three-phase winding current and the stator current respectively; and are the inner and outer rotor positions respectively.

[0009] Preferably, in S2, within the predicted vehicle speed range, the power balance relationship of the plug-in hybrid electric vehicle system model of the dual-rotor motor after convexification is: (6) In the formula, Output power for the dual-rotor motor, Output power to the battery.

[0010] Preferably, in S2, the objective function of the plug-in hybrid electric vehicle system model of the convex dual-rotor motor is: (11) In the formula, is the prediction range, is the time step, is the battery output power, It is the battery charge state.

[0011] Preferably, in S2, the process of solving the optimized convexified plug-in hybrid electric vehicle system model of the dual-rotor motor by the improved alternating direction multiplier method is as follows: first, an inequality constraint is added to the objective function, the optimization objective function is reconstructed, the optimization objective function is Lagrangian expanded, the Lagrangian function is obtained, the parameters and penalty factors in the Lagrangian function are updated by the alternating direction multiplier method, and the solution is terminated when the set condition or number of iterations is reached.

[0012] Preferably, the reconstruction optimization objective function is: (11) In the formula, is the prediction range, is the time step, is the battery output power, It is the battery charge state.

[0013] Preferably, in S2, the optimal torque and speed of the internal combustion engine are obtained according to the optimal internal combustion engine power and the optimal working curve of the internal combustion engine, and the optimal torque of the internal combustion engine is the given torque of the internal combustion engine; the given torque of the dual-rotor motor includes the given torque of the inner motor and the given torque of the outer motor, and the given torque of the inner motor is the optimal torque of the internal combustion engine, and the given torque of the outer motor is the difference between the required torque and the given torque of the inner motor.

[0014] Preferably, the specific process of S3 is: the transient model of the internal combustion engine collects the speed and electromagnetic speed of the inner motor in the dual-rotor motor in real time to estimate the output torque of the internal combustion engine, and utilizes the fast response characteristics of the external motor to compensate for the output torque ripple and output torque lag of the internal combustion engine to obtain the speed and torque of the internal combustion engine; during the start-up of the internal combustion engine, the external motor compensates the additional power to the internal motor.

[0015] A multi-time scale energy management system for a dual-rotor hybrid electric vehicle, comprising: The model building unit is used to build a steady-state dual-rotor motor power model and a transient dual-rotor motor torque model for a dual-rotor hybrid vehicle, and further obtain a plug-in hybrid vehicle system model of the dual-rotor motor.

[0016] The long-time unit is used to predict the vehicle speed according to the current vehicle operation status through the back propagation neural network optimized by the improved particle swarm algorithm, and convexify the plug-in hybrid vehicle system model of the dual-rotor motor within the predicted vehicle speed range, and solve the convex plug-in hybrid vehicle system model through the improved alternating direction multiplier method to obtain the optimal internal combustion engine power and the optimal working curve of the internal combustion engine, and further obtain the internal combustion engine given torque and the dual-rotor motor given torque of the twin hybrid vehicle; The transient unit is used to construct the internal combustion engine transient model and the dual-rotor motor coupling torque model of the plug-in hybrid vehicle system model of the dual-rotor motor; the state of the plug-in hybrid vehicle system is monitored and estimated in real time through the internal combustion engine transient model, and the internal combustion engine speed and torque, as well as the dual-rotor motor torque are obtained by combining the dual-rotor motor coupling torque model with the given torque of the internal combustion engine of the twin hybrid vehicle.

[0017] Compared with the prior art, the present invention has the following beneficial effects: The invention discloses a multi-time scale energy management method for a dual-rotor hybrid vehicle, which is used to solve the coordinated optimization of fuel economy and dynamic performance and the real-time application problem of energy management strategy. The method of the invention first constructs a multi-time scale framework, optimizes the fuel economy of the vehicle in a long time scale, and optimizes the dynamic performance of the vehicle in a short time scale. The long-time scale energy management strategy solves the nonlinear optimization problem by using an improved alternating direction multiplier method, obtains the optimal power distribution within the prediction range, and improves the calculation efficiency; the short-time scale energy management strategy estimates the output torque of the internal combustion engine and observes the coupling torque of the dual-rotor motor, and compensates the additional power brought by the speed regulation of the internal combustion engine through the external motor to ensure the stability of the output shaft torque. Simulation and experimental results show that compared with other energy management methods, the invention optimizes the performance of the vehicle in two time scales, improves its dynamic performance while ensuring the fuel economy of the vehicle, and ensures its real-time application capability. Different optimization targets are carried out in two time scales, and the electromagnetic coupling of the dual-rotor motor and the dynamic characteristics of the internal combustion engine are fully considered, so as to achieve the coordinated optimization of the fuel economy and dynamic performance of the system, and has the potential for real-time application.

[0018] Furthermore, in the long time scale, the present invention adopts the nonlinear model predictive control energy management optimized by the improved alternating direction multiplier method to significantly improve the fuel economy of the vehicle. Compared with the rule-based energy management strategy, the fuel consumption is reduced by 25.05% and the fuel consumption rate is reduced by 15.88%. In the short time scale, the output shaft torque is optimized through composite torque coordination, and the output shaft torque fluctuation is reduced by 45.24%. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1Schematic diagram of the dual-rotor hybrid system structure.

[0020] Figure 2 Schematic diagram of the multi-time-scale energy management strategy for a dual-rotor hybrid system.

[0021] Figure 3 Illustration of coordinated control of control sequences at different time scales.

[0022] Figure 4 Schematic diagram of the short-time scale composite torque coordination control strategy.

[0023] Figure 5 Comparison chart of internal combustion engine operating points under different optimization algorithms.

[0024] Figure 6 This is a comparison chart of dynamic performance optimization effects. DETAILED DESCRIPTION

[0025] In the following, the terms "first", "second", "third", and "fourth" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, a feature defined as "first", "second", "third", and "fourth" may explicitly or implicitly include one or more of the features.

[0026] The co-shooting method provided in the embodiment of the present application can be applied to terminal devices such as mobile phones, tablet computers, wearable devices, vehicle-mounted devices, augmented reality (AR) / virtual reality (VR) devices, laptop computers, ultra-mobile personal computers (UMPC), netbooks, personal digital assistants (PDA), etc. The embodiment of the present application does not impose any restrictions on the specific type of the terminal device.

[0027] It should be noted that the terms "first", "second", etc. in the specification and drawings of the present invention are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention 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 inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0028] The present invention provides an energy management method for a dual-rotor hybrid power system, which optimizes the fuel economy and dynamic performance of the vehicle under a multi-time scale framework. In the long time scale, the future vehicle speed is predicted by the current vehicle operation information, and the optimization objective function is solved within the prediction range to obtain the optimal power distribution within the prediction range; in the short time scale, a transient response model of the vehicle power system is constructed, including: internal combustion engine torque estimation and dual-rotor motor transient coupling torque model, and the output shaft torque is stabilized by compensating for the internal combustion engine torque loss and coupling torque.

[0029] The specific steps include: S1, according to the structural characteristics of the dual-rotor hybrid system, analyze the impact of electromagnetic coupling between the internal and external motors of the dual-rotor motor on energy management and system dynamic performance, build a plug-in dual-rotor hybrid vehicle model and a permanent magnet reluctance dual-rotor motor electromagnetic coupling model, and the constructed system model will be used for the prediction model in the model predictive control on a long time scale and the coupling torque observation on a short time scale. The specific process is: The influence of the internal electromagnetic coupling of the permanent magnet reluctance dual-rotor motor on the average output torque and efficiency of the motor under different working conditions was analyzed by finite element simulation, and the transient torque model of the dual-rotor motor and the steady-state power model of the dual-rotor motor considering electromagnetic coupling were constructed; then, according to the structural characteristics of the dual-rotor hybrid system, a plug-in hybrid vehicle model based on the permanent magnet reluctance dual-rotor motor was constructed, which is referred to as the dual-rotor hybrid system model in the subsequent description. The dual-rotor hybrid system model mainly includes an internal combustion engine model, a dual-rotor motor model, a battery model, a vehicle dynamics model and a transmission system model. Among them, the internal combustion engine model adopts the traditional table lookup model, the battery model is a series structure of an ideal voltage source and a variable resistor, the vehicle dynamics model selects the longitudinal dynamics model of the vehicle, and the transmission system model is a mechanism model. Different from the traditional dual-rotor motor modeling method, the dual-rotor motor model considering coupling established by the present invention analyzes the influence of the internal electromagnetic coupling of the dual-rotor motor on the system output torque and efficiency, reconstructs the motor power model of the traditional dual-rotor motor model, and improves the accuracy of the model.

[0030] Among them, the transient torque model of the dual-rotor motor considering electromagnetic coupling is shown in formula (1): (1) In the formula, is a table lookup function, is the dual-rotor motor torque given by the long-time-scale energy management strategy, and They are the inner rotor three-phase winding current and the stator current respectively; and are the inner and outer rotor positions respectively.

[0031] The steady-state power model of the dual-rotor motor considering electromagnetic coupling is shown in formula (2): (2) In the formula, and They are the internal and external motor powers, and are the average coupling torques of the inner and outer motors on a long time scale, and are the outer rotor speed and the internal combustion engine speed respectively, is the character extraction function, and They are the internal and external motor efficiencies respectively. Due to the influence of electromagnetic coupling between the internal and external motors, the efficiency of the internal motor is not only related to its operating point, but also affected by the working state of the external motor; the same is true for the external motor. Therefore, the efficiency of the internal motor and the efficiency of the external motor are shown in formula (3): (3) In the formula, and They are the lookup functions for the internal and external motor efficiencies respectively.

[0032] In the constructed S2 and S3, a multi-time scale energy management system is constructed, e.g. Figure 2 As shown, it includes a long-time scale energy management system with fuel economy as the optimization target and a short-time scale energy management system with dynamic performance as the optimization target.

[0033] Coordination of control sequences on long and short time scales Figure 3 At the current moment t ,The long time scale first determines the optimal power allocation within the prediction range according to the system operation state, and takes the first element as the given input of the system input and the short time scale energy management.,Then, through modeling and monitoring of the system transient response in the short time scale,,compensation for the real time output torque of the system is achieved to ensure the smoothness of the output torque.

[0034] S2. In the long-time scale energy management system, a nonlinear model predictive control energy management system based on the improved alternating direction multiplier method is constructed, and rolling optimization is performed in combination with the optimization objectives, system constraints and system status to obtain the optimal power allocation of the system.

[0035] S201: Based on the current vehicle operation information, the future driving conditions of the vehicle are predicted by improving the back propagation neural network optimized by the particle swarm algorithm, and the required driving power and torque are calculated according to the vehicle longitudinal dynamics model. The speed prediction model is shown in formula (4): (4) In the formula, To predict the nonlinear relationship of the model, The future i The predicted speed at each moment, is the forecast range.

[0036] S202: Within the speed prediction range, the dual-rotor hybrid power system model is convexified, and the optimization target and system constraints of the dual-rotor hybrid power system model are reconstructed.

[0037] The optimization objective of a dual-rotor hybrid system is non-convex, which makes the real-time application of energy management algorithms dependent on the speed of solving the optimization problem. In order to reduce the solution time of the optimization problem and improve the fuel economy of the hybrid system, the nonlinear model of the dual-rotor hybrid system is convexified and the optimization problem is solved by an improved alternating direction multiplier method.

[0038] Considering the influence of electromagnetic coupling, the power model of the dual-rotor motor can be simulated by a quadratic function between the input electrical power and the output mechanical power. The input power of the inner motor and the input power of the outer motor are shown in formula (5): (5) In the formula, and are the inner and outer motor speeds respectively, and are the inner and outer motor torques respectively, and Output mechanical power for the inner and outer motors; and are the fitting coefficients, and , , and This is satisfied within the operating speed range of the dual-rotor motor.

[0039] At each time step within the predicted vehicle speed range, the output shaft speed and the required drive power are known. Assuming that the internal combustion engine operates at the optimal operating curve, the input-output relationship in formula (5) can be reduced to a time-varying convex function of the output power.

[0040] Therefore, at the kth time step, the power balance relationship of the dual-rotor hybrid system is shown in formula (6): (6) In the formula, Output power for the dual-rotor motor, is the battery output power, and the input-output relationship of the dual-rotor motor is shown in formula (7): (7) In the formula, is the fitting coefficient.

[0041] Assuming that the internal combustion engine operates at the optimal operating curve, the fuel consumption rate can be calculated solely from the internal combustion engine power The convex function representation of is shown in formula (8): (8) In the formula, are the fitting coefficients and , .

[0042] Assuming that the battery open circuit voltage and battery internal resistance do not change with the battery SOC, the battery dynamics are as shown in formula (9): (9) in, Output power to the battery; is the open circuit voltage of the battery; is the internal resistance of the battery.

[0043] In order to ensure that the above relationship is monotonically increasing, the system constraints are as shown in formula (10): (10) In the formula, the subscript and Represent the lower and upper limits of the physical quantity constraints respectively.

[0044] Therefore, the reconstructed objective function is a convex function and is determined only by the battery power, as shown in formula (11): (11) In the formula, is the prediction range, is the time step, is the battery output power, It is the battery charge state.

[0045] S203: Using an improved alternating direction multiplier method to solve the convex optimization problem at each prediction step, the optimal internal combustion engine output power within the prediction range is obtained.

[0046] when When , there is enough energy in the battery at each time step in the prediction range to allow the motor to run at maximum power, then the solution of formula (11) is Otherwise, it is necessary to solve the optimization problem in formula (11). In order to improve the performance of real-time execution, the present invention proposes a nonlinear model predictive control based on the improved alternating direction multiplier method. At the same time, the virtual variable , by separating the variables, the solver iteration is simplified. By adding inequality constraints to the objective function, formula (11) can be reconstructed into a new optimization objective function, as shown in formula (12), (12) In the formula, the indicator function The expression of is shown in formula (13), (13) The extended Lagrangian function of the optimization objective function is shown in formula (14): (14) In the formula, is the penalty factor, is the Lagrange multiplier, is a unit vector, The element is The lower triangular matrix of .

[0047] The parameter update in the alternating direction multiplier method is shown in formula (15): (15) In the formula (16) Penalty Factor The update rule of is shown in formula (17): (17) in, The element is The lower triangular matrix of is a dummy variable, is a unit vector.

[0048] In general, the iterations of the alternating direction multiplier method are and , or stop when the number of iterations exceeds a set threshold. Error Usually determined in advance by the dimensionality of the variables.

[0049] S204: According to the optimal internal combustion engine power and dual-rotor hybrid system structure obtained in S203, the internal combustion engine is set to work at the optimal working curve to obtain the optimal torque distribution of the system, including: the internal combustion engine given torque and the dual-rotor motor given torque. The specific process is as follows: according to the optimal working curve of the internal combustion engine and the optimal power of the internal combustion engine obtained in S203, the optimal torque and speed of the internal combustion engine are obtained; the given torque of the internal motor is equal to the optimal torque of the internal combustion engine, and the given torque of the external motor is the difference between the required torque and the given torque of the internal motor.

[0050] S3: Figure 4As shown, in the short-time scale energy management system, the different dynamic response characteristics of the power system components and the electromagnetic coupling characteristics of the dual-rotor motor are taken into consideration, and a composite torque coordination control strategy is designed to coordinate the control system output torque. Combined with the power distribution results of the long-time scale energy management system, the final hybrid system input reference torque and speed are obtained, specifically including the reference torque and speed of the internal combustion engine, and the reference torque of the dual-rotor motor.

[0051] S301: Construct a system transient response model, and construct an internal combustion engine transient model and a dual-rotor motor coupling torque model on a short time scale.

[0052] S302: Monitor and estimate the hybrid power system state in real time according to the system transient model, and perform torque coordination control under different operating conditions, including: internal combustion engine start-up, internal combustion engine speed regulation, and hybrid drive mode. Specifically: According to the speed and electromagnetic torque of the inner motor in the dual-rotor motor, the output torque of the internal combustion engine is estimated, and the output torque ripple and output torque lag of the internal combustion engine are compensated by using the fast response characteristics of the outer motor; according to the dual-rotor motor coupling torque model, the coupling torque is obtained by monitoring the inner and outer motor currents and the inner and outer rotor positions, and then compensated. During the internal combustion engine starting and speed regulation process, the inner motor requires additional power for regulation, and the additional power is compensated by the outer motor to ensure the stability of the output torque, as shown in formula (18), (18) In the formula, and For the given external motor torque and given internal combustion engine torque of the long-time energy management strategy, Estimate the torque for the internal combustion engine, For a given speed of the internal combustion engine, and It is the coupling compensation torque for the inner and outer motors.

[0053] After simulation and experimental analysis, the fuel economy comparison of the multi-time scale energy management, dynamic programming, nonlinear model predictive control and rule-based energy management strategies proposed in the present invention is shown in Table 1. Among them, the dynamic programming has the lowest fuel consumption, which is because the complete operating condition information is known in advance. In addition, the effective fuel consumption rate of the proposed strategy is the lowest, because its working points are mostly concentrated in the low fuel consumption area. Compared with the nonlinear model prediction, the proposed multi-time scale energy management strategy modifies the working point of the internal combustion engine by considering the dynamic characteristics of the powertrain and electromagnetic coupling, so the internal combustion engine has higher working efficiency and lower fuel consumption. In addition, its fuel consumption is 95.35% similar to the offline theoretical optimal value, which is 25.05% lower than the rule-based energy management strategy. The operation of the system under different control strategies is shown in the figure. Figure 5As shown in the figure, due to the limitation of battery power, different methods keep the remaining battery power within a reasonable range. Compared with the theoretical optimal value obtained by dynamic programming, the proposed multi-time scale energy management strategy is more inclined to maintain the battery power near its lower limit due to the limitation of its prediction range. In addition, compared with rule-based methods and nonlinear model predictive control, dynamic programming and the proposed strategy keep the internal combustion engine power and motor torque within a wider range, thereby increasing the frequency of appearing in the high-efficiency zone.

[0054] Dynamic performance optimization results such as Figure 6 As shown. Compared with the single-time-scale energy management strategy, the output shaft torque of the proposed method can quickly reach a stable value due to the coordinated control of the internal combustion engine speed regulation and power output. Since the expected power of the internal combustion engine suddenly increases, the internal combustion engine will not output torque at first, and the speed of the internal combustion locomotive can quickly reach the expected value through the internal motor. At the same time, the external motor quickly generates torque to compensate for the additional torque generated by the internal combustion engine speed regulation, which also ensures the stability of the internal combustion engine speed. On the other hand, considering the influence of electromagnetic coupling, the proposed strategy speeds up the response time of the external machine, thereby ensuring the stability of the output shaft torque. The proposed method can stabilize the output power of the internal combustion engine and reduce the output shaft torque fluctuation by 45.24%, thereby improving the dynamic performance of the vehicle.

[0055] Table 1 Fuel consumption under different control strategies

[0056] In summary, the present invention relates to a multi-time scale energy management method for a dual-rotor hybrid system considering electromagnetic coupling, including: a long-time scale nonlinear model predictive control energy management strategy based on an improved alternating direction multiplier method and a short-time scale composite torque coordinated control strategy, wherein: the long-time scale energy management strategy monitors the running state of the vehicle in real time, predicts the vehicle speed within a certain range in the future through a back propagation neural network optimized by an improved particle swarm algorithm, collects the current vehicle speed and battery charge state into the long-time scale energy management system, takes fuel economy as the optimization goal, and solves the optimal power allocation under the constraints of the system physical constraints. In order to improve its fuel economy and real-time application capability, the dual-rotor hybrid system is convexified, and an improved alternating direction multiplier method is proposed to solve the optimization problem, which significantly improves its computational efficiency while ensuring fuel economy; the short-time scale energy management strategy establishes a system transient response model, collects the internal and external motor speeds, internal and external motor currents and internal and external rotor positions in real time, estimates the output torque of the internal combustion engine, and compensates for its output torque ripple and output torque lag. In addition, when the internal motor adjusts the speed of the internal combustion engine, the additional power will cause the output shaft torque to be lost. The reverse torque is compensated by the external motor to ensure the smooth output torque. When the internal and external motors work at the same time, the existence of electromagnetic coupling causes the output torque of the internal and external motors to be less than the given torque. The coupling torque is observed and compensated through the transient coupling torque model. Through the above-mentioned composite torque coordination control strategy, the system output shaft torque can be guaranteed to be smooth and the vehicle dynamic performance can be improved.

[0057] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A multi-time scale energy management method for a dual-rotor hybrid electric vehicle, characterized in that: The following steps are involved: S1, for dual-rotor hybrid vehicles, a dual-rotor motor power model based on steady state and a dual-rotor motor torque model based on transient state are constructed, and a plug-in hybrid vehicle system model of the dual-rotor motor is further obtained; S2, according to the current vehicle operation status, the vehicle speed is predicted by the back propagation neural network optimized by the improved particle swarm algorithm. Within the predicted vehicle speed range, the plug-in hybrid vehicle system model of the dual-rotor motor is convexified, and the convexified plug-in hybrid vehicle system model is solved by the improved alternating direction multiplier method to obtain the optimal internal combustion engine power and the optimal working curve of the internal combustion engine, and further obtain the internal combustion engine given torque and the dual-rotor motor given torque of the twin hybrid vehicle; S3, constructing an internal combustion engine transient model and a dual-rotor motor coupling torque model of a plug-in hybrid vehicle system model with a dual-rotor motor; monitoring and estimating the state of the plug-in hybrid vehicle system in real time through the internal combustion engine transient model, taking the given torque of the internal combustion engine of the dual-rotor hybrid vehicle as the target, and combining the dual-rotor motor coupling torque model to obtain the internal combustion engine speed and torque, as well as the dual-rotor motor torque.

2. A multi-time scale energy management method for a dual-rotor hybrid electric vehicle according to claim 1, characterized in that: In S1, the steady-state dual-rotor motor power model is: （2） In the formula, and They are the internal and external motor powers, and are the average coupling torques of the inner and outer motors on a long time scale, and are the outer rotor speed and the internal combustion engine speed respectively, is the character extraction function, and are the internal and external motor efficiencies respectively.

3. The multi-time scale energy management method for a dual-rotor hybrid electric vehicle according to claim 1, characterized in that: In S1, the transient-based dual-rotor motor torque model is: （1） In the formula, is a table lookup function, is the dual-rotor motor torque given by the long-time-scale energy management strategy, and They are the inner rotor three-phase winding current and the stator current respectively; and are the inner and outer rotor positions respectively.

4. The multi-time scale energy management method for a dual-rotor hybrid electric vehicle according to claim 1, characterized in that: In S2, within the predicted vehicle speed range, the power balance relationship of the plug-in hybrid vehicle system model of the dual-rotor motor after convexification is: （6） In the formula, Output power for the dual-rotor motor, Output power to the battery.

5. The multi-time scale energy management method for a dual-rotor hybrid electric vehicle according to claim 1, characterized in that: In S2, the objective function of the plug-in hybrid electric vehicle system model with a convex dual-rotor motor is: （11） In the formula, is the prediction range, is the time step, is the battery output power, It is the battery charge state.

6. A multi-time scale energy management method for a dual-rotor hybrid electric vehicle according to claim 5, characterized in that: In S2, the process of solving the optimized convexified plug-in hybrid electric vehicle system model of the dual-rotor motor by the improved alternating direction multiplier method is as follows: first, an inequality constraint is added to the objective function, the optimization objective function is reconstructed, the optimization objective function is Lagrangian expanded, the Lagrangian function is obtained, the parameters and penalty factors in the Lagrangian function are updated by the alternating direction multiplier method, and the solution is completed when the set condition or number of iterations is reached.

7. A multi-time scale energy management method for a dual-rotor hybrid electric vehicle according to claim 6, characterized in that: The reconstructed optimization objective function is: （11） In the formula, is the prediction range, is the time step, is the battery output power, It is the battery charge state.

8. The multi-time scale energy management method for a dual-rotor hybrid electric vehicle according to claim 7, characterized in that: In S2, the optimal torque and speed of the internal combustion engine are obtained according to the optimal internal combustion engine power and the optimal working curve of the internal combustion engine. The optimal torque of the internal combustion engine is the given torque of the internal combustion engine; the given torque of the dual-rotor motor includes the given torque of the inner motor and the given torque of the outer motor. The given torque of the inner motor is the optimal torque of the internal combustion engine, and the given torque of the outer motor is the difference between the required torque and the given torque of the inner motor.

9. The multi-time scale energy management method for a dual-rotor hybrid electric vehicle according to claim 1, characterized in that: The specific process of S3 is as follows: the transient model of the internal combustion engine collects the speed and electromagnetic speed of the inner motor in the dual-rotor motor in real time to estimate the output torque of the internal combustion engine, and uses the fast response characteristics of the outer motor to compensate for the output torque ripple and output torque lag of the internal combustion engine to obtain the speed and torque of the internal combustion engine; during the start-up of the internal combustion engine, the outer motor compensates the additional power to the inner motor.

10. A multi-time scale energy management system for a dual-rotor hybrid electric vehicle, characterized in that: include: A model building unit, for building a steady-state dual-rotor motor power model and a transient-state dual-rotor motor torque model for a dual-rotor hybrid vehicle, and further obtaining a plug-in hybrid vehicle system model of the dual-rotor motor; The long-time unit is used to predict the vehicle speed according to the current vehicle operation status through the back propagation neural network optimized by the improved particle swarm algorithm, and convexify the plug-in hybrid vehicle system model of the dual-rotor motor within the predicted vehicle speed range, and solve the convex plug-in hybrid vehicle system model through the improved alternating direction multiplier method to obtain the optimal internal combustion engine power and the optimal working curve of the internal combustion engine, and further obtain the internal combustion engine given torque and the dual-rotor motor given torque of the twin hybrid vehicle; The transient unit is used to construct the internal combustion engine transient model and the dual-rotor motor coupling torque model of the plug-in hybrid vehicle system model of the dual-rotor motor; the state of the plug-in hybrid vehicle system is monitored and estimated in real time through the internal combustion engine transient model, and the internal combustion engine speed and torque, as well as the dual-rotor motor torque are obtained by combining the dual-rotor motor coupling torque model with the given torque of the internal combustion engine of the dual-rotor hybrid vehicle.

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