Multi-scenario dynamic power control method for hybrid agricultural electric vehicles

Abstract

The multi-scenario dynamic power control method for hybrid electric agricultural vehicles solves the problems of poor dynamic response and poor real-time performance of existing hybrid electric agricultural vehicle energy management, and belongs to the field of energy management control algorithms. The present invention includes: real-time acquisition of bus voltage, bus power, output power of fuel generator, battery, and supercapacitor, determining the working scene of the agricultural electric vehicle, and then determining the change direction of the bus power, the acquired bus power adopts a self-sliding window for power setting, and obtains the power reference value of the next cycle of the fuel generator; low-frequency bandwidth transient power decomposition of the acquired bus power is performed to obtain the power reference value of the next cycle of the battery; according to the real-time change of the bus voltage, the power reference value of the next cycle of the supercapacitor is obtained; according to the obtained power reference value, combined with the change direction of the bus power, the power of the fuel generator, battery and supercapacitor in the next cycle is updated.

Description

Technical Field

[0001] The invention relates to a multi-scenario dynamic power control method, which is applicable to a hybrid electric agricultural vehicle and belongs to the field of energy management control algorithms. Background Art

[0002] The application of hybrid technology can effectively combine the advantages of pure electric vehicles and fuel vehicles, and is an important way to solve the problem of insufficient endurance of agricultural electric vehicles. In order to further improve the practicality of hybrid technology, it is necessary to study the energy management of hybrid agricultural electric vehicles. Under the current hybrid technology, the energy management strategy only considers the fuel utilization efficiency and abnormal problems of the battery management system during operation. For agricultural electric vehicles using hybrid technology, battery performance and durability are the key to ensuring stable and efficient operation of the vehicle. The thermal management technology of power batteries has completed the battery heating technology in low temperature environments, and manages and controls the heat generation, transmission, and dissipation of the power battery thermal management system. Based on the current research status of hybrid technology, the energy management strategy is mainly aimed at the performance optimization of the energy unit, and does not control the dynamic power of the system when switching multiple scenes from the perspective of maximizing the efficiency of the whole machine. At the same time, it does not consider the real-time nature of energy management, that is, the real-time nature of energy management is poor. Summary of the invention

[0003] In view of the problems of poor dynamic response and poor real-time performance of energy management of existing hybrid electric agricultural vehicles, the present invention provides a multi-scenario dynamic power control method for hybrid electric agricultural vehicles.

[0004] A multi-scenario dynamic power control method for a hybrid electric agricultural vehicle of the present invention is applicable to a hybrid energy system of an agricultural electric vehicle, wherein the energy storage devices in the hybrid energy system of the agricultural electric vehicle include supercapacitors and batteries; the control method includes:

[0005] S1. Real-time collection of bus voltage, bus power and input energy power, determining the working scene of the agricultural electric vehicle, and determining the change direction of the bus power according to the working scene. The multiple scenes include agricultural electric vehicles going uphill and agricultural electric vehicles going downhill. The input energy power is the sum of the output power of the fuel generator, the output power of the battery, and the output power of the supercapacitor;

[0006] S2. Predict the power reference value required for the next cycle based on the collected current cycle data, including:

[0007] The bus power of the current cycle is adjusted using a self-sliding window to predict the power reference value of the fuel generator in the next cycle. P g ( t n+1 ); tn Indicates the current cycle, t n+1 Indicates the next cycle;

[0008] Perform low-frequency bandwidth transient power decomposition on the bus power of the current cycle to obtain the power reference value of the battery in the next cycle P b ( t n+1 );

[0009] According to the real-time change of bus voltage, the power reference value of the supercapacitor in the next cycle is obtained. P c ( t n+1 );

[0010] S3. According to the power reference value obtained P g ( t n+1 ), P b ( t n+1 )and P c ( t n+1 ), combined with the change direction of the bus power determined by S1, the power of the fuel generator, battery and supercapacitor for the next cycle is updated.

[0011] As a preferred method, the power is set by using a self-sliding window according to the bus power of the current cycle to predict the power reference value of the fuel generator in the next cycle. P g ( t n+1 ):

[0012] A self-sliding window with a fixed width is set to slide along the time series, and the bus power is adjusted using the window to obtain the sampling sequence of the window. :

[0013]

[0014] in, p ( t n-i )express t n Bus power sampling value in the cycle, width of the sliding window N =2 k +1, k Determined by the window width;

[0015] The exponential smoothing method is used to calculate the steady-state power reference value of the next cycle. P 0( t n+1 ) is used for prediction, and its expression is:

[0016]

[0017] in, express t n Periodic bus power sampling value, is the smoothing factor;

[0018] The steady-state power reference value of the next cycle P 0( t n+1 ) as the power reference value of the next cycle of the fuel generator P g ( t n+1 ).

[0019] As a preferred method, the bus power of the current cycle is decomposed into low-frequency bandwidth transient power to obtain the power reference value of the battery in the next cycle. P b ( t n+1 ):

[0020] Fixed number of decomposition modes M=M 0, according to the principle of minimizing the sum of bandwidth values, the frequency domain analytical signal around the determined center frequency is obtained through iterative calculation :

[0021]

[0022] in, M 0 is M The initial value of α is the balance factor, λ ( ω ) is the frequency domain signal corresponding to the Lagrange multiplier, represents the angular frequency, represents the j-th angular frequency of the decomposition;

[0023] Based on the obtained frequency domain analytical signal sequence right M Make repairs;

[0024] After the repair M The obtained frequency domain analytical signal sequence is And its corresponding amplitude spectrum, the frequency domain analytical signal sequence Perform inverse Fourier transform, compare amplitudes, and select low-frequency and high-amplitude time-domain analytical signals based on the amplitude spectrum as the battery power reference value for the next battery cycle P b ( t n+1 ).

[0025] As a preferred embodiment, based on the obtained frequency domain analytical signal sequence right M How to perform trimming:

[0026] Gradually increasing M , when the frequency domain analytic signal sequence Energy The relationship with bus power satisfies:

[0027]

[0028] At this time M The value is trimmed M value, To set the difference, express The corresponding time domain signal is express t n Periodic bus power sampling value.

[0029] Preferably, the S2 further comprises:

[0030] when , then the predicted power reference value of the fuel generator in the next cycle is P g ( t n+1 ), and only the battery power reference value of the next cycle of the battery P b ( t n+1 ) is updated to: ;

[0031] is the set error value, w is the power difference coefficient;

[0032] when The reference value of the next cycle is still the power reference value obtained above. P g ( t n+1 ), P b ( t n+1 )and P c (t n+1 ).

[0033] The beneficial effects of the present invention are as follows: the present invention can timely judge the power direction according to the multi-scenario working status of the agricultural electric vehicle, and predict the required power of the next cycle based on the sampling data of the current cycle, so as to give full play to the working characteristics of each energy device; by introducing supercapacitors in the energy storage device, the dynamic response capability of the system is enhanced, and the stability of the bus voltage is guaranteed in real time; after the power closed loop is completed with a given reference power, the output power of the fuel generator and the energy storage device is adjusted in time through power feedback control, which can effectively avoid the power imbalance problem caused by incorrect sampling data of a certain cycle. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 This is a structural diagram of the hybrid energy system for an agricultural electric vehicle of the present invention;

[0035] Figure 2 It is a flow chart of the multi-scenario dynamic power control method of the hybrid electric agricultural vehicle of the present invention;

[0036] Figure 3 This is a block diagram of dynamic power calculation and prediction control of the present invention;

[0037] Figure 4 This is the simulation result when the system does not use dynamic power control;

[0038] Figure 5 The figure is a simulation result of a system adopting the dynamic power control of the present invention. DETAILED DESCRIPTION

[0039] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0040] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments may be combined with each other.

[0041] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments, but they are not intended to limit the present invention.

[0042] A multi-scenario dynamic power control method for a hybrid electric agricultural vehicle in this embodiment is applicable to a hybrid energy system for an agricultural electric vehicle. The structure of the hybrid energy system for an agricultural electric vehicle is as follows: Figure 1As shown, it includes a fuel generator, an AC / DC power converter, an energy storage device and a bidirectional DC / DC power converter, a DC bus, a bidirectional DC / AC power converter, an electric motor and a dynamic power control device; wherein the energy storage device includes a battery and a super capacitor and each is connected in series with a bidirectional DC / DC power converter. Compared with other electric vehicle energy systems, a high power density super capacitor is introduced to improve the dynamic response capability of the system. The dynamic power control device can update the power command in real time according to the working status of the electric vehicle.

[0043] The function realized by the dynamic power control device is the multi-scenario dynamic power control method of the hybrid electric agricultural vehicle of this embodiment. The specific process is as follows: Figure 2 As shown, specifically including:

[0044] Step 1: Real-time collection of bus voltage, bus power, fuel generator power, battery power and supercapacitor power. According to the relationship between bus power and input energy power, determine the working scenario of agricultural electric vehicles. The input energy power is the sum of fuel generator power, battery power and supercapacitor power. When the bus power is positive and greater than the input energy power, the working scenario is the agricultural electric vehicle going uphill. When the bus power is negative and less than the input energy power, the working scenario is the agricultural electric vehicle going downhill. When going uphill, the bus power increases significantly in the positive direction, and when going downhill, the bus power increases significantly in the negative direction. Specifically, it can be equivalent to pulse loads of different time scales. Its working time is related to the slope distance, the power amplitude is related to the slope steepness, and the power consumption is not constant, which can be summarized as low average power and high peak power. Considering that the hybrid energy system of agricultural electric vehicles belongs to an independent microgrid with small capacity and small inertia, in order to avoid the impact of pulse loads on the operating characteristics and control of independent microgrids and ensure the normal operation of loads, it is necessary to cooperate with power-type energy storage equipment (supercapacitors) for high-frequency instantaneous energy supply.

[0045] In the hybrid energy system, the fuel generator is one of the main energy sources. During the long-term flat working stage, the fuel generator works at a constant power output to provide the steady-state average power required by the DC bus; when there are large power changes such as uphill and downhill, the equivalent pulse power is mainly provided by power-type energy storage devices (batteries, supercapacitors). The hybrid power supply system meets the power demand of the load in different operating stages and maintains the stability of the DC bus voltage. The power control of the energy unit not only adopts closed-loop control, but also introduces power feedback to adjust the predicted value of the reference power of the next cycle, effectively avoiding the power imbalance problem caused by incorrect sampling data in a certain cycle.

[0046] Step 2: Based on the collected data of the current cycle, predict the power reference value required for the next cycle:

[0047] The block diagram of hybrid multi-scenario dynamic power calculation and predictive control is as follows Figure 3 As shown, the calculation of the reference value includes three parts: the steady-state power of the fuel generator, the low-frequency power of the battery, and the high-frequency power of the supercapacitor, including:

[0048] (1) Use a self-sliding window to set the bus power of the current cycle and predict the power reference value of the fuel generator in the next cycle. P g ( t n+1 ); t n Indicates the current cycle, t n+1 Indicates the next cycle;

[0049] In a preferred embodiment, a self-sliding window with a fixed width is set to slide along the time series, and the window is used to adjust the real-time sampled power. N = k +1, input as shown t n Cycle k +1 bus power sampling value sequence p ( t n-i ), take the arithmetic mean of the data in the window as the output value, and get the sampling sequence of the window :

[0050]

[0051] based on P 0( t n ), using exponential smoothing method to calculate the steady-state power reference value of the next cycle P 0( t n+1 ) is used for prediction, and its expression is:

[0052]

[0053] in, express t n Periodic bus power sampling value, is the smoothing factor.

[0054] The steady-state power reference value of the next cycle will be obtained P 0( t n+1 ) as the power reference value of the next cycle of the fuel generator P g ( t n+1).

[0055] (2) Perform a specific bandwidth transient power decomposition on the bus power of the current cycle to obtain the power reference value of the battery in the next cycle. P b ( t n+1 ):

[0056] In a preferred embodiment, the number of decomposed modes is given in the first few cycles. M=M 0, M 0 is the initial value of the modal number. According to the principle of minimizing the sum of bandwidths, the constrained optimization equation is equivalent to an unconstrained optimization equation through the augmented Lagrangian function, and the signal sequence is analyzed. satisfy:

[0057] In the formula, α is the balance factor, λ ( ω ) is the frequency domain signal corresponding to the Lagrange multiplier, represents the angular frequency, Indicates the decomposition j An angular frequency. represents the operational gradient, is the unit pulse function, represents the imaginary part, Indicates time;

[0058] The alternating direction method of the multiplier is used to solve the problem, and then the ( n +1) Sub-frequency domain analysis signal , specifically expressed as:

[0059]

[0060] Based on the obtained frequency domain analytical signal sequence According to the relationship between signal energy and bus power, the decomposed modal number M Make adjustments, specifically:

[0061] Gradually increasing M , when the frequency domain analytic signal sequence Energy Gradually approaching the bus power, when the difference between the two satisfies ε The design requirements are:

[0062]

[0063] At this time M The value is trimmed M value, To set the difference, the subsequent sequence operations use the newly obtainedM value.

[0064] After the repair M The obtained frequency domain analytical signal sequence is And its corresponding amplitude spectrum, the frequency domain analytical signal sequence Perform inverse Fourier transform and filter out low-frequency and high-amplitude time-domain analytical signals based on the amplitude spectrum as the battery power reference value for the next battery cycle.

[0065] (3) Supercapacitors are used to provide instantaneous high-frequency power and self-regulate bus voltage in real time to ensure the stability of bus voltage during scene switching. Their output power is determined by the real-time changes in bus voltage.

[0066] In a preferred embodiment, according to the real-time change of the bus voltage, the voltage closed-loop control of the bus voltage is used to obtain the power reference value of the supercapacitor in the next cycle. P c ( t n+1 ).

[0067] In a preferred embodiment, after the power closed loop is completed, feedback control is performed on the bus power change of the next cycle. The bus power value of the next cycle is sampled and compared with the calculated reference value:

[0068] When the difference between the bus power and the reference value meets the set error ε p ,Right now: The reference value of the next cycle is still the power reference value obtained above. P g ( t n+1 ), P b ( t n+1 )and P c ( t n+1 );

[0069] When the difference between the bus power and the reference value exceeds the set error range ε p ,Right now When the power difference is increased to the reference value of the next cycle of the battery, that is, the battery power reference value of the next cycle of the battery P b ( t n+1 ) is updated to: ; In the formula, w is the power difference factor.

[0070] Step 3: Based on the power reference value obtained P g ( t n+1 ), P b ( t n+1 )and P c ( t n+1 ), combined with the determined direction of change of the bus power, the power of the fuel generator, battery and supercapacitor for the next cycle is updated.

[0071] According to the judgment of the working scene of the agricultural electric vehicle (uphill, downhill), the power flow direction is determined, and the power reference value of the next cycle of the fuel generator is obtained. P g ( t n+1 ), after power judgment, the discharge power control loop or the charging power control loop of the fuel generator is used for control, and the output power of the fuel generator is updated in time by controlling the AC / DC converter.

[0072] According to the judgment of the working scene of the agricultural electric vehicle (uphill, downhill), the power flow direction is determined, and the power reference value of the next cycle of the battery is obtained. P b ( t n+1 ), and timely update of battery output power is achieved by controlling the bidirectional DC / DC converter.

[0073] According to the judgment of the working scene of the agricultural electric vehicle (uphill, downhill), the power flow direction is determined, and the power reference value of the supercapacitor in the next cycle is obtained. P c ( t n+1 ), and timely update of battery output power is achieved by controlling the bidirectional DC / DC converter.

[0074] Step 3 achieves smooth transition and efficiency maximization of energy during scene switching.

[0075] Based on the dynamic power control method proposed in this embodiment, the DC bus voltage under the condition that the load power increases from 1kW to 3kW v bus , generator current i ac , battery current i b , Supercapacitor current i c The simulation results are as follows Figure 4and Figure 5 As shown in Figure 2, it can be seen that when dynamic power control is not adopted, Figure 4 , the bus voltage will oscillate during the large-scale switching of load power, and the dynamic response will be poor until the generator power reaches stability and the system recovers stability; after adopting dynamic power control, such as Figure 5 During the large-scale switching of load power, the bus voltage remains stable. The supercapacitor responds quickly to the power increase, and the battery reacts immediately to achieve dynamic power regulation. As the battery power increases, the calculated reference power value of the generator increases, the generator power gradually increases, and the battery power decreases until it reaches a steady state. The battery power is reduced to 0, and the steady-state power is provided entirely by the generator.

[0076] The method of this embodiment can also be implemented in a computer-readable storage device, which stores a computer program. When the computer program is executed by a processor, the steps of the above-mentioned multi-scenario dynamic power control method for hybrid electric agricultural vehicles are implemented.

[0077] The method of this embodiment can also be implemented as a multi-scenario dynamic power control device for a hybrid agricultural electric vehicle, including a storage device, a processor, and a computer program stored in the storage device and executable on the processor. The processor executes the computer program to implement the steps of the multi-scenario dynamic power control method for the hybrid agricultural electric vehicle.

[0078] The method of this embodiment can also be implemented as a computer program product, including a computer program, which, when executed by a processor, implements the steps of the above-mentioned multi-scenario dynamic power control method for hybrid electric agricultural vehicles.

[0079] Although the present invention is described herein with reference to specific embodiments, it should be understood that these embodiments are merely examples of the principles and applications of the present invention. It should therefore be understood that many modifications may be made to the exemplary embodiments and that other arrangements may be devised without departing from the spirit and scope of the present invention as defined by the appended claims. It should be understood that the various dependent claims and features described herein may be combined in a manner different from that described in the original claims. It should also be understood that features described in conjunction with individual embodiments may be used in other described embodiments.

Claims

1. A multi-scenario dynamic power control method for hybrid electric agricultural vehicles, characterized in that: Applicable to hybrid energy systems of agricultural electric vehicles, where energy storage devices include supercapacitors and batteries; The control method comprises: S1. Real-time collection of bus voltage, bus power and input energy power, determining the working scene of the agricultural electric vehicle, and determining the change direction of the bus power according to the working scene. The multiple scenes include agricultural electric vehicles going uphill and agricultural electric vehicles going downhill. The input energy power is the sum of the output power of the fuel generator, the output power of the battery, and the output power of the supercapacitor; S2. Predict the power reference value required for the next cycle based on the collected current cycle data, including: The bus power of the current cycle is adjusted using a self-sliding window to predict the power reference value P of the fuel generator in the next cycle. g (t n+1 ): A self-sliding window with a fixed width is set to slide along the time series, and the bus power is adjusted using the window to obtain the sampling sequence P0(t n ): Among them, p(t n-i ) indicates t n The bus power sampling value in the cycle, the width of the sliding window is N=k+1, k is determined by the window width; The exponential smoothing method is used to calculate the steady-state power reference value P0(t n+1 ) is used for prediction, and its expression is: P0(t n+1 )=μp(t n )+(1-μ)P0(t n ) Among them, p(t n ) indicates t n Periodic bus power sampling value, μ is the smoothing factor; The steady-state power reference value P0(t n+1 ) as the power reference value P of the next cycle of the fuel generator g (t n+1 ); t n Indicates the current cycle, t n+1 Indicates the next cycle; Perform low-frequency bandwidth transient power decomposition on the bus power of the current cycle to obtain the power reference value P of the battery in the next cycle. b (t n+1 ): Fixed decomposition mode number M = M0, according to the principle of minimizing the sum of bandwidth, the frequency domain analytical signal around the determined center frequency is obtained through iterative calculation Among them, M0 is the initial value of M, α is the balance factor, λ(ω) is the frequency domain signal corresponding to the Lagrange multiplier, ω represents the angular frequency, ω j represents the j-th angular frequency of the decomposition; Based on the obtained frequency domain analytical signal sequence To trim M: Gradually increase M, when the frequency domain analytic signal sequence Energy The relationship with bus power satisfies: Then the M value at this time is the trimmed M value, To set the difference, express The corresponding time domain signal, p(t n ) indicates t n Periodic bus power sampling value; Under the modified M value, the frequency domain analytical signal sequence is Perform inverse Fourier transform, compare the amplitudes, and select the low-frequency and high-amplitude time-domain analytical signal based on the amplitude spectrum as the battery power reference value P for the next cycle of the battery b (t n+1 ); According to the real-time change of bus voltage, the power reference value P of the supercapacitor in the next cycle is obtained. c (t n+1 ); S3, according to the power reference value P g (t n+1 ), P b (t n+1 ) and P c (t n+1 ), combined with the change direction of the bus power determined by S1, the power of the fuel generator, battery and supercapacitor for the next cycle is updated.

2. The multi-scenario dynamic power control method of hybrid electric agricultural electric vehicle according to claim 1 is characterized in that: According to the real-time change of bus voltage, the power reference value P of the supercapacitor in the next cycle is obtained by using the voltage closed-loop control of the bus voltage. c (t n+1 ).

3. A computer-readable storage device storing a computer program, characterized in that: When the computer program is executed by the processor, the steps of the multi-scenario dynamic power control method for a hybrid electric agricultural vehicle as described in any one of claims 1 to 2 are implemented.

4. A multi-scenario dynamic power control device for a hybrid electric agricultural vehicle, comprising a storage device, a processor, and a computer program stored in the storage device and capable of running on the processor, characterized in that: The processor executes the computer program to implement the steps of the multi-scenario dynamic power control method for a hybrid electric agricultural vehicle as described in any one of claims 1 to 2.

5. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the multi-scenario dynamic power control method for a hybrid electric agricultural vehicle as described in any one of claims 1 to 2 are implemented.

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