Power regulation method, system, device and medium for vehicle hybrid energy storage device

Abstract

The application provides a power regulation method, system, device and medium of a vehicle hybrid energy storage device. The method comprises: predicting an error compensation value of the hybrid energy storage device according to a current change amount of a fuel cell of the vehicle at a current time; obtaining a state of charge of an electric energy storage device and a load current of the vehicle at the current time to obtain an adaptive weight value of the hybrid energy storage device; inputting a preset reference value, the error compensation value and the adaptive weight value into a model in a model predictive control algorithm to give a current change amount of the fuel cell of the vehicle at a next time, and regulate power distribution of the hybrid energy storage device at the next time; wherein the preset reference value is a preset state of charge of the electric energy storage device and a preset current value of the fuel cell. The application can realize effective power distribution, thereby greatly improving the service life of the fuel cell.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of fuel cells, in particular to a power regulation method, system, device and medium for a vehicle hybrid energy storage device. BACKGROUND

[0002] Under the background of carbon neutrality, the global vehicle industry urgently needs to explore new energy fuels to reduce dependence on traditional petrochemical fuels. Hydrogen, as a new energy, has the advantages of being clean, non-polluting, widely available, and high in combustion energy. Fuel cells (FC) have become the main form of hydrogen energy application due to their high energy conversion efficiency and clean operation process, and have received widespread attention. Although fuel cells can be used as the main energy source for fuel cell vehicles, due to their internal electrochemical reactions, their dynamic response is relatively slow. Therefore, fuel cell vehicles usually introduce electric energy storage devices (such as lithium ion batteries, supercapacitors, lithium ion capacitors, etc.) with faster dynamic response characteristics to form a hybrid energy storage device.

[0003] The hybrid energy storage device still faces many problems in the power distribution between the fuel cell and the electric energy storage device. The existing technology generally only considers single aspects such as system voltage stability or FC current slope. However, there are still many aspects that have not been covered and should be considered simultaneously. For example, the state of charge (SOC) of the electric energy storage device should be within a reasonable range within an operating cycle and should remain unchanged after an operating cycle. The FC current should be as small as possible to avoid expensive hydrogen consumption. In addition, rapid changes in FC current will shorten the life of the FC, so the current change rate of the FC should be as small as possible. Therefore, it is necessary to provide a power regulation method, system, device and medium for a vehicle hybrid energy storage device. SUMMARY

[0004] In view of the above shortcomings of the prior art, the purpose of the present application is to provide a power regulation method, system, device and medium for a vehicle hybrid energy storage device to improve the problem that the hybrid energy storage device of the vehicle does not consider all factors affecting its current time operation in the prior art, resulting in unreasonable allocation of current for the next moment of the hybrid energy storage device.

[0005] To achieve the above object and other related objects, the present application provides a power regulation method for a vehicle hybrid energy storage device, the hybrid energy storage device of a hybrid electric vehicle comprising a fuel cell and an electric energy storage device, the electric energy storage device comprising one or more of a lithium ion battery, a supercapacitor, and a lithium ion capacitor, the method comprising the following processes:

[0006] predicting an error compensation value of the hybrid energy storage device according to a given current change amount of the fuel cell at the current time of the vehicle;

[0007] obtain the adaptive weight value of the hybrid energy storage device by acquiring the state of charge of the electric energy storage device and the load current of the vehicle at the current time;

[0008] input the preset reference value, the error compensation value and the adaptive weight value into a model in a model predictive control algorithm, give the current variation of the fuel cell of the vehicle at the next time, and regulate the power distribution of the hybrid energy storage device at the next time; wherein the preset reference value is a preset state of charge of the electric energy storage device and a preset current value of the fuel cell.

[0009] In an embodiment of the present application, the adaptive weight value of the hybrid energy storage device is obtained by acquiring the state of charge of the electric energy storage device and the load current of the vehicle at the current time, comprising:

[0010] calculate the difference between the state of charge of the electric energy storage device and the preset reference value as the deviation value of the state of charge;

[0011] based on the deviation value and the load current, calculate the adaptive weight value of the hybrid energy storage device according to a preset weight calculation method.

[0012] In an embodiment of the present application, the adaptive weight value includes a first adaptive state weight, a second adaptive state weight and an adaptive input weight.

[0013] In an embodiment of the present application, if the load current is less than a preset threshold, the first adaptive state weight takes a minimum value when the deviation value is a first preset offset; if the load current is greater than or equal to the preset threshold, the first adaptive state weight takes a minimum value when the deviation value is a second preset offset.

[0014] In an embodiment of the present application, the adaptive weight value of the hybrid energy storage device is obtained by acquiring the state of charge of the electric energy storage device and the load current of the vehicle at the current time, comprising:

[0015] input the preset reference value and the adaptive weight value, the error compensation value into the model in the model predictive control algorithm, and give the current variation of the fuel cell at the next time according to the minimization of the cost function;

[0016] input the current variation of the fuel cell at the next time into a preset proportional integral controller, predict the duty cycle of the boost converter at the next time, and regulate the power distribution at the next time; wherein the boost converter is connected in parallel with the electric energy storage device after being connected in series with the fuel cell.

[0017] In an embodiment of the present application, the model in the model predictive control algorithm is represented as a state space equation, which is: x k+1 = Ax k +(B u +k c C2)u k +B d d k +k c C1, wherein A is a state coefficient matrix, B u is an input coefficient matrix, B d is a disturbance coefficient matrix, C1 and C2 are compensation factors, x k is a state variable at time k, u k is input data at time k, d k is a disturbance at time k, k c is a compensation weight, and x k+1 is a state variable at time k+1.

[0018] In an embodiment of the present application, before the step of predicting the error compensation value of the hybrid energy storage device according to the current change amount of the fuel cell of the vehicle at the current time, the method further comprises: constructing a cost function of the hybrid energy storage device according to a constraint condition preset for the fuel cell and the electric energy storage device and an operation requirement of the hybrid energy storage device, to obtain the model in the model predictive control algorithm.

[0019] In an embodiment of the present application, a power regulation system for a hybrid energy storage device of a vehicle is further provided. The hybrid energy storage device of the hybrid vehicle comprises a fuel cell and an electric energy storage device, the electric energy storage device comprises one or more of a lithium ion battery, a super capacitor and a lithium ion capacitor, and the system comprises:

[0020] an error compensation obtaining module configured to predict an error compensation value of the hybrid energy storage device according to a current change amount of the fuel cell of the vehicle at the current time;

[0021] a weight obtaining module configured to obtain a state of charge of the electric energy storage device and a load current of the vehicle at the current time, to obtain an adaptive weight value of the hybrid energy storage device;

[0022] a regulation module configured to input a preset reference value, the error compensation value at the current time and the adaptive weight value into a model in a model predictive control algorithm, to give a current change amount of the fuel cell of the vehicle at the next time, and to regulate power distribution of the hybrid energy storage device at the next time; wherein the preset reference value is a preset state of charge of the electric energy storage device and a preset current value of the fuel cell.

[0023] In an embodiment of the present application, a power regulating device of a vehicle hybrid energy storage device is also provided, comprising a processor coupled with a memory, the memory storing program instructions which, when executed by the processor, implement any of the above methods.

[0024] In an embodiment of the present application, a computer readable storage medium is also provided, comprising a program which, when running on a computer, implements any of the above methods.

[0025] In summary, in the present application, by obtaining the current change of the fuel cell, an error compensation value can be obtained. By calculating the state of charge of the current electric energy storage device and the load current, a corresponding adaptive weight value can be obtained. The error compensation value and the adaptive weight value, as well as the preset reference value, are input into the model in the model predictive control algorithm to obtain the current change of the fuel cell. The power of the hybrid energy storage device at the next moment can be regulated by the current change of the fuel cell. The final power distribution is more reasonable, and the service life of the fuel cell is improved. BRIEF DESCRIPTION OF DRAWINGS

[0026] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments or prior art description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0027] Figure 1 A semi-active power system topology diagram of a fuel cell hybrid electric vehicle in the present application is shown;

[0028] Figure 2 An overview schematic diagram of a control scheme in the present application is shown;

[0029] Figure 3 An adaptive weight q1 based on SC SOC deviation in the present application is shown;

[0030] Figure 4 An adaptive weight q2 and r of SC SOC deviation in the present application is shown;

[0031] Figure 5 A fluctuation curve diagram of simulating SC SOC in the present application is shown;

[0032] Figure 6 A curve diagram of simulating FC current change rate in the present application is shown;

[0033] Figure 7A flow chart showing a power regulation method of a vehicle hybrid energy storage device according to an embodiment of the present application;

[0034] Figure 8 A flow chart showing step S20 according to an embodiment of the present application;

[0035] Figure 9 A flow chart showing step S30 according to an embodiment of the present application;

[0036] Figure 10 A schematic diagram showing a principle structure of a power regulation system of a vehicle hybrid energy storage device according to an embodiment of the present application.

[0037] Element number explanation:

[0038] 100, a power regulation system of a vehicle hybrid energy storage device; 110, an error compensation acquisition module; 120, a weight acquisition module; 130, a regulation module. DETAILED DESCRIPTION

[0039] The present application is described in greater detail by the following specific examples. Other advantages and effects of the present application can be easily understood by those skilled in the art from this disclosure. The present application can be implemented or applied in other different specific embodiments, and various modifications or changes can be made to the details of the present application based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict. It should also be understood that the terms used in the embodiments of the present application are intended to describe specific specific embodiments, not to limit the protection scope of the present application. The test methods in the following embodiments are not specified, and are generally performed under conventional conditions or under conditions recommended by the manufacturer.

[0040] Please refer to Figures 1 to 10 It should be noted that the structures, proportions, sizes, etc. shown in the drawings of the present application are only used to illustrate the content disclosed in the present application, to enable those skilled in the art to understand and read, and do not limit the conditions under which the present application can be implemented, so they do not have technical significance. Any modification of the structure, change of the proportion relationship or adjustment of the size, without affecting the effect and purpose that can be achieved by the present application, should still fall within the scope of the technical content disclosed by the present application. At the same time, the terms such as "up", "down", "left", "right", "middle" and "one" used in the present application are only for the convenience of clear description, not to limit the scope of the present application, and the change or adjustment of the relative relationship, without substantially changing the technical content, is also considered as the scope of the present application.

[0041] When the embodiments give numerical ranges, it is understood that unless the disclosure specifically states otherwise, each numerical range's two endpoints, and any number that falls between the two endpoints, are optional. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art in the field of the application, and any method and material similar or equivalent in function, in result to be described in the embodiments of the application can be used to implement the application.

[0042] Referring to Figures 1 to 2 , the application provides a power regulation method of a vehicle hybrid energy storage device. For a fuel cell hybrid electric vehicle, a fuel cell and a boost converter are connected in series and used as a main power source, and an electric energy storage device is used as an energy buffer and connected in parallel with the boost converter. Based on a conventional semi-active topology structure, an adaptive weight and a model error compensation are introduced on the basis of a model predictive control algorithm, and a new power management strategy is proposed. Compared with a conventional model predictive control algorithm, the new power management strategy has more excellent performance in terms of hydrogen consumption, stability of a state of charge of the electric energy storage device, and alleviation of fuel cell aging.

[0043] Referring to Figure 1 and Figure 7 , in an embodiment of the application, the power regulation method of the vehicle hybrid energy storage device comprises the following processes.

[0044] S10, predicting an error compensation value of the hybrid energy storage device according to a current change amount of the fuel cell given by the vehicle at a current time.

[0045] Figure 1 In the formula, the load includes components such as a DC / AC inverter and a motor. L fc is an input inductance of the analog boost converter, R fc represents a parasitic resistance of L fc , D fcis a duty cycle signal generated by the PI controller. The current variation of the fuel cell at the current time is a value that the current of the fuel cell changes at the current time compared with the previous time. Since the model in the model predictive control (MPC) algorithm is a model based on a linear system, considering that the hybrid energy storage system (HESS) described in the application is nonlinear, linearization of the hybrid energy storage system will cause errors to occur, so that the subsequent output results of the model are inaccurate. In order to improve this situation, in the application, the concept of error compensation is introduced, and the error compensation value is input to the model in the model predictive control algorithm to realize the correction of the output value of the model in the model predictive control algorithm at the next time. Specifically, the difference between the actual value and the predicted value of the state variable can be expanded by the Taylor series shown in formula (1):

[0046] E(u k )=C1+C2u k +C3u k 2 +… (1)

[0047] Wherein, E(u k ) is the difference between the predicted value and the actual value of the state variable at k time, u k is the input data at k time (i.e. the current variation of the fuel cell at k time), C1, C2, C3 are corresponding compensation factors. Wherein, the state variable includes the current value of the fuel cell, and the state of charge of the electric energy storage device. It should be noted that the type of electric energy storage device includes but is not limited to lithium ion battery, super capacitor, lithium ion capacitor and other energy storage devices with better power characteristics than fuel cell. The type of electric energy storage device can be one or more, which is not limited herein. The application takes super capacitor as an example to describe the related action principle, and the action principle of other types of electric energy storage devices is similar to that of super capacitor, which is not described herein. Since the difference E(u k ) can be regarded as an error compensation value, in order to reduce the calculation cost, in an embodiment of the application, the square and higher powers of u k are ignored, and the compensation equation obtained is shown in formula (2), and the error compensation value C(u k ) of the hybrid energy storage device at the current k time is obtained based on formula (2) as an error compensation part.

[0048] C(u k )=C1+C2u k (2)

[0049] Therefore, through the input data u k(i.e. the amount of change in the current of the fuel cell), i.e. to obtain an error compensation value, and input the error compensation value as an error compensation part into the model in the model predictive control algorithm to adjust the power distribution of the super capacitor and the fuel cell in the hybrid energy storage device at the next time. It can be understood that the error compensation value can also be calculated by ignoring the Nth power and higher powers (N is an integer greater than 2) of u k The skilled person can adaptively select an appropriate method according to the actual calculation accuracy and calculation cost needs, and the method is not limited here.

[0050] S20, obtaining the state of charge of the electric energy storage device and the load current of the vehicle at the current time to obtain the adaptive weight value of the hybrid energy storage device.

[0051] Please refer to Figure 1 , Figure 2 and Figure 8 In an embodiment of the present application, step S20 includes the following process:

[0052] S21, calculating the difference between the state of charge of the electric energy storage device and the preset reference value as the deviation value of the state of charge;

[0053] S22, calculating the adaptive weight value of the hybrid energy storage device according to the preset weight calculation method based on the deviation value and the load current.

[0054] Please refer to Figure 1 Since the present application uses the model in the model predictive control algorithm to perform power prediction control on the HESS of the vehicle, the vehicle in the present application refers to an electric vehicle with fuel cell hybrid power. The target of the power management strategy of the vehicle is to appropriately split and distribute power to the fuel cell and the super capacitor, reduce the amount of hydrogen consumption, and alleviate the degradation of the fuel cell. However, according to the driving conditions of the vehicle and the working conditions of the hybrid energy storage device, one target may need to be optimized with higher priority. Considering that if the related weight of the model in the model predictive control algorithm is a fixed value, once the driving conditions of the vehicle change, timely regulation and control cannot be achieved, resulting in inaccurate power distribution to the fuel cell and the super capacitor, which greatly reduces the control performance. In order to solve this problem, the present application introduces the concept of adaptive weight, taking the deviation value of the state of charge of the super capacitor relative to the preset reference value as the adaptive weight, which can express the deviation value of the state of charge SOC d as shown in formula (3):

[0055] SOC d = SOC k -SOC ref (3)

[0056] wherein, SOCk SOCk is the state of charge of the super capacitor at time k ref SOCref is the preset state of charge of the super capacitor. Specifically, the related weights of the model in the model predictive control algorithm are expressed in the form of deviation value SOC d to achieve the adjustment of the weights by deviation value SOC d Specifically, the weights are as shown in formula (4):

[0057]

[0058] wherein q1 is the first adaptive state weight, q2 is the second adaptive state weight, r is the adaptive input weight, a q1 , b q11 , b q12 , a q2 , b q2 , a r and b r are preset adaptive parameter values, which are related to the settings of vehicle parameters, O d , O c are offset values, i load is the load current. According to the above formula (4), the corresponding q1 can be selected for calculation according to whether the value of the load current at the current time k is negative. And based on the corresponding values of the state of charge SOC k of the super capacitor at the current time k and the preset reference value SOC ref , the values of the three adaptive weights q1, q2 and r of the model in the model predictive control algorithm can be obtained, so as to change the output values of the model in the subsequent model predictive control algorithm by adjusting the corresponding weight values.

[0059] Further, please refer to Figure 3 and Figure 4 Since q1, q2 and r are all related to the deviation value SOC d , if the deviation value SOC d is small, it means that the state of charge of the super capacitor (i.e. SC SOC) has been well controlled at this time, in order to further maintain this small deviation value SOC d , q1 can be reduced at this time, while q2 and r can be increased. On the other hand, when the deviation value SOC d is large, q1 needs to be increased at this time, while q2 and r can be reduced. It should be noted that Figure 4 there are two longitudinal coordinates in the graph, q2 corresponds to the left longitudinal coordinate (i.e. 0.009 to 0.011), and r corresponds to the right longitudinal coordinate (i.e. 0.8 to 1.05). Further, the adaptive parameter values in formula (4) and the simulation parameters related to the fuel cell and the super capacitor are shown in Table 1:

[0060] Table 1 Simulation parameter table

[0061]

[0062] In an embodiment of the application, since the adaptive weight value comprises the first adaptive state weight q1, the second adaptive state weight q2 and the adaptive input weight r, if the load current is less than a preset threshold, the deviation value is a first preset offset, the first adaptive state weight takes a minimum value; if the load current is greater than or equal to the preset threshold, the deviation value is a second preset offset, the first adaptive state weight takes a minimum value. Specifically, in combination with Table 1 and Figure 4 It can be seen that q2 and r reach a peak value when the deviation value SOC d is zero. On the other hand, as Figure 3 drawn in two q1 curves: one corresponds to the value distribution of q1 when the load current is greater than or equal to zero, and the other corresponds to the value distribution of q1 when the load current is less than zero. For q1, two offsets O d and O c are introduced. Wherein, when the load current i load is greater than or equal to zero, the first adaptive weight value q1 takes a minimum value when the first preset offset O d is -1%; when the load current i load is less than zero, the first adaptive weight value q1 takes a minimum value when the second preset offset O c is 1%. It can be seen that q1 does not take a minimum value when the deviation value of the state of charge is zero, but by introducing the offset O c , so that q1 takes a minimum value when the deviation value SOC d of the state of charge is -1 (i.e. the offset O c is -1). Wherein, the preset threshold can be adaptively set as needed, and in the embodiment, the preset threshold is set to zero. By introducing two offsets O d and O c , it is ensured that the state of charge of the super capacitor can be maintained at a relatively stable level, so that the hybrid energy storage device can operate normally. Taking the offset O c as an example, when the load current is less than zero, the super capacitor is charged. If the preset state of charge of the super capacitor is preset to 50%, when the state of charge of the super capacitor reaches 49%, the deviation value SOC d of the state of charge is -1, q1 takes a minimum value. This means that the super capacitor may still be charging, but the current is very small. If a large regenerative power pulse is generated during the next sampling interval, the super capacitor can still be charged.

[0063] S30, inputting the preset reference value, the error compensation value and the adaptive weight value into a model in the model predictive control algorithm, giving a current change of the fuel cell, and regulating power distribution of the hybrid energy storage device at the next moment; wherein the preset reference value is a preset state of charge of the electric energy storage device and a preset current value of the fuel cell.

[0064] Please refer to Figure 1 、 Figure 2 and Figure 9 In an embodiment of the present application, step S30 comprises the following process:

[0065] S31, inputting the preset reference value, the error compensation value and the adaptive weight value into the model in the model predictive control algorithm, and giving a current change of the fuel cell according to minimization of a cost function;

[0066] S32, inputting the current change of the fuel cell into a preset proportional integral controller, predicting a duty cycle of the boost converter at the next moment, and regulating power distribution at the next moment; wherein the boost converter is connected in parallel with the electric energy storage device after being connected in series with the fuel cell.

[0067] After obtaining the error compensation value and the adaptive weight value at the current moment, the model predictive control algorithm in the model is used to obtain the current change of the fuel cell at the next moment by inputting the error compensation value, the adaptive weight value and the preset reference value into the model in the model predictive control algorithm. The duty cycle of the boost converter at the next moment can be obtained through a proportional integral (PI) controller, and the power distribution of the hybrid energy storage device is adjusted based on the duty cycle. Further, in an embodiment of the present application, before predicting the error compensation value of the hybrid energy storage device according to the current change of the fuel cell given by the vehicle at the current moment, the model in the model predictive control algorithm is constructed according to a constraint condition of the fuel cell and the electric energy storage device and an operation requirement of the hybrid energy storage device. The cost function of the model predictive control algorithm is as shown in formula (5):

[0068]

[0069] wherein X k is a set of N state variables predicted at k moment, U k is a set of N-1 input data predicted at k moment, Q is a symmetric positive definite weight coefficient matrix of state variables, R is a symmetric positive definite weight coefficient matrix of input data, X ref is a set of preset reference values of state variables, and N is a prediction step. Specifically, x k|k is the state variable at time k predicted at time k, x k+N|k is the state variable at time k+N predicted at time k predicted at time k, u k|k is the input data at time k predicted at time k, u k+N-1|k is the input data at time k+N-1 predicted at time k predicted at time k, u ref includes a reference to the state variable: x ref = [SOC ref , 0] T , SOC ref is a preset state of charge of the super capacitor. R = r. Wherein, q1 is a first adaptive state weight value, q2 is a second adaptive state weight value, and r is an adaptive input weight value. It should be noted that,

[0070] After the preset reference value and the error compensation value and the adaptive weight value at the current time k are input into the model in the model predictive control algorithm, the input data that minimizes the cost function J(x k , U k ) in formula (5) is obtained. And according to the following spatial state equation, the predicted value of the state variable at the next time is obtained. It can be understood that if the prediction step is set to N, the preset reference value and the error compensation value and the adaptive weight value at the current time k are input into the model in the model predictive control algorithm, the model in the model predictive control algorithm in the present application will obtain the current change Δi fc (k), Δi fc (k+1), Δi fc (k+2),..., Δi fc (k+N-1) of N fuel cells, and the current change of adjacent fuel cells is separated by a fixed sampling period T s . Through the PI controller, N corresponding duty cycle data D fc (k), D fc (k+1), D fc (k+2),..., D fc (k+N-1) can be obtained to form a duty cycle sequence. The first duty cycle data D fc (k) in the sequence is output as an adjustment signal, and by adjusting the duty cycle of the boost converter, the current flowing through the fuel cell and the current flowing through the super capacitor are changed, thereby realizing power regulation of the hybrid energy storage device at the next time.

[0071] Further, in the present application, the predicted value of the state variable is obtained through the state space equation, and the model in the model predictive control algorithm can be represented as the space state equation shown in formula (6) in the discrete state space by using Euler discretization:

[0072] x k+1 =Ax k +B u u k +B d d k (6)

[0073] Wherein, x k+1 is the state variable at k+1 time, A is the state coefficient matrix, B u is the input coefficient matrix, B d is the disturbance coefficient matrix, d k is the disturbance at k time, x k is the state variable at k time, and u k is the input data at k time. Considering the error of the state variable, the error compensation part is added to formula (6), and the state space equation is obtained as:

[0074] x k+1 =Ax k +B u u k +B d d k +k c C(u k ) (7)

[0075] Wherein, k c is the compensation weight, and further, formula (7) can be updated as formula (8) in combination with formula (2):

[0076] x k+1 =Ax k +(B u +k c C2)u k +B d d k +k c C1 (8)

[0077] It can be understood that C1 and C2 can be adaptively set to corresponding values according to actual needs by those skilled in the art, and no requirement is made herein. For the specific MPC scheme considered in the present application, the state variable, input data and disturbance at k time are represented as formulas (9) to (11) respectively:

[0078] x k =[SOC(k),i fc (k)] T(9)

[0079] u k = Δi fc (k) (10)

[0080] d k = i load (k) (11)

[0081] wherein SOC(k) is the state of charge of the super capacitor at the k moment, i fc (k) is the current of the fuel cell at the k moment, Δi fc (k) is the current variation of the fuel cell at the k moment, i load (k) is the load current at the k moment. Further, the above coefficient matrixes are respectively represented as shown in formulas (12) to (14):

[0082]

[0083]

[0084]

[0085] Please refer to Figure 2 wherein η boost is the efficiency of the boost converter, v fc (k) is the voltage value of the fuel cell at the k moment, T s is the preset sampling period, C sc is the capacitance value of the super capacitor, v sc (k) is the output voltage of the super capacitor at the k moment, v max is the maximum working voltage of the fuel cell. By substituting formulas (9) to (14) into formula (8), the predicted value of the state variable at the next moment can be calculated. Then when the next moment comes, three adaptive weight values are generated according to the predicted value and the actual value of the state variable, the error compensation value is obtained according to the current variation of the fuel cell, and the error caused by the linearization of the nonlinear hybrid energy storage device is compensated according to the adaptive weight value and the error compensation value, and the predicted value of the current variation and the state variable of the fuel cell at the next moment is given by the model in the model predictive control algorithm. It should be noted that the state variable x k+1 in the present application includes the predicted current value of the fuel cell at the k+1 moment and the predicted state of charge of the super capacitor, and the corresponding x k is the current value of the fuel cell at the k moment and the state of charge of the super capacitor.

[0086] Further, according to the power conservation, it can be known that the load power is shown as formula (15):

[0087] P load= η boost P fc +P sc (15)

[0088] where P fc is the power of fuel cell, P sc is the power of super capacitor, and P load is the load power. Therefore, by changing the current of fuel cell and the current of super capacitor, the overall power distribution of hybrid energy storage device can be changed.

[0089] Further, when simulating, fuel cell model, super capacitor model and boost converter model need to be constructed. For fuel cell model, since the voltage of single fuel cell is relatively low and changes with load current. Therefore, in practical application, multiple fuel cells are usually connected in series to form a stack to obtain a certain output voltage. The output voltage of fuel cell at time k is shown in equation (16):

[0090] v fc (k) = V ofc -R ofc i fc (k) (16)

[0091] where V ofc is the open circuit voltage of fuel cell, R ofc is the equivalent resistance of fuel cell, v fc (k) is the output voltage of fuel cell at continuous time domain k, and i fc (k) is the current of fuel cell at continuous time domain k. Obviously, if the current i fc (k) of fuel cell at time k increases, the output voltage of corresponding fuel cell decreases, thereby affecting the power of fuel cell at time k.

[0092] For super capacitor model, since the characteristic of super capacitor is the change rate of state of charge (SOC), which is related to the output current of super capacitor, as shown in equation (17):

[0093]

[0094] where C sc is the capacitance value of super capacitor, v max is the maximum working voltage of fuel cell, and i sc (k) is the output current of super capacitor at time k.

[0095] Please refer to Figure 1For the boost converter model, since the boost converter is used to bridge the voltage difference between the fuel cell and the supercapacitor, the output current of the boost converter is as shown in formula (18):

[0096]

[0097] Among them, v sc (k) represents the output voltage of the supercapacitor at time k, i dc (k) represents the output current of the boost converter at time k, η boost The efficiency of the boost converter is given. Clearly, as shown in equation (19), the load current i at time k is... load (k) is the output current i of the boost converter at time k. dc (k), and the output current i of the supercapacitor at time k. sc (k) Composition:

[0098] i load (k)=i sc (k)+i dc (k) (19)

[0099] Therefore, by substituting formulas (18) and (19) into formula (17), formula (17) can be rewritten as formula (20):

[0100]

[0101] Please see Figure 5 It can be seen that the state of charge (SOC) of the supercapacitor fluctuates well and within bounds. During the approximately 550s load change, the SOC reaches a minimum of 58.45% and a maximum of 64.04%. Throughout the simulation, the SOC fluctuation remains within 5.59%, and the supercapacitor eventually reaches a SOC approximately equal to its initial value. This indicates that the method described in this invention can maintain the SOC of the supercapacitor within a reasonable range (during the driving cycle) and at a reasonable level (in subsequent driving cycles), thereby reducing the possibility of overcharging or over-discharging.

[0102] Please see Figure 6 It can be seen that the current change rate of the fuel cell is less than 8 A / s, and the fuel cell reaches its maximum current change rate of 7.6672 A / s at around 210 s. This indicates that the method described in this invention can effectively alleviate the degradation of the fuel cell. This proves the effectiveness of the method described in this invention.

[0103] From the table, in terms of hydrogen consumption, although the MPC without adaptive compensation reduces by 5.14% compared with the Dual-loop, the hydrogen consumption of the application reduces by 6.98%, which is more significant, thereby effectively reducing the hydrogen consumption. During the vehicle driving process, the super capacitor state of charge change is significantly reduced, effectively alleviating the rapid change of the super capacitor state of charge. In addition, for the maximum current change rate of the fuel cell, compared with the MPC (the maximum current change rate of the fuel cell is 21.1621A / s), the maximum current change rate of the fuel cell of the application is as low as 18.1322A / s. Therefore, the ACMPC scheme proposed in the application is superior to the previous two schemes in reducing hydrogen consumption, alleviating fuel cell degradation, and maintaining the state of charge change of the super capacitor.

[0104] Table 2 Comparison table of three power management strategies

[0105]

[0106] In summary, the power regulation method of the vehicle hybrid energy storage device in the application is as follows: obtaining the current change amount Δi fc (k) of the fuel cell of the vehicle at the current time k, and the state of charge (SC SOC) of the super capacitor and the load current i load . Based on the preset Taylor expansion method, the error compensation value is obtained according to the current change amount Δi fc (k) of the fuel cell at the current time k. And based on the current state of charge of the super capacitor and the load current, the corresponding adaptive weight value is obtained according to the preset weight calculation method. The preset charge state SOC ref of the super capacitor and the preset current value i fcref of the fuel cell, the error compensation value u k and the corresponding adaptive weight value are jointly input into the model in the model predictive control algorithm, and the current change amount Δi fc (k+1) of the fuel cell at the next time k+1 and the predicted value of the state variable at the next time k+1 are given. Then the current change amount of the fuel cell at the time k+1 is input into the PI controller to obtain the duty cycle D fc of the boost converter. The duty cycle can regulate the boost converter to change the current value of the fuel cell and the state of charge of the super capacitor at the next time, and realize the power regulation of the hybrid energy storage device.

[0107] The step division of the above method is only for the purpose of clear description, and can be combined into one step or split into multiple steps in implementation, as long as the same logical relationship is included, and all are within the protection scope of the application; adding irrelevant modifications or introducing irrelevant designs in the algorithm or process, but not changing the core design of the algorithm and process, are within the protection scope of the application.

[0108] Please refer to Figure 10 . The power regulation system 100 of the vehicle hybrid energy storage device includes an error compensation acquisition module 110, a weight acquisition module 120, and a regulation module 130. The error compensation acquisition module 110 is configured to predict an error compensation value of the hybrid energy storage device according to a current variation of the fuel cell given by the vehicle at the current time. The weight acquisition module 120 is configured to acquire a state of charge of the electric energy storage device and a load current of the vehicle at the current time, and obtain an adaptive weight value of the hybrid energy storage device. The regulation module 130 is configured to input a preset reference value, the error compensation value at the current time, and the adaptive weight value into a model in a model predictive control algorithm that is pre-trained, to give a current variation of the fuel cell of the vehicle at the next time, and to regulate power distribution of the hybrid energy storage device at the next time. The preset reference value is a preset state of charge of the electric energy storage device and a preset current value of the fuel cell.

[0109] It should be noted that, in order to highlight the innovative part of the application, modules not closely related to solving the technical problems proposed in the application are not introduced in this embodiment, but this does not mean that there are no other modules in this embodiment.

[0110] In addition, those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the system described above can refer to the corresponding process in the foregoing method embodiments, which will not be described here. In the embodiments provided by the application, it should be understood that the disclosed system, device and method can be implemented by other ways. For example, the device embodiments described above are only schematic, for example, the division of the modules is only a logical function division, and the actual implementation can be in another division manner, for example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the shown or discussed mutual elements can be indirect coupling or communication connection through some interfaces, devices or units, and can be electrical, mechanical or other forms.

[0111] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules, i.e., may be located in one place, or may be distributed to multiple network modules. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

[0112] In addition, each functional module in various embodiments of the application can be integrated into one processing module, or each module can exist physically alone, or two or more modules can be integrated into one module. The integrated module can be realized in the form of hardware or in the form of a software functional unit.

[0113] The embodiment also provides a power regulation device of a vehicle hybrid energy storage device, which comprises a processor and a memory coupled with the processor, and the memory stores program instructions, and the program instructions stored in the memory are executed by the processor to implement the above task management method. The processor can be a general processor, including a central processing unit (CPU), a network processor (NP), etc.; can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components; the memory can include a random access memory (RAM) and can also include a non-volatile memory (Non-Volatile Memory), such as at least one disk memory. The memory can be an internal memory of a random access memory (RAM) type, and the processor and the memory can be integrated into one or more independent circuits or hardware, such as an application specific integrated circuit (ASIC). It should be noted that the computer program in the above memory can be implemented in the form of a software functional unit and sold or used as an independent product, which can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a plurality of instructions for causing a computer device (which can be a personal computer, an electronic device, or a network device, etc.) to execute all or part of the steps of the embodiments of the present application.

[0114] The embodiment also provides a computer readable storage medium storing computer instructions for causing a computer to execute the power regulation method of the vehicle hybrid energy storage device. The storage medium can be an electronic medium, a magnetic medium, an optical medium, an electromagnetic medium, an infrared medium, or a semiconductor system or a propagation medium. The storage medium can also include a semiconductor or solid-state memory, a magnetic tape, a removable computer disk, a random access memory (RAM), a read-only memory (ROM), a hard disk, and an optical disk. The optical disk can include a compact disk-read only memory (CD-ROM), a compact disk-read / write (CD-RW), and a DVD.

[0115] In summary, in the present application, considering that the calculation cost of the nonlinear MPC scheme is usually high, and the accuracy of the linear MPC scheme is usually affected. Therefore, by combining the linear MPC scheme and the adaptive compensation, the ACMPC scheme is constructed, so that the linear accuracy of the model in the final model predictive control algorithm is effectively improved, the effective power distribution can be realized, so that the service life of the fuel cell is greatly improved, the high-cost hydrogen consumption is avoided, and the state of charge of the electric energy storage device is kept in a reasonable range, and the resource consumption is effectively slowed down. Therefore, the present application effectively overcomes some practical problems in the prior art, and has high utilization value and use significance.

[0116] The above embodiments only exemplarily illustrate the principles and effects of the present application, and are not used to limit the present application. Any person skilled in the art can modify or change the above embodiments without departing from the spirit and scope of the present application. Therefore, all equivalent modifications or changes completed by those skilled in the art without departing from the spirit and technical thought disclosed by the present application should be covered by the claims of the present application.

Claims

1. A method of power conditioning for a vehicle hybrid energy storage device, comprising: The hybrid energy storage device of the hybrid vehicle includes a fuel cell and an electric energy storage device, the electric energy storage device including one or more of a lithium ion battery, a super capacitor, and a lithium ion capacitor, and the method includes the following processes: predicting an error compensation value of the hybrid energy storage device according to a current change amount of the fuel cell at a current time of the vehicle; obtaining a state of charge of the electric energy storage device and a load current of the vehicle at the current time to obtain an adaptive weight value of the hybrid energy storage device; inputting a preset reference value and the error compensation value and the adaptive weight value into a model in a model predictive control algorithm to give a current change amount of the fuel cell at a next time of the vehicle, and to regulate power distribution of the hybrid energy storage device at the next time; wherein the preset reference value is a preset state of charge of the electric energy storage device and a preset current value of the fuel cell; wherein the adaptive weight value includes a first adaptive state weight, and when the load current is less than a preset threshold value and a deviation value is a first preset offset, the first adaptive state weight takes a minimum value; when the load current is greater than or equal to the preset threshold value and the deviation value is a second preset offset, the first adaptive state weight takes a minimum value; wherein the deviation value is obtained by calculating a difference between the state of charge of the electric energy storage device and the preset reference value.

2. The method of claim 1, wherein, The obtaining of the state of charge of the electric energy storage device and the load current of the vehicle at the current time to obtain the adaptive weight value of the hybrid energy storage device includes: calculating a difference between the state of charge of the electric energy storage device and the preset reference value as a deviation value of the state of charge; calculating the adaptive weight value of the hybrid energy storage device according to a preset weight calculation method based on the deviation value and the load current.

3. The method of claim 2, wherein, The adaptive weight value further includes a second adaptive state weight and an adaptive input weight.

4. The method of claim 1, wherein, The inputting of the preset reference value and the error compensation value and the adaptive weight value at the current time into the model in the model predictive control algorithm to give the current change amount of the fuel cell at the next time of the vehicle, and to regulate the power distribution of the hybrid energy storage device at the next time includes: inputting the preset reference value and the adaptive weight value and the error compensation value into the model in the model predictive control algorithm to give the current change amount of the fuel cell at the next time according to minimization of a cost function; inputting the current change amount of the fuel cell at the next time into a preset proportional integral controller to predict a duty cycle of a boost converter at the next time to regulate the power distribution at the next time; wherein the boost converter is connected in parallel with the electric energy storage device after being connected in series with the fuel cell.

5. The method of claim 4, wherein, The model in the model predictive control algorithm is represented as a state space equation, which is: x k+1 = Ax k +(B u +k c C2)u k +B d d k +k c C1, wherein A is a state coefficient matrix, B u is an input coefficient matrix, B d is a disturbance coefficient matrix, C1 and C2 are compensation factors, x k is a state variable at time k, u k is input data at time k, d k is a disturbance at time k, k c is a compensation weight, and x k+1 is a state variable at time k+1.

6. The method of claim 1, wherein, Before the predicting of the error compensation value of the hybrid energy storage device according to the current change amount of the fuel cell at the current time of the vehicle, the method further includes constructing a cost function of the hybrid energy storage device according to preset constraint conditions of the fuel cell and the electric energy storage device and operating requirements of the hybrid energy storage device to obtain the model in the model predictive control algorithm.

7. A power conditioning system for a vehicle hybrid energy storage device, comprising: The hybrid energy storage device of the hybrid vehicle comprises a fuel cell and an electric energy storage device, the electric energy storage device comprising one or more of a lithium ion battery, a super capacitor, a lithium ion capacitor, and the system comprising: an error compensation obtaining module configured to predict an error compensation value of the hybrid energy storage device according to a current variation of the fuel cell of the vehicle at a current time; a weight obtaining module configured to obtain a state of charge of the electric energy storage device and a load current of the vehicle at the current time, and obtain an adaptive weight value of the hybrid energy storage device; a regulating module configured to input a preset reference value, the error compensation value at the current time, and the adaptive weight value into a model in a model predictive control algorithm, give a current variation of the fuel cell of the vehicle at a next time, and regulate power distribution of the hybrid energy storage device at the next time; wherein the preset reference value is a preset state of charge of the electric energy storage device and a preset current value of the fuel cell; wherein the adaptive weight value comprises a first adaptive state weight, the first adaptive state weight taking a minimum value when the load current is less than a preset threshold and a deviation value is a first preset offset, the first adaptive state weight taking a minimum value when the load current is greater than or equal to the preset threshold and the deviation value is a second preset offset; wherein the deviation value is obtained by calculating a difference between the state of charge of the electric energy storage device and the preset reference value.

8. A power conditioning apparatus for a vehicle hybrid energy storage device, characterized by: A processor coupled with a memory, the memory storing program instructions that, when executed by the processor, implement the method of any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: A program that, when executed on a computer, performs the method of any one of claims 1 to 6.

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