Hybrid power system based on braking working condition prediction, energy management method and system

Abstract

A hybrid power system based on braking working condition prediction, and an energy management method and system. A braking estimation coefficient is predicted on the basis of the maximum driving speed before braking, so that output power of a fuel cell system is more reasonable, effectively improving fuel economy of the hybrid power system, and reducing operating costs of a vehicle. Moreover, on the basis of the limitation of an output set point, fluctuation of the output power of the fuel cell system is reduced, the aging process of the system is effectively slowed down, and the maintenance costs of the system are reduced.

Description

Hybrid power systems and energy management methods based on braking condition prediction Technical Field

[0001] This invention relates to the field of hybrid power and network control systems for digital rail vehicles, and particularly to hybrid power systems and energy management methods and systems based on braking condition prediction. Background Technology

[0002] In terms of vehicle power systems, hybrid power systems composed of fuel cells / supercapacitors are relatively mature and have been widely used. Currently, most online control technologies are based on optimizing the overall power consumption function to control the system. However, considering that supercapacitors do not produce or consume hydrogen, current electro-hydrogen conversion methods mainly optimize the instantaneous power of the entire system by equating the instantaneous charging and discharging power of the supercapacitor with the instantaneous loss of hydrogen energy.

[0003] However, energy management methods for hybrid power systems still have certain shortcomings. While existing technologies can improve the utilization rate of hydrogen energy, the accuracy of the electro-hydrogen conversion still needs further improvement, and many parameters in existing technologies rely too heavily on empirical values, lacking universality. Therefore, in terms of energy management of hybrid power systems, the combined effect of load fluctuations and control accuracy will have a cumulative impact on control accuracy, which is detrimental to stable system operation. Summary of the Invention

[0004] This invention provides a hybrid power system and energy management method and system based on braking condition prediction. By predicting the state of the supercapacitor during braking, it effectively solves the problem of electricity-hydrogen conversion in the energy management process, thereby improving the control accuracy of the system and achieving the goal of reducing vehicle operation and maintenance costs.

[0005] A hybrid power system energy management method based on braking condition prediction includes the following steps:

[0006] S100, Calculate the power consumption parameters of the fuel cell system and the supercapacitor system;

[0007] S200: Obtain the vehicle operating status signal using the hybrid power system;

[0008] S300: Determine whether the vehicle is in braking state based on the vehicle operating status signal. If the vehicle is in braking state, execute S4; otherwise, execute S6.

[0009] S400: Acquire vehicle speed sensor signal;

[0010] S500: Calculate and update the braking estimation coefficient k based on vehicle speed;

[0011] S600, Calculate the output power setpoint P of the DC / DC converter cascaded with the fuel cell system based on the braking estimation coefficient k. FCset .

[0012] The power consumption parameters of the hybrid power system include the power consumption curve fitting coefficients a, b, and c of the fuel cell system, and the power consumption parameters R and R of the supercapacitor system. S .

[0013] The formulas for calculating the fitting coefficients a, b, and c of the power consumption curve of a fuel cell system are as follows:

[0014] In the formula, Q FC P represents the instantaneous power consumption of the fuel cell. FC This refers to the net output power of the fuel cell;

[0015] The power consumption parameter R of the supercapacitor system is the resistance value of the parallel resistor in the equivalent circuit. S This represents the resistance value of the series resistor in the equivalent circuit.

[0016] Vehicle operating status signals include at least braking status, traction status, and inertia status.

[0017] The method for obtaining vehicle operating status signals is as follows:

[0018] The vehicle obtains gear position information through the vehicle's Ethernet bus. When the gear position signal value is less than 0, the vehicle is in a braking state. When the gear position signal is equal to 0, the vehicle is in an inertial state. When the gear position signal is greater than 0, the vehicle is in a traction state.

[0019] The vehicle speed sensor signal acquired in S400 is the speed sensor measurement value at the instant the vehicle enters braking state.

[0020] The formula for calculating the braking estimation system k is as follows:

[0021] In the formula, p1, p2, p3, and p4 are the fitting coefficients obtained by fitting a quartic equation based on historical operating data; U coa This refers to the voltage value of the supercapacitor system at the end of the vehicle's braking state.

[0022] The voltage value U of the supercapacitor system at the end of the braking state of the vehicle coa The calculation formula is as follows:

[0023] In the formula, q1 is the fitting coefficient for the quadratic term; q2 is the fitting coefficient for the linear term; q3 is the fitting coefficient for the constant term; v max This represents the maximum speed during the driving phase.

[0024] The output power setpoint P of the DC / DC converter cascaded in the fuel cell system FCset The calculation formula is as follows:

[0025] In the formula, U SC P is the terminal voltage of the supercapacitor at this moment; req The vehicle's main controller receives the bus demand power data from the traction system; k is the braking estimation coefficient; R s These are the power consumption parameters of the supercapacitor system.

[0026] An energy management system for a hybrid power system, used to implement the above-described energy management method, includes:

[0027] The power consumption parameter calculation module is used to calculate the power consumption parameters of the hybrid power system.

[0028] The operating status acquisition module is used to acquire the operating status signal of the vehicle using the hybrid power system.

[0029] The judgment module is used to determine whether the vehicle is in a braking state;

[0030] The vehicle speed acquisition module is used to acquire vehicle speed sensor signals;

[0031] The braking estimation coefficient calculation module calculates the braking estimation coefficient based on vehicle speed; and

[0032] The output power calculation module, based on the braking estimation system, calculates the output power setpoint of the DC / DC converter cascaded with the fuel cell system.

[0033] A hybrid power system, including the aforementioned energy management system.

[0034] Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention adopts a method of predicting the braking estimation coefficient based on the maximum driving speed before braking, which makes the output power of the fuel cell system more reasonable, effectively improves the fuel economy of the hybrid power system, and reduces the operating cost of the vehicle; at the same time, based on the limitation of the output set point, the power output fluctuation of the fuel cell system is reduced, effectively slowing down the aging process of the system and reducing the maintenance cost of the system. Attached Figure Description

[0035] Figure 1 shows the topology of the hybrid power system;

[0036] Figure 2 is a flowchart of the energy management method for a hybrid power system;

[0037] Figure 3 shows the vehicle braking power recovery at different peak speeds;

[0038] Figure 4 is a comparison of the output power of the fuel cell using the energy management method of this application and existing methods;

[0039] Figure 5 is a voltage comparison diagram of the supercapacitor using the energy management method of this application and existing methods. Detailed Implementation

[0040] The following detailed description of a specific embodiment of the present invention is provided in conjunction with the accompanying drawings. However, it should be understood that the scope of protection of the present invention is not limited to the specific embodiment.

[0041] Example 1

[0042] As shown in Figures 2-4, an embodiment of the present invention provides an energy management method for a hybrid power system based on braking condition prediction, comprising the following steps:

[0043] S100, Calculate the power consumption parameters of the hybrid power system;

[0044] S200: Obtain the vehicle operating status signal using the hybrid power system;

[0045] S300: Determine whether the vehicle is in braking state based on the vehicle operating status signal. If the vehicle is in braking state, execute S4; otherwise, execute S6.

[0046] S400: Acquire vehicle speed sensor signal;

[0047] S500: Calculate and update the braking estimation coefficient k based on vehicle speed;

[0048] S600, Calculate the output power setpoint P of the DC / DC converter cascaded with the fuel cell system based on the braking estimation coefficient k. FCset .

[0049] The hybrid power system includes a fuel cell system and a supercapacitor system. The power consumption parameters of the hybrid power system in S100 include the power consumption curve fitting coefficients a, b, and c of the fuel cell system, and the power consumption parameters R and Rsupercapacitor of the supercapacitor system. s ;

[0050] Among them, the fitting coefficients a, b, and c of the fuel cell system power consumption curve are the values ​​of the quadratic term, the linear term, and the constant term obtained by fitting the curve using the least squares method with a quadratic function as the basis function. The specific calculation formula is as follows:

[0051] In the formula, Q FC P represents the instantaneous power consumption of the fuel cell. FC This refers to the net output power of the fuel cell;

[0052] The supercapacitor system adopts a first-order series-parallel equivalent circuit. The power consumption parameter R of the supercapacitor system is the resistance value of the parallel resistor in the equivalent circuit. s The values ​​of the two are the series resistance values ​​in the equivalent circuit, and both can be calculated based on the series and parallel relationships of the supercapacitor cells in the actual system.

[0053] The vehicle operating status signals in S200 include at least braking status, traction status, and inertia status;

[0054] The S300 determines the vehicle's operating status based on the following steps:

[0055] The main controller obtains the gear information triggered by the vehicle driver through the vehicle Ethernet bus. When the gear signal value is less than 0, the vehicle is in a braking state; when the gear signal is equal to 0, the vehicle is in an inert state; and when the gear signal is greater than 0, the vehicle is in a traction state.

[0056] In S400, the vehicle speed sensor signal is obtained as the speed sensor measurement value at the moment the vehicle enters the braking state. This step is triggered and executed only at the moment the vehicle enters the braking state.

[0057] The calculation steps for the braking estimation coefficient k in S500 are as follows:

[0058] First, based on the vehicle's maximum speed in each operating section, calculate the voltage value U of the supercapacitor at the end of the braking state at the end of that section. coa The specific calculation method is as follows:

[0059] Figure 3 shows the vehicle braking power recovery at different peak speeds;

[0060] Based on measured data, by investigating the voltage of the supercapacitor at the braking end under different maximum speeds, an empirical fitting relationship is established to calculate the braking end voltage U. coa The approximate value is given by the following formula:

[0061] In the formula, q1 is the fitting coefficient for the quadratic term; q2 is the fitting coefficient for the linear term; q3 is the fitting coefficient for the constant term; v max This represents the maximum speed during the driving phase.

[0062] Meanwhile, for each braking condition, the average efficiency of the supercapacitor during the braking process can be calculated by the following formula:

[0063] In the formula, T bra The time represents the duration of each braking condition, and the integral time represents the charging efficiency of the supercapacitor. It should be noted that the final time is T. bra The terminal voltage is U SC (Tbra )=U coa ;

[0064] Considering the time required to calculate the integral in a real-time control system, a fitting method is used to ensure the real-time accuracy of k. Specifically, the braking estimation system k is updated using the following formula:

[0065] In the formula, p1, p2, p3, and p4 are the fitting coefficients obtained by fitting a quartic equation based on historical operating data; U coa This refers to the voltage value of the supercapacitor system at the end of the braking state of the vehicle;

[0066] The output power setpoint P of the DC / DC converter cascaded in the S600 fuel cell system FCset The calculation formula is as follows:

[0067] In the formula, U SC P is the terminal voltage of the supercapacitor at this moment; req The vehicle's main controller receives the bus demand power data from the traction system; k is the braking estimation coefficient (the braking estimation system value can be preset based on engineering experience in the initial state); R S These are the power consumption parameters of the supercapacitor system.

[0068] The following is an example of this embodiment:

[0069] Under the same operating conditions, the comparison results between the method of this embodiment and the existing power tracking and equivalent hydrogen consumption methods are as follows. The comparison of the fuel cell output power and supercapacitor voltage is shown in Figures 4 and 5. The calculated energy consumption and average fuel cell efficiency for each of the three operating ranges are shown in the table below:

[0070] Therefore, it can be seen that the method in this embodiment has a significant advantage in reducing energy consumption compared to the two existing methods.

[0071] Example 2

[0072] Based on Embodiment 1, this embodiment proposes an energy management system for a hybrid power system, including:

[0073] The power consumption parameter calculation module is used to calculate the power consumption parameters of the hybrid power system.

[0074] The operating status acquisition module is used to acquire the operating status signal of the vehicle using the hybrid power system.

[0075] The judgment module is used to determine whether the vehicle is in a braking state;

[0076] The vehicle speed acquisition module is used to acquire vehicle speed sensor signals;

[0077] The braking estimation coefficient calculation module calculates the braking estimation coefficient based on vehicle speed; and

[0078] The output power calculation module, based on the braking estimation system, calculates the output power setpoint of the DC / DC converter cascaded with the fuel cell system.

[0079] Example 3

[0080] As shown in Figure 1, this embodiment proposes a hybrid power system based on embodiment one or embodiment two, and also includes a proton exchange membrane fuel cell system, a supercapacitor system, a vehicle traction system, a main controller and its acquisition circuit.

[0081] The hybrid power system and its DC / DC converter are the controlled entities, forming a power source and load topology with the vehicle traction system via a DC bus. The main controller collects sensor signals, specifically the speed of the vehicle traction system, the power demand, and the terminal voltage signal of the supercapacitor system, to achieve real-time energy management of the DC / DC converter in the hybrid power system.

[0082] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit and essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

[0083] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A hybrid power system energy management method based on braking condition prediction, characterized in that, Includes the following steps: S100. Calculate the power consumption parameters of the hybrid power system consisting of the fuel cell system and the supercapacitor system. S200: Obtain the vehicle operating status signal using the hybrid power system; S300: Determine whether the vehicle is in braking state based on the vehicle operating status signal. If the vehicle is in braking state, execute S4; otherwise, execute S6. S400: Acquire vehicle speed sensor signal; S500: Calculate and update the braking estimation coefficient k based on vehicle speed; S600, Calculate the output power setpoint P of the DC / DC converter cascaded with the fuel cell system based on the braking estimation coefficient k. FCset .

2. The hybrid power system energy management method based on braking condition prediction as described in claim 1, characterized in that, The power consumption parameters of the hybrid power system include the power consumption curve fitting coefficients a, b, and c of the fuel cell system and the power consumption parameters R and R of the supercapacitor system. S ; The formulas for calculating the fitting coefficients a, b, and c of the power consumption curve of the fuel cell system are as follows: In the formula, Q FC P represents the instantaneous power consumption of the fuel cell. FC This refers to the net output power of the fuel cell; The power consumption parameter R of the supercapacitor system is the parallel resistance value in the equivalent circuit, and R1 is the series resistance value in the equivalent circuit.

3. The hybrid power system energy management method based on braking condition prediction as described in claim 1, characterized in that, The vehicle operating status signals include at least braking status, traction status, and inertia status.

4. The hybrid power system energy management method based on braking condition prediction as described in claim 3, characterized in that, The method for acquiring the vehicle operating status signal is as follows: The vehicle obtains gear position information through the vehicle's Ethernet bus. When the gear position signal value is less than 0, the vehicle is in a braking state. When the gear position signal is equal to 0, the vehicle is in an inertial state. When the gear position signal is greater than 0, the vehicle is in a traction state.

5. The hybrid power system energy management method based on braking condition prediction as described in claim 1, characterized in that, The vehicle speed sensor signal acquired in S400 is the speed sensor measurement value at the instant the vehicle enters the braking state.

6. The hybrid power system energy management method based on braking condition prediction as described in claim 1, characterized in that, The formula for calculating the braking estimation system k is as follows: In the formula, p1, p2, p3, and p4 are the fitting coefficients obtained by fitting a quartic equation based on historical operating data; U coa This refers to the voltage value of the supercapacitor system at the end of the vehicle's braking state.

7. The hybrid power system energy management method based on braking condition prediction as described in claim 6, characterized in that, The voltage value U of the supercapacitor system at the end of the braking state of the vehicle coa The calculation formula is as follows: In the formula, q1 is the fitting coefficient for the quadratic term; q2 is the fitting coefficient for the linear term; q3 is the fitting coefficient for the constant term; v max This represents the maximum speed during the driving phase.

8. The hybrid power system energy management method based on braking condition prediction as described in claim 2, characterized in that, The output power setpoint P of the DC / DC converter cascaded with the fuel cell system FCset The calculation formula is as follows: In the formula, U SC P is the terminal voltage of the supercapacitor at this moment; req The vehicle's main controller receives the bus demand power data from the traction system; k is the braking estimation coefficient; R S These are the power consumption parameters of the supercapacitor system.

9. An energy management system for a hybrid power system, used to implement the energy management method according to any one of claims 1-8, characterized in that, include: The power consumption parameter calculation module is used to calculate the power consumption parameters of the hybrid power system. The operating status acquisition module is used to acquire the operating status signal of the vehicle using the hybrid power system. The judgment module is used to determine whether the vehicle is in a braking state; The vehicle speed acquisition module is used to acquire vehicle speed sensor signals; Braking estimation coefficient calculation module, which calculates braking estimation coefficients based on vehicle speed; as well as The output power calculation module, based on the braking estimation system, calculates the output power setpoint of the DC / DC converter cascaded with the fuel cell system.

10. A hybrid power system, characterized in that, Including the energy management system as described in claim 9.

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