Plug-in hybrid power bus energy management system and method based on extremum search

Abstract

The invention discloses a plug-in hybrid power bus energy management system and method based on extremum search, and the method comprises the steps: initializing a management system, specifically, initializing a threshold parameter initial value; outer-layer extremum searching is carried out based on current input data to obtain global reference parameters, and the input data comprise current outer-layer reference parameters and complete driving period working condition data; performing inner-layer extremum search on the basis of the global reference parameter in combination with the driving condition of the current road section to obtain an adaptive threshold parameter of each road section; vehicle energy management, including mode switching, torque distribution, and battery management, is performed based on global reference parameters and / or adaptive threshold parameters in combination with real-time vehicle states. According to the method, the limitation of a traditional method based on a fixed parameter rule is overcome, meanwhile, low calculation requirements, high reliability and nearly optimal performance are achieved, and the deployment feasibility in large-scale mass production of vehicles is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and more specifically, to an energy management system and method for a plug-in hybrid electric bus based on extremum search. Background Art

[0002] The existing energy management strategies for hybrid electric buses are mainly divided into three categories, namely rule-based methods, optimization-based methods, and learning-based methods. Among them, the above three methods have their respective technical drawbacks.

[0003] Specifically, the disadvantages of the rule-based method are that the parameters are fixed, it cannot adapt to dynamic working conditions (such as slope and passenger capacity changes), the energy distribution is sub-optimal, and it cannot cope with the dynamic changes of bus routes; the disadvantages of the optimization-based method are high computational complexity, dependence on accurate models, and difficulty in real-time deployment; while the disadvantages of the learning-based method are that it requires a large amount of training data, has limited generalization, poor real-time performance, a long development cycle, and poor stability. Summary of the Invention

[0004] The object of the present invention is to provide an energy management system and method for a plug-in hybrid electric bus based on extremum search, which is used to solve the energy management control of a plug-in hybrid electric bus (PHEB), especially for the control of dynamic working conditions (such as the periodicity, slope change, and passenger capacity fluctuation of urban buses) on fixed bus routes.

[0005] The first aspect of the present invention provides an energy management system for a plug-in hybrid electric bus based on extremum search, including:

[0006] A vehicle dynamics model, a power battery model, and an energy cost model, wherein,

[0007] The vehicle dynamics model is used to calculate the required torque of the vehicle under different working conditions to switch the mode of the energy management strategy, and the vehicle dynamics model includes the power system structure and key vehicle parameters;

[0008] The power battery model is used to monitor the state of charge (SOC) of the battery to switch the mode of the control strategy, and to calculate the power consumption. The power battery model includes the battery type and key battery parameters;

[0009] The energy cost model is used as the optimization target of the extremum search algorithm to adjust the rule threshold parameters. The energy cost model includes fuel parameters, power parameters, and energy consumption data. Among them, the optimization target includes minimizing the equivalent fuel cost in the vehicle dynamics model, and the energy consumption data includes the power consumption in the power battery model.

[0010] In this solution, the power system structure includes an engine, a motor, a clutch, an AMT transmission, a main reducer, and wheels; the key vehicle parameters include vehicle mass, transmission system efficiency, AMT transmission ratio, main reducer ratio, engine torque, motor torque, braking torque, rolling resistance coefficient, road gradient, gravitational acceleration, vehicle frontal area, air resistance coefficient, air density, vehicle speed, rotational mass factor, vehicle acceleration, and wheel radius; the demand torque calculation formula is as follows:

[0011]

[0012] Among them, T ω is the wheel torque, η T is the transmission system efficiency, i g is the AMT transmission ratio, i f is the main reducer ratio, T e is the engine torque, T m is the motor torque, T b is the braking torque, M is the vehicle mass, g is the gravitational acceleration, f r is the rolling resistance coefficient, ρ is the air density, A is the vehicle frontal area, C D is the air resistance coefficient, α is the road gradient, V is the vehicle speed, δ is the rotational mass factor, a is the vehicle acceleration, and r is the wheel radius.

[0013] In this solution, the battery type includes lithium-ion batteries, and the key battery parameters include open-circuit voltage, internal resistance, and battery capacity. The calculation formula for monitoring the state of charge SOC of the battery is as follows:

[0014]

[0015] P m = V oc I - I 2 R int ;

[0016] P ess = V oc I;

[0017] Among them, k is the sampling time, V oc is the open-circuit voltage, R int is the internal resistance, Q B is the battery capacity, I is the current, P ess is the load power, P m is the motor power.

[0018] In this solution, the fuel parameters include the price of compressed natural gas, the power parameters include the grid electricity price, and the energy consumption data includes the gas consumption and electricity consumption. The calculation formula for the consumption per 100 kilometers is as follows:

[0019]

[0020] Among them, COST is the fuel consumption per 100 kilometers, p g is the price of compressed natural gas, p c is the grid electricity price, FC is the fuel consumption of gas, EC is the power consumption, S dc is the driving cycle mileage.

[0021] In the second aspect of the present invention, an energy management method for a plug-in hybrid bus based on extremum search is provided, which is applied to any one of the energy management systems for a plug-in hybrid bus based on extremum search. Among them, the method includes the following steps:

[0022] Initialize the management system, specifically including initializing the initial values of the threshold parameters;

[0023] Perform outer-layer extremum search based on the current input data to obtain global reference parameters. Among them, the input data includes the current outer-layer reference parameters and the complete driving cycle condition data;

[0024] Perform inner-layer extremum search based on the global reference parameters combined with the current road section driving conditions to obtain the adaptive threshold parameters for each road section;

[0025] Perform energy management on the vehicle based on the global reference parameters and / or the adaptive threshold parameters combined with the real-time vehicle state, including mode switching, torque distribution, and battery management.

[0026] In this solution, the method further includes iterative optimization during the optimization process of performing outer-layer extremum search for global reference parameters and inner-layer extremum search for adaptive threshold parameters. Among them, when the cost reduction rate is less than the preset threshold or the maximum number of iterations is reached, the iteration is stopped.

[0027] In this solution, the performing outer-layer extremum search based on the current input data to obtain global reference parameters specifically includes:

[0028] Obtain the current rule threshold parameter u k and the driving condition data ω;

[0029] Perform two-way perturbation calculation for positive perturbation and negative perturbation based on the current rule threshold parameter;

[0030] Run the energy management strategy under positive perturbation and negative perturbation to calculate the positive equivalent cost and negative equivalent cost under the current driving condition data ω;

[0031] Calculate the approximate gradient based on the positive equivalent cost and the negative equivalent cost

[0032] Based on the approximate gradient and the regular threshold parameter u k Calculate the global reference parameter u k+1 , where the calculation formula is as follows:

[0033]

[0034] where u k+1 is the global reference parameter, u k is the regular threshold parameter, a k is the learning rate, is the approximate gradient, M f is the momentum coefficient, ξ is the noise, and k is the sampling time.

[0035] In this solution, the inner extreme value search is performed based on the global reference parameter combined with the current road section driving conditions to obtain the adaptive threshold parameters of each road section, specifically including:[[]]

[0036] Obtain the global reference parameter u k+1 and the current road section driving conditions, where the current road section driving conditions include slope and passenger capacity segmented data;

[0037] Based on the incremental parameters of each road section, perform segmented perturbation and then calculate the gradient to obtain the incremental parameter δ i,k+1 ;

[0038] Add the global reference parameter u k+1 and the incremental parameter δ i,k+1 to obtain the adaptive threshold parameter u corresponding to different road sections i k,i .

[0039] In this solution, the mode management is performed based on the global reference parameter and / or the adaptive threshold parameter combined with the real-time vehicle state, specifically including:[[]]

[0040] Perform energy management based on the global reference parameter and / or the adaptive threshold parameter combined with the real-time vehicle state, where

[0041] When the mode is switched, if the required torque is less than the maximum torque of the engine, the mode is switched to the pure electric drive EV mode;

[0042] If the state of charge SOC of the battery is greater than the critical value of the state of charge, the mode is switched to the charge depletion CD mode, otherwise the mode is switched to the charge sustaining CS mode.

[0043] A third aspect of the present invention provides a computer-readable storage medium, which includes a program of an energy management method for a plug-in hybrid bus based on extremum search for a machine. When the program of the energy management method for the plug-in hybrid bus based on extremum search is executed by a processor, the steps of an energy management method for a plug-in hybrid bus based on extremum search as described in any one of the above are implemented.

[0044] An energy management system and method for a plug-in hybrid bus based on extremum search disclosed by the present invention overcome the limitations of traditional deterministic rule-based methods, and at the same time achieve low computational requirements, high reliability and near-optimal performance, ensuring the feasibility of deployment in mass-produced vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 FIG. shows a schematic structural diagram of an energy management system for a plug-in hybrid bus based on extremum search of the present invention;

[0046] Figure 2 FIG. shows a schematic diagram of the power system of a plug-in hybrid bus of an energy management system for a plug-in hybrid bus based on extremum search of the present invention;

[0047] Figure 3 FIG. shows a schematic diagram of the steps of an energy management method for a plug-in hybrid bus based on extremum search of the present invention;

[0048] Figure 4 FIG. shows a schematic diagram of the verification platform architecture of an energy management method for a plug-in hybrid bus based on extremum search of the present invention;

[0049] Figure 5 FIG. shows a bus line diagram of an energy management method for a plug-in hybrid bus based on extremum search of the present invention;

[0050] Figure 6 FIG. shows a schematic diagram of the speed-position curve of an energy management method for a plug-in hybrid bus based on extremum search of the present invention;

[0051] Figure 7 FIG. shows a schematic diagram of the relationship between the slope and the distance of an energy management method for a plug-in hybrid bus based on extremum search of the present invention;

[0052] Figure 8 FIG. shows a schematic diagram of the relationship between the passenger transport distance and the passenger capacity of an energy management method for a plug-in hybrid bus based on extremum search of the present invention;

[0053] Figure 9 FIG. shows a schematic diagram of the SOC iterative evolution trajectory of an energy management method for a plug-in hybrid bus based on extremum search of the present invention. Detailed implementation manners

[0054] In order to more clearly understand the above objects, features and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific implementation manners. It should be noted that, without conflict, the embodiments of the present application and the features in the embodiments may be combined with each other.

[0055] In the following description, many specific details are set forth in order to fully understand the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited by the specific embodiments disclosed below.

[0056] Figure 1 The structural schematic diagram of a plug-in hybrid bus energy management system based on extremum seeking of the present application is shown.

[0057] As Figure 1 shown, the present application discloses a plug-in hybrid bus energy management system based on extremum seeking, including:

[0058] A vehicle dynamics model, a power battery model and an energy cost model, wherein,

[0059] The vehicle dynamics model is used to calculate the required torque of the vehicle under different working conditions to switch the mode of the energy management strategy, and the vehicle dynamics model includes a power system structure and key vehicle parameters;

[0060] The power battery model is used to monitor the state of charge (SOC) of the battery to switch the mode of the control strategy, and is used to calculate the power consumption. The power battery model includes battery types and key battery parameters;

[0061] The energy cost model is used as the optimization target of the extremum seeking algorithm to adjust the rule threshold parameters. The energy cost model includes fuel parameters, power parameters and energy consumption data. Among them, the optimization target includes minimizing the equivalent fuel cost in the vehicle dynamics model, and the energy consumption data includes the power consumption in the power battery model.

[0062] It should be noted that, in this embodiment, as Figure 2As shown, it is a schematic diagram of the power system of a plug-in hybrid bus, adopting a typical coaxial parallel hybrid configuration. Among them, different from traditional AMT (Automated Manual Transmission) vehicles, a motor is coaxially integrated between the clutch and the AMT. The vehicle dynamics model is used to calculate the required torque of the vehicle under different working conditions to switch the mode of the energy management strategy. The vehicle dynamics model includes the power system structure and key vehicle parameters, which will be described in detail in the subsequent specification. Further, the plug-in hybrid bus uses lithium-ion batteries known for their high specific energy and power performance. On the premise of ignoring the influence of temperature changes and battery aging, a simple and effective internal resistance battery model is adopted for modeling, that is, corresponding to the mode for monitoring the state of charge (SOC) of the battery to switch the control strategy, and the power battery model for calculating power consumption. Among them, the power battery model includes battery type and key battery parameters, and the corresponding dynamic equations will be introduced in detail in the subsequent specification content.

[0063] Further, the energy management system of the plug-in hybrid bus based on extremum search in this embodiment further includes an energy cost model. Among them, the energy cost model is used as the optimization target of the extremum search algorithm to adjust the rule threshold parameters. The energy cost model includes fuel parameters, power parameters, and energy consumption data. Among them, the optimization target includes minimizing the equivalent fuel cost in the vehicle dynamics model. The energy consumption data includes the power consumption in the power battery model. Similarly, the subsequent specification will describe it in detail.

[0064] According to the embodiment of the present invention, the power system structure includes an engine, a motor, a clutch, an AMT transmission, a main reducer, and wheels; the key vehicle parameters include vehicle mass, transmission system efficiency, AMT transmission ratio, main reducer transmission ratio, engine torque, motor torque, braking torque, rolling resistance coefficient, road slope, gravitational acceleration, vehicle front area, air resistance coefficient, air density, vehicle speed, rotational mass factor, vehicle acceleration, and wheel radius; the calculation formula for the required torque is as follows:

[0065]

[0066] Among them, T ω is the wheel torque, η T is the transmission system efficiency, i g is the AMT transmission ratio, i f is the main reducer transmission ratio, T e is the engine torque, T m is the motor torque, T b is the braking torque, M is the vehicle mass, g is the gravitational acceleration, fr is the rolling resistance coefficient, ρ is the air density, A is the frontal area of the vehicle, C D is the aerodynamic drag coefficient, α is the road slope, V is the vehicle speed, δ is the rotating mass factor, a is the vehicle acceleration, and r is the wheel radius.

[0067] It should be noted that in this embodiment, the power system structure includes an engine, a motor, a clutch, an AMT transmission, a main reducer, and wheels; and the key vehicle parameters are used to calculate the required torque. Among them, for the wheel torque calculated in this embodiment, the core objective of the energy management strategy is to make the wheel torque match the corresponding required torque by controlling the torque distribution of the engine and the motor, that is, the wheel torque is approximately equal to the required torque. Therefore, the wheel torque can be directly calculated as the required torque. Specifically, the calculation formula for the required torque is as follows:

[0068]

[0069] Among them, T ω is the wheel torque, η T is the transmission system efficiency, i g is the transmission ratio of the AMT transmission, i f is the transmission ratio of the main reducer, T e is the engine torque, T m is the motor torque, T b is the braking torque, M is the vehicle mass, g is the acceleration due to gravity, f r is the rolling resistance coefficient, ρ is the air density, A is the frontal area of the vehicle, C D is the aerodynamic drag coefficient, α is the road slope, V is the vehicle speed, δ is the rotating mass factor, a is the vehicle acceleration, and r is the wheel radius.

[0070] According to the embodiment of the present invention, the battery type includes a lithium-ion battery, and the key battery parameters include the open-circuit voltage, internal resistance, and battery capacity. The calculation formula for monitoring the state of charge SOC of the battery is as follows:

[0071]

[0072] P m = V oc I - I 2 R int ;

[0073] P ess = V oc I;

[0074] Among them, k is the sampling time, V oc is the open-circuit voltage, R int is the internal resistance, q B is the battery capacity, I is the current, Pess is the load power, P m is the motor power.

[0075] It should be noted that, in this embodiment, the battery type includes lithium-ion batteries, and the key battery parameters include open-circuit voltage, internal resistance, and battery capacity. Correspondingly, the mathematical expression of the power battery model is to calculate the SOC. Among them, when calculating the SOC, the key battery parameters open-circuit voltage V oc , internal resistance R int and battery capacity Q B are required. Among them, by real-time monitoring the battery SOC, the operation mode of the vehicle can be switched, specifically including the EV (Electric Vehicle) mode, CD (Charge-Depleting) mode, and CS (Charge-Sustaining) mode.

[0076] According to the embodiment of the present invention, the fuel parameter includes the price of compressed natural gas, the power parameter includes the grid electricity price, and the energy consumption data includes the gas consumption and power consumption. The calculation formula for the consumption per 100 kilometers is as follows:

[0077]

[0078] Among them, COST is the consumption per 100 kilometers, is the price of compressed natural gas, p c is the grid electricity price, FC is the gas consumption, EC is the power consumption, S dc is the driving cycle mileage.

[0079] It should be noted that, in this embodiment, as the optimization objective of the extremum search algorithm, the fuel parameter, power parameter, and energy consumption data need to be considered. Specifically, the fuel parameter includes the price of compressed natural gas, the power parameter includes the grid electricity price, and the energy consumption data includes the gas consumption and power consumption. Among them, when calculating the consumption per 100 kilometers, the price of compressed natural gas p g , grid electricity price p c , gas consumption FC, power consumption EC, and driving cycle mileage S dc are specifically applied.

[0080] Figure 3 shows the flowchart of an energy management method for a plug-in hybrid bus based on extremum search in the present application.

[0081] As Figure 3 shown, the present application discloses an energy management method for a plug-in hybrid bus based on extremum search, including the following steps:

[0082] S302, initialize the management system, specifically including initializing the initial value of the threshold parameter;

[0083] S304, performing an outer layer extreme value search based on current input data to obtain a global reference parameter, wherein the input data includes the current outer layer reference parameter and complete driving cycle operating condition data;

[0084] S306, performing an inner extreme value search based on the global reference parameter and the current road section driving condition to obtain an adaptive threshold parameter for each road section;

[0085] S308, performing energy management of the vehicle based on the global reference parameter and / or the adaptive threshold parameter in combination with the real-time vehicle status, including mode switching, torque distribution and battery management.

[0086] It should be noted that, in this embodiment, the rule-based energy management strategy is used as the control object, and the extreme value search algorithm is used to adaptively adjust the rule threshold parameters. The real-time and robustness of the strategy are guaranteed by the rule-based method, while the adaptability and optimality of the optimization method are combined. Specifically, a real-time adaptive rule energy management strategy based on a double-layer extreme value search algorithm is developed. The method in this embodiment overcomes the limitations of traditional deterministic rule-based methods, while achieving low computing requirements, high reliability and near-optimal performance, ensuring the feasibility of deployment in large-scale mass-produced vehicles, and optimizing the global benchmark parameters to adapt to conventional bus routes and periodic driving conditions.

[0087] Specifically, after initializing the management system, the initial value of SOC and the initial value of threshold parameters are also initialized synchronously, and then an outer extreme value search is performed based on the current input data to obtain the global benchmark parameters, wherein the input data includes the current outer extreme value parameters and the complete driving cycle operating condition data. The specific optimization process will be described in detail in the subsequent manual. Accordingly, an inner extreme value search is performed based on the global benchmark parameters combined with the current road driving conditions to obtain the adaptive threshold parameters of each road section. The inner extreme value search will also be described in detail in the subsequent manual.

[0088] Furthermore, after obtaining the global baseline parameters and adaptive threshold parameterization, the vehicle can perform energy management based on the global baseline parameters and / or adaptive threshold parameters in combination with the real-time vehicle status, including mode switching, torque distribution and battery management, wherein the torque distribution is distributed with reference to the wheel torque calculation in the above-mentioned system embodiment, and the battery management is also distributed with reference to the battery update during the power battery SOC calculation in the above-mentioned system embodiment, and the mode switching will be described in detail in the subsequent specification.

[0089] According to an embodiment of the present invention, the method also includes performing iterative optimization during the optimization process of the global benchmark parameters of the outer extreme value search and the adaptive threshold parameters of the inner extreme value search, wherein the iteration is stopped when the cost reduction is less than a preset threshold or the maximum number of iterations is reached.

[0090] It should be noted that in this embodiment, when performing performance evaluation, this embodiment combines specific settings for verification on the No. 303 bus line in Chongqing, China. Among them, as Figure 4 shown, it shows a schematic diagram of the verification platform architecture. Collect data to construct a typical urban driving condition. The bus line is as Figure 5 shown. The driving condition starts from Nanping Station ( Figure 5 the "Start" point in Figure 5 ), and ends at Longzhouwan Hub Station ( Figure 6 the "Final" point in Figure 7 ). The one-way full length of the route is "24" kilometers, and there are "36" designated stops. As Figure 8 shown, it shows a speed-position curve. Three representative speed-position curves are selected from the "25" groups of experimental curves. In addition, the driving condition characteristics also include road slope information and passenger mass data. Specifically, as

[0091] shown, it shows a schematic diagram of the relationship between slope and distance. As Figure 9 shown, it shows a schematic diagram of the relationship between passenger travel distance and passenger capacity. Figure 9 It can be clearly seen from

[0092] that the SOC trajectory gradually approaches the solution achieved by dynamic programming from the sub-optimal state based on rule control, which indicates that the adaptability to the driving condition is improved, so as to achieve more efficient power distribution during operation and significantly reduce fuel consumption.

[0093] Obtain the current rule threshold parameter u k and the driving condition data ω;

[0094] Perform two-way perturbation calculations for positive perturbation and negative perturbation based on the current rule threshold parameter;

[0095] Run the energy management strategy under positive perturbation and negative perturbation to calculate the positive equivalent cost and negative equivalent cost under the current driving condition data ω;

[0096] Calculate the approximate gradient based on the positive equivalent cost and negative equivalent cost

[0097] Based on the approximate gradient and the regular threshold parameter u k calculate the global reference parameter u k+1 , where the calculation formula is as follows:

[0098]

[0099] where u k+1 is the global reference parameter, u k is the regular threshold parameter, a k is the learning rate, is the approximate gradient, M f is the momentum coefficient, ξ is the noise, and k is the sampling time.

[0100] It should be noted that in this embodiment, when performing the outer extreme value search, the input parameters include the current regular threshold parameter u k and the driving condition data ω. The processing process includes two-way perturbation, cost calculation, gradient estimation, and parameter update. Specifically, based on the current regular threshold parameter, perform two-way perturbation to calculate the positive perturbation and the negative perturbation, and then run the energy management strategy under the positive perturbation and the negative perturbation to calculate the positive equivalent cost and the negative equivalent cost under the current driving condition data ω.

[0101] Furthermore, calculate the approximate gradient based on the positive equivalent cost and the negative equivalent cost so as to calculate the global reference parameter u based on the approximate gradient k and the regular threshold parameter u k+1 to complete the parameter update.

[0102] According to the embodiment of the present invention, the inner extreme value search is performed based on the global reference parameter in combination with the driving conditions of the current section to obtain the adaptive threshold parameters of each section, which specifically includes:[[]]

[0103] Obtain the global reference parameter u k+1 and the driving conditions of the current section, where the driving conditions of the current section include slope and passenger capacity segmented data;

[0104] Perform segmented perturbation based on the incremental parameters of each section and then perform gradient calculation to obtain the incremental parameter δ i,k+1 ;

[0105] Superimpose the global reference parameter u k+1 and the incremental parameter δ i,k+1 to obtain the adaptive threshold parameter u k,i corresponding to different sections i.

[0106] It should be noted that in this embodiment, when performing the inner extreme value search, the input parameters include the global reference parameter u k+1and the driving conditions of the current road section, corresponding to the segmented data of slope and passenger capacity. Specifically, the processing process includes segmented perturbation, gradient estimation, parameter update, and actual parameter synthesis. Specifically, the global reference parameter u is obtained k+1 and the driving conditions of the current road section, where the driving conditions of the current road section include slope and segmented passenger capacity data, so as to perform segmented perturbation based on the incremental parameters of each road section and then calculate the gradient to obtain the incremental parameters Among them, is the differential parameter for different road sections i.

[0107] According to the embodiments of the present invention, mode management is performed on the vehicle based on the global reference parameter and / or the adaptive threshold parameter in combination with the real-time vehicle state, specifically including:

[0108] Energy management is performed on the vehicle based on the global reference parameter and / or the adaptive threshold parameter in combination with the real-time vehicle state. Among them,

[0109] When the mode is switched, if the required torque is less than the maximum torque of the engine, the mode is switched to the pure electric drive EV mode;

[0110] If the state of charge SOC of the battery is greater than the critical value of the state of charge, the mode is switched to the charge depletion CD mode, otherwise the mode is switched to the charge sustaining CS mode.

[0111] It should be noted that in this embodiment, the EV+CD+CS rule is adopted as the core energy management strategy. Since the rule has adaptability, its threshold is set as an adjustable parameter. In the EV mode, the vehicle is mainly driven by the motor, and most of the energy is provided by the battery pack. In the CD mode, the motor and the engine drive simultaneously, resulting in a gradual decrease in the state of charge SOC of the battery. However, different threshold parameters will affect the rationality of power distribution during the CD process. In the CS mode, the engine provides the main driving demand while charging the battery pack to keep the battery SOC within the set range until the vehicle stops.

[0112] Specifically, when the mode is switched, if the required torque is less than the maximum torque of the engine, the mode is switched to the pure electric drive EV mode. If the state of charge SOC of the battery is greater than the critical value of the state of charge, the mode is switched to the charge depletion CD mode, otherwise the mode is switched to the charge sustaining CS mode.

[0113] The third aspect of the present invention provides a computer-readable storage medium, which includes a program for an energy management method of a plug-in hybrid bus based on extremum search. When the program for the energy management method of the plug-in hybrid bus based on extremum search is executed by a processor, the steps of an energy management method of a plug-in hybrid bus based on extremum search as described in any one of the above are implemented.

[0114] An energy management system and method for a plug-in hybrid bus based on extremum seeking disclosed by the present invention overcomes the limitations of traditional deterministic rule-based methods, and at the same time achieves low computational requirements, high reliability, and near-optimal performance, ensuring the feasibility of deployment in mass-produced vehicles.

[0115] In several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined, or can be integrated into another system, or some features can be ignored, or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed with each other can be through some interfaces. The indirect coupling or communication connection of devices or units can be electrical, mechanical, or other forms.

[0116] The units described as separate components above may or may not be physically separated. The components shown as units may or may not be physical units; they can be located in one place or distributed to multiple network units; some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0117] In addition, in each embodiment of the present invention, each functional unit can be all integrated in a processing unit, or each unit can be separately a unit, or two or more units can be integrated in a unit; the above-mentioned integrated units can be implemented in the form of hardware, or in the form of hardware plus software functional units.

[0118] Those of ordinary skill in the art can understand that all or part of the steps for implementing the above method embodiments can be completed by hardware related to program instructions. The foregoing program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps including the above method embodiments; and the foregoing storage medium includes: various media that can store program codes such as removable storage devices, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disks, or optical discs.

[0119] Alternatively, if the above integrated units of the present invention are implemented in the form of software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiments of the present invention, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in the various embodiments of the present invention. The foregoing storage medium includes: various media that can store program codes, such as removable storage devices, ROM, RAM, magnetic disks, or optical discs.

Claims

1. A plug-in hybrid bus energy management system based on extremum seeking, characterized in that, Including: A vehicle dynamics model, a power battery model, and an energy cost model. Among them, The vehicle dynamics model is used to calculate the required torque of the vehicle under different working conditions to switch the mode of the energy management strategy. The vehicle dynamics model includes the power system structure and vehicle key parameters; The power battery model is used to monitor the state of charge (SOC) of the battery to switch the mode of the control strategy, and to calculate the power consumption. The power battery model includes the battery type and battery key parameters; The energy cost model is used as the optimization objective of the extremum search algorithm to adjust the rule threshold parameters. The energy cost model includes fuel parameters, power parameters, and energy consumption data. Among them, the optimization objective includes minimizing the equivalent fuel cost in the vehicle dynamics model, and the energy consumption data includes the power consumption in the power battery model.

2. The energy management system for a plug-in hybrid bus based on extremum seeking according to claim 1, wherein The power system structure includes an engine, a motor, a clutch, an AMT transmission, a main reducer, and wheels; the vehicle key parameters include vehicle mass, transmission system efficiency, AMT transmission ratio, main reducer ratio, engine torque, motor torque, braking torque, rolling resistance coefficient, road gradient, gravitational acceleration, vehicle front area, air resistance coefficient, air density, vehicle speed, rotating mass factor, vehicle acceleration, and wheel radius; the calculation formula for the required torque is as follows: Among them, T ω is the wheel torque, η T is the transmission system efficiency, i g is the transmission ratio of the AMT gearbox, i f is the transmission ratio of the main reducer, T e is the engine torque, T m is the motor torque, T b is the braking torque, M is the vehicle mass, g is the acceleration due to gravity, f r is the rolling resistance coefficient, ρ is the air density, A is the frontal area of the vehicle, C D is the aerodynamic drag coefficient, α is the road slope, V is the vehicle speed, δ is the rotating mass factor, a is the vehicle acceleration, r is the wheel radius.

3. The energy management system for a plug-in hybrid bus based on extremum seeking according to claim 2, characterized in that, The battery type includes lithium-ion batteries, and the battery key parameters include open-circuit voltage, internal resistance, and battery capacity. The calculation formula for monitoring the state of charge (SOC) of the battery is as follows: P m = V oc I - I 2 R int ; R ess = V oc I; Among them, k is the sampling time, V oc is the open-circuit voltage, R int is the internal resistance, Q B is the battery capacity, I is the current, P ess is the load power, P m is the motor power.

4. The energy management system for a plug-in hybrid bus based on extremum seeking according to claim 3, wherein The fuel parameters include the price of compressed natural gas, the power parameters include the grid electricity price, and the energy consumption data includes the gas consumption and power consumption. The calculation formula for the consumption per 100 kilometers is as follows: Among them, COST is the consumption per 100 kilometers, p g is the price of compressed natural gas, p c is the grid electricity price, FC is the gas consumption, EC is the electricity consumption, S dc is the driving cycle mileage.

5. A power management method for a plug-in hybrid bus based on extremum search, characterized in that, Applied to an energy management system for a plug-in hybrid bus based on extremum search according to any one of claims 1-4. Among them, the method includes the following steps: Initialize the management system, specifically including initializing the initial values of the threshold parameters; Perform an outer-layer extremum search based on the current input data to obtain global reference parameters. Among them, the input data includes the current outer-layer reference parameters and the complete driving cycle working condition data; Perform an inner-layer extremum search based on the global reference parameters combined with the current road section driving conditions to obtain the adaptive threshold parameters for each road section; Perform energy management on the vehicle based on the global reference parameters and / or the adaptive threshold parameters combined with the real-time vehicle state, including mode switching, torque distribution, and battery management.

6. The energy management method for a plug-in hybrid bus based on extremum seeking according to claim 5, characterized in that, The method further includes performing iterative optimization during the optimization process of the outer-layer extremum search for global reference parameters and the inner-layer extremum search for adaptive threshold parameters. Among them, when the cost reduction is less than the preset threshold or the maximum number of iterations is reached, the iteration stops.

7. A method for energy management of a plug-in hybrid bus based on extremum seeking according to claim 6, characterized in that, The performing an outer-layer extremum search based on the current input data to obtain global reference parameters specifically includes: Obtain the current rule threshold parameter u k and the driving condition data ω; Perform two-way perturbation calculations for positive and negative perturbations based on the current rule threshold parameters; Run the energy management strategy under positive and negative perturbations to calculate the positive equivalent cost and negative equivalent cost under the current driving condition data ω; Calculating approximate gradients based on positive equivalent cost and negative equivalent cost Based on the approximate gradient and the regular threshold parameter u k calculate the global reference parameter u k+1 , where the calculation formula is as follows: Among them, u k+1 is the global reference parameter, u k is the regular threshold parameter, a k is the learning rate, is the approximate gradient, M f is the momentum coefficient, ξ is the noise, and k is the sampling time.

8. A method for energy management of a plug-in hybrid bus based on extremum seeking according to claim 7, characterized in that, Performing inner extreme value search based on the global reference parameters in combination with the driving conditions of the current road section to obtain the adaptive threshold parameters for each road section, specifically including: Obtain the global reference parameter u k+1 and the current road section driving conditions, where the current road section driving conditions include slope and passenger capacity segmented data; The incremental parameter δ is obtained by performing gradient calculation after segmental perturbation based on the incremental parameter of each road segment i,k+1 ; Add the global reference parameter u k+1 and the incremental parameter δ i,k+1 to obtain the adaptive threshold parameter u corresponding to different road segments i k,i .

9. The energy management method for a plug-in hybrid bus based on extremum seeking according to claim 8, wherein Performing mode management on the vehicle based on the global reference parameters and / or the adaptive threshold parameters in combination with the real-time vehicle state, specifically including: Performing energy management on the vehicle based on the global reference parameters and / or the adaptive threshold parameters in combination with the real-time vehicle state, wherein During mode switching, if the required torque is less than the maximum torque of the engine, the mode is switched to the pure electric drive EV mode; If the state of charge SOC of the battery is greater than the critical value of the state of charge, the mode is switched to the charge depletion CD mode, otherwise the mode is switched to the charge sustaining CS mode.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a program for an energy management method of a plug-in hybrid bus based on extreme value search. When the program for the energy management method of the plug-in hybrid bus based on extreme value search is executed by a processor, the steps of an energy management method of a plug-in hybrid bus based on extreme value search as described in any one of claims 5 to 9 are implemented.

Images