Regenerative braking energy storage and feedback system based on super capacitor

Abstract

The invention relates to the technical field of regenerative braking energy feedback, in particular to a regenerative braking energy storage and feedback system based on a super capacitor, which comprises a real-time monitoring port, an energy management port and a power distribution optimization port, the monitoring module is used for monitoring the running speed and acceleration of the vehicle and the charging and discharging current, voltage, power fluctuation and residual life of the supercapacitor in real time; the energy management port is used for performing energy distribution between the super capacitor and the battery; and the power distribution control port is used for adjusting the charging and discharging strategy of the super capacitor in real time according to the driving state of the vehicle.

Description

Technical Field

[0001] The present invention relates to the technical field of regenerative braking energy feedback, and in particular to a regenerative braking energy storage and feedback system based on a supercapacitor. Background Art

[0002] With the growing global demand for energy and the increasing awareness of environmental protection, electric vehicles (EVs) and hybrid electric vehicles (HEVs) have developed rapidly. Improving energy efficiency and extending driving range are the core requirements of these vehicles, and regenerative braking technology has become an important means to achieve this goal. The goal of the regenerative braking system is to recover the kinetic energy during the vehicle's deceleration process and convert it into usable electrical energy for storage for subsequent power needs.

[0003] Traditional regenerative braking systems mainly rely on batteries to store electrical energy, but batteries have problems such as low power density, slow charge and discharge rates, and limited cycle life. Supercapacitors have become highly potential energy storage devices in regenerative braking systems due to their high power density, fast charge and discharge, and long life. However, when designing and implementing a regenerative braking system with supercapacitors as the core, how to maximize energy recovery efficiency, extend the service life of supercapacitors, balance energy flow, and optimize the response speed of the system are still urgent issues to be solved. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention proposes a regenerative braking energy storage and feedback system based on supercapacitors. The present invention monitors the vehicle's driving speed, acceleration, and the supercapacitor's charging and discharging current, voltage, power fluctuation, and remaining life in real time, distributes energy between the supercapacitor and the battery, and adjusts the supercapacitor's charging and discharging strategy in real time according to the vehicle's driving status, solving the problems of low energy recovery efficiency, large power fluctuation, and limited supercapacitor life in traditional systems. Through the comprehensive application of nonlinear dynamic modeling, intelligent control, multi-objective optimization, and adaptive predictive control, the system achieves maximum energy recovery, extends the life of supercapacitors, and ensures the stability and efficiency of the system under various driving conditions.

[0005] To achieve the above object, the technical solution of the present invention is as follows:

[0006] A regenerative braking energy storage and feedback system based on supercapacitors, the system comprising a real-time monitoring port, an energy management port and a power distribution optimization port. The real-time monitoring port is used to monitor the vehicle's driving speed, acceleration, and the supercapacitor's charging and discharging current, voltage, power fluctuation, and remaining life in real time; the energy management port is used to distribute energy between the supercapacitor and the battery; the power distribution control port is used to adjust the supercapacitor's charging and discharging strategy in real time according to the vehicle's driving status.

[0007] A further improvement of the present invention is that the energy management port includes an energy recovery power model construction module and a charge and discharge management module. The energy recovery power model construction module is used to construct a nonlinear energy recovery power model and output the recovery power; the charge and discharge management module is used to run the charge and discharge control strategy and adjust the charge and discharge current of the supercapacitor according to the real-time voltage of the supercapacitor.

[0008] A further improvement of the present invention is that the power allocation optimization port includes a multi-objective optimization module and an adaptive predictive control module. The multi-objective optimization module is used to construct a multi-objective optimization function to adaptively adjust the charging and discharging behavior of the supercapacitor; the adaptive predictive control module is used to predict the changes in the vehicle's operating state in the future based on the vehicle's driving speed and acceleration, thereby adjusting the energy recovery strategy.

[0009] A further improvement of the present invention is that the energy recovery power model construction module is used to construct a nonlinear energy recovery power model and output the recovery power. The specific formula of the nonlinear energy recovery power model is:

[0010] P regen =αv 2 ×(1-βe -kv );

[0011] Where v is the vehicle speed, α, β, and k are adjustment parameters of the nonlinear energy recovery power model.

[0012] A further improvement of the present invention is that the charge and discharge management module is used to run a charge and discharge control strategy to adjust the charge and discharge current of the supercapacitor according to the real-time voltage of the supercapacitor; the specific formula of the charge and discharge control strategy is: Among them, I SC (t) is the charge and discharge current of the supercapacitor, V SET (t) is the target voltage of the supercapacitor at the current moment, V SC (t) is the actual voltage of the supercapacitor at the current moment, k1 is the influencing factor of the difference between the target voltage and the actual voltage of the supercapacitor, and k2 is the influencing factor of the actual voltage change rate of the supercapacitor.

[0013] A further improvement of the present invention is that the multi-objective optimization module is used to construct a multi-objective optimization function to adaptively adjust the charging and discharging behavior of the supercapacitor; the specific formula of the multi-objective optimization function is:

[0014] F=ω1×P regen +ω2×L SC +ω3×σ power ;

[0015] Among them, L SC is the remaining life of the supercapacitor, σ power is the standard deviation of supercapacitor power fluctuation, ω1, ω2, and ω3 are the weights of energy recovery power, remaining life of supercapacitor, and standard deviation of supercapacitor power fluctuation, respectively.

[0016] A further improvement of the present invention is that the adaptive prediction control module is used to predict the changes in the vehicle's operating state in the future period of time based on the vehicle's driving speed and acceleration, thereby adjusting the energy recovery strategy. The specific content of predicting the changes in the vehicle's operating state in the future period of time is: the vehicle's historical driving speed, historical acceleration and time series are synchronously mixed and combined into the form of a feature vector, 70% of the feature data are input into the neural network model as a training set for training to obtain an initial neural network model, and 30% of the feature data are used as a test set to test the initial neural network model, and the optimal initial neural network model that meets the accuracy of the historical vehicle driving speed and acceleration change curve data is output as the final neural network model; the vehicle's real-time driving speed, real-time acceleration and predicted time period length are input into the final neural network model to obtain the changes in the vehicle's operating state in the future period of time.

[0017] The technical effects of the present invention are as follows:

[0018] The present invention monitors the vehicle's driving speed, acceleration, and the supercapacitor's charging and discharging current, voltage, power fluctuation, and remaining life in real time, distributes energy between the supercapacitor and the battery, and adjusts the supercapacitor's charging and discharging strategy in real time according to the vehicle's driving status, solving the problems of low energy recovery efficiency, large power fluctuation, and limited supercapacitor life in traditional systems. Through the comprehensive application of nonlinear dynamic modeling, intelligent control, multi-objective optimization, and adaptive predictive control, the system maximizes energy recovery, extends the life of the supercapacitor, and ensures the stability and efficiency of the system under various driving conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0020] Figure 1 It is a structural schematic diagram of the supercapacitor-based regenerative braking energy storage and feedback system of the present invention. DETAILED DESCRIPTION

[0021] Example 1

[0022] This embodiment proposes a regenerative braking energy storage and feedback system based on supercapacitors. By real-time monitoring of vehicle speed, acceleration, and supercapacitor charge and discharge current, voltage, power fluctuation, and remaining life, energy is distributed between supercapacitors and batteries, and the supercapacitor charge and discharge strategy is adjusted in real time according to the vehicle's driving state, solving the problems of low energy recovery efficiency, large power fluctuation, and limited supercapacitor life in traditional systems. Through the comprehensive application of nonlinear dynamic modeling, intelligent control, multi-objective optimization, and adaptive predictive control, the system achieves maximum energy recovery, extends the life of supercapacitors, and ensures the stability and efficiency of the system under various driving conditions.

[0023] like Figure 1 As shown, a regenerative braking energy storage and feedback system based on supercapacitors includes a real-time monitoring port, an energy management port and a power distribution optimization port. The real-time monitoring port is used to monitor the vehicle speed, acceleration, and the charging and discharging current, voltage, power fluctuation, and remaining life of the supercapacitor in real time; the energy management port is used to distribute energy between the supercapacitor and the battery; the power distribution control port is used to adjust the charging and discharging strategy of the supercapacitor in real time according to the driving status of the vehicle.

[0024] In this embodiment, the energy management port includes an energy recovery power model construction module and a charge and discharge management module. The energy recovery power model construction module is used to construct a nonlinear energy recovery power model and output the recovery power; the charge and discharge management module is used to run the charge and discharge control strategy and adjust the charge and discharge current of the supercapacitor according to the real-time voltage of the supercapacitor.

[0025] In this embodiment, the power allocation optimization port includes a multi-objective optimization module and an adaptive predictive control module. The multi-objective optimization module is used to construct a multi-objective optimization function to adaptively adjust the charging and discharging behavior of the supercapacitor; the adaptive predictive control module is used to predict the changes in the vehicle's operating state in the future based on the vehicle's driving speed and acceleration, thereby adjusting the energy recovery strategy.

[0026] In this embodiment, the energy recovery power model construction module is used to construct a nonlinear energy recovery power model and output the recovery power. The specific formula of the nonlinear energy recovery power model is:

[0027] P regen =αv 2 ×(1-βe -kv );

[0028] Where v is the vehicle speed, α, β, and k are adjustment parameters of the nonlinear energy recovery power model.

[0029] In this embodiment, the charge and discharge management module is used to run the charge and discharge control strategy to adjust the charge and discharge current of the supercapacitor according to the real-time voltage of the supercapacitor; the specific formula of the charge and discharge control strategy is: Among them, I SC (t) is the charge and discharge current of the supercapacitor, V SET (t) is the target voltage of the supercapacitor at the current moment, V SC (t) is the actual voltage of the supercapacitor at the current moment, k1 is the influencing factor of the difference between the target voltage and the actual voltage of the supercapacitor, and k2 is the influencing factor of the actual voltage change rate of the supercapacitor.

[0030] In this embodiment, the multi-objective optimization module is used to construct a multi-objective optimization function to adaptively adjust the charging and discharging behavior of the supercapacitor; the specific formula of the multi-objective optimization function is:

[0031] F=ω1×P regen +ω2×L SC +ω3×σ power ;

[0032] Among them, L SC is the remaining life of the supercapacitor, σ power is the standard deviation of supercapacitor power fluctuation, ω1, ω2, and ω3 are the weights of energy recovery power, remaining life of supercapacitor, and standard deviation of supercapacitor power fluctuation, respectively.

[0033] In this embodiment, the adaptive prediction control module is used to predict the changes in the vehicle's operating state in the future based on the vehicle's speed and acceleration, thereby adjusting the energy recovery strategy. The specific content of predicting the changes in the vehicle's operating state in the future is: the vehicle's historical speed, historical acceleration and time series are synchronously mixed and combined into the form of a feature vector, 70% of the feature data are input into the neural network model as a training set for training to obtain an initial neural network model, and 30% of the feature data are used as a test set to test the initial neural network model, and the optimal initial neural network model that meets the accuracy of the historical vehicle speed and acceleration change curve data is output as the final neural network model; the vehicle's real-time speed, real-time acceleration and predicted time period length are input into the final neural network model to obtain the changes in the vehicle's operating state in the future.

[0034] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0035] It should be understood that determining B based on A does not mean determining B only based on A. B can also be determined based on A and / or other information.

[0036] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When a computer instruction or computer program is loaded or executed on a computer, a process or function according to an embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center through a wired network or / and a wireless network. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0037] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0038] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0039] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of the units is only one, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0040] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0041] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0042] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0043] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A regenerative braking energy storage and feedback system based on supercapacitors, characterized in that: The system includes a real-time monitoring port, an energy management port and a power distribution optimization port. The real-time monitoring port is used to monitor the vehicle speed, acceleration, and the charging and discharging current, voltage, power fluctuation, and remaining life of the supercapacitor in real time; The energy management port is used to distribute energy between the supercapacitor and the battery; the power distribution control port is used to adjust the charging and discharging strategy of the supercapacitor in real time according to the driving state of the vehicle.

2. The supercapacitor-based regenerative braking energy storage and feedback system according to claim 1, characterized in that: The energy management port includes an energy recovery power model construction module and a charge and discharge management module, wherein the energy recovery power model construction module is used to construct a nonlinear energy recovery power model and output the recovery power; The charge and discharge management module is used to run the charge and discharge control strategy and adjust the charge and discharge current of the supercapacitor according to the real-time voltage of the supercapacitor.

3. The supercapacitor-based regenerative braking energy storage and feedback system according to claim 2, characterized in that: The power allocation optimization port includes a multi-objective optimization module and an adaptive prediction control module, wherein the multi-objective optimization module is used to construct a multi-objective optimization function to adaptively adjust the charging and discharging behavior of the supercapacitor; The adaptive prediction control module is used to predict the change of the vehicle's operating state in the future according to the vehicle's running speed and acceleration, so as to adjust the energy recovery strategy.

4. The supercapacitor-based regenerative braking energy storage and feedback system according to claim 3, characterized in that: The energy recovery power model building module is used to build a nonlinear energy recovery power model and output the recovery power. The specific formula of the nonlinear energy recovery power model is: P regen =αv 2 ×(1-βe -kv ); Where v is the vehicle speed, α, β, and k are adjustment parameters of the nonlinear energy recovery power model.

5. The supercapacitor-based regenerative braking energy storage and feedback system according to claim 4, characterized in that: The charge and discharge management module is used to run the charge and discharge control strategy to adjust the charge and discharge current of the supercapacitor according to the real-time voltage of the supercapacitor; the specific formula of the charge and discharge control strategy is: Among them, I SC (t) is the charge and discharge current of the supercapacitor, V STT (t) is the target voltage of the supercapacitor at the current moment, V SC (t) is the actual voltage of the supercapacitor at the current moment, k1 is the influencing factor of the difference between the target voltage and the actual voltage of the supercapacitor, and k2 is the influencing factor of the actual voltage change rate of the supercapacitor.

6. The supercapacitor-based regenerative braking energy storage and feedback system according to claim 5, characterized in that: The multi-objective optimization module is used to construct a multi-objective optimization function to adaptively adjust the charging and discharging behavior of the supercapacitor; the specific formula of the multi-objective optimization function is: F=ω1×P regen +ω2×L SC +ω3×σ power ; Among them, L SC is the remaining life of the supercapacitor, σ power is the standard deviation of supercapacitor power fluctuation, ω1, ω2, and ω3 are the weights of energy recovery power, remaining life of supercapacitor, and standard deviation of supercapacitor power fluctuation, respectively.

7. The supercapacitor-based regenerative braking energy storage and feedback system according to claim 6, characterized in that: The adaptive prediction control module is used to predict the changes in the vehicle's operating state in the future based on the vehicle's speed and acceleration, so as to adjust the energy recovery strategy. The specific content of predicting the changes in the vehicle's operating state in the future is: synchronously mixing and combining the vehicle's historical speed, historical acceleration and time series into the form of a feature vector, inputting 70% of the feature data as a training set into the neural network model for training to obtain an initial neural network model, using 30% of the feature data as a test set to test the initial neural network model, and outputting the optimal initial neural network model that meets the accuracy of the historical vehicle speed and acceleration change curve data as the final neural network model; inputting the vehicle's real-time speed, real-time acceleration and predicted time period length into the final neural network model to obtain the changes in the vehicle's operating state in the future.

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