Regenerative braking energy feedback optimization method based on multi-energy conversion

Abstract

The invention relates to the technical field of braking energy feedback, in particular to a regenerative braking energy feedback optimization method based on multi-energy conversion, and the method comprises the steps: collecting an initialization parameter, collecting the change of a motion state parameter in a time sequence, and constructing a vehicle power model; collecting changes of the motor state parameters, the battery state parameters and the capacitor state parameters in a time sequence, and constructing a motor power model, a battery model and a capacitor model; an energy distribution strategy and an energy recovery efficiency objective function are constructed, and the battery charging power, the capacitor charging power, the battery current, the capacitor current, the battery charge state and the capacitor voltage are constrained according to limiting conditions; and updating the battery state parameters and the capacitor state parameters in real time. And the actual working condition and the state of the energy storage device are dynamically adjusted, so that energy recovery and utilization are more sufficient.

Description

Background Art

[0002] As the global energy crisis and environmental pollution become increasingly serious, new energy vehicles (such as electric vehicles and hybrid vehicles) have received widespread attention. As an important component of new energy vehicles, regenerative braking technology can recover the vehicle's kinetic energy and convert it into electrical energy when the vehicle decelerates or brakes, and store it in an energy storage device (such as a battery or supercapacitor) for use when the vehicle accelerates again, thereby improving energy efficiency, extending vehicle range, and reducing energy consumption and emissions.

[0003] Multi-energy conversion refers to the use of multiple energy conversion methods and multiple energy storage devices (such as batteries, supercapacitors, flywheels, etc.) during regenerative braking to achieve efficient energy recovery and utilization. Compared with a single energy conversion and energy storage method, multi-energy conversion can better adapt to complex working conditions and energy needs, improve energy recovery efficiency, and optimize energy management. Existing energy management systems are usually based on preset control strategies and cannot dynamically adjust the actual working conditions and the status of energy storage devices, resulting in insufficient energy recovery and utilization. Summary of the invention

[0004] The main purpose of the present invention is to provide a regenerative braking energy feedback optimization method based on multi-energy conversion, which effectively solves the above-mentioned problems mentioned in the background technology.

[0005] The technical solution of the present invention is as follows:

[0006] A regenerative braking energy feedback optimization method based on multi-energy conversion is proposed, which includes the following steps:

[0007] S1, collect initialization parameters, and at the same time collect the changes of motion state parameters in time series to build a vehicle dynamic model;

[0008] S2, collecting changes in motor state parameters, battery state parameters and capacitor state parameters in time series, and constructing motor power model, battery model and capacitor model;

[0009] S3. Construct an energy allocation strategy and an energy recovery efficiency objective function, and constrain the battery charging power, capacitor charging power, battery current, capacitor current, battery state of charge, and capacitor voltage according to the constraints;

[0010] S4. Update the battery status parameters and the capacitor status parameters in real time.

[0011] A further improvement of the present invention is that S1 comprises the following specific steps:

[0012] S11, collecting initialization parameters, wherein the initialization parameters specifically include vehicle mass m, road slope angle θ, rolling resistance coefficient fr, frontal area A and air resistance coefficient Cd;

[0013] S12, collecting the changes of motion state parameters in time series, wherein the motion state parameters specifically include the driving speed v(t) and the traction force F tractive (t), where t is the time node;

[0014] S13, constructing a vehicle dynamics model, wherein the specific formula of the vehicle dynamics model is:

[0015]

[0016] Among them, F resist (t) is the driving resistance, g is the acceleration due to gravity, and ρ is the air density.

[0017] A further improvement of the present invention is that S2 comprises the following specific steps:

[0018] S21, collecting changes in motor state parameters in time series, and constructing a motor power model. The specific formula of the motor power model is:

[0019]

[0020] Among them, P regen (t) is the motor regenerative power, is the motor efficiency, T em (t) is the motor torque, ω em (t) is the motor speed;

[0021] S22, collecting changes in battery status parameters in time series, and constructing a battery model, wherein the specific formula of the battery model is:

[0022] P bat (t) = V bat (t)×I bat (t);

[0023]

[0024] Among them, P bat (t) is the battery charging power, V bat (t) is the battery voltage, I bat (t) is the battery current, SOC bat (t) is the battery charge state, Q bat is the battery capacity;

[0025] S23, collecting changes in capacitor state parameters in time series, and constructing a capacitor model, wherein the specific formula of the capacitor model is:

[0026] P sc (t) = V sc (t)×I sc (t);

[0027]

[0028] Among them, P sc (t) is the capacitor charging power, V sc (t) is the capacitor voltage, I sc (t) is the capacitor current, C sc is the capacitance value.

[0029] A further improvement of the present invention is that S3 comprises the following specific steps:

[0030] S31. Construct an energy allocation strategy. The specific formula is:

[0031] P sc (t) = α(t) × P regen (t);

[0032] P bat (t) = (1-α(t)) × P regen (t);

[0033] Wherein, α(t) is the energy distribution coefficient. When the capacitor voltage is lower than the capacitor voltage constraint value, α(t)=1; when the capacitor voltage is higher than the capacitor voltage constraint value, α(t)=0;

[0034] S32, constructing an energy recovery efficiency objective function, wherein the specific formula of the energy recovery efficiency objective function is:

[0035]

[0036] in, is the total energy recovery efficiency, is the capacitor charging efficiency, is the battery charging efficiency, t0 is the starting point of the acquisition time, t f The end point of the collection time.

[0037] A further improvement of the present invention is that the specific formula of S4 is:

[0038]

[0039] Among them, SOC bat (t+Δt) is the updated value of battery state of charge, V sc(t+Δt) is the updated value of the capacitor voltage, and Δt is the time node interval.

[0040] The technical effects of the present invention are as follows:

[0041] A regenerative braking energy feedback optimization method based on multi-energy conversion is constructed. The present invention constructs a vehicle power model by collecting initialization parameters and changes in motion state parameters in time series; collects changes in motor state parameters, battery state parameters and capacitor state parameters in time series to construct motor power models, battery models and capacitor models; constructs energy allocation strategies and energy recovery efficiency objective functions, and constrains battery charging power, capacitor charging power, battery current, capacitor current, battery state of charge and capacitor voltage according to restrictions; and updates battery state parameters and capacitor state parameters in real time. The actual working conditions and the state of the energy storage device are dynamically adjusted, resulting in more adequate energy recovery and utilization. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] 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:

[0043] Figure 1 It is a flow chart of the regenerative braking energy feedback optimization method based on multi-energy conversion of the present invention. DETAILED DESCRIPTION

[0044] The present invention aims to propose a regenerative braking energy feedback optimization method based on multi-energy conversion, by collecting initialization parameters and the changes of motion state parameters in time series, constructing a vehicle power model; collecting the changes of motor state parameters, battery state parameters and capacitor state parameters in time series, constructing motor power model, battery model and capacitor model; constructing energy allocation strategy and energy recovery efficiency objective function, and constraining battery charging power, capacitor charging power, battery current, capacitor current, battery state of charge and capacitor voltage according to restriction conditions; updating battery state parameters and capacitor state parameters in real time. Dynamically adjusting the actual working conditions and the state of the energy storage device, resulting in more adequate energy recovery and utilization.

[0045] Embodiment 1:

[0046] This embodiment proposes a regenerative braking energy feedback optimization method based on multi-energy conversion, such as Figure 1 The following specific steps are shown:

[0047] S1, collect initialization parameters, and at the same time collect the changes of motion state parameters in time series to build a vehicle dynamic model;

[0048] S2, collecting changes in motor state parameters, battery state parameters and capacitor state parameters in time series, and constructing motor power model, battery model and capacitor model;

[0049] S3. Construct an energy allocation strategy and an energy recovery efficiency objective function, and constrain the battery charging power, capacitor charging power, battery current, capacitor current, battery state of charge, and capacitor voltage according to the constraints;

[0050] S4. Update the battery status parameters and the capacitor status parameters in real time.

[0051] In this embodiment, S1 includes the following specific steps:

[0052] S11, collecting initialization parameters, wherein the initialization parameters specifically include vehicle mass m, road slope angle θ, rolling resistance coefficient fr, frontal area A and air resistance coefficient Cd;

[0053] S12, collecting the changes of motion state parameters in time series, wherein the motion state parameters specifically include the driving speed v(t) and the traction force F tractive (t), where t is the time node;

[0054] S13, constructing a vehicle dynamics model, wherein the specific formula of the vehicle dynamics model is:

[0055]

[0056] Among them, F resist (t) is the driving resistance, g is the acceleration due to gravity, and ρ is the air density.

[0057] In this embodiment, S2 includes the following specific steps:

[0058] S21, collecting changes in motor state parameters in time series, and constructing a motor power model. The specific formula of the motor power model is:

[0059]

[0060] Among them, P regen (t) is the motor regenerative power, is the motor efficiency, T em (t) is the motor torque, ω em (t) is the motor speed;

[0061] S22, collecting changes in battery status parameters in time series, and constructing a battery model, wherein the specific formula of the battery model is:

[0062] P bat (t) = V bat (t)×Ibat (t);

[0063]

[0064] Among them, P bat (t) is the battery charging power, V bat (t) is the battery voltage, I bat (t) is the battery current, SOC bat (t) is the battery charge state, Q bat is the battery capacity;

[0065] S23, collecting changes in capacitor state parameters in time series, and constructing a capacitor model, wherein the specific formula of the capacitor model is:

[0066] P sc (t) = V sc (t)×I sc (t);

[0067]

[0068] Among them, P sc (t) is the capacitor charging power, V sc (t) is the capacitor voltage, I sc (t) is the capacitor current, C sc is the capacitance value.

[0069] In this embodiment, S3 includes the following specific steps:

[0070] S31. Construct an energy allocation strategy. The specific formula is:

[0071] P sc (t) = α(t) × P regen (t);

[0072] P bat (t) = (1-α(t)) × P regen (t);

[0073] Wherein, α(t) is the energy distribution coefficient. When the capacitor voltage is lower than the capacitor voltage constraint value, α(t)=1; when the capacitor voltage is higher than the capacitor voltage constraint value, α(t)=0;

[0074] S32, constructing an energy recovery efficiency objective function, wherein the specific formula of the energy recovery efficiency objective function is:

[0075]

[0076]

[0077] in, is the total energy recovery efficiency, is the capacitor charging efficiency, is the battery charging efficiency, t0 is the starting point of the acquisition time, t f The end point of the collection time.

[0078] In this embodiment, the specific formula of S4 is:

[0079]

[0080] Among them, SOC bat (t+Δt) is the updated value of battery state of charge, V sc (t+Δt) is the updated value of the capacitor voltage, and Δt is the time node interval.

[0081] Embodiment 2:

[0082] This embodiment provides an electronic device, including: a processor and a memory, wherein the memory stores a computer program that can be called by the processor; the processor executes the above-mentioned regenerative braking energy feedback optimization method based on multi-energy conversion by calling the computer program stored in the memory.

[0083] The electronic device may have relatively large differences due to different configurations or performances, and may include one or more processors (Central Processing Units, CPU) and one or more memories, wherein the memory stores at least one computer program, which is loaded and executed by the processor to implement the regenerative braking energy feedback optimization method based on multi-energy conversion provided by the above method embodiment. The electronic device may also include other components for realizing the functions of the device, for example, the electronic device may also have components such as a wired or wireless network interface and an input and output interface for data input and output. This embodiment will not be described in detail here.

[0084] Those skilled in the art know that the present invention can be implemented as a system, method or computer program product. Therefore, the present disclosure can be specifically implemented in the following forms, namely: it can be complete hardware, it can be complete software (including firmware, resident software, microcode, etc.), and it can also be a combination of hardware and software, which is generally referred to as "circuit", "module" or "system" herein. In addition, in some embodiments, the present invention can also be implemented in the form of a computer program product in one or more computer-readable media, and the computer-readable medium contains computer-readable program code.

[0085] Any combination of one or more computer-readable media may be used. A computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, apparatus, or device.

[0086] The present invention is described with reference to flowcharts and block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process or block in the flowchart and block diagram, as well as the combination of processes and blocks in the flowchart or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts. Figure 1 Process or multiple processes and boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0087] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 Process or multiple processes and boxes Figure 1 The steps for the functions specified in one or more boxes.

[0088] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the enlightenment of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the purpose of the present invention and the claims, which all fall within the protection of the present invention.

Claims

1. A regenerative braking energy feedback optimization method based on multi-energy conversion, characterized in that: The specific steps include: S1, collect initialization parameters, and at the same time collect the changes of motion state parameters in time series to build a vehicle dynamic model; S2, collecting changes in motor state parameters, battery state parameters and capacitor state parameters in time series, and constructing motor power model, battery model and capacitor model; S3. Construct an energy allocation strategy and an energy recovery efficiency objective function, and constrain the battery charging power, capacitor charging power, battery current, capacitor current, battery state of charge, and capacitor voltage according to the constraints; S4. Update and adjust the battery status parameters and the capacitor status parameters in real time.

2. The regenerative braking energy feedback optimization method based on multi-energy conversion according to claim 1 is characterized in that: The S1 comprises the following specific steps: S11, collecting initialization parameters, wherein the initialization parameters specifically include vehicle mass m, road slope angle θ, rolling resistance coefficient fr, frontal area A and air resistance coefficient Cd; S12, collecting the changes of motion state parameters in time series, wherein the motion state parameters specifically include the driving speed v(t) and the traction force F tractive (t), where t is the time node; S13, constructing a vehicle dynamics model, wherein the specific formula of the vehicle dynamics model is: Among them, F resist (t) is the driving resistance, g is the acceleration due to gravity, and ρ is the air density.

3. The regenerative braking energy feedback optimization method based on multi-energy conversion according to claim 2 is characterized in that: The S2 comprises the following specific steps: S21, collecting changes in motor state parameters in time series, and constructing a motor power model. The specific formula of the motor power model is: Among them, P regen (t) is the motor regenerative power, is the motor efficiency, T em (t) is the motor torque, ω em (t) is the motor speed; S22, collecting changes in battery status parameters in time series, and constructing a battery model, wherein the specific formula of the battery model is: P bat (t)=V bat (t)×I bat (t); Among them, P bat (t) is the battery charging power, V bat (t) is the battery voltage, I bat (t) is the battery current, SOC bat (t) is the battery charge state, Q bat is the battery capacity; S23, collecting changes in capacitor state parameters in time series, and constructing a capacitor model, wherein the specific formula of the capacitor model is: P sc (t)=V sc (t)×I sc (t); Among them, P sc (t) is the capacitor charging power, V sc (t) is the capacitor voltage, I sc (t) is the capacitor current, C sc is the capacitance value.

4. The regenerative braking energy feedback optimization method based on multi-energy conversion according to claim 3 is characterized in that: The S3 includes the following specific steps: S31. Construct an energy allocation strategy. The specific formula is: P sc (t)=α(t)×P regen (t); P bat (t)=(1-α(t))×P regen (t); Wherein, α(t) is the energy distribution coefficient. When the capacitor voltage is lower than the capacitor voltage constraint value, α(t)=1; when the capacitor voltage is higher than the capacitor voltage constraint value, α(t)=0; S32, constructing an energy recovery efficiency objective function, wherein the specific formula of the energy recovery efficiency objective function is: in, is the total energy recovery efficiency, is the capacitor charging efficiency, is the battery charging efficiency, t0 is the starting point of the acquisition time, t f The end point of the collection time.

5. The regenerative braking energy feedback optimization method based on multi-energy conversion according to claim 4 is characterized in that: The specific formula of S4 is: Among them, SOC bat (t+Δt) is the updated value of battery state of charge, V sc (t+Δt) is the updated value of the capacitor voltage, and Δt is the time node interval.

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