Brake energy recovery method based on lexicographical order

Abstract

The invention relates to the technical field of motorcycle energy recovery, in particular to a braking energy recovery method based on a lexicographical order. In the running process of the electric motorcycle, angle data of a braking hand brake are obtained, and the mechanical braking torque is determined according to the angle data. And target functions and target function constraints corresponding to the braking deceleration, the braking energy recovery and the braking smoothness respectively are determined. And according to the mechanical braking torque, the motor current, the motor voltage, the motor rotating speed and the motor braking optimization model of the electric motorcycle at the current moment, target control signals meeting target function constraints and target function constraints corresponding to braking deceleration, braking energy recovery and braking smoothness respectively are solved. And determining the braking torque of the motor according to the target control signal. And braking energy is recovered according to the motor braking torque. Multi-target conflicts are reasonably handled, braking control is achieved based on an existing electric motorcycle braking system structure, and good practicability, safety and the energy-saving effect are achieved.

Description

Technical Field

[0001] This specification relates to the field of motorcycle energy recovery technology, and in particular to a lexicographical method for brake energy recovery. Background Technology

[0002] Driven by the rapid development of new energy technologies and the increasing global awareness of environmental protection, electric motorcycles have become an important part of the automotive industry. In the electric motorcycle sector, energy recovery technology is key to improving range and riding experience.

[0003] In the design and optimization of vehicle braking energy recovery systems, coordinating multiple control objectives—such as braking deceleration, energy recovery efficiency, and braking smoothness—is key to improving overall vehicle performance and driving experience. Because the relative importance of each objective varies across different driving scenarios, the system's requirements for control strategies also change accordingly. These differences are often difficult to accurately express and handle using traditional control methods.

[0004] Currently, the linear weighted method is frequently used in multi-objective optimization problems. It constructs a comprehensive evaluation function to merge multiple objectives into a single optimization objective. However, this method has significant shortcomings: First, its evaluation function, numerically, fails to reflect the actual differences in importance between objectives under different conditions. Although adjusting the weight coefficients can reflect the relative importance of objectives to some extent, regardless of the value of the weight coefficients, it cannot theoretically guarantee that the deviation of the corresponding objective will be optimized first; that is, it cannot ensure that the key objective is satisfied first. Second, when changes in driving conditions alter the importance of objectives, the linear weighted method struggles to adjust the weight coefficients in a timely and accurate manner, affecting the system's adaptability and robustness in real-time control and limiting its application in dynamic multi-objective optimization scenarios.

[0005] The root cause of the above problems lies in the fact that the linear weighting method, as a scalarization method, essentially transforms a multi-objective optimization problem that is originally independent and potentially conflicting into a single-objective optimization problem. This approach ignores the independence and inherent differences between the objectives, making it impossible to analyze and control each objective separately, thus making it difficult to achieve overall optimal system performance under complex real-world conditions.

[0006] Therefore, this specification provides a lexicographical method for brake energy recovery. Summary of the Invention

[0007] This specification provides a lexicographical method for regenerative braking to address the aforementioned problems in the prior art.

[0008] The following technical solution is adopted in this specification: This specification provides a lexicographically ordered braking energy recovery method, including: S1. During the operation of the electric motorcycle, acquire angle data collected by an angle sensor installed on the brake handbrake, and determine the mechanical braking torque based on the angle data; S2. Determine the objective functions and objective function constraints corresponding to braking deceleration, braking energy recovery, and braking ride comfort, respectively; S3. Based on the mechanical braking torque, the current motor current, motor voltage, and motor speed of the electric motorcycle, and the preset motor braking optimization model, solve for the objective functions and objective function constraints that satisfy the braking deceleration, braking energy recovery, and braking smoothness, respectively. S4. Determine the motor's driving torque based on the target control signal; S5. Based on the electric motor's driving torque and the mechanical braking torque, brake control is performed on the electric motorcycle, and braking energy is recovered based on the electric motor's driving torque.

[0009] Based on the aforementioned technical means, this solution optimizes the control signal by comprehensively considering three objectives: braking deceleration, braking energy recovery, and braking smoothness. This ensures that during the braking control of an electric motorcycle, braking smoothness is guaranteed, providing the driver with a good riding experience. Simultaneously, it ensures that the braking deceleration meets braking requirements, guaranteeing driving safety. Furthermore, it maximizes braking energy recovery, extending the electric motorcycle's range. It effectively handles multi-objective conflicts and utilizes the existing electric motorcycle braking system structure to achieve braking control, demonstrating good practicality, safety, and energy-saving effects.

[0010] Furthermore, the expression for the preset motor braking optimization model in S3 is:

[0011]

[0012]

[0013]

[0014]

[0015]

[0016]

[0017] in, , , , , , , , These are the motor current, motor voltage, motor speed, control signal, mechanical braking torque, input power, motor torque, and output power of the electric motorcycle at the current moment. For resistance, The electromotive force constant is . For inductance, The torque constant is The coefficient of friction of the motor. It is the moment of inertia; These are the preset transfer function constants; The preset time period of change; For control signals.

[0018] Furthermore, the expression for the objective function corresponding to the braking deceleration described in S2 is:

[0019]

[0020]

[0021]

[0022] in, The objective function corresponding to the braking deceleration; These are preset braking deceleration weighting parameters; Let k be the actual change in rotational speed of the electric motorcycle at time t in the future, where t is an integer and t≤N. When t is 0, k+t is the current time. Let N be the expected change in rotational speed of the electric motorcycle at time t in the future; N is the number of times the objective function corresponding to the braking deceleration is predicted. Let be the mechanical braking torque of the electric motorcycle at time t in the future; Let be the angle data of the electric motorcycle at time t in the future.

[0023] Furthermore, the expression for the objective function corresponding to the regenerative braking energy described in S2 is:

[0024]

[0025]

[0026] in, Let the objective function be the braking energy recovery function. The preset braking energy recovery weight parameters; Let N be the input power of the electric motorcycle at time t in the future, where t is an integer and t≤N. When t is 0, k+t is the current time. N is the number of times the objective function corresponding to the braking deceleration is predicted. Let be the output power of the electric motorcycle at time t in the future; Let be the electric motor torque of the electric motorcycle at time t in the future; Let be the mechanical braking torque of the electric motorcycle at time t in the future; Let be the motor voltage of the electric motorcycle at time t in the future; Let be the motor current of the electric motorcycle at time t in the future.

[0027] Furthermore, the expression for the objective function corresponding to the braking smoothness described in S2 is:

[0028] in, Let the objective function be the braking smoothness. These are preset braking smoothness weighting parameters.

[0029] Furthermore, the expression for the objective function constraint described in S2 is:

[0030]

[0031]

[0032] in, , , These are motor current, motor voltage, and motor speed, respectively. , These are the preset minimum and maximum values ​​of the motor current, respectively; , These are the preset minimum and maximum motor speeds, respectively; , These are the preset minimum and maximum values ​​of the motor voltage, respectively.

[0033] Furthermore, in S3, the target control signal that satisfies the objective functions and objective function constraints corresponding to the braking deceleration, braking energy recovery, and braking ride comfort, respectively, specifically includes: S31. Under the condition that the constraints of the motor braking optimization model and the objective function are satisfied, the first control signal is solved according to the objective function corresponding to the braking deceleration; S32. Within the range corresponding to the first control signal, solve for the second control signal according to the objective function corresponding to the regenerative braking energy; S33. Within the range corresponding to the second control signal, solve for the target control signal based on the objective function corresponding to the braking smoothness.

[0034] Based on the aforementioned technical methods, firstly, under the constraints of the motor braking model and the basic objective function, a first control signal is obtained, focusing solely on braking deceleration, to achieve the optimal braking deceleration. Then, within the range encompassed by the first control signal, a second control signal is derived, aiming to maximize braking energy recovery, thus satisfying both the braking deceleration requirement and optimizing energy recovery. Finally, within the range encompassed by the second control signal, a unique or optimal target control signal is determined, prioritizing braking smoothness. This achieves the technical effect of ensuring braking safety (deceleration priority), maximizing energy recovery potential under safe conditions, and ultimately optimizing driving quality (braking smoothness) within a confined space.

[0035] Furthermore, the expression for solving the first control signal in S31 is:

[0036] in, This is the first control signal.

[0037] Furthermore, the expression for solving the second control signal in S32 is:

[0038] in, This is the second control signal; The expression for the second control signal satisfying the range corresponding to the first control signal is:

[0039] in, This is the preset first relaxation factor.

[0040] Furthermore, the expression for solving the target control signal in S33 is as follows:

[0041] in, The target control signal; The expression that the target control signal satisfies within the range corresponding to the second control signal is:

[0042] in, This is the preset second relaxation factor.

[0043] The above-mentioned technical solutions adopted in this specification can achieve the following beneficial effects: This solution optimizes the control signal by comprehensively considering three objectives: braking deceleration, regenerative braking, and braking smoothness. This ensures smooth braking for a comfortable driving experience while simultaneously guaranteeing sufficient deceleration to meet braking requirements and ensure driving safety. Furthermore, it maximizes regenerative braking, extending the electric motorcycle's range. It effectively handles multiple conflicting objectives and leverages the existing electric motorcycle braking system structure for braking control, demonstrating excellent practicality, safety, and energy efficiency. Attached Figure Description

[0044] The accompanying drawings, which are included to provide a further understanding of this specification and form part of this specification, illustrate exemplary embodiments and are used to explain this specification, but do not constitute an undue limitation thereof. In the drawings: Figure 1 A flowchart illustrating a lexicographically ordered braking energy recovery method provided for embodiments of this specification; Figure 2 This specification provides a corresponding Figure 1 A schematic diagram of the structure of an electronic device. Detailed Implementation

[0045] To make the objectives, technical solutions, and advantages of this specification clearer, the technical solutions of this specification will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this specification, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments in this specification without creative effort are within the scope of protection of this application.

[0046] In embodiments of this application, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0047] The technical solutions provided in the various embodiments of this specification are described in detail below with reference to the accompanying drawings.

[0048] Figure 1A flowchart illustrating a lexicographically ordered regenerative braking energy recovery method provided in this specification includes the following steps: S1: During the operation of the electric motorcycle, acquire angle data collected by the angle sensor installed on the brake handbrake, and determine the mechanical braking torque based on the angle data.

[0049] This specification describes the process of regenerative braking of an electric motorcycle. In the embodiments described herein, the regenerative braking process can be executed by a brake controller. However, this specification does not limit the type of device or platform used to perform the regenerative braking process; for example, a personal computer, mobile terminal, vehicle infotainment system, or an onboard (or built-in) Electronic Control Unit (ECU) can also be used. For ease of description, the brake controller will be used as an example for the following explanation.

[0050] In one or more embodiments of this specification, an angle sensor is installed on the handbrake of the electric motorcycle to detect the angle of rotation of the handbrake when the driver attempts to turn it. Therefore, during the operation of the electric motorcycle, the brake controller can acquire the angle data collected by the angle sensor and thus determine the driver's intention to decelerate. The brake controller can then determine the mechanical braking torque of the motorcycle's mechanical brakes based on the angle data.

[0051] The expression for calculating mechanical braking torque is:

[0052] in, It is the mechanical braking torque. This is the preset braking coefficient. This is angle data.

[0053] S2: Determine the objective functions and objective function constraints corresponding to braking deceleration, braking energy recovery, and braking ride comfort, respectively.

[0054] S3: Based on the mechanical braking torque, the current motor current, motor voltage, and motor speed of the electric motorcycle at the current moment, and the preset motor braking optimization model, solve for the target control signal that satisfies the objective function and objective function constraint corresponding to the braking deceleration, the braking energy recovery, and the braking smoothness, respectively.

[0055] In one or more embodiments of this specification, after the brake controller begins preparing for braking, the objective functions and objective function constraints corresponding to braking deceleration, braking energy recovery, and braking smoothness can be determined respectively. Then, based on the mechanical braking torque, the current motor current, motor voltage, and motor speed of the electric motorcycle, and a preset motor braking optimization model, the objective functions corresponding to braking deceleration, braking energy recovery, and braking smoothness are solved to obtain target control signals that satisfy the objective functions and objective function constraints corresponding to braking deceleration, braking energy recovery, and braking smoothness respectively.

[0056] The expression for the preset motor braking optimization model is as follows:

[0057]

[0058]

[0059]

[0060]

[0061]

[0062]

[0063] in, , , , , , , , These are the current motor current, motor voltage, motor speed, control signal, mechanical braking torque, input power, motor braking torque, and output power of the electric motorcycle at the current moment. The motor-related parameters in the motor braking optimization model include: For resistance, The electromotive force constant is . For inductance, The torque constant is The coefficient of friction of the motor. Let be the moment of inertia. These are the preset transfer function constants. With a preset time period for variation, the motor braking optimization model can be used to predict the future N times. and The preset change time period is the time interval between two adjacent moments. The control signal, in this specification, can be a PWM control signal (PWM stands for Pulse Width Modulation, which can be used to control motors; publicly available methods exist for determining motor torque based on PWM) or a voltage control quantity. The initial value can be a preset value.

[0064] The objective function corresponding to braking deceleration is expressed as follows:

[0065]

[0066]

[0067]

[0068] in, Let be the objective function corresponding to braking deceleration. These are the preset braking deceleration weight parameters. Let k be the actual change in rotational speed of the electric motorcycle at time t in the future, where t is an integer and t≤N. When t is 0, k+t is the current time. Let N be the expected change in rotational speed of the electric motorcycle at time t in the future. N is the number of times the objective function predicts the braking deceleration. Let t be the mechanical braking torque of the electric motorcycle at time t in the future. For the angle data of the electric motorcycle at time t in the future, in this specification, The default value is the angle data at the current moment.

[0069] It is worth noting that the brake controller can determine the desired speed change based on the mechanical braking torque and a preset change time period. It can also determine the actual speed change based on the current motor speed of the electric motorcycle and the motor speed of the previous moment.

[0070] The expression for calculating the desired change in rotational speed is:

[0071] in, This represents the desired change in rotational speed. This is a preset time period for changes. It is the mechanical braking torque. This is the preset moment of inertia.

[0072] The objective function for regenerative braking is expressed as follows:

[0073]

[0074]

[0075] in, This is the objective function for regenerative braking. The preset braking energy recovery weight parameters. Let be the input power of the electric motorcycle at time t in the future, where t is an integer and t≤N. When t is 0, k+t represents the current time. N is the number of times the objective function predicts the braking deceleration. Let be the output power of the electric motorcycle at time t in the future. Let t be the electric motor torque of the electric motorcycle at time t in the future. Let t be the mechanical braking torque of the electric motorcycle at time t in the future. Let be the motor voltage of the electric motorcycle at time t in the future. Let t be the motor current of the electric motorcycle at time t in the future.

[0076] The objective function for braking ride comfort is expressed as follows:

[0077] in, Let be the objective function corresponding to braking smoothness. These are preset braking smoothness weighting parameters.

[0078] The expression for the objective function constraint is:

[0079]

[0080]

[0081] in, , , These are motor current, motor voltage, and motor speed, respectively. , These are the preset minimum and maximum values ​​of the motor current, respectively. , These are the preset minimum and maximum motor speeds, respectively. , These are the preset minimum and maximum values ​​of the motor voltage, respectively.

[0082] Therefore, in this specification, step S3 solves for target control signals that satisfy the objective functions and objective function constraints corresponding to the braking deceleration, braking energy recovery, and braking smoothness, respectively, specifically including steps S31 to S33. It is worth noting that the aforementioned motor braking optimization model, objective function constraints, and each objective function can be combined to form a motor braking optimization controller.

[0083] S31 is the braking controller that, under the constraints of the motor braking optimization model and the objective function, solves for the first control signal based on the objective function corresponding to the braking deceleration.

[0084] The expression for the first control signal is:

[0085] in, This is the first control signal.

[0086] S32 is the braking controller that solves for the second control signal within the range corresponding to the first control signal, based on the objective function corresponding to braking energy recovery.

[0087] The expression for the second control signal is:

[0088] in, This is the second control signal.

[0089] The expression for the second control signal satisfying the range corresponding to the first control signal is:

[0090] in, This is the preset first relaxation factor.

[0091] S33 is the brake controller that, within the range corresponding to the second control signal, solves for the target control signal based on the objective function corresponding to braking smoothness.

[0092] The expression for solving the target control signal is:

[0093] in, For target control signals.

[0094] The expression for the target control signal satisfying the range corresponding to the second control signal is:

[0095] in, This is the preset second relaxation factor.

[0096] In the above steps S31~S33, Input to the target function The motor braking optimization controller is based on the objective function. Solve for the control signal that meets the requirements, i.e., the first control signal. First control signal For a range of values, in the first control signal When representing voltage (i.e., voltage control quantity), it can be a voltage range value, such as... The range is Furthermore, then... As a constraint, it is substituted into the solution of the second control signal. Similarly, the motor braking optimization controller is based on the objective function. In order to satisfy Output a second control signal based on the constraint range. ,like The range is Finally, As constraints are substituted into the solution of the objective control signal, the motor braking optimization controller is based on the objective function. The final output satisfies And satisfied It also satisfies Target control signal ,like The range is Finally Output or from Select an optimal value from the range and output it to the electric motorcycle model to implement braking control.

[0097] Under the constraints of the motor braking model and basic objective function, a first control signal that satisfies the optimal braking deceleration is obtained, focusing solely on braking deceleration. Then, within the range encompassed by the first control signal, a second control signal is derived with the objective of maximizing braking energy recovery, satisfying both the braking deceleration requirement and optimizing energy recovery. Finally, within the range encompassed by the second control signal, a unique or optimal target control signal is determined with braking smoothness as the objective. This achieves the technical effect of ensuring braking safety (deceleration priority), maximizing energy recovery potential under safe conditions, and ultimately optimizing driving quality (braking smoothness) within a limited space.

[0098] The aforementioned motor braking optimization controller can be used as a model predictive controller (MPC). By solving the objective function, it predicts the optimal solution at multiple future time points or step sizes, and determines the final output of the motor braking optimization controller from the optimal solutions at these future time points. In other words, it predicts the future... Step, by optimizing the input for the future Each step Predicting the future The state value of each step , so that the future Step state value Satisfy the objective function .

[0099] S4: Determine the motor's driving torque based on the target control signal.

[0100] S5: Based on the electric motor's driving torque and the mechanical braking torque, brake control is performed on the electric motorcycle, and braking energy is recovered based on the electric motor's driving torque.

[0101] In one or more embodiments of this specification, the brake controller may further determine the motor braking torque of the motor based on the target control signal.

[0102] This system controls the braking of the electric motorcycle through the motor's driving torque and mechanical braking torque, and recovers braking energy based on the motor's driving torque. This braking energy recovery is an inherent function of the motor itself. The braking current generated by the motor's driving torque flows from the motor to the battery, effectively charging the battery. Therefore, braking energy is recovered and stored through the motor's driving torque. Furthermore, a motor braking optimization controller outputs a target control signal that maximizes braking energy recovery, causing the motor to operate according to the corresponding motor drive torque, thereby maximizing braking energy recovery.

[0103] based on Figure 1 The lexicographical order-based regenerative braking method shown here optimizes the control signal by comprehensively considering three objectives: braking deceleration, regenerative braking, and braking smoothness. This ensures smooth braking for a comfortable driving experience in electric motorcycle braking control, while simultaneously guaranteeing sufficient deceleration to meet braking requirements and ensure driving safety. Furthermore, it maximizes regenerative braking, extending the electric motorcycle's range. This method effectively handles multi-objective conflicts and leverages the existing electric motorcycle braking system structure for braking control, demonstrating good practicality, safety, and energy-saving performance.

[0104] Furthermore, in one or more embodiments of this specification, the brake controller can input the target control signal and the mechanical braking torque into a preset electric motorcycle model to obtain the motor current, motor voltage, and motor speed output by the electric motorcycle model at the next moment. For ease of subsequent description, the motor current, motor voltage, and motor speed output by the electric motorcycle model at the next moment are referred to as the system state value. Moreover, if the angle sensor continues to collect new angle data, the brake controller sends the motor current, motor voltage, and motor speed output by the electric motorcycle model at the next moment to the motor brake optimization controller. Simultaneously, the brake controller determines a new mechanical braking torque based on the collected new angle data. Based on the new mechanical braking torque and the aforementioned system state value (i.e., the motor current, motor voltage, and motor speed at this time, equivalent to the current motor current, motor voltage, and motor speed), the motor brake optimization controller continues to solve for the new target control signal.

[0105] The expression for the electric motorcycle model is:

[0106] in, , , These are the motor current, motor speed, and motor voltage output by the electric motorcycle model at the next moment.

[0107] This specification also provides a computer-readable storage medium storing a computer program that can be used to execute the above-described... Figure 1 A lexicographical method for brake energy recovery is provided.

[0108] This instruction manual also provides Figure 2 The diagram shows a schematic structural representation of the electronic device. Figure 2 As shown, at the hardware level, this electronic device includes a processor, internal bus, network interface, memory, and non-volatile memory, and may also include other hardware required for business operations. The processor reads the corresponding computer program from the non-volatile memory into memory and then runs it to achieve the above. Figure 1 A lexicographical method for brake energy recovery is provided.

[0109] Of course, in addition to software implementation, this specification does not exclude other implementation methods, such as logic devices or a combination of hardware and software. In other words, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0110] In the 1990s, improvements to a technology could be clearly distinguished as either hardware improvements (e.g., improvements to the circuit structure of diodes, transistors, switches, etc.) or software improvements (improvements to the methodology). However, with technological advancements, many methodological improvements today can be considered direct improvements to the hardware circuit structure. Designers almost always obtain the corresponding hardware circuit structure by programming the improved methodology into the hardware circuit. Therefore, it cannot be said that a methodological improvement cannot be implemented using hardware physical modules. For example, a Programmable Logic Device (PLD) (such as a Field Programmable Gate Array (FPGA)) is such an integrated circuit whose logic function is determined by the user programming the device. Designers can program and "integrate" a digital system onto a PLD themselves, without needing chip manufacturers to design and manufacture dedicated integrated circuit chips. Furthermore, nowadays, instead of manually manufacturing integrated circuit chips, this programming is mostly implemented using "logic compiler" software. Similar to the software compiler used in program development, the original code before compilation must also be written in a specific programming language, called a Hardware Description Language (HDL). There are many HDLs, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, and RHDL (Ruby Hardware Description Language). Currently, the most commonly used are VHDL (Very-High-Speed ​​Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should also understand that by simply performing some logic programming on the method flow using one of these hardware description languages ​​and programming it into an integrated circuit, the hardware circuit implementing the logical method flow can be easily obtained.

[0111] The controller can be implemented in any suitable manner. For example, it can take the form of a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro)processor, logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers, and embedded microcontrollers. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicon Labs C8051F320. A memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art will also recognize that, in addition to implementing the controller in purely computer-readable program code form, the same functionality can be achieved by logically programming the method steps to make the controller take the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers. Therefore, such a controller can be considered a hardware component, and the means included therein for implementing various functions can also be considered as structures within the hardware component. Alternatively, the means for implementing various functions can be considered as both software modules implementing the method and structures within the hardware component.

[0112] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.

[0113] For ease of description, the above devices are described in terms of function, divided into various units. Of course, in implementing this specification, the functions of each unit can be implemented in one or more software and / or hardware components.

[0114] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0115] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0116] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0117] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0118] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0119] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0120] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0121] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0122] Those skilled in the art will understand that the embodiments of this specification can be provided as methods, systems, or computer program products. Therefore, this specification may take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this specification may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0123] This specification can be described in the general context of computer-executable instructions that are executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform a specific task or implement a specific abstract data type. This specification can also be practiced in distributed computing environments, where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0124] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0125] The above description is merely an embodiment of this specification and is not intended to limit this specification. Various modifications and variations can be made to this specification by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this specification should be included within the scope of the claims of this specification.

Claims

1. A lexicographical order-based method for brake energy recovery, characterized in that, include: S1. During the operation of the electric motorcycle, acquire angle data collected by an angle sensor installed on the brake handbrake, and determine the mechanical braking torque based on the angle data; S2. Determine the objective functions and objective function constraints corresponding to braking deceleration, braking energy recovery, and braking ride comfort, respectively; S3. Based on the mechanical braking torque, the current motor current, motor voltage, and motor speed of the electric motorcycle, and the preset motor braking optimization model, solve for the objective functions and objective function constraints that satisfy the braking deceleration, braking energy recovery, and braking smoothness, respectively. S4. Determine the motor's driving torque based on the target control signal; S5. Based on the electric motor's driving torque and the mechanical braking torque, brake control is performed on the electric motorcycle, and braking energy is recovered based on the electric motor's driving torque.

2. The lexicographical order-based braking energy recovery method as described in claim 1, characterized in that, The expression for the preset motor braking optimization model in S3 is: in, , , , , , , , These are the motor current, motor voltage, motor speed, control signal, mechanical braking torque, input power, motor torque, and output power of the electric motorcycle at the current moment. For resistance, The electromotive force constant is . For inductance, The torque constant is The coefficient of friction of the motor. It is the moment of inertia; These are the preset transfer function constants; The preset time period of change; For control signals.

3. The lexicographical order-based braking energy recovery method as described in claim 2, characterized in that, The expression for the objective function corresponding to the braking deceleration described in S2 is: in, The objective function corresponding to the braking deceleration; These are preset braking deceleration weighting parameters; Let k be the actual change in rotational speed of the electric motorcycle at time t in the future, where t is an integer and t≤N. When t is 0, k+t is the current time. Let N be the expected change in rotational speed of the electric motorcycle at time t in the future; N is the number of times the objective function corresponding to the braking deceleration is predicted. Let be the mechanical braking torque of the electric motorcycle at time t in the future; Let be the angle data of the electric motorcycle at time t in the future.

4. The lexicographical order-based braking energy recovery method as described in claim 3, characterized in that, The expression for the objective function corresponding to the regenerative braking energy described in S2 is: in, Let the objective function be the braking energy recovery function. The preset braking energy recovery weight parameters; Let N be the input power of the electric motorcycle at time t in the future, where t is an integer and t≤N. When t is 0, k+t is the current time. N is the number of times the objective function corresponding to the braking deceleration is predicted. Let be the output power of the electric motorcycle at time t in the future; Let be the electric motor torque of the electric motorcycle at time t in the future; Let be the mechanical braking torque of the electric motorcycle at time t in the future; Let be the motor voltage of the electric motorcycle at time t in the future; Let be the motor current of the electric motorcycle at time t in the future.

5. The lexicographical order-based braking energy recovery method as described in claim 4, characterized in that, The expression for the objective function corresponding to the braking ride comfort described in S2 is: in, Let the objective function be the braking smoothness. These are preset braking smoothness weighting parameters.

6. The lexicographical order-based braking energy recovery method as described in claim 5, characterized in that, The expression for the objective function constraint described in S2 is: in, , , These are motor current, motor voltage, and motor speed, respectively. , These are the preset minimum and maximum values ​​of the motor current, respectively; , These are the preset minimum and maximum motor speeds, respectively; , These are the preset minimum and maximum values ​​of the motor voltage, respectively.

7. The lexicographical order-based braking energy recovery method as described in claim 6, characterized in that, The target control signals obtained in S3 satisfy the objective functions and objective function constraints corresponding to the braking deceleration, braking energy recovery, and braking ride comfort, respectively, and specifically include: S31. Under the condition that the constraints of the motor braking optimization model and the objective function are satisfied, the first control signal is solved according to the objective function corresponding to the braking deceleration; S32. Within the range corresponding to the first control signal, solve for the second control signal according to the objective function corresponding to the regenerative braking energy; S33. Within the range corresponding to the second control signal, solve for the target control signal based on the objective function corresponding to the braking smoothness.

8. The lexicographical order-based braking energy recovery method as described in claim 7, characterized in that, The expression for solving the first control signal in S31 is: in, This is the first control signal.

9. The lexicographical order-based braking energy recovery method as described in claim 8, characterized in that, The expression for solving the second control signal in S32 is: in, This is the second control signal; The expression for the second control signal satisfying the range corresponding to the first control signal is: in, This is the preset first relaxation factor.

10. The lexicographical order-based braking energy recovery method as described in claim 9, characterized in that, The expression for solving the target control signal in S33 is: in, The target control signal; The expression that the target control signal satisfies within the range corresponding to the second control signal is: in, This is the preset second relaxation factor.

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