A parameter optimization method and system for electric vehicle controller

Abstract

The present invention provides a parameter optimization method and system for an electric vehicle controller, which relates to the technical field of electric vehicles. The method includes: constructing a mathematical model of a hybrid electric vehicle; the hybrid electric vehicle includes a main power system, a secondary power system, a load and a wheel, the main power system is used to convert chemical fuel into mechanical energy, the secondary power system is used to convert electrical energy and mechanical energy into each other, and the main power system and the secondary power system are connected to the wheel power through the load; according to the mathematical model, with the goal of minimizing power loss, a particle swarm optimization algorithm is used to optimize speed and determine the optimal speed control curve; based on the optimal speed control curve, with the goal of minimizing fuel loss, a particle swarm optimization algorithm is used to optimize energy and determine the optimal electric vehicle control parameters. The present invention simplifies the calculation process, greatly reduces the high calculation complexity, reduces the calculation cost, and improves the real-time optimization in the actual driving process.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicles, and in particular to a parameter optimization method and system for an electric vehicle controller. Background Art

[0002] Optimizing the controller parameters of hybrid electric vehicles (HEVs) is of great significance in improving vehicle performance, economy, environmental protection, and user experience. It is one of the key technologies for realizing efficient, environmentally friendly, and intelligent transportation.

[0003] At present, in the process of parameter optimization of the controller of hybrid electric vehicles, driving speed and energy optimization are often considered simultaneously. The one-step solution method can be closer to the global optimal solution rather than the local optimal solution, thereby improving fuel economy and driving performance.

[0004] However, the one-step solution method will face more complex multi-objective optimization problems with high computational complexity. In actual driving, the controller needs to calculate the optimal speed and energy management strategy in real time. The one-step solution method places higher requirements on computing power and response speed, resulting in increased computing costs. Summary of the invention

[0005] In order to solve the technical problem that the one-step solution method in the prior art will face more complex multi-objective optimization problems with high computational complexity, and in the actual driving process, the controller needs to calculate the optimal speed and energy management strategy in real time, and the one-step solution method puts forward higher requirements on computing power and response speed, resulting in increased computing costs, the present invention provides a parameter optimization method and system for an electric vehicle controller.

[0006] The technical solution provided by the embodiment of the present invention is as follows:

[0007] First aspect:

[0008] An embodiment of the present invention provides a method for optimizing parameters of an electric vehicle controller, comprising:

[0009] S1: Construct a mathematical model of hybrid electric vehicles;

[0010] The hybrid electric vehicle comprises a main power system, a secondary power system, a load and wheels, the main power system is used to convert chemical fuel into mechanical energy, the secondary power system is used to convert electrical energy into mechanical energy, and the main power system and the secondary power system are connected to the wheel power through the load;

[0011] S2: According to the mathematical model, with the goal of minimizing power loss, speed optimization is performed through a particle swarm optimization algorithm to determine an optimal speed control curve;

[0012] S3: Based on the optimal speed control curve and aiming at minimizing fuel loss, the particle swarm optimization algorithm is used to perform energy optimization and determine the optimal electric vehicle control parameters.

[0013] Second aspect:

[0014] An embodiment of the present invention provides a parameter optimization system for an electric vehicle controller, comprising:

[0015] processor;

[0016] A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the parameter optimization method for the electric vehicle controller as described in the first aspect is implemented.

[0017] The third aspect:

[0018] An embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the method for optimizing the parameters of the electric vehicle controller as described in the first aspect is implemented.

[0019] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0020] In the present invention, a two-step optimization scheme is adopted. First, the speed is optimized by the particle swarm optimization algorithm with the goal of minimizing power loss to determine the optimal speed control curve. Then, based on the optimal speed control curve and with the goal of minimizing fuel loss, energy is optimized by the particle swarm optimization algorithm to determine the optimal electric vehicle control parameters. The calculation process is simplified, the calculation complexity is greatly reduced, the calculation cost is reduced, and the real-time optimization in the actual driving process is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0022] Figure 1 A schematic flow chart of a method for optimizing parameters of an electric vehicle controller provided by an embodiment of the present invention;

[0023] Figure 2A schematic diagram of the structure of a parameter optimization method for an electric vehicle controller provided by an embodiment of the present invention;

[0024] Figure 3 A schematic diagram of the structure of a parameter optimization system for an electric vehicle controller provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0025] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0026] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0027] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same. "of", "corresponding, relevant" and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same.

[0028] In the embodiments of the present invention, sometimes the subscripts such as W 1 It may be written as a non-subscript form such as W1, and the meaning is the same when the difference is not emphasized.

[0029] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0030] Reference Manual Attached Figure 1 , shows a flow chart of a parameter optimization method for an electric vehicle controller provided by an embodiment of the present invention.

[0031] Reference Manual Attached Figure 2 , showing a structural schematic diagram of a parameter optimization method for an electric vehicle controller provided by an embodiment of the present invention.

[0032] The embodiment of the present invention provides a parameter optimization method for an electric vehicle controller, which can be implemented by a parameter optimization device for an electric vehicle controller, and the parameter optimization device for an electric vehicle controller can be a terminal or a server. The processing flow of the parameter optimization method for an electric vehicle controller can include the following steps:

[0033] S1: Construct a mathematical model of hybrid electric vehicles.

[0034] A hybrid electric vehicle includes a main power system, a secondary power system, a load and wheels. The main power system is used to convert chemical fuel into mechanical energy, and the secondary power system is used to convert electrical energy into mechanical energy. The main power system and the secondary power system are connected to the wheel power through the load.

[0035] It should be noted that by building a mathematical model that includes all key components, the dynamics and energy flow of hybrid electric vehicles can be fully and accurately described. By modeling the entire system, more efficient energy management strategies can be developed and optimized to maximize the vehicle's fuel economy and driving performance.

[0036] In a possible implementation, the mathematical model of the hybrid electric vehicle specifically includes: kinematic equations, power distribution dynamic equations, power distribution schemes, power loss equations, fuel loss equations, and constraints.

[0037] In a possible implementation, the kinematic equation is specifically:

[0038]

[0039] in, P v The power required by the wheels, m v Indicates the vehicle mass, a represents acceleration, F R Indicates the rolling resistance of the tire. F D represents air resistance, v Indicates speed.

[0040] The rolling resistance of tires is:

[0041]

[0042] in, f R represents the tire friction coefficient, g Represents the acceleration due to gravity.

[0043] The specific air resistance is:

[0044]

[0045] in, f D Represents the air resistance coefficient.

[0046] In the present invention, by specifying the kinematic equations and describing in detail the calculation methods of each resistance, the accuracy and practicality of the hybrid electric vehicle mathematical model can be significantly improved. This not only helps to optimize vehicle design and energy management, improve driving experience and system reliability, but also reduces development costs and improves environmental performance.

[0047] In a possible implementation manner, the power allocation dynamic equation is specifically:

[0048]

[0049] in, P r represents the output power of the main power system, P b represents the output power of the secondary power system, P p represents the load propulsion power, P v The power required by the wheels, η p represents the load propulsion efficiency, sign() represents the sign function, when hour, ,when hour, .

[0050] It should be noted that accurate dynamic equations for power distribution can optimize energy use and ensure that fuel and battery power are used in the best way under different driving conditions. By describing the power distribution between the primary power system and the secondary power system in detail, the actual power flow can be more accurately reflected, improving the accuracy of the model.

[0051] In a possible implementation manner, the power allocation scheme specifically includes:

[0052] when When , the power distribution between the main power system and the secondary power system is:

[0053]

[0054] in, P b,max Indicates the maximum output power of the secondary power system.

[0055] when When , the power distribution between the main power system and the secondary power system is:

[0056] .

[0057] It should be noted that at this time, the power is provided by the secondary power system alone, without the participation of the main power system.

[0058] when When , the power distribution between the main power system and the secondary power system is:

[0059] .

[0060] in, P b,min Indicates the minimum output power of the secondary power system.

[0061] when When , the power distribution between the main power system and the secondary power system is:

[0062] .

[0063] It should be noted that at this time, during the regenerative braking stage, energy is recovered by the secondary power system.

[0064] In a possible implementation manner, the power loss equation specifically includes:

[0065]

[0066] in, P loss Indicates power loss, P loss,f Indicates the power loss of the power system. P loss,p Indicates load power loss.

[0067] It should be noted that at this time, the secondary power system provides the minimum power output to maintain basic functions.

[0068] In the present invention, by optimizing power distribution, it is possible to ensure that the main power system and the secondary power system operate within the most efficient working range and reduce energy loss. During deceleration and braking, the secondary power system is used first to recover energy and improve energy utilization. When power demand is high, the main and secondary power systems work together to provide better power response and performance. When power demand is low, the secondary power system works alone to reduce the frequent start and stop of the engine and improve driving stability.

[0069] The power loss of the power system is as follows:

[0070]

[0071] in, P f represents the chemical power of the fuel, P r represents the output power of the main power system, V oc represents the open circuit voltage,i b,max Indicates the maximum battery current, i b,min Indicates the minimum battery current, P b,max Indicates the maximum output power of the secondary power system.

[0072] The chemical power of the fuel is:

[0073]

[0074] in, P f represents the chemical power of the fuel, q LHV Indicates the lower heating value of the fuel. q f0 represents the idle fuel mass rate, α f Indicates the fuel conversion factor.

[0075] The load power loss is specifically:

[0076] .

[0077] In the present invention, by describing in detail the calculation method of power system power loss, fuel chemical power and load power loss, the accuracy and practicality of the mathematical model of hybrid electric vehicles can be significantly improved. This not only helps to optimize energy management and improve fuel economy, but also improves system reliability and driving performance, and has important environmental protection and energy saving significance. Detailed power loss analysis also supports design optimization and real-time control, improving the vehicle's intelligence level and user experience.

[0078] In a possible implementation, the fuel loss equation is specifically:

[0079]

[0080] in, m f Indicates the fuel consumption mass, T represents the total observation time, q f0 Indicates the idle fuel mass rate, represents the thrust phase, represents the battery discharge interval during the thrust phase, represents the battery charging interval during the thrust phase, α f represents the power conversion factor, t Indicates time, dt represents the differentiation of time, η dcIt indicates the conversion efficiency of electrical energy to mechanical energy.

[0081] In the present invention, by specifying the fuel loss equation, the accuracy and practicality of the hybrid electric vehicle mathematical model can be significantly improved. This not only helps to optimize energy management and improve fuel economy, but also improves driving performance and environmental performance, and improves system reliability and user experience. Detailed fuel loss analysis also supports design optimization and real-time control, further improving the vehicle's intelligence level and market competitiveness.

[0082] In a possible implementation, the constraints specifically include:

[0083] Driving constraints:

[0084]

[0085] in, F r represents the driving force of the main power system, P b represents the driving force of the secondary power system, F v Indicates the driving force required by the wheels, η p represents the load propulsion efficiency, sign() represents the sign function, when hour, ,when hour, .

[0086] It should be noted that the driving constraint ensures that the driving force jointly provided by the primary power system and the secondary power system can meet the driving force required by the wheels.

[0087] Stability constraints:

[0088]

[0089] in, a represents acceleration, a min represents the minimum acceleration, a max Indicates the maximum acceleration.

[0090] It should be noted that the smoothness constraint ensures that the vehicle's acceleration is within a reasonable range to ensure smooth and comfortable driving.

[0091] Battery status constraints:

[0092]

[0093] Among them, SOC represents the battery charging state, SOC minIndicates the minimum battery charge, SOC max Indicates the maximum battery charge level.

[0094] It should be noted that the battery state constraint ensures that the battery's state of charge is within a safe range to avoid over-discharge or over-charge.

[0095] Active power system power constraints:

[0096]

[0097] in, v Indicates speed, P r,max Indicates the maximum output power of the main power system.

[0098] It should be noted that the power constraint of the main power system ensures that the output power of the main power system is within its capability to prevent overload.

[0099] Auxiliary power system power constraints:

[0100]

[0101] in, P b,min represents the minimum output power of the auxiliary power system, P b,max Indicates the maximum output power of the auxiliary power system.

[0102] It should be noted that the auxiliary power system power constraint ensures that the output power of the auxiliary power system (such as batteries and electric motors) is within its capacity to prevent overload or failure to meet demand.

[0103] S2: Based on the mathematical model, with the goal of minimizing power loss, speed optimization is performed through the particle swarm optimization algorithm to determine the optimal speed control curve.

[0104] In a possible implementation, S2 specifically includes:

[0105] Build speed optimization objective function:

[0106]

[0107] in, F 1 represents the speed optimization objective function, X 1 Represents the speed optimization parameters, which include the speed of the car at each moment. T represents the total observation time, P loss Indicates power loss, t Indicates time, dtrepresents the differentiation of time, s j Indicates violation of j The penalty value of the constraint condition, β j Indicates j The penalty coefficient of the constraint condition, m Represents the total number of constraints.

[0108] Among them, those skilled in the art can set the size of the penalty coefficient of each constraint condition according to actual conditions, and the present invention does not limit this.

[0109] It should be noted that by minimizing power loss, the energy utilization efficiency of the vehicle can be maximized and unnecessary energy waste can be reduced. The optimized speed control curve can ensure that the vehicle can operate in the best way under different driving conditions and improve the overall energy efficiency. At the same time, the introduction of penalty terms in the speed optimization objective function can ensure the satisfaction of constraints, improve the practicality of the optimization results and the safety of the system, balance the optimization objectives and constraints, enhance the robustness and fault tolerance of the algorithm, and significantly improve the optimization effect.

[0110] With the goal of minimizing the speed optimization objective function, the particle swarm optimization algorithm is used to perform speed optimization and determine the optimal speed control curve.

[0111] Specifically, the inverse of the speed optimization objective function is used as the fitness function of the particle swarm optimization algorithm.

[0112] Initialize the population, which contains multiple particles, each particle represents a feasible speed control curve. The speed control curve contains the speed of the electric vehicle at each moment.

[0113] Calculate the fitness of each particle.

[0114] Determine the individual optimal position and the global optimal position.

[0115]

[0116] in, Indicates t +1 iteration i The speed of a particle, Indicates t The iteration i The speed of a particle, Indicates t The first iteration i The inertia weight factor of each particle, c 1 represents the individual learning factor, c 2 represents the global learning factor,r 1 and r 2 Both represent random numbers between 0 and 1. Indicates t The iteration i The individual optimal position of each particle is Indicates t The iteration i The global optimal position of a particle, Indicates t The iteration i The position of a particle, Indicates t +1 iteration i The position of a particle.

[0117] In the present invention, by determining the individual optimal position and the global optimal position, and iteratively updating the position and velocity of the particles, the PSO algorithm can perform optimization efficiently and accurately.

[0118] Optionally, an inertia weight factor ω Specifically:

[0119]

[0120] in, Indicates t The iteration i The inertia weight factor of each particle, ω max represents the maximum value of the inertia weight factor, ω min represents the minimum value of the inertia weight factor, e represents a natural constant, η Represents the scaling parameter, r 3 Represents a random number between 0 and 1. Indicates t The iteration i The fitness change parameter of each particle.

[0121] Among them, those skilled in the art can set the scaling parameters according to actual conditions. η The present invention does not limit the size.

[0122] In the present invention, the inertia weight factor is dynamically adjusted according to the number of iterations and the fitness change parameter, so that the algorithm can adaptively adjust the search strategy according to the current optimization state. In the early stage of optimization, a larger inertia weight factor is helpful for global search; in the later stage of optimization, a smaller inertia weight factor is helpful for local search.

[0123] Furthermore, the fitness change parameters are specifically:

[0124]

[0125] in, Indicates t The iteration i The fitness change of each particle is Indicates t The iteration i The fitness value of a particle, Indicates t -1 iteration i The fitness value of a particle.

[0126] In the present invention, the fitness change of the particle can be accurately judged by calculating the fitness change degree, and the reaction of the algorithm to the fitness change can be refined. When the fitness change is large, the inertia weight is increased rapidly to encourage the exploration of new solution space; when the fitness change is small or stable, the inertia weight is reduced to refine the search for the current solution.

[0127] Recalculate the fitness of each particle.

[0128] When the fitness of the current particle is better than the individual optimal solution of the particle itself, the individual optimal solution of the particle itself is updated. When the fitness of the current particle is worse than the individual optimal solution of the particle itself, the individual optimal solution of the particle itself is updated with a certain replacement probability. When the fitness of the current particle is better than the global optimal solution, the global optimal solution is updated.

[0129] In the present invention, by introducing the replacement probability, even if the fitness of the current particle does not exceed the individual optimal solution, there is a chance to update the individual optimal solution, thereby increasing the possibility of exploring new solution spaces and avoiding premature convergence to the local optimal solution. At the same time, the replacement probability introduces a certain degree of randomness, making the algorithm more robust and adaptable, and able to cope with diverse and complex optimization problems.

[0130] Among them, the replacement probability is specifically:

[0131]

[0132] in, P represents the replacement probability, e represents a natural constant, δ ( ) represents the fitness function, C t Indicates t The temperature at the iteration.

[0133] Among them, the temperature is updated according to the following formula:

[0134]

[0135] in, ε represents the temperature drop coefficient, T i+1 Indicates i The temperature at +1 iteration, T i Indicates i The temperature at the iteration.

[0136] It should be noted that as the number of iterations increases, the temperature gradually decreases, making the algorithm more focused on local search in the later stage, improving the convergence speed and optimization effect.

[0137] The specific temperature reduction coefficient is:

[0138]

[0139] in, A 1 represents the cooling oscillation factor in the first half cycle, B 1 Indicates the cooling floating factor in the first half of the cycle, A 2 represents the cooling oscillation factor in the second half of the cycle, B 2 Indicates the cooling floating factor in the second half of the cycle.

[0140] It should be noted that as the number of iterations increases, the temperature gradually decreases, allowing the algorithm to focus on local search in the later stage, refine the solution accuracy, and accelerate convergence. At the same time, the introduction of an oscillation factor introduces moderate fluctuations in the cooling process to prevent the temperature from dropping too quickly and maintain a certain search flexibility. Through different cooling strategies in the first half of the cycle and the second half of the cycle, the algorithm can balance the needs of exploration and development at different stages, with extensive exploration in the early stage and detailed development in the later stage.

[0141] Determine whether the maximum number of iterations has been reached. If so, output the speed control curve represented by the particle with the highest fitness value as the optimal speed control curve. Otherwise, return to continue iterating.

[0142] It should be noted that in the early stage of the algorithm, when the temperature is high, the probability of accepting a worse solution is greater, which helps to jump out of the local optimal solution and increase the chance of global search. By dynamically adjusting the temperature and replacement probability, more global searches can be performed in the early stage of the algorithm to find the potential global optimal solution.

[0143] In the present invention, speed optimization is performed by particle swarm optimization algorithm, and a specific speed optimization objective function is constructed, which can significantly improve the energy utilization efficiency, driving performance and system reliability of hybrid electric vehicles, reduce operation and maintenance costs, and improve environmental performance. The optimization method that comprehensively considers power loss and constraint conditions ensures that the vehicle can operate in the optimal state under various driving conditions, providing better user experience and market competitiveness.

[0144] S3: Based on the optimal speed control curve and aiming at minimizing fuel loss, the particle swarm optimization algorithm is used to perform energy optimization and determine the optimal electric vehicle control parameters.

[0145] Optionally, the electric vehicle control parameters include: engine power output, engine speed, fuel consumption quality, battery charging state and power source switching strategy, etc.

[0146] In a possible implementation, an energy optimization objective function is constructed:

[0147]

[0148] in, F 2 represents the energy optimization objective function, X 2 Represents electric vehicle parameters, which include fuel consumption quality and battery charging status at each moment. m f Indicates the fuel consumption mass, T represents the total observation time, s j Indicates violation of j The penalty value of the constraint condition, β j Indicates j The penalty coefficient of the constraint condition, m Represents the total number of constraints.

[0149] With the goal of minimizing the energy optimization objective function, the particle swarm optimization algorithm is used to perform energy optimization and determine the optimal electric vehicle control parameters.

[0150] It should be noted that the process of optimizing energy and determining the optimal electric vehicle control parameters through the particle swarm optimization algorithm can refer to the specific process of speed optimization described above. To avoid repetition, the present invention will not repeat it. Specifically, each particle represents a feasible combination of electric vehicle control parameters. The electric vehicle control parameter combination includes control parameters such as fuel consumption quality and battery charging state.

[0151] In the present invention, by constructing an energy optimization objective function and optimizing and determining the optimal electric vehicle control parameters using a particle swarm optimization algorithm, the energy efficiency, fuel economy and vehicle performance of hybrid electric vehicles can be significantly improved, while satisfying multiple constraints and improving system reliability and environmental performance. This method can achieve comprehensive optimization, provide more efficient, economical and environmentally friendly solutions, and adapt to complex needs in practical applications.

[0152] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0153] In the present invention, a two-step optimization scheme is adopted. First, the speed is optimized by the particle swarm optimization algorithm with the goal of minimizing power loss to determine the optimal speed control curve. Then, based on the optimal speed control curve and with the goal of minimizing fuel loss, energy is optimized by the particle swarm optimization algorithm to determine the optimal electric vehicle control parameters. The calculation process is simplified, the calculation complexity is greatly reduced, the calculation cost is reduced, and the real-time optimization in the actual driving process is improved.

[0154] Reference Manual Attached Figure 3 , showing a structural schematic diagram of a parameter optimization system for an electric vehicle controller provided by the present invention.

[0155] The present invention further provides a parameter optimization system 20 for an electric vehicle controller, which is applied to the parameter optimization method for the electric vehicle controller, and comprises:

[0156] Processor 201;

[0157] The memory 202 stores computer-readable instructions, and when the computer-readable instructions are executed by the processor 201, the parameter optimization method of the electric vehicle controller as described in the method embodiment is implemented.

[0158] The electric vehicle controller parameter optimization system 20 provided by the present invention can execute the above-mentioned electric vehicle controller parameter optimization method and achieve the same or similar technical effects. To avoid repetition, the present invention will not elaborate on them.

[0159] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0160] In the present invention, the mask pattern is optimized by precisely adjusting the position of each segment of the pattern, which can reduce the process variation perception EPE, PV band area and CPU running time, improve the geometric accuracy of the pattern, enhance the efficiency of pattern optimization, and reduce the difficulty of pattern optimization.

[0161] It should be understood that the processor in the embodiment of the present invention may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0162] It should also be understood that the memory in the embodiments of the present invention may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0163] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), 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 the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the 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. The 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, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer 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.

[0164] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0165] In the present invention, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0166] It should be understood that in various embodiments of the present invention, 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 invention.

[0167] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein 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.

[0168] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0169] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses 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 a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, 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.

[0170] 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.

[0171] 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.

[0172] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0173] An embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein when the program is executed by a processor, the method for optimizing the parameters of an electric vehicle controller as described in the method embodiment is implemented.

[0174] A computer-readable storage medium provided by the present invention can implement the steps and effects of the parameter optimization method of the electric vehicle controller of the above method embodiment. To avoid repetition, the present invention will not go into details.

[0175] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0176] In the present invention, the mask pattern is optimized by precisely adjusting the position of each segment of the pattern, which can reduce the process variation perception EPE, PV band area and CPU running time, improve the geometric accuracy of the pattern, enhance the efficiency of pattern optimization, and reduce the difficulty of pattern optimization.

[0177] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

[0178] There are a few points to note:

[0179] (1) The drawings of the embodiments of the present invention only involve structures related to the embodiments of the present invention. Other structures may refer to conventional designs.

[0180] (2) For the sake of clarity, in the drawings used to describe the embodiments of the present invention, the thickness of layers or regions is exaggerated or reduced, that is, these drawings are not drawn according to the actual scale. It is understood that when an element such as a layer, film, region or substrate is referred to as being "on" or "under" another element, the element may be "directly" "on" or "under" the other element or there may be intermediate elements.

[0181] (3) In the absence of conflict, the embodiments of the present invention and the features therein may be combined with each other to obtain new embodiments.

[0182] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. The protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A parameter optimization method for an electric vehicle controller, characterized in that: include: S1: Construct a mathematical model of hybrid electric vehicles; The hybrid electric vehicle comprises a main power system, a secondary power system, a load and wheels, the main power system is used to convert chemical fuel into mechanical energy, the secondary power system is used to convert electrical energy into mechanical energy, and the main power system and the secondary power system are connected to the wheel power through the load; S2: According to the mathematical model, with the goal of minimizing power loss, speed optimization is performed through a particle swarm optimization algorithm to determine an optimal speed control curve; Among them, S2 specifically includes: Build speed optimization objective function: Among them, F1 represents the speed optimization objective function, X1 represents the speed optimization parameters, which include the speed of the car at each moment, T represents the total observation time, and P loss represents power loss, t represents time, dt represents the differentiation of time, s j represents the penalty value for violating the jth constraint, β j represents the penalty coefficient of the jth constraint, and m represents the total number of constraints; With the goal of minimizing the speed optimization objective function, the particle swarm optimization algorithm is used to optimize the speed and determine the optimal speed control curve; The power loss is specifically: P loss =P loss,f +P loss,p Among them, P loss Indicates power loss, P loss,f Indicates the power loss of the power system, P loss,p Indicates load power loss; The power loss of the power system is as follows: Among them, P f Represents the chemical power of fuel, P r Indicates the output power of the main power system, V oc represents the open circuit voltage, i b,max Indicates the maximum battery current, i b,min Indicates the minimum battery current, P b represents the output power of the secondary power system, P p Indicates load propulsion power, P b,max Indicates the maximum output power of the secondary power system; The load power loss is specifically: P loss,p =P p -P v Among them, P v Indicates the power required by the wheels; S3: Based on the optimal speed control curve and aiming at minimizing fuel loss, the particle swarm optimization algorithm is used to perform energy optimization and determine the optimal electric vehicle control parameters; Among them, S3 specifically includes: Construct energy optimization objective function: Among them, F2 represents the energy optimization objective function, X2 represents the electric vehicle parameters, which include the fuel consumption quality and battery charging state at each moment, and m f represents the fuel consumption mass, T represents the total observation time, s j represents the penalty value for violating the jth constraint, β j represents the penalty coefficient of the jth constraint, and m represents the total number of constraints; The fuel consumption quality is specifically: Among them, m f represents the fuel consumption mass, T represents the total observation time, q f0 represents the fuel mass rate at idle speed, Φ represents the thrust stage, Φ d represents the battery discharge interval in the thrust phase, Φ c represents the battery charging interval in the thrust phase, α f represents the power conversion coefficient, t represents time, dt represents the differentiation of time, η dc Indicates the efficiency of conversion between electrical energy and mechanical energy; With the goal of minimizing the energy optimization objective function, the particle swarm optimization algorithm is used to perform energy optimization and determine the optimal electric vehicle control parameters.

2. The parameter optimization method of the electric vehicle controller according to claim 1, characterized in that: The mathematical model of the hybrid electric vehicle specifically includes: kinematic equations, power distribution dynamic equations, power distribution schemes, power loss equations, fuel loss equations and constraints.

3. The parameter optimization method of the electric vehicle controller according to claim 2, characterized in that: The kinematic equation is specifically: P v =(m v a+F R +F D )v Among them, P v Indicates the power required by the wheel, m v represents the vehicle mass, a represents the acceleration, F R Indicates tire rolling resistance, F D represents air resistance, v represents speed; The tire rolling resistance is specifically: F R =f R m v g Among them, f R represents the friction coefficient of the tire, and g represents the acceleration due to gravity; The air resistance is specifically: F D =f D v 2 Among them, f D Represents the air resistance coefficient.

4. The parameter optimization method of the electric vehicle controller according to claim 2, characterized in that: The power allocation dynamic equation is specifically: Among them, P r Indicates the output power of the main power system, P b represents the output power of the secondary power system, P p Indicates load propulsion power, P v represents the power required by the wheel, η p represents the load propulsion efficiency, sign() represents the sign function, when P v ≥0, sign(P v )=1, when P v When <0, sign(P v )=-1; The power allocation scheme specifically includes: When P p >P b,max When , the power distribution between the main power system and the secondary power system is: P b =P b,max Among them, P b,max Indicates the maximum output power of the secondary power system; When 0<P p ≤P b,max When , the power distribution between the main power system and the secondary power system is: P r ＝0 When P b,min ≤P p When <0, the power distribution between the primary power system and the secondary power system is as follows: P r =0 P b =P p =η p P v ; Among them, P b,min It represents the minimum output power of the secondary power system; When P p <P b,min When , the power distribution between the main power system and the secondary power system is: P r =0 P b =P b,min 。 5. The parameter optimization method of the electric vehicle controller according to claim 2, characterized in that: The constraints specifically include: Driving constraints: Among them, F r represents the driving force of the main power system, F b represents the driving force of the secondary power system, F v represents the driving force required by the wheel, η p represents the load propulsion efficiency, sign() represents the sign function, when F v ≥0, sign(F v )=1, when F v When <0, sign(F v )=-1; Stability constraints: a min ≤a≤a max Where a represents acceleration, a min represents the minimum acceleration, a max Indicates the maximum acceleration; Battery status constraints: SOC min ≤SOC≤SOC max Among them, SOC represents the battery charging state, SOC min Indicates the minimum battery charge, SOC max Indicates the maximum battery charge; Active power system power constraints: 0≤vF r ≤P r,max Where v represents the speed, P r,max Indicates the maximum output power of the main power system; Auxiliary power system power constraints: P b,min ≤vF b ≤P b,max Among them, P b,min Indicates the minimum output power of the auxiliary power system, P b,max Indicates the maximum output power of the auxiliary power system.

6. A parameter optimization system for an electric vehicle controller, characterized in that: include: processor; A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the parameter optimization method of the electric vehicle controller as claimed in any one of claims 1 to 5 is implemented.

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