WO2020119341A1 - 一种汽车电液复合转向系统的多学科集成建模及优化方法 - Google Patents
一种汽车电液复合转向系统的多学科集成建模及优化方法 Download PDFInfo
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- the invention belongs to the technical field of automobile steering systems, and particularly relates to a multi-disciplinary integrated modeling and optimization method for automobile electro-hydraulic composite steering systems.
- Automobile electro-hydraulic steering system because of the integration of multiple disciplines such as mechanics, electronics, hydraulics, and control, needs to consider the coupling relationship between various disciplines in the design stage to obtain better global performance.
- Existing automobile steering system modeling methods mainly include: use Matlab and other software to write mathematical formulas to derive dynamic models and control models, use Catia and other three-dimensional software to design three-dimensional models, and use Adams and other dynamic simulation software to establish dynamic simulation models, and Various special software such as Fluent, AMEsim, etc. analyze the sub-model of the steering system.
- the above method can perform high-efficiency modeling and analysis on the traditional electric power steering system and hydraulic power steering system, but for the electro-hydraulic composite steering system that combines the electric power assist function and the hydraulic power assist function, it is difficult to adopt a comprehensive integrated modeling method To systematically analyze and optimize various disciplines, but only able to build models separately and then conduct joint analysis, the efficiency is relatively low, and the accuracy of analysis under the multidisciplinary coupling relationship cannot be guaranteed.
- a multidisciplinary integrated modeling method for automotive electro-hydraulic composite steering system which combines the advantages of multiple software platforms for multidisciplinary integrated modeling, which can improve the accuracy and efficiency of development and design. Based on multidisciplinary integrated modeling for optimization, it can quickly obtain the overall performance of the system, which is helpful for the simplicity and efficiency of parameter optimization design.
- the object of the present invention is to provide a multi-disciplinary integrated modeling and optimization method for automobile electro-hydraulic composite steering system.
- the multidisciplinary integrated modeling method of the automobile electro-hydraulic composite steering system of the present invention includes the following steps:
- Step 1 Establish multi-disciplinary simulation model of automobile electro-hydraulic composite steering system based on AMEsim software
- Step 2 Establish the dynamic optimization model of automobile electro-hydraulic composite steering system based on matlab software
- Step 3 Based on the isight software, a multi-disciplinary simulation model and a dynamic optimization model of the automotive electro-hydraulic composite steering system are merged to establish a multi-disciplinary integrated optimization model of the automotive electro-hydraulic composite steering system.
- step 1 specifically includes:
- step 1.5 Take the input data file configured in step 1.4 as input, run the executable file configured in step 1.3, execute the AMEpilot guidance function of AMEsim software, call the multi-disciplinary simulation model of the electro-hydraulic composite steering system, and parse the output data with the suffix .out format file.
- step 2 specifically includes:
- step 3 specifically includes:
- the multi-disciplinary simulation model of the automobile electro-hydraulic composite steering system in step 1 includes a steering wheel input module, an electric power assist module, a hydraulic power assist module and a mechanical module;
- the mechanical module includes a torsion bar connected in sequence, a steering shaft, a steering column, a rack and pinion steering gear, a steering trapezoid, and a steering wheel;
- the steering wheel input module simulates the rotation angle and torque input by the driver, and sequentially transmits to the torsion bar of the mechanical module, Steering shaft, steering column, rack and pinion steering gear, steering trapezoid, steering wheel;
- electric power assist module simulates the electric power assist torque generated by the motor, which is transmitted to the worm gear mechanism, the worm gear mechanism acts on the torsion bar and steering shaft of the mechanical module In the meantime, the electric power assist torque and the driver torque are superimposed;
- the hydraulic power assist module simulates the production of a certain flow of hydraulic oil, which is transferred from the oil tank to the oil pump, the directional valve, and finally acts on both sides of the hydraulic cylinder to generate the hydraulic assist torque; hydraulic pressure The boosting torque acts on the rack and pinion steering gear of the mechanical module, superimposed with the electric boosting
- the dynamic model in step 2.1 is:
- J s is the steering wheel moment of inertia
- ⁇ s is the driver input rotation angle
- T dri is the driver input torque
- B s is the steering shaft viscous damping coefficient
- k s is the stiffness
- ⁇ e is the steering pinion rotation angle
- J ds is the equivalent rotational inertia of the steering output shaft and the reduction mechanism
- B ds is the damping coefficient
- G is the reduction ratio of the reduction mechanism
- T eps is the assisting torque of the booster motor
- T sen is the torque output from the torque sensor
- T w is the gear tooth Bar force
- J m1 is the moment of inertia of the assist motor
- ⁇ m1 is the angle of the assist motor
- B m1 is the damping coefficient of the assist motor
- T em1 is the electromagnetic torque of the assist motor
- m r is the equivalent rack mass
- x r is the pinion gear Displacement
- the dynamic optimization model in step 2.2 is:
- the optimization model in step 3.3.2 is:
- f 1 (X), f 2 (X), f 3 (X) are the optimization goals
- X is the optimization parameter
- K s is the steering shaft stiffness
- R p is the pinion radius
- a p is the hydraulic cylinder transverse cross-sectional area
- d p is the hydraulic diameter of the pipe
- J m1 to power the motor inertia K a gain for the valve.
- the optimization method of the automobile electro-hydraulic composite steering system of the present invention includes the following steps:
- Step 1) According to the requirements of the electro-hydraulic composite steering system, the above multi-disciplinary integrated modeling method is used to establish a multi-disciplinary integrated model of the electro-hydraulic composite steering system;
- Step 2) Discipline decomposition, the three disciplines obtained are: driving comfort discipline, steering economics discipline, and vehicle safety discipline; for each discipline obtained by decomposition, set several discipline goals;
- Step 3 Discipline objectives are transferred to the corresponding discipline optimization modules, and the three disciplines are optimized at the subsystem level. After optimization, the obtained subsystem optimal goals are transferred to the system-level optimization module;
- Step 4) The system-level optimization module takes the comprehensive steering performance as the goal, takes the subsystem optimization results and system-level constraints as constraints, performs system-level optimization of the comprehensive steering performance, and returns the optimal parameters obtained by the system-level optimization to the subsystem;
- Step 5 It is judged whether the result of the above optimal parameters meets the requirements, and if it is satisfied, the pareto solution is output and the optimization is ended, otherwise return to step 3).
- the discipline objectives of the driving comfort discipline include steering wheel hand strength and steering wheel shake
- the discipline objectives of the economic discipline include mechanical system energy consumption, electrical system energy consumption, and hydraulic system energy consumption
- vehicle safety discipline subject objectives Including yaw rate and lateral acceleration.
- the subsystem-level optimization uses a multi-objective particle swarm optimization algorithm as the optimization algorithm.
- system-level optimization in step 4) uses a multi-objective genetic algorithm as the optimization algorithm.
- the invention integrates a software platform of multiple disciplines, can perform multi-disciplinary integrated modeling, and improve modeling efficiency.
- the invention is based on a multi-disciplinary integrated modeling method and uses multi-disciplinary optimization to optimize the automobile electro-hydraulic compound steering system, which can simultaneously take into account multiple disciplines, obtain the best solution set, and improve the convergence and optimization efficiency of the optimal design.
- FIG. 1 is a flowchart of a multi-disciplinary integrated modeling method of an automobile electro-hydraulic composite steering system of the present invention
- Figure 2 is a multi-disciplinary optimization flow chart of the method of the present invention.
- a multi-disciplinary integrated modeling method for an automobile electro-hydraulic composite steering system of the present invention includes the following steps:
- Step 1 Establish multi-disciplinary simulation model of automobile electro-hydraulic composite steering system based on AMEsim software
- Step 2 Establish the dynamic optimization model of automobile electro-hydraulic composite steering system based on matlab software
- Step 3 Establish multi-disciplinary integrated optimization model of automotive electro-hydraulic composite steering system based on isight software.
- step 1 specifically includes:
- Serial number element Serial number Numerical value 1 Axis of rotation 14 Rotation axis node 2 First-order signal hysteresis 15 Rack and pinion 3 Torque sensor 16 Speed sensor 4 Viscous friction 17 Rotary valve 5 Angle sensor 18 Hydraulic hose 6 Rotary spring 19 Hydraulic pump 7 Linear spring 20 Hydraulic cylinder 8 Safety valve twenty one Permanent magnet motor 9 Directional valve twenty two Turbine shaft 10 tank twenty three Inverter 11 current sensor twenty four Table function 12 battery 25 generator 13 Quality components 26 Damping element
- step 2 Take the input data file configured in step 1.4 as input, run the executable file configured in step 1.3, execute the AMEpilot guidance function of AMEsim software, call the multi-disciplinary simulation model of the electro-hydraulic composite steering system, and parse the output data with the suffix .out format file.
- the step 2 specifically includes:
- the step 3 specifically includes:
- the multi-disciplinary simulation model of the automobile electro-hydraulic composite steering system in step 1 includes a steering wheel input module, an electric power assist module, a hydraulic power assist module and a mechanical module;
- the mechanical module includes a torsion bar connected in sequence, a steering shaft, a steering column, a rack and pinion steering gear, a steering trapezoid, and a steering wheel;
- the steering wheel input module simulates the rotation angle and torque input by the driver, and sequentially transmits to the torsion bar of the mechanical module, Steering shaft, steering column, rack and pinion steering gear, steering trapezoid, steering wheel;
- the electric power assist module simulates the electric power assist torque generated by the motor and transmits it to the worm gear mechanism.
- the worm gear mechanism acts on the torsion bar and steering shaft of the mechanical module
- the electric power assist torque and the driver torque are superimposed
- the hydraulic power assist module simulates the production of a certain flow of hydraulic oil, which is transferred from the oil tank to the oil pump, the directional valve, and finally acts on both sides of the hydraulic cylinder to generate the hydraulic assist torque
- the boosting torque acts on the rack and pinion steering gear of the mechanical module, superimposed with the electric boosting torque and the driver's torque.
- the dynamic model in step 2.1 is:
- J s is the steering wheel moment of inertia
- ⁇ s is the driver input rotation angle
- T dri is the driver input torque
- B s is the steering shaft viscous damping coefficient
- k s is the stiffness
- ⁇ e is the steering pinion rotation angle
- J ds is the equivalent rotational inertia of the steering output shaft and the reduction mechanism
- B ds is the damping coefficient
- G is the reduction ratio of the reduction mechanism
- T eps is the assisting torque of the booster motor
- T sen is the torque output from the torque sensor
- T w is the gear tooth Bar force
- J m1 is the moment of inertia of the assist motor
- ⁇ m1 is the angle of the assist motor
- B m1 is the damping coefficient of the assist motor
- T em1 is the electromagnetic torque of the assist motor
- m r is the equivalent rack mass
- x r is the pinion gear Displacement
- step 2.2 The dynamic optimization model in step 2.2 is:
- the optimization model in step 3.3.2 is:
- f 1 (X), f 2 (X), f 3 (X) are the optimization goals
- X is the optimization parameter
- K s is the steering shaft stiffness
- R p is the pinion radius
- a p is the hydraulic cylinder transverse cross-sectional area
- d p is the hydraulic diameter of the pipe
- J m1 to power the motor inertia K a gain for the valve.
- an optimization method of an automobile electro-hydraulic composite steering system includes the following steps:
- Step 1) According to the requirements of the electro-hydraulic composite steering system, the above multi-disciplinary integrated modeling method is used to establish a multi-disciplinary integrated model of the electro-hydraulic composite steering system;
- Step 2) Discipline decomposition, the three disciplines obtained are: driving comfort discipline, steering economics discipline, and vehicle safety discipline; for each discipline obtained by decomposition, set several discipline goals;
- Step 3 Discipline objectives are transferred to the corresponding discipline optimization modules, and the three disciplines are optimized at the subsystem level. After optimization, the obtained subsystem optimal goals are transferred to the system-level optimization module;
- Step 4) The system-level optimization module takes the comprehensive steering performance as the goal, takes the subsystem optimization results and system-level constraints as constraints, performs system-level optimization of the comprehensive steering performance, and returns the optimal parameters obtained by the system-level optimization to the subsystem;
- Step 5 It is judged whether the result of the above optimal parameters meets the requirements, and if it is satisfied, the pareto solution is output and the optimization is ended, otherwise return to step 3).
- the discipline objectives of the driving comfort discipline include steering wheel hand strength and steering wheel shake;
- the discipline objectives of the economic discipline include mechanical system energy consumption, electrical system energy consumption, and hydraulic system energy consumption;
- the vehicle safety discipline discipline objectives include yaw Angular velocity, lateral acceleration.
- the multi-objective particle swarm optimization algorithm is used as the optimization algorithm in the subsystem-level optimization.
- the specific steps are as follows:
- a Separately define the particle number, individual cognitive factor coefficient, social cognitive factor coefficient, inertial weight coefficient, weight reduction rate, and maximum evolutionary algebra of the particle swarm; generate the initial particle swarm, respectively, according to the driving comfort discipline and the economic discipline 1.
- the objective function value of the vehicle safety discipline generates initial particles and randomly generates some particles;
- step f Determine whether the external storage set is full, and if it is full, execute the maintenance strategy, remove the non-inferior solutions with small objective function values of each discipline, and ensure the diversity of the particle group; if not, then directly execute step f;
- update the particle swarm position and velocity information evolve to obtain the next generation particle swarm, adjust the individual historical optimal position pbest and the global optimal position gbest;
- step 4 uses a multi-objective genetic algorithm as the optimization algorithm, and the specific steps are as follows:
- the individuals in the population are sorted non-dominated according to the optimal objective at the subsystem level to calculate the individual crowding degree;
- m Determine whether the evolutionary algebra has reached the set value, if not, then loop step j-1; if it is reached, then complete the evolution, output the population obtained by evolution, and decode to obtain a non-inferior solution, which is the system-level optimal parameter.
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- Theoretical Computer Science (AREA)
- Aviation & Aerospace Engineering (AREA)
- Computational Mathematics (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Pure & Applied Mathematics (AREA)
- Computer Hardware Design (AREA)
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- Automation & Control Theory (AREA)
- Steering Control In Accordance With Driving Conditions (AREA)
Abstract
一种汽车电液复合转向系统的多学科集成建模方法及优化方法，基于AMEsim软件建立汽车电液复合转向系统多学科仿真模型，基于matlab软件建立汽车电液复合转向系统动力学优化模型，基于isight软件建立汽车电液复合转向系统多学科集成优化模型；采用所建的多学科集成模型对汽车电液复合转向系统进行优化，融合了多种软件平台的优势功能，有助于电液复合转向系统参数优化设计的简便化、高效化，提高开发设计的精度和效率。
Description
本发明属于汽车转向系统技术领域，具体涉及一种汽车电液复合转向系统的多学科集成建模及优化方法。
汽车电液转向系统由于融合了机械、电子、液压、控制等多个学科，在设计阶段需要考虑各个学科之间的耦合关系，才能够获得较好的全局性能。现有的汽车转向系统建模方法主要包括：利用Matlab等软件编写数学公式推导动力学模型及控制模型、利用Catia等三维软件设计三维模型、利用Adams等动力学仿真软件建立动力学仿真模型，以及各类专用软件如Fluent、AMEsim等分析转向系统的子模型。上述方法能够对传统的电动助力转向系统、液压助力转向系统进行较高效率的建模分析，但是针对融合了电动助力功能和液压助力功能的电液复合转向系统，难以通过综合的集成建模方法，系统地对各个学科进行分析及优化，而是只能够先单独地建立模型，再联合进行分析，效率较为低下，也无法保证多学科耦合关系下分析的精确性。
因此，提出一种汽车电液复合转向系统的多学科集成建模方法，融合多种软件平台的优势功能进行多学科集成建模，能够提高开发设计的精度和效率。基于多学科集成建模进行优化，能够快速获取系统的综合性能，有助于参数优化设计的简便化、高效化。
发明内容
针对于上述现有技术的不足，本发明的目的在于提供一种汽车电液复合转向系统的多学科集成建模及优化方法，通过融合多软件平台综合建模，获得简便、高效的多学科集成模型，以克服现有技术中存在的问题。
为达到上述目的，本发明采用的技术方案如下：
本发明的一种汽车电液复合转向系统的多学科集成建模方法，包括以下步骤：
步骤1：基于AMEsim软件建立汽车电液复合转向系统多学科仿真模型；
步骤2：基于matlab软件建立汽车电液复合转向系统动力学优化模型；
步骤3：基于isight软件，融合汽车电液复合转向系统多学科仿真模型和动力学优化模型，建立汽车电液复合转向系统多学科集成优化模型。
进一步地，所述步骤1具体包括：
1.1选择元件搭建电液复合转向系统多学科仿真模型，初始化模型参数；
1.2通过AMEsim软件定义输出参数，设置输出参数类型；
1.3配置后缀为.bat的可执行文件；
1.4根据多学科仿真模型，配置后缀为.in格式的输入数据文件；
1.5以步骤1.4配置的输入数据文件为输入，运行步骤1.3配置的可执行文件，执行AMEsim软件的AMEpilot引导功能，调用电液复合转向系统多学科仿真模型，解析得到后缀为.out格式的输出数据文件。
进一步地，所述步骤2具体包括：
2.1建立电液复合转向系统数学公式，推导系统动力学模型；
2.2根据动力学模型推导性能函数，编写matlab动力学优化模型。
进一步地，所述步骤3具体包括：
3.1建立isight和AMEsim联合仿真接口；
3.1.1配置isight软件的simcode模块组件，导入步骤1.3中配置的可执行文件；
3.1.2配置simcode的输入输出参数，输入参数为步骤1.4中的输入数据文件，输出参数为步骤1.5中的输出数据文件；
3.2建立isight和matlab联合仿真接口；
3.2.1配置isight软件的matlab模块组件；
3.2.2导入步骤2.2中编写的matlab动力学优化模型；
3.3配置isight软件的optimization组件模块；
3.3.1依次连接optimization组件模块、simcode组件模块、matlab组件模块；
3.3.2设定优化参数、约束条件、优化目标建立优化模型，选择优化算法并设定算法参数；
3.3.3完成多学科集成建模，得到汽车电液复合转向系统多学科集成优化模型，进行多学科优化。
优选地，所述步骤1中的汽车电液复合转向系统多学科仿真模型包含方向盘输入模块，电动助力模块，液压助力模块及机械模块；
机械模块包括依次连接的扭杆，转向轴、转向管柱、齿轮齿条转向器、转向梯形、转向车轮；方向盘输入模块模拟驾驶员输入的转角和转矩，依次传递给机械模块的扭杆，转向轴、转向管柱、齿轮齿条转向器、转向梯形、转向车轮；电动助力模块模拟电机产 生的电动助力力矩，传递给蜗轮蜗杆机构，蜗轮蜗杆机构作用在机械模块的扭杆和转向轴之间，将电动助力力矩与驾驶员转矩进行叠加；液压助力模块模拟产生一定流量的液压油，从油箱依次传递至油泵、换向阀、最后作用于液压缸两侧，产生液压助力力矩；液压助力力矩作用于机械模块的齿轮齿条转向器上，与电动助力力矩、驾驶员转矩进行叠加。
优选地，所述步骤2.1中的动力学模型为：
式中：J
s为转向盘转动惯量，θ
s为驾驶员输入转角；T
dri为驾驶员输入力矩，B
s为转向轴粘性阻尼系数，k
s为刚度，θ
e为转向小齿轮转角，J
ds为转向输出轴与减速机构的等效转动惯量，B
ds为阻尼系数，G为减速机构减速比，T
eps为助力电机助力转矩，T
sen为转矩传感器输出力矩，T
w为齿轮齿条作用力，J
m1为助力电机转动惯量，θ
m1为助力电机转角，B
m1为助力电机阻尼系数，T
em1为助力电机电磁转矩，m
r为等效齿条质量，x
r为小齿轮位移，B
r为齿条阻尼系数，r
p为小齿轮半径，F
hyd为转向液压缸助力，F
z为车轮在齿条上的等效阻力，J
m2为油泵电机与油泵的转动惯量，θ
m2为油泵电机转角，B
m2为油泵电机阻尼系数，T
em2为油泵电机电磁转矩，T
ehps为油泵工作力矩。
优选地，所述步骤2.2中的动力学优化模型为：
优选地，所述步骤3.3.2中的优化模型为：
式中：f
1(X)、f
2(X)、f
3(X)分别为优化目标，X为优化参数，K
s为转向轴刚度、R
p为小齿轮半径、A
p为液压缸横截面积、d
p为液压管道直径、J
m1为助力电机转动惯量、K
a为换向阀增益。
本发明的一种汽车电液复合转向系统的优化方法，包括步骤如下：
步骤1)根据电液复合转向系统需求，采用上述多学科集成建模方法，建立电液复合转向系统多学科集成模型；
步骤2)进行学科分解，得到的三个学科分别为：驾驶舒适性学科、转向经济性学科、车辆安全性学科；对分解得到的每一个学科，分别设定若干个学科目标；
步骤3)将学科目标分别传递给对应的学科优化模块，对三个学科分别进行子系统级优化，优化后将得到的子系统最优目标传递给系统级优化模块；
步骤4)系统级优化模块以综合转向性能为目标，以子系统优化结果和系统级约束条件为约束，进行综合转向性能的系统级优化，并将系统级优化得到的最优参数返回子系统；
步骤5)判断上述最优参数结果是否满足要求，满足则输出pareto解，结束优化，否则返回步骤3)。
进一步地，所述驾驶舒适性学科的学科目标包括方向盘手力、方向盘抖动；转向经济性学科的学科目标包括机械系统能耗、电气系统能耗、液压系统能耗；车辆安全性学科的学科目标包括横摆角速度、侧向加速度。
进一步地，所述步骤4)中子系统级优化采用多目标粒子群算法作为优化算法。
进一步地，所述步骤4)中系统级优化采用多目标遗传算法作为优化算法。
本发明的有益效果：
本发明与现有的汽车转向系统建模方法相比，融合了多个学科的软件平台，能够进行多学科集成建模，提高建模效率。
本发明基于多学科集成建模方法，采用多学科优化对汽车电液复合转向系统进行优化，能够同时兼顾多个学科，获取最佳解集，提高优化设计的收敛性和优化效率。
图1为本发明汽车电液复合转向系统的多学科集成建模方法流程图；
图2为本发明方法多学科优化流程图。
为了便于本领域技术人员的理解，下面结合实施例与附图对本发明作进一步的说 明，实施方式提及的内容并非对本发明的限定。
参照图1所示，本发明的一种汽车电液复合转向系统的多学科集成建模方法，包括以下步骤：
步骤1：基于AMEsim软件建立汽车电液复合转向系统多学科仿真模型；
步骤2：基于matlab软件建立汽车电液复合转向系统动力学优化模型；
步骤3：基于isight软件建立汽车电液复合转向系统多学科集成优化模型。
进一步地，所述步骤1具体包括：
1.1选择元件搭建电液复合转向系统多学科仿真模型，初始化模型参数，保存为后缀是.ame的多学科仿真模型文件；所选元件如表1所示：
表1
| 序号 | 元件 | 序号 | 数值 |
| 1 | 旋转轴 | 14 | 旋转轴节点 |
| 2 | 一阶信号滞后器 | 15 | 齿轮齿条 |
| 3 | 转矩传感器 | 16 | 转速传感器 |
| 4 | 粘滞摩擦器 | 17 | 转阀 |
| 5 | 转角传感器 | 18 | 液压软管 |
| 6 | 旋转弹簧 | 19 | 液压泵 |
| 7 | 线性弹簧 | 20 | 液压缸 |
| 8 | 安全阀 | 21 | 永磁电机 |
| 9 | 换向阀 | 22 | 蜗轮蜗杆 |
| 10 | 油箱 | 23 | 逆变器 |
| 11 | 电流传感器 | 24 | 表格函数 |
| 12 | 电池 | 25 | 发电机 |
| 13 | 质量元件 | 26 | 阻尼元件 |
1.2通过AMEsim软件的输出模块，定义输出参数，设置输出参数类型；输出参数如表2所示：
表2
| 序号 | 参数 | 类型 | 序号 | 参数 | 类型 |
| 1 | 液压缸直径 | 单一参数 | 9 | 液压系统能耗 | 复合参数 |
| 2 | 电机转动惯量 | 单一参数 | 10 | 电气系统能耗 | 复合参数 |
| 3 | 活塞行程 | 单一参数 | 11 | 机械系统能耗 | 复合参数 |
| 4 | 转向轴刚度 | 单一参数 | 12 | 系统总能耗 | 复合参数 |
| 5 | 小齿轮半径 | 单一参数 | 13 | 横摆角速度 | 单一参数 |
| 6 | 液压缸横截面积 | 单一参数 | 14 | 侧向加速度 | 单一参数 |
| 7 | 液压管道直径 | 单一参数 | 15 | 方向盘手力 | 单一参数 |
| 8 | 换向阀增益 | 单一参数 | 16 | 方向盘抖动 | 单一参数 |
1.3新建后缀为.txt的记事本文件，输入的文本内容为goAMEpilot，以与步骤1.1得到的多学科仿真模型文件相同的名称，命名该记事本文件，将记事本文件的后缀修改为.bat并保存，配置后缀为.bat的可执行文件；
1.4运行多学科仿真模型，配置后缀为.in格式的输入数据文件；
1.5以步骤1.4配置的输入数据文件为输入，运行步骤1.3配置的可执行文件，执行AMEsim软件的AMEpilot引导功能，调用电液复合转向系统多学科仿真模型，解析得到后缀为.out格式的输出数据文件。所述步骤2具体包括：
2.1建立电液复合转向系统数学公式，推导系统动力学模型；
2.2根据动力学模型推导性能函数，编写matlab动力学优化模型。
所述步骤3具体包括：
3.1建立isight和AMEsim联合仿真接口；
3.1.1配置isight软件的simcode模块组件，导入步骤1.3中配置的可执行文件；
3.1.2配置simcode的输入输出参数，输入参数为步骤1.4中的输入数据文件，输出参数为步骤1.5中的输出数据文件；
3.2建立isight和matlab联合仿真接口；
3.2.1配置isight软件的matlab模块组件；
3.2.2导入步骤2.2中编写的matlab动力学优化模型；
3.3配置isight软件的optimization组件模块；
3.3.1依次连接optimization组件模块、simcode组件模块、matlab组件模块；
3.3.2设定优化参数、约束条件、优化目标建立优化模型，选择优化算法并设定算法参数；
3.3.3完成多学科集成建模，得到汽车电液复合转向系统多学科集成优化模型，进行多学科优化。
所述步骤1中的汽车电液复合转向系统多学科仿真模型包含方向盘输入模块，电动助力模块，液压助力模块及机械模块；
机械模块包括依次连接的扭杆，转向轴、转向管柱、齿轮齿条转向器、转向梯形、转向车轮；方向盘输入模块模拟驾驶员输入的转角和转矩，依次传递给机械模块的扭杆，转向轴、转向管柱、齿轮齿条转向器、转向梯形、转向车轮；电动助力模块模拟电机产生的电动助力力矩，传递给蜗轮蜗杆机构，蜗轮蜗杆机构作用在机械模块的扭杆和转向轴之间，将电动助力力矩与驾驶员转矩进行叠加；液压助力模块模拟产生一定流量的液压油，从油箱依次传递至油泵、换向阀、最后作用于液压缸两侧，产生液压助力力矩；液压助力力矩作用于机械模块的齿轮齿条转向器上，与电动助力力矩、驾驶员转矩进行叠加。
所述步骤2.1中的动力学模型为：
式中：J
s为转向盘转动惯量，θ
s为驾驶员输入转角；T
dri为驾驶员输入力矩，B
s为转向轴粘性阻尼系数，k
s为刚度，θ
e为转向小齿轮转角，J
ds为转向输出轴与减速机构 的等效转动惯量，B
ds为阻尼系数，G为减速机构减速比，T
eps为助力电机助力转矩，T
sen为转矩传感器输出力矩，T
w为齿轮齿条作用力，J
m1为助力电机转动惯量，θ
m1为助力电机转角，B
m1为助力电机阻尼系数，T
em1为助力电机电磁转矩，m
r为等效齿条质量，x
r为小齿轮位移，B
r为齿条阻尼系数，r
p为小齿轮半径，F
hyd为转向液压缸助力，F
z为车轮在齿条上的等效阻力，J
m2为油泵电机与油泵的转动惯量，θ
m2为油泵电机转角，B
m2为油泵电机阻尼系数，T
em2为油泵电机电磁转矩，T
ehps为油泵工作力矩。
所述步骤2.2中的动力学优化模型为：
所述步骤3.3.2中的优化模型为：
式中：f
1(X)、f
2(X)、f
3(X)分别为优化目标，X为优化参数，K
s为转向轴刚度、R
p为小齿轮半径、A
p为液压缸横截面积、d
p为液压管道直径、J
m1为助力电机转动惯量、K
a为换向阀增益。
参照图2所示，一种汽车电液复合转向系统的优化方法，包括步骤如下：
步骤1)根据电液复合转向系统需求，采用上述多学科集成建模方法，建立电液复合转向系统多学科集成模型；
步骤2)进行学科分解，得到的三个学科分别为：驾驶舒适性学科、转向经济性学科、车辆安全性学科；对分解得到的每一个学科，分别设定若干个学科目标；
步骤3)将学科目标分别传递给对应的学科优化模块，对三个学科分别进行子系统级优化，优化后将得到的子系统最优目标传递给系统级优化模块；
步骤4)系统级优化模块以综合转向性能为目标，以子系统优化结果和系统级约束条件为约束，进行综合转向性能的系统级优化，并将系统级优化得到的最优参数返回子系统；
步骤5)判断上述最优参数结果是否满足要求，满足则输出pareto解，结束优化，否则返回步骤3)。
所述驾驶舒适性学科的学科目标包括方向盘手力、方向盘抖动；转向经济性学科的 学科目标包括机械系统能耗、电气系统能耗、液压系统能耗；车辆安全性学科的学科目标包括横摆角速度、侧向加速度。
所述步骤3)中子系统级优化采用多目标粒子群算法作为优化算法，具体步骤如下：
a.分别定义粒子群的粒子数、个体认知因子系数、社会认知因子系数、惯性权重系数、权重下降率、最大进化代数；生成初始粒子群，分别按驾驶舒适性学科、转向经济性学科、车辆安全性学科的目标函数值产生初始粒子，并随机生成部分粒子；
b.分别初始化粒子群位置、速度信息，并计算每个粒子对驾驶舒适性学科、转向经济性学科、车辆安全性学科的学科目标的适应度函数值；
c.将粒子当前的位置设置为粒子个体最优位置pbest，寻找当前粒子群中并适应度函数值最高的粒子并将其设置为全局最优位置gbest，将gbest的粒子作为非劣解加入外部存储集合；
d.计算各粒子的目标函数值，采用轮盘赌法选择当前状态的非劣解，并用当前状态的非劣解更新外部储存集合的非劣解；
e.判断外部存储集合是否装满，若装满则执维护策略，剔除各学科目标函数值较小的非劣解，保证粒子群的多样性；若未装满则直接执行步骤f；
f.在解空间范围内，更新粒子群位置、速度信息，进化得到下一代粒子群，调整个体历史最优位置pbest和全局最优位置gbest；
g.循环步骤d-f，达到最大进化代数时停止，输出外部储存集合的非劣解，即为子系统级最优目标。
所述步骤4)中系统级优化采用多目标遗传算法作为优化算法，具体步骤如下：
h.编码，设置种群数量、进化代数、目标函数数量、维度、交叉概率、变异概率，初始化种群；
i.对种群中的个体按子系统级最优目标进行非支配排序，计算个体拥挤度；
j.执行种群的选择、交叉和变异操作，得到新种群；
k.将新种群与原有种群合并，对合并后的种群按综合转向性能的进行非支配排序和个体拥挤度计算，将所有个体按照排序等级和拥挤度进行排序，排序等级较低、拥挤度较差的个体被剔除，选择排序等级较高、拥挤度较大的优良个体并组成下一代种群；
l.对下一代种群中的个体进行非支配排序，计算个体拥挤度；
m.判断进化代数是否达到设置值，若未达到则循环步骤j-l；若达到则完成进化，输出进化得到的的种群，解码得到非劣解，即为系统级最优参数。
本发明具体应用途径很多，以上所述仅是本发明的优选实施方式，应当指出，对于本技术领域的普通技术人员来说，在不脱离本发明原理的前提下，还可以作出若干改进，这些改进也应视为本发明的保护范围。
Claims (10)
- 一种汽车电液复合转向系统的多学科集成建模方法，其特征在于，包括以下步骤：步骤1：基于AMEsim软件建立汽车电液复合转向系统多学科仿真模型；步骤2：基于matlab软件建立汽车电液复合转向系统动力学优化模型；步骤3：基于isight软件，融合汽车电液复合转向系统多学科仿真模型和动力学优化模型，建立汽车电液复合转向系统多学科集成优化模型。
- 根据权利要求1所述的汽车电液复合转向系统的多学科集成建模方法，其特征在于，所述步骤1具体包括：1.1选择元件搭建电液复合转向系统多学科仿真模型，初始化模型参数；1.2通过AMEsim软件定义输出参数，设置输出参数类型；1.3配置后缀为.bat的可执行文件；1.4根据多学科仿真模型，配置后缀为.in格式的输入数据文件；1.5以步骤1.4配置的输入数据文件为输入，运行步骤1.3配置的可执行文件，执行AMEsim软件的AMEpilot引导功能，调用电液复合转向系统多学科仿真模型，解析得到后缀为.out格式的输出数据文件。
- 根据权利要求2所述的汽车电液复合转向系统的多学科集成建模方法，其特征在于，所述步骤2具体包括：2.1建立电液复合转向系统数学公式，推导系统动力学模型；2.2根据动力学模型推导性能函数，编写matlab动力学优化模型。
- 根据权利要求3所述的汽车电液复合转向系统的多学科集成建模方法，其特征在于，所述步骤3具体包括：3.1建立isight和AMEsim联合仿真接口；3.1.1配置isight软件的simcode模块组件，导入步骤1.3中配置的可执行文件；3.1.2配置simcode的输入输出参数，输入参数为步骤1.4中的输入数据文件，输出参数为步骤1.5中的输出数据文件；3.2建立isight和matlab联合仿真接口；3.2.1配置isight软件的matlab模块组件；3.2.2导入步骤2.2中编写的matlab动力学优化模型；3.3配置isight软件的optimization组件模块；3.3.1依次连接optimization组件模块、simcode组件模块、matlab组件模块；3.3.2设定优化参数、约束条件、优化目标建立优化模型，选择优化算法并设定算 法参数；3.3.3完成多学科集成建模，得到汽车电液复合转向系统多学科集成优化模型，进行多学科优化。
- 根据权利要求1所述的汽车电液复合转向系统的多学科集成建模方法，其特征在于，所述步骤1中的汽车电液复合转向系统多学科仿真模型包含方向盘输入模块，电动助力模块，液压助力模块及机械模块；机械模块包括依次连接的扭杆，转向轴、转向管柱、齿轮齿条转向器、转向梯形、转向车轮；方向盘输入模块模拟驾驶员输入的转角和转矩，依次传递给机械模块的扭杆，转向轴、转向管柱、齿轮齿条转向器、转向梯形、转向车轮；电动助力模块模拟电机产生的电动助力力矩，传递给蜗轮蜗杆机构，蜗轮蜗杆机构作用在机械模块的扭杆和转向轴之间，将电动助力力矩与驾驶员转矩进行叠加；液压助力模块模拟产生一定流量的液压油，从油箱依次传递至油泵、换向阀、最后作用于液压缸两侧，产生液压助力力矩；液压助力力矩作用于机械模块的齿轮齿条转向器上，与电动助力力矩、驾驶员转矩进行叠加。
- 根据权利要求1所述的汽车电液复合转向系统的多学科集成建模方法，其特征在于，所述步骤2.1中的动力学模型为：
式中：J s为转向盘转动惯量，θ s为驾驶员输入转角；T dri为驾驶员输入力矩，B s为转向轴粘性阻尼系数，k s为刚度，θ e为转向小齿轮转角，J ds为转向输出轴与减速机构的等效转动惯量，B ds为阻尼系数，G为减速机构减速比，T eps为助力电机助力转矩，T sen为转矩传感器输出力矩，T w为齿轮齿条作用力，J m1为助力电机转动惯量，θ m1为助力电机转角，B m1为助力电机阻尼系数，T em1为助力电机电磁转矩，m r为等效齿条质量，x r为小齿轮位移，B r为齿条阻尼系数，r p为小齿轮半径，F hyd为转向液压缸助力，F z为车轮 在齿条上的等效阻力，J m2为油泵电机与油泵的转动惯量，θ m2为油泵电机转角，B m2为油泵电机阻尼系数，T em2为油泵电机电磁转矩，T ehps为油泵工作力矩。 - 根据权利要求6所述的汽车电液复合转向系统的多学科集成建模方法，其特征在于，所述步骤2.2中的动力学优化模型为：
- 根据权利要求7所述的汽车电液复合转向系统的多学科集成建模方法，其特征在于，所述步骤3.3.2中的优化模型为：
式中，f 1(X)、f 2(X)、f 3(X)分别为优化目标，X为优化参数，K s为转向轴刚度、R p为小齿轮半径、A p为液压缸横截面积、d p为液压管道直径、J m1为助力电机转动惯量、K a为换向阀增益。 - 一种汽车电液复合转向系统的优化方法，其特征在于，包括步骤如下：步骤1)根据电液复合转向系统需求，采用上述权利要求1至8中任意一项所述的多学科集成建模方法，建立电液复合转向系统多学科集成模型；步骤2)进行学科分解，得到的三个学科分别为：驾驶舒适性学科、转向经济性学科、车辆安全性学科；对分解得到的每一个学科，分别设定若干个学科目标；步骤3)将学科目标分别传递给对应的学科优化模块，对三个学科分别进行子系统级优化，优化后将得到的子系统最优目标传递给系统级优化模块；步骤4)系统级优化模块以综合转向性能为目标，以子系统优化结果和系统级约束条件为约束，进行综合转向性能的系统级优化，并将系统级优化得到的最优参数返回子系统；步骤5)判断上述最优参数结果是否满足要求，满足则输出pareto解，结束优化，否则返回步骤3)。
- 根据权利要求9所述的汽车电液复合转向系统的优化方法，其特征在于，所述驾驶舒适性学科的学科目标包括方向盘手力、方向盘抖动；转向经济性学科的学科目标包括机械系统能耗、电气系统能耗、液压系统能耗；车辆安全性学科的学科目标包括横 摆角速度、侧向加速度。
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