吕宝占, 李朋宁, 吕雅然,等.工程车辆直线电机馈能悬架能量回收分析与参数优化[J].河南理工大学学报（自然科学版）,2024,43(3):107-114.

LYU B Z , LI P N , LYU Y R ,et al.Energy recovery analysis and parameter optimization of linear motor energy regenerative suspension of construction vehicles[J].Journal of Henan Polytechnic University(Natural Science) ,2024,43(3):107-114.

工程车辆直线电机馈能悬架能量回收分析与参数优化

吕宝占1, 李朋宁1, 吕雅然2, 邓晓亭3

1.河南理工大学 机械与动力工程学院，河南 焦作  454000  2.中国矿业大学（北京） 机械与电气工程学院，北京 100083  3.南京农业大学 工学院， 江苏 南京  210031

摘要: 目的  为了回收工程车辆在行驶过程中由于路面不平度引起的车身振动能量，同时解决馈能悬架系统的动力学性能和馈能性能不协调问题， 方法   首先，提出一种直线电机与减振器并联的馈能悬架系统结构，通过建立馈能悬架系统的二自由度动力学模型，利用天棚控制提高车辆的平顺性；其次，以悬架减振器阻尼作为优化设计变量，结合直线电机式馈能悬架的馈能性能、平顺性和安全性建立优化目标函数，采用遗传算法控制策略对馈能悬架的馈能性能进行优化，以获得全局最优解，解决馈能悬架的平顺性和馈能性能的协调问题；最后，通过MATLAB/Simulink对馈能悬架系统性能进行仿真。    结果   仿真结果表明：与被动悬架相比，天棚控制下的馈能悬架车身加速度、轮胎动载荷、悬架动行程均方根值分别减小25.40%，17.76%和32.58%；利用遗传算法对馈能悬架的动力性能和馈能协调性能优化，优化后馈能悬架的动力学性能相比优化前有一定恶化，但与被动悬架的动力学性能相比提高显著，而蓄电池平均回收能量提高了15.22%，馈能性能提高显著。    结论   本文提出的直线电机和减振器并联结构的馈能悬架系统装置能够回收工程车辆悬架系统的振动能量，采用遗传算法协调控制策略能够兼顾馈能悬架的动力学性能和馈能性能，并使馈能性能得到显著提高。

关键词:馈能悬架;直线电机;天棚控制;遗传算法

doi:10.16186/j.cnki.1673-9787.2022020042

基金项目:国家自然科学基金资助项目（51275249， 51505232）；河南省科技攻关项目（182102310843）；河南省高校基本科研业务费专项项目（NSFRF200403）

收稿日期:2022/02/20

修回日期:2023/02/16

出版日期:2024/05/15

Energy recovery analysis and parameter optimization of linear motor energy regenerative suspension of construction vehicles

LYU Baozhan1, LI Pengning1, LYU Yaran2, DENG Xiaoting3

1.School of Mechanical and Power Engineering， Henan Polytechnic University，Jiaozuo  454000，Henan，China  2.School of Mechanical and Electrical Engineering，China University of Mining and Technology（Beijing），Beijing， 100083，China  3.College of Engineering，Nanjing Agricultural University，Nanjing  210031，Jiangsu，China

Abstract:  Objectives   To recover the body vibration energy caused by road roughness during the driving process of construction vehicles， and to solve the incongruous problem of the dynamic performance and energy regenerative performance of the suspension，    Methods   an energy regenerative suspension structure with linear motor and shock absorber in parallel was proposed. Firstly， a two-degree-of-freedom dynamic model of energy regenerative suspension was established，and ceiling control was used to improve the smooth running of the vehicle. Secondly，in order to solve the coordination problem of ride comfort and performance of energy-fed suspension，the genetic algorithm control strategy was adopted to optimize the performance of energy-fed suspension.The suspension shock absorber damping was taken as the optimization design variable，and the optimization objective function of energy-fed performance，ride comfort and safety of linear motor energy-fed suspension was established to obtain the global optimal solution. Finally， the performance of energy-fed suspension was simulated by MATLAB/Simulink.    Results   The simulation results showed that the root-mean-square（ RMS ） values of body acceleration，tire dynamic load，and suspension dynamic travel  of energy-fed suspension under ceiling control were reduced by 25.40%，17.76% and 32.58%，respectively，compared with passive suspension. The dynamic performance of energy regenerative suspension and energy regenerative performance were coordinated optimization by genetic algorithm. The dynamic performance of energy regenerative suspension after optimization deteriorated to some degree compared with that before optimization， but was significantly improved compared with that of passive suspension. The average energy recovery of battery was significantly increased by 15.22%.    Conclusions   The energy regenerative suspension device with parallel structure of linear motor and shock absorber can recover the vibration energy of suspension system of engineering vehicles.The genetic algorithm coordinated control strategy adopted can coordinate the dynamic performance and energy regenerative performance of the energy regenerative suspension， and the energy regenerative performance was significantly improved. </sec>

Key words:energy regenerative suspension;linear motor;ceiling control;genetic algorithm