为提高车辆平顺性及悬架馈能效率,基于半桥整流及非对称阻尼特性原理,提出了一种新型抗侧倾电液互联馈能悬架(energy-harvesting shock absorbers based on hydraulically interconnected suspension,ESA-HIS)。基于流量-压降关系建立电液互联悬架数学模型并通过单缸台架试验验证其数学模型的准确性。开展ESA-HIS特性仿真分析,同时在随机路面工况及双移线工况下评估车辆的平顺性及抗侧倾特性。仿真结果表明:在2 Hz、20 mm的垂向激励条件下,ESA-HIS拉伸行程峰值阻尼力达到液压互联馈能悬架系统(energy-harvesting hydraulically interconnected suspension,EH-HIS)的1.77倍,同时馈能效率提升至32.81%;随机路面工况下装备有ESA-HIS的车辆能够获得良好的平顺性,相较于装备EH-HIS的车辆在60 km/h、70 km/h、80 km/h的车速下质心垂向加速度均方根值降低了4.37%、4.98%、6.47%,且双移线工况下车辆侧倾角相对于装备传统悬架及EH-HIS的车辆分别降低了45.89%和11.96%。

The Kalman filter (KF) can accurately estimate vehicle states under the assumption of Gaussian noise. However, when the measurement system is subject to complex non-Gaussian noise disturbances, the estimation performance of traditional methods degrades significantly. To address this issue, this paper proposes an adaptive and robust nonlinear KF method for vehicle state estimation, namely the cubature KF with maximum correntropy criterion and variational Bayesian under error compensation term (VB-MCC-CKF-E). The proposed framework mainly consists of regression model construction, robust state estimation, and adaptive noise variance estimation. Specifically, a batch regression model is first constructed to account for linearization errors, and the MCC is introduced within this framework to derive the MCC-CKF-E algorithm. Then, a VB approach is employed to perform online adaptive estimation of the time-varying statistics of the measurement noise covariance. Furthermore, the computational complexity of the VB-MCC-CKF-E is analyzed, demonstrating that it meets real-time application requirements. Finally, simulation experiments for vehicle state estimation under complex non-Gaussian noise scenarios show that the proposed VB-MCC-CKF-E outperforms extended KF, CKF, and other MCC-based variants in terms of both robustness and estimation accuracy.

针对轮胎侧向力非线性变化导致车辆动力学模型失准,进而影响路径跟踪性能的问题,将变侧偏刚度参数引入预测函数控制框架,提出了一种自适应预测函数控制算法(Adaptive Predictive Function Control, APFC).分析了轮胎侧向力的非线性特性对轮胎侧偏刚度的影响,建立了基于平方根容积卡尔曼滤波的轮胎侧向力实时估计器,设计了一种考虑真实轮胎侧向力变化实时修改轮胎侧偏刚度的自适应调节策略.利用Simulink和CarSim联合仿真试验和实车试验对所提策略进行了验证.仿真试验结果表明:在高/低附着双移线工况下,横向位置偏差均方根值较模型预测控制(Model predictive control, MPC)策略分别降低19.43%和31.67%;在高附着大曲率工况下横向位置偏差均方根值较MPC控制策略降低了37.89%.实车试验结果表明:APFC控制策略较MPC控制策略平均计算时间缩短了54.09%,横向位置偏差均方根值较MPC控制策略降低了38.23%.

为提高不同工况下车辆的减振性能，同时回收由路面不平引起的车辆振动能量，提出一种适应不同工况的电动静液压互联式馈能悬架系统。在分析互联悬架结构的基础上建立其数学模型，改变悬架输入参数，通过仿真分析4种工况下互联悬架的阻尼力特性与馈能特性。对比液压内部参数对互联悬架特性的影响，优化主要参数，在保证悬架阻尼力特性的前提下，使馈能功率最大化。在蛇形和凸块路面条件下进一步验证了该互联悬架抗侧倾和抗俯仰性能。结果表明：不同工况下互联悬架阻尼力特性一致且馈能特性相近，阻尼力及平均馈能功率随路面输入频率和振幅的增加而增加；优化主要参数后馈能特性明显提升；与安装传统悬架相比，安装该系统的车辆拥有更优异的平顺性和操稳性。

针对轮毂电机引起电机激励与车辆悬架系统耦合导致的振动负效应问题,提出一种基于双滑模面的主动悬架自适应滑模控制策略,搭建主动悬架动力学模型和永磁同步电机模型,分析定转子偏心距和车速对不平衡电磁力的影响,并开发了以理想天地棚为参考模型的自适应滑模控制器。仿真结果表明:相比被动悬架和传统的天棚控制,自适应滑模控制悬架系统使轮毂电机偏心距降低了12.4%,减小了不平衡电磁力,有效抑制了电机与汽车垂向振动耦合效应;同时使车身加速度和悬架动挠度降低了16.1%和4.2%,并保持轮胎动载荷在合理范围内,显著提升了汽车的乘坐舒适性和电机运行稳定性。

The suspension system is an important component of the vehicle, with the development of controllable suspension, the performance requirements of the vehicle suspension system are getting higher and higher. Usually, the suspension system is simplified as a linear system in the full vehicle control, but the actual continuous damping control (CDC) semi-active suspension force will be nonlinear with the motion of the damper. In addition, the control performance is also affected by the accuracy of the actual actuator output force. In this paper, a hierarchical control method for a CDC semi-active suspension linear parameter varying (LPV) system is designed. The nonlinear LPV model of CDC semi-active suspension is established, based on which the upper-level robust LPV-H infinity control strategy is designed. A dataset is established based on damping characteristics experiments, and a lower-level force tracking predictive control strategy is designed using cubic spline interpolation and a random forest model improved with Bayesian optimization. Simulation results show that the designed hierarchical control method can accurately realize the tracking prediction of the damping force and effectively improve the ride comfort of the full vehicle. And the full vehicle test platform is further built for road tests to evaluate the system performance. The results under randomized road conditions show that the root mean square values of the full vehicle vertical, pitch, and roll acceleration are reduced by 41.38%, 32.11%, and 31.27%, respectively, compared with the passive suspension. The method better improves the full vehicle performance.

A novel hybrid electromagnetic-hydraulically interconnected actuator suspension (EHIAS) is proposed to improve the ride comfort, handling stability, and body stability of the vehicle while converting vibration energy into usable electricity. The mathematical model of EHIAS including the model of hydraulically interconnected suspension (HIS) and the model of the linear motor is established, while the 14-degree-of-freedom model of the whole vehicle is described. Further, the correctness of the mathematical model and the nonlinearity of damping force in the HIS system are verified by bench tests. Furthermore, a comparison of EHIAS and HIS in terms of damping characteristics is conducted and the energy recovery characteristics of EHIAS are also investigated. Subsequent investigations evaluate the dynamic performance of vehicles equipped with HIS, EHIAS, and traditional independent suspensions. The bench test results show that HIS has a nonlinear damping force and the relationship between damping force and velocity is a concave function. The simulation results show that EHIAS provides significantly higher equivalent damping coefficients than HIS at low suspension velocities. The analysis also indicates that on A-class road, the EHIAS system exhibits marginally inferior ride comfort compared to the traditional independent suspensions but significantly outperforms the HIS. In addition, EHIAS provides superior ride comfort and handling stability compared to HIS and the traditional independent suspension on B-D class roads. Moreover, during double lane-change condition, EHIAS enhances anti-roll performance by 3.3 % over HIS and 81.24 % over the traditional independent suspension. © 2025 Elsevier Ltd

提出一种基于特征重构与多时间尺度的健康状态（SOH）和剩余寿命（RUL）联合估计方法。首先，从锂电池老化数据集中提取健康特征，使用变分模态分解算法重构特征数据；在此基础上，利用贝叶斯算法优化卷积长短期记忆网络构建SOH估计模型，通过微观尺度下的算法迭代与宏观尺度下的卷积神经网络模型相结合来实现SOH与RUL准确且高效的估计。利用美国国家航空航天局（NASA）和马里兰大学（CALCE）公开数据集进行验证，结果显示：SOH的估计误差稳定保持在1%以内，估计精度较特征重构前提升约35%;RUL预测结果的平均绝对误差（MAE）和均方根误差（RMSE）分别保持在0.42和0.78以内，实现了SOH和RUL的高精度估计。

A novel energy-regenerative hydraulically interconnected suspension (ER-HIS) is proposed to enhance the dynamic performance of vehicles while simultaneously converting the vibration energy into useable electricity. The mathematical model of ER-HIS is established based on the relationship between the flow rate and the pressure drop. The mathematical model of ER-HIS is verified by the bench test of 1/4 sub-model and the physical simulation model. Further, a comparative analysis is conducted on the force characteristics and energy harvesting capacity between the ER-HIS and the existing energy-regenerative hydraulically interconnected suspension. Thereafter, the simulation is conducted to compare the dynamic performance of vehicles equipped with the ER-HIS and the traditional suspension. The bench test results show that the response of the sub-model prototype is in good agreement with the simulation results of the mathematical model. The simulation results show that ER-HIS exhibits higher regenerated power and energy recovery efficiency compared to the existing energy-regenerative hydraulically interconnected suspension. Moreover, ER-HIS can provide better ride comfort and road-holding ability at low driving speeds, while recovering part of the vibration energy. The analysis also indicates that ER-HIS demonstrates superior anti-roll and anti-pitch performances compared to the traditional suspension under double lane change and braking conditions. © 2025 Elsevier Ltd

针对煤矿井下无人运输车作业场景中存在的低光照、多粉尘及目标形态多变等因素导致传统检测模型易出现漏检、错检和实时性不足的问题，本文提出了一种高效精准的煤矿井下无人运输车辆目标检测模型——YOLOv8-CBD。首先，结合Dual思想融合CSPNet与HetConv，提出了一种新的CSPConv模块，用于替代原YOLOv8网络中的C2f模块，在保证模型检测精度的同时，降低了模型的计算复杂度；其次，引入加权双向特征金字塔网络（BiFPN）来改进YOLOv8的颈部网络，根据不同的特征重要性分配不同的权重，有效地融合了不同尺度的目标特征，保证目标物特征信息的高效利用；同时，将传统的检测头替换为Dynamic Head，使检测头在复杂背景中的表现得到显著提升，强化模型对小目标的检测能力；最后，使用Focaler-IoU代替CIoU，重构IoU损失的线性区间映射，实现模型损失值在不同难度回归样本间的动态调节，增强模型在复杂煤矿井下环境中的定位和检测能力。利用煤矿井下无人运输车辆目标检测数据集对所提出的YOLOv8-CBD模型进行测试，试验结果表明：YOLOv8-CBD模型的mAP@50达到91.5%、...