Fully leveraging the four-wheel independent drive characteristics of distributed-drive electric vehicles has become essential for enhancing their driving range. However, conventional regenerative braking strategies applied to such vehicles often fail to consider individual wheel slip ratios, which can easily lead to wheel lock and low energy recovery efficiency. To address these issues, this paper proposes a novel energy management method that integrates hybrid braking control with intelligent connected speed planning. A hierarchical control strategy for the hybrid braking system is first developed, explicitly accounting for the slip ratio of each wheel. The upper-level controller calculates the slip ratio for each wheel based on vehicle speed and wheel speed information and subsequently determines the braking torque distribution between the front and rear axles. The lower-level controller then allocates the motor braking torque and hydraulic braking torque to each wheel, subject to system constraints such as battery status and motor torque limits. Building on this framework, vehicle state and road information are incorporated as inputs to formulate a Markov decision process, which optimizes traffic efficiency, energy economy, and ride comfort as multiple objectives. The deep deterministic policy gradient (DDPG) algorithm is employed to achieve collaborative optimization of speed planning and energy management. Simulation results demonstrate that the proposed DDPG-based control strategy outperforms both rule-based control methods and classical dynamic programming algorithms in terms of comprehensive performance across traffic efficiency, energy consumption, and ride comfort. These findings validate its superiority in complex traffic conditions.

For hybrid energy storage systems requiring efficient energy management to achieve optimal power allocation between the power battery and supercapacitor, this study proposes an optimal energy management method integrating whole-process particle swarm optimization with fuzzy logic control, which simultaneously considers braking safety and energy efficiency optimization. First, a zonal braking force distribution strategy based on the I-curve, ECE regulations curve, and front wheel lockup curve is designed to maximize energy recovery while ensuring braking safety. On this basis, a whole-process “driving–braking” fuzzy logic control strategy for power distribution is constructed, aiming at maximizing braking energy recovery efficiency and minimizing energy consumption per 100 km. The parameters of the membership functions in the fuzzy controller are optimized using the particle swarm optimization algorithm to achieve global optimization of the control process. Finally, simulation validation of the optimization results demonstrates that, compared with traditional logic threshold control under NEDC conditions, the proposed strategy improves braking energy recovery efficiency by 10.32%, reduces energy consumption per 100 km by 0.96 kWh, and decreases the peak current of the power battery by 6.4%, thereby effectively enhancing vehicle economy and extending battery lifespan.

To enhance the balance between lateral stability and energy efficiency, we propose an adaptive compound controller based on phase plane analysis for four-wheel independent drive electric vehicles (4WID-EVs). The adaptive stability and energy-saving controller (SEC) is designed with a three-layer structure. The upper-layer controller employs model predictive control (MPC) to compute the external yaw moment based on the desired yaw rate and side slip angle derived from a reference model. The adaptive-layer controller utilizes a phase plane diagram to evaluate vehicle stability and reduces unnecessary external yaw moment consumption by accounting for the vehicle’s steering state and battery’s state-of-charge (SOC) level. The lower-layer controller implements an optimal torque distribution algorithm to minimize an objective function that considers tire workload, energy consumption, and smooth motor control. Numerical simulations are performed in MATLAB/Simulink using three distinct steering angles to evaluate the performance of the proposed control strategy. At each steering angle, the SEC’s stability and energy efficiency are compared to those of the energy-saving controller (EC) and stability controller (SC) under varying battery charge levels. The results indicate that, at small steering angles, the vehicle operates in a highly stable state, enabling a reduction in the external yaw moment to achieve substantial energy savings. As the steering angle increases, the vehicle approaches a critical stability state, where the external yaw moment is applied to maintain lateral stability. Furthermore, as the SOC decreases, the SEC strategy will increasingly prioritize energy savings. Simulation results verify that the SEC strategy effectively balances lateral stability and energy savings while maintaining consistent performance across a range of operating conditions.

We propose a compound control framework to improve the path tracking accuracy of a four-wheel independent steering and driving (4WISD) vehicle in complex environments. The framework consists of a deep reinforcement learning (DRL)-based auxiliary controller and a dual-layer controller. Samples in the 4WISD vehicle control framework have the issues of skewness and sparsity, which makes it difficult for the DRL to converge. We propose a group intelligent experience replay (GER) mechanism that non-dominantly sorts the samples in the experience buffer, which facilitates within-group and between-group collaboration to achieve a balance between exploration and exploitation. To address the generalization problem in the complex nonlinear dynamics of 4WISD vehicles, we propose an actor-critic architecture based on the method of two-stream information bottleneck (TIB). The TIB method is used to remove redundant information and extract high-dimensional features from the samples, thereby reducing generalization errors. To alleviate the overfitting of DRL to known data caused by IB, the reverse information bottleneck (RIB) alters the optimization objective of IB, preserving the discriminative features that are highly correlated with actions and improving the generalization ability of DRL. The proposed method significantly improves the convergence and generalization capabilities of DRL, while effectively enhancing the path tracking accuracy of 4WISD vehicles in high-speed, large-curvature, and complex environments.

To enhance the braking stability of electric vehicles and maximize braking energy recovery, this paper proposes a regenerative braking force distribution strategy based on Electro-Mechanical Braking (EMB) in a single-pedal mode. The braking intention during single-pedal operation is identified using an Adaptive Neuro-Fuzzy Inference System (ANFIS), with the effectiveness of this method validated through data collection and analysis on a six-degree-of-freedom test rig, showing significant improvement in intention recognition accuracy. An innovative distribution method for front and rear axle braking forces is developed, and a fuzzy controller is designed with battery State of Charge ( SOC ), vehicle velocity ( v ), braking intensity ( z ), and braking intention ( I ) as inputs, and the regenerative braking ratio coefficient ( k ) as the output. The controller is optimized using the Sparrow Search Algorithm (SSA), further enhancing braking energy recovery efficiency. Co-simulation with Simulink and AVL Cruise software demonstrates the strategy’s effectiveness. Results indicate that under the Worldwide Harmonized Light Vehicles Test Cycle (WLTC) and China Light-Duty Vehicle Test Cycle (CLTC) conditions, the braking energy recovery efficiency of the proposed strategy reaches 21.54% and 25.39%, respectively. These findings confirm that the EMB-based single-pedal regenerative braking force distribution strategy significantly improves both braking stability and energy recovery efficiency in electric vehicles, offering valuable insights for future braking strategy development.

This study delves deeply into the traffic light intersection control issue for connected autonomous vehicles (CAVs). First, we employed the vehicle dynamics simulation software, CarSim, to model the vehicle and utilized the intelligent driving simulation software, PreScan, to establish a road environment model. Subsequently, we designed a rule-based traffic light crossing controller (RB-TLCC) for CAVs in MATLAB/Simulink and implemented a co-simulation using CarSim and PreScan. Furthermore, we conducted driver-in-the-loop studies using the Logitech G27 driving simulator kit and compared the experimental results with those from the RB-TLCC. Our findings indicate that RB-TLCC enhances the regenerative braking energy by 23.5%, improves traffic efficiency by 17.9%, and increases driving smoothness by 50.7%. Additionally, based on Markov Chain theory, we proposed a multi-objective optimization model (MO-OM) for CAVs traffic light intersection crossing using the Proximal Policy Optimization algorithm (PPO). A comparison was conducted under two different operating conditions between RB-TLCC and dynamic programming algorithms (DP). The results indicated that the MO-OM proposed in this study exhibited the best comprehensive control performance among the three control methods. While adhering to traffic regulations, it enhances the recuperation of braking energy, efficiency of vehicle passage, and smoothness of travel at traffic light intersections. This study offers effective methodologies for enhancing the performance of CAVs in traffic light intersection control and holds significant reference value for the advancement of future intelligent transportation systems (ITS).

为提高后悬架车轮定位参数性能,以某运动型多用途车(SUV)多连杆后独立悬架为研究对象,建立多连杆独立后悬架多体动力学模型.首先进行后悬架车轮定位参数敏感度分析,找出对定位参数敏感度最高的悬架硬点坐标;其次基于统一目标法与蒙特卡罗法,以敏感度最高的硬点坐标作为设计变量,悬架定位参数变化最小为优化目标进行优化分析;对比后悬架车轮定位参数优化结果,车轮外倾角、前束角的变化范围有明显减小;通过动力学建模与分析,提高了多连杆后悬架的运动学性能.

为了减少某重型卡车多连杆转向系统转角误差,确定转向机构的结构强度,建立了重型卡车多连杆转向系统多体动力学模型;首先利用模型进行了转向系统运动学分析,确定转向系统转角误差,采用试验设计方法,求解转向系统转向误差最小的机构;其次,进行转向系统动力学分析,得出转向系统杆件的最大受力,并应用有限元方法,优化转向系统的结构强度;结果表明,转向系统转向误差明显减小,提高了转向系统的可靠性;转向系统的性能达到某卡车设计要求.

The dynamic characteristics of electric drive systems are crucial in electric vehicles. Based on the dynamic finite element method and previous studies, this study proposes and analyzes a new mathematical model for a motor longitudinally mounted on a centralized electric drive system of a pure electric vehicle. First, we analyze the largest torque ripple of a fractional slot concentrated winding inner-mounted permanent magnet synchronous motor designed for commercial electric vehicles. This torque ripple is identified as one of the excitations influencing the dynamic performance of the electric drive system. Second, a new dynamic mathematical model for the electric drive system is established. Third, we investigate the linear vibration responses of the system subject to torque ripple and transmission error. Finally, the relationships between critical motor parameters and dynamic mesh force are revealed. The results demonstrate that the proposed theoretical method can effectively determine the dynamic characteristics of the electric drive system, thereby providing valuable theoretical guidance for the design and optimization of the motor and electric drive system.

High−precision and robust localization is critical for intelligent vehicle and transportation systems, while the sensor signal loss or variance could dramatically affect the localization performance. The vehicle localization problem in an environment with Global Navigation Satellite System (GNSS) signal errors is investigated in this study. The error state Kalman filtering (ESKF) and Rauch–Tung–Striebel (RTS) smoother are integrated using the data from Inertial Measurement Unit (IMU) and GNSS sensors. A segmented RTS smoothing algorithm is proposed in order to estimate the error state, which is typically close to zero and mostly linear, which allows more accurate linearization and improved state estimation accuracy. The proposed algorithm is evaluated using simulated GNSS signals with and without signal errors. The simulation results demonstrate its superior accuracy and stability for state estimation. The designed ESKF algorithm yielded an approximate 3% improvement in long straight line and turning scenarios compared to classical EKF algorithm. Additionally, the ESKF−RTS algorithm exhibited a 10% increase in the localization accuracy compared to the ESKF algorithm. In the double turning scenarios, the ESKF algorithm resulted in an improvement of about 50% in comparison to the EKF algorithm, while the ESKF−RTS algorithm improved by about 50% compared to the ESKF algorithm. These results indicated that the proposed ESKF−RTS algorithm is more robust and provides more accurate localization.

The use of regenerative braking systems is an important approach for improving the travel mileage of electric vehicles, and the use of an auxiliary hydraulic braking energy recovery system can improve the efficiency of the braking energy recovery process. In this paper, we present an algorithm for optimizing the energy recovery efficiency of a hydraulic regenerative braking system (HRBS) based on fuzzy Q-Learning (FQL). First, we built a test bench, which was used to verify the accuracy of the hydraulic regenerative braking simulation model. Second, we combined the HRBS with the electric vehicle in ADVISOR. Third, we modified the regenerative braking control strategy by introducing the FQL algorithm and comparing it with a fuzzy-control-based energy recovery strategy. The simulation results showed that the power savings of the vehicle optimized by the FQL algorithm were improved by about 9.62% and 8.91% after 1015 cycles and under urban dynamometer driving schedule (UDDS) cycle conditions compared with a vehicle based on fuzzy control and the dynamic programming (DP) algorithm. The regenerative braking control strategy optimized by the fuzzy reinforcement learning method is more efficient in terms of energy recovery than the fuzzy control strategy.

In order to reduce the energy consumption caused by the frequent braking of vehicles at signalized intersections, an optimized speed trajectory control method is proposed, based on braking energy recovery efficiency (BERE) in connection with an automated system for vehicle real-time interaction with roadside facilities and regional central control. Our objectives were as follows; firstly, to establish the simulation model of the hybrid energy regenerative braking system (HERBS) and to verify it by bench test. Secondly, to build up the genetic algorithm (GA) optimization model for the deceleration stopping of the HERBS. Then, to obtain signal light status and timing information to be the constraints; the BERE is to be the optimized objective, resulting in optimization for the speed trajectory under the deceleration stopping condition of a single signalized intersection. Finally, vehicle simulations in ADVISOR software are utilized to validate the optimization results. The results show that the BERE during deceleration stopping at a single signalized intersection after the speed trajectory optimization is 36.21% higher than that of inexperienced drivers, and 7.82% higher than that of experienced drivers.

In order to solve the problems of wheel locking and loss of vehicle control due to understeering or oversteering during the braking energy-recovery process of the hydraulic regenerative braking system (HRBS), aiming at the characteristics of chassis domain control that can realize coordinated work among various chassis systems, a cooperative control strategy of HRBS based on chassis domain control was proposed. Firstly, a HRBS test bench was built, and the accuracy of the simulation model was verified by comparing it with the test. Next, the proposed cooperative control strategy was designed, which coordinates the wheel anti-lock actuation system (WAAS) to adjust the wheel cylinder pressure to solve the wheel locking problem of HRBS in the process of braking energy recovery and coordinate the vehicle anti-loss control actuation system (VACAS) to generate a yaw compensation moment to solve the vehicle loss of the control problem of HRBS in the process of braking energy recovery by detecting the wheel slip ratio, yaw rate and sideslip angle. Finally, the established control strategy was verified through the co-simulation of Carsim and Matlab software, and the results showed that the control strategy proposed in this paper could not only avoid wheel locking and loss of vehicle control during turning braking on low-adhesion roads, but also improve the energy-recovery efficiency by 29.64% compared with a vehicle that only controls the slip ratio.

针对企业制造的某型号液压闭门器的寿命测试合格率低的问题,对该闭门器工作时柱塞所受阻尼力进行了理论计算、仿真分析和试验测试研究.首先,在柱塞处于不同位置时,结合液压流体力学理论分别建立了闭门器的阻尼力数学模型,利用MATLAB辅助计算得到了不同状态下柱塞齿条所受压力与速度的关系曲线,对闭门器阻尼力进行了理论分析;然后,利用Fluent建立了闭门器内部流道模型,通过流场仿真得到了流道压力分布;最后,通过闭门器台架试验测得了安装闭门器后的开关门力,以及不同位置段的关门时间,并结合仿真结果对该阻尼力数学模型进行了验证.研究结果表明:理论关门力和实际关门力误差为2.6 N～3.5 N,所建立的闭门器阻尼力数学模型准确、可靠,可作为今后闭门器结构优化的基础.

为了实现重型叉车前驱动桥桥壳国产化,对重型叉车前驱动桥桥壳进行了结构强度研究,提出了多体动力学和有限元结合的方法.首先,建立了重型叉车多体动力学模型,分析了叉车前驱动桥桥壳在叉车满载快速举升工况、紧急制动工况、过颠簸块工况等典型工况下的动态载荷,确定了前驱动桥桥壳在各种典型工况下的最大载荷;其次,建立了叉车前驱动桥桥壳的有限元模型,分析了前驱动桥桥壳在各种典型工况最大载荷下的应力与应变;最后,采用应变测试仪测量了前驱动桥桥壳应力与应变.研究结果表明:有限元计算数值与实验测试数值基本一致;重型叉车国产化前驱动桥桥壳结构设计,能够满足重型叉车作业时对高强度、高承载驱动桥壳的需求,可替代进口产品.

In this paper, an optimization algorithm of energy recovery efficiency is proposed for parallel hydraulic hybrid systems (PHHS) using dynamic programming (DP). Global optimal solution of pump displacement and transmission ratio under the known urban drive cycles is obtained by using the DP approach, where the total amount of energy recovery is defined as the cost function, and the pump displacement and the transmission ratio of the torque coupler are defined as the deciding variables. Two major steps are involved in verifying the proposed approach. Firstly, a PHHS Simulink model is accurately obtained by repeated comparison with the bench test. Subsequently, we derive a parallel hydraulic hybrid vehicle (PHHV) from adding a hydraulic hybrid system to an electric vehicle in ADVISOR (advanced vehicle simulator). This vehicle is used to validate the effectiveness of the proposed method in energy recovery efficiency.

介绍叉车护顶架的安全性能测试标准及要求.按照标准关于冲击下落试验的要求,基于LS-DYNA建立试验载荷冲击护顶架的显式动力学计算模型,同时开展该型护顶架的现场冲击试验.研究表明:模拟计算结果同试验结果符合较好,护顶架永久变形误差为2.5％.建立的有限元仿真模型具有较高的可信度,可为进一步改进和完善护顶架的结构提供依据和参考.

针对叉车正常作业时驾驶员遭受意外跌落物体威胁的问题,对某新型电叉配套的护顶架安全性能进行了研究.按照国标关于护顶架动载试验的要求,提出了一种利用LS-DYNA完全重启动方式,实现试验载荷多次冲击护顶架的方法,基于此建立了动载试验仿真的显式动力学计算模型,同时开展了该型护顶架的动载试验;对护顶架动载试验18个测点处模拟和试验结果获取的永久变形进行了对比,同时还研究了模拟和试验两种方法得到的护顶架构件最大永久变形,并与标准进行了校核.研究结果表明:该新型护顶架安全性能达不到测试标准要求,需更改;但建立的有限元仿真模型具有较高的可信度,为进一步改进和完善护顶架的结构提供了依据和参考.

针对大型液压挖掘机铲斗与松散岩石之间非线性挖掘阻力计算与无法直接测量的问题,以及非线性挖掘阻力大小与松散岩石形状、尺寸之间的关系问题,对实际松散岩石的尺寸分布和形状分布进行了研究.利用筛分法对松散岩石颗粒的尺寸分布进行了测量,利用随机法对松散岩石的形状分布进行了测量,建立了与实际吻合的松散岩石的离散元模型;对挖掘机工作装置机械机构与松散岩石耦合作用力相互传递进行了研究,提出了挖掘机工作装置多体动力学模型与松散岩石离散元模型的耦合方法,对工作装置的斗杆油缸压力和铲斗油缸压力与实际测量进行了对比分析.研究结果表明:挖掘机工作装置多体动力学模型与松散岩石离散元耦合模型能够计算挖掘机铲斗的挖掘阻力,松散岩石大块岩石和片状岩石的比例增加,铲斗挖掘阻力增大.

针对传统液压再生制动汽车在高强度制动工况下再生制动特性差的问题,对系统的再生制动过程进行了研究,提出了一种用两个初始压力不同的小容积蓄能器作为液压再生制动系统储能单元的方法.搭建了液压再生制动系统试验台架,通过台架实验分析了蓄能器各主要参数对再生制动过程的影响,在ADVISOR平台中搭建了双蓄能器并联式液压再生制动车辆模型,对系统的制动特性进行了仿真研究.研究结果表明:液压再生制动系统提供的制动力矩与蓄能器压力成线性关系,且蓄能器体积越小,压力上升越快;采用双蓄能器进行液压再生制动可有效增大系统再生制动力矩的取值范围,提高系统能量回收效率.