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学者姓名：陈志勇
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Abstract ：
Both the stochastic traffic information and state of charge (SOC) greatly impact the plug-in parallel hybrid electric vehicle performance. Uncertain cycles and driving styles affect the effectiveness of velocity prediction and further cause the instability of SOC estimate. These uncertain stochastic factors interfere with the solution of torque demand in different degrees in each control cycle. To address this issue, a stochastic model predictive control (SMPC) considering short-term forecast optimal SOC is proposed. Firstly, multiple linear regression of engine and battery is developed for energy management strategy (EMS), respectively. Then, the velocity prediction model is developed based Markov chain considering the driver styles, and reference SOC is optimized by dynamic programming with the forthcoming information. Finally, the SMPC-based EMS with the short-term optimal SOC is constituted. The verification results show Markov based on driver styles has better predictive performance than radial basis function neural networks and back propagation neural networks. The fuel economy of the proposed strategy improves by about 11.8% compared with normal model predictive control and is close to that of the globally optimal dynamic programming. The test results indicate that the SMPC with the short-term optimal SOC can promote EMS to improve the fuel economy. IEEE
Keyword ：
Adaptation models Adaptation models Batteries Batteries Energy management Energy management Energy management strategy Energy management strategy Engines Engines Multiple linear regression Multiple linear regression Plug-in hybrid electric vehicle Plug-in hybrid electric vehicle Predictive models Predictive models State of charge State of charge Stochastic model predictive control Stochastic model predictive control Torque Torque Velocity prediction Velocity prediction
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| GB/T 7714 | Lin, X. , Chen, X. , Chen, Z. et al. Stochastic Model Predictive Control Strategy with Short-term Forecast Optimal SOC for a Plug-in Hybrid Electric Vehicle [J]. | IEEE Transactions on Transportation Electrification , 2024 : 1-1 . |
| MLA | Lin, X. et al. "Stochastic Model Predictive Control Strategy with Short-term Forecast Optimal SOC for a Plug-in Hybrid Electric Vehicle" . | IEEE Transactions on Transportation Electrification (2024) : 1-1 . |
| APA | Lin, X. , Chen, X. , Chen, Z. , Xie, L. . Stochastic Model Predictive Control Strategy with Short-term Forecast Optimal SOC for a Plug-in Hybrid Electric Vehicle . | IEEE Transactions on Transportation Electrification , 2024 , 1-1 . |
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The road gradient and trip range are of great significance in fuel consumption and emissions of a range-extended electric vehicle (REEV). However, the traditional energy management strategy failed to consider the road gradient. To address this issue, a multi-objective optimization adaptive control strategy is proposed to improve the fuel consumption and emissions of the REEVs. Firstly, a multi-objective optimization adaptive control strategy is developed based the equivalent consumption minimization strategy integrated with adaptive equivalent factor (EF). The EF is updating according to the road slope by using a proportional-integral controller. To investigate the impacts of the road gradient on emissions, the numerical models between road gradient and emissions are established. Furthermore, an optimal torque distribution strategy is proposed according to the weights of fuel and emissions, which realizes the tracking of the SOC trajectory and improves the fuel economy and emission performance of the vehicle. Finally, various strategies are carried out to verify the superiority of the proposed strategy by numerical validations. Compared with the control strategy considered fuel consumption only, the validation results show that the engine CO, HC, and NOx are reduced by 9.47, 2.33, and 4.10%, respectively, while compromising fuel economy by 3.3%.
Keyword ：
Emissions Emissions Energy management Energy management Equivalent consumption minimization strategy Equivalent consumption minimization strategy Fuel economy Fuel economy Multi-objective optimization Multi-objective optimization Range-extended electric vehicle Range-extended electric vehicle
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| GB/T 7714 | Lin, Xinyou , Chen, Zhiyong , Zhang, Jiajin et al. A Multi-objective Optimization Control Strategy of a Range-Extended Electric Vehicle for the Trip Range and Road Gradient Adaption [J]. | INTERNATIONAL JOURNAL OF AUTOMOTIVE TECHNOLOGY , 2024 . |
| MLA | Lin, Xinyou et al. "A Multi-objective Optimization Control Strategy of a Range-Extended Electric Vehicle for the Trip Range and Road Gradient Adaption" . | INTERNATIONAL JOURNAL OF AUTOMOTIVE TECHNOLOGY (2024) . |
| APA | Lin, Xinyou , Chen, Zhiyong , Zhang, Jiajin , Wu, Chaoyu . A Multi-objective Optimization Control Strategy of a Range-Extended Electric Vehicle for the Trip Range and Road Gradient Adaption . | INTERNATIONAL JOURNAL OF AUTOMOTIVE TECHNOLOGY , 2024 . |
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Abstract ：
针对具有不确定性的自动驾驶电动车辆的运动控制问题，提出了一种基于参数预测的径向基函数(RBF)神经网络自适应协调控制方案。首先，考虑系统参数的不确定性及外部干扰的影响，利用预瞄方法建立可表征车辆循迹跟车行为的动力学模型；其次，采用RBF神经网络补偿器对系统不确定性进行自适应补偿，设计车辆横纵向运动的广义协调控制律；之后，考虑前车车速及道路曲率影响，以车辆在循迹跟车控制过程中的能耗及平均冲击度最小为优化目标，利用粒子群优化(PSO)算法对协调控制律中的增益参数K进行滚动优化，并最终得到一系列优化后的样本数据；在此基础上，设计、训练一个反向传播(BP)神经网络，实现对广义协调控制律中增益参数K的实时预测，以保证车辆的经济性及乘坐舒适性。仿真结果证实了所提控制方案的有效性。
Keyword ：
不确定性 不确定性 参数预测 参数预测 径向基函数神经网络 径向基函数神经网络 粒子群优化算法 粒子群优化算法 自动驾驶电动车辆 自动驾驶电动车辆
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| GB/T 7714 | 陈志勇 , 李攀 , 叶明旭 et al. 自动驾驶电动车辆基于参数预测的径向基函数神经网络自适应控制 [J]. | 中国机械工程 , 2024 , 35 (06) : 982-992 . |
| MLA | 陈志勇 et al. "自动驾驶电动车辆基于参数预测的径向基函数神经网络自适应控制" . | 中国机械工程 35 . 06 (2024) : 982-992 . |
| APA | 陈志勇 , 李攀 , 叶明旭 , 林歆悠 . 自动驾驶电动车辆基于参数预测的径向基函数神经网络自适应控制 . | 中国机械工程 , 2024 , 35 (06) , 982-992 . |
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Abstract ：
The eco-driving strategy is of great significance in driving cost for plug-in hybrid electric vehicles in driving trips, especially at signalized intersections. To address the issue of further energy saving, this study proposes an ecological approach and departure-driving strategy by using syncretic learning with trapezoidal collocation algorithm. First, a syncretic learning-based speed predictor is built by merging back propagation neural networks and radial basis function neural networks. Second, the syncretic learning-based speed predictor and trapezoidal collocation algorithm are combined to optimize the speed trajectory. Third, the torque between the engine and the motor is distributed by the dynamic programming algorithm. Then, model predictive control optimizes torque output in the control time domain. Finally, the driving interval optimization method is designed to avoid mixed-integer programming problems and redundant constraints, which make vehicles cross intersections without stopping. The numerical verification results show that the trapezoidal collocation algorithm with syncretic learning has more advantages than other methods in speed trajectory planning. Compared with the original trajectory, the driving time through the intersection is reduced and the total driving cost is lowered by 19.82%. Validation results confirm the effectiveness of the proposed strategy in energy consumption management at signalized intersections. This study proposes an ecological approach and departure-driving strategy by using syncretic learning with trapezoidal collocation algorithm. The speed predictor is combined with the trapezoidal collocation algorithm to obtain the speed trajectory with higher accuracy. The driving interval optimization method is proposed to make vehicles cross the intersections without stopping.image (c) 2024 WILEY-VCH GmbH
Keyword ：
plug-in hybrid electric vehicles plug-in hybrid electric vehicles speed prediction speed prediction speed trajectory planning speed trajectory planning torque distribution torque distribution trapezoidal collocation algorithm trapezoidal collocation algorithm
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| GB/T 7714 | Lin, Xinyou , Chen, Xiankang , Chen, Zhiyong et al. Ecological Approach and Departure-Driving Strategy Optimized by Using Syncretic Learning with Trapezoidal Collocation Algorithm for the Plug-In Hybrid Electric Vehicles [J]. | ENERGY TECHNOLOGY , 2024 , 12 (4) . |
| MLA | Lin, Xinyou et al. "Ecological Approach and Departure-Driving Strategy Optimized by Using Syncretic Learning with Trapezoidal Collocation Algorithm for the Plug-In Hybrid Electric Vehicles" . | ENERGY TECHNOLOGY 12 . 4 (2024) . |
| APA | Lin, Xinyou , Chen, Xiankang , Chen, Zhiyong , Wu, Jiayun . Ecological Approach and Departure-Driving Strategy Optimized by Using Syncretic Learning with Trapezoidal Collocation Algorithm for the Plug-In Hybrid Electric Vehicles . | ENERGY TECHNOLOGY , 2024 , 12 (4) . |
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Due to the complete randomness of sampling, the traditional PRM algorithm was often difficult to be applied to the robot path planning in the working environment, including narrow channels. To this end, an improved probabilistic roadmap method (Improved PRM) integrating global goal-oriented sampling and local node enhancement was proposed and utilized to the path planning of a planar grid map scene and a 6-DOF robot. Firstly, the global goal-directed sampling was combined with the random sampling in the proposed Improved PRM, and the probability of global sampling points falling into narrow channels was raised by the mixed sampling, so as to achieve the heuristic map enhancement. Secondly, nodes in narrow channels were extracted by using the node weight idea, and a local node enhancement strategy based on Gaussian distribution was used to expand new nodes in narrow channels to enhance the connectivity of the map and the success rate of path planning. Finally, the redundant node elimination strategy was presented to optimize the initial path planned by the algorithm. The simulation results of the Improved PRM algorithm in the planar grid map showed that the success rate of the algorithm for robot path planning was more than 89.3%. Besides, the comprehensive evaluation and path quality evaluation were both higher than that of other algorithms. In the simulation experiment of a 6-DOF robot, the average path cost obtained by the Improved PRM algorithm was about 42.7% lower than that of the traditional PRM algorithm. Meanwhile, the probability of successfully passing through the narrow channel was also 68 percentage points higher than that of the traditional PRM algorithm. Therefore, compared with other algorithms, the Improved PRM algorithm had advantages in improving the success rate of path planning, reducing path nodes, and ensuring path quality in the working environment with narrow channels. © 2023 Chinese Society of Agricultural Machinery. All rights reserved.
Keyword ：
global goal-oriented sampling global goal-oriented sampling Improved PRM Improved PRM narrow channels narrow channels path planning path planning robot robot
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| GB/T 7714 | Chen, Z. , Wu, J. . Improved Probability Path Graph Method for Robots Based on Goal-oriented Sampling; [基于目标导向采样的机器人改进概率路图法研究] [J]. | Transactions of the Chinese Society for Agricultural Machinery , 2023 , 54 (6) : 410-418,426 . |
| MLA | Chen, Z. et al. "Improved Probability Path Graph Method for Robots Based on Goal-oriented Sampling; [基于目标导向采样的机器人改进概率路图法研究]" . | Transactions of the Chinese Society for Agricultural Machinery 54 . 6 (2023) : 410-418,426 . |
| APA | Chen, Z. , Wu, J. . Improved Probability Path Graph Method for Robots Based on Goal-oriented Sampling; [基于目标导向采样的机器人改进概率路图法研究] . | Transactions of the Chinese Society for Agricultural Machinery , 2023 , 54 (6) , 410-418,426 . |
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鉴于采样的完全随机性，传统PRM算法往往较难适用于具有狭窄通道工作环境下的机器人路径规划。为此，本文提出了一种融合全局目标导向采样、局部节点增强的改进概率路图法(Improved PRM),并将其应用于平面栅格地图场景及六自由度机器人的路径规划。首先将全局目标导向采样与随机采样有机结合，通过混合采样的方式来提高全局采样点落在狭窄通道内的概率，实现启发式地图增强；其次，经由节点权重思想对位于狭窄通道中的节点进行提取，并利用基于高斯分布的局部节点增强策略在狭窄通道中扩展新节点，增强地图连通性，以提高路径规划的成功率；最后，采用冗余节点剔除策略对算法规划的初始路径进行优化。Improved PRM算法在平面栅格地图中的仿真结果表明，该算法对于机器人路径规划的成功率可达89.3%以上，且综合评价指数及路径质量评价指数均高于其他算法；在六自由度机器人的仿真实验中，Improved PRM算法得到的平均路径代价比传统PRM算法降低约42.7%,成功通过狭窄通道概率也比传统PRM提高68个百分点。因此，相比文中所提其他算法，在具有狭窄通道的工作环境中，改进概率路图法在提高路径规划成功率、减少路径节点、保证路径质量等方面具有优势。
Keyword ：
全局目标导向采样 全局目标导向采样 改进概率路图法 改进概率路图法 机器人 机器人 狭窄通道 狭窄通道 路径规划 路径规划
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| GB/T 7714 | 陈志勇 , 吴精华 . 基于目标导向采样的机器人改进概率路图法研究 [J]. | 农业机械学报 , 2023 , 54 (06) : 410-418,426 . |
| MLA | 陈志勇 et al. "基于目标导向采样的机器人改进概率路图法研究" . | 农业机械学报 54 . 06 (2023) : 410-418,426 . |
| APA | 陈志勇 , 吴精华 . 基于目标导向采样的机器人改进概率路图法研究 . | 农业机械学报 , 2023 , 54 (06) , 410-418,426 . |
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为解决复杂环境下六自由度机械臂的路径规划问题，提出一种基于采样规则目标导向设计、父节点重选的Biased-RRT修正算法.该算法在原目标偏置策略的基础上对随机采样点的选取规则进行重新设定，引导算法搜索树在尽可能向目标区域扩展的同时有效避开复杂障碍物.在节点扩展方面，依据新节点距离目标点的远近采用变步长扩展方式，即在距离远时选用大步长，加快搜索树扩展；进入目标区域后选用小步长，防止节点扩展陷入局部死循环.在路径优化方面，本算法通过引入基于路径代价最小的重选父节点操作及多余路径节点剔除操作，使规划出的路径相对优化.最后，利用3次样条插值技术为机械臂各关节规划出一条光滑、连续且无障的运动曲线.仿真结...
Keyword ：
Biased-RRT修正算法 Biased-RRT修正算法 机械臂 机械臂 样条插值 样条插值 父节点重选 父节点重选 路径规划 路径规划
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| GB/T 7714 | 陈志勇 , 黄泽麟 , 曾德财 et al. 复杂环境下六自由度机械臂路径规划的Biased-RRT修正算法 [J]. | 福州大学学报(自然科学版) , 2022 , 50 (05) : 658-666 . |
| MLA | 陈志勇 et al. "复杂环境下六自由度机械臂路径规划的Biased-RRT修正算法" . | 福州大学学报(自然科学版) 50 . 05 (2022) : 658-666 . |
| APA | 陈志勇 , 黄泽麟 , 曾德财 , 于潇雁 . 复杂环境下六自由度机械臂路径规划的Biased-RRT修正算法 . | 福州大学学报(自然科学版) , 2022 , 50 (05) , 658-666 . |
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Abstract ：
本发明涉及一种辅助穿鞋装置，包括底座、鞋夹、穿鞋辅助件、支撑板，所述支撑板安装在底座上并与之滑动配合，支撑板能在底座上左右滑动，穿鞋辅助件安装在底座后侧，鞋夹左右对称设置两个并安装在底座前侧，底座上安装有分别驱动鞋夹、穿鞋辅助件进行前后移动的移动机构A、移动机构B，移动机构A、移动机构B由驱动件A带动相向或相背运动，底座上安装有驱动穿鞋辅助件升降的升降机构，本装置结构简单，设计合理，操作方便，为不方便弯腰的老年人辅助穿鞋，可以根据老人自身的鞋码进行调试，且具备自动换鞋功能。
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| GB/T 7714 | 陈志勇 , 方国宝 , 邓祖滨 et al. 辅助穿鞋装置 : CN202021076736.4[P]. | 2020-06-12 . |
| MLA | 陈志勇 et al. "辅助穿鞋装置" : CN202021076736.4. | 2020-06-12 . |
| APA | 陈志勇 , 方国宝 , 邓祖滨 , 程志鹏 , 陈海城 . 辅助穿鞋装置 : CN202021076736.4. | 2020-06-12 . |
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本实用新型涉及一种优先保障行人通行的窄桥系统，包括桥梁一侧的被连接道路和桥梁另一侧的待连接道路，所述被连接道路的路面高于待连接道路，位于被连接道路和待连接道路之间设置有路面高度与待连接道路保持一致的移动桥板，被连接道路的两侧及中部对应设置有与移动桥板相连接的人行坡面和车行坡面，位于被连接道路和待连接道路之间的两侧分别设置有用于阻挡人行坡面的楔形限宽机构，所述移动桥板的一端设置有用于待连接道路一端嵌入的凹部，所述移动桥板的下侧设置有驱动其向被连接道路一侧移动露出凹部的驱动机构，所述楔形限宽机构由移动桥板向被连接道路一侧移动过程中驱动开启以露出人行坡面。该系统可在优先保障行人通行模式下，禁止任何车辆驶入；在允许小型客车通行模式下，有效限制大型超标车辆强行过桥。
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| GB/T 7714 | 陈志勇 , 林浩勋 , 陈晓凡 et al. 一种优先保障行人通行的窄桥系统 : CN202020104962.2[P]. | 2020/1/17 . |
| MLA | 陈志勇 et al. "一种优先保障行人通行的窄桥系统" : CN202020104962.2. | 2020/1/17 . |
| APA | 陈志勇 , 林浩勋 , 陈晓凡 , 李舒婷 . 一种优先保障行人通行的窄桥系统 : CN202020104962.2. | 2020/1/17 . |
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The coaxial Tri-rotor micro air vehicle (MAV) is composed of three coaxial rotors where the aerodynamic characteristics of is complicated in flight especially when the wind effect is introduced. In this paper, the hovering performance of a full-scale coaxial Tri-rotor MAV is analyzed with both the simulations and wind tunnel experiments. Firstly, the wind effect on the aerodynamic performance of coaxial Tri-rotor MAV is established with different rotor speed (1500-2300 rpm) and horizontal wind (0-4 m/s). Secondly, the thrust and power consumption of coaxial Tri-rotor (L/D = 1.6) were obtained with low-speed wind tunnel experiments. Furthermore, the streamline distribution, pressure distribution, velocity contour and vortex distribution with different horizontal wind conditions are obtained by numerical simulations. Finally, combining the experiment results and simulation results, it is noted that the horizontal wind may accelerate the aerodynamic coupling, which resulting in the greater thrust variation up to 9% of the coaxial Tri-rotor MAV at a lower rotor speed. Moreover, the aerodynamic performance is decreased with more power consumption at higher rotor speed where the wind and the downwash flow are interacted with each other. Compared with no wind flow, the shape of the downwash flow and the deformation of the vortex affect the power loading and figure of metric accordingly.
Keyword ：
coaxial Tri-rotor MAV coaxial Tri-rotor MAV horizontal wind horizontal wind low-speed wind tunnel low-speed wind tunnel numerical simulation numerical simulation
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| GB/T 7714 | Lei, Yao , Ye, Yiqiang , Chen, Zhiyong . Horizontal Wind Effect on the Aerodynamic Performance of Coaxial Tri-Rotor MAV [J]. | APPLIED SCIENCES-BASEL , 2020 , 10 (23) . |
| MLA | Lei, Yao et al. "Horizontal Wind Effect on the Aerodynamic Performance of Coaxial Tri-Rotor MAV" . | APPLIED SCIENCES-BASEL 10 . 23 (2020) . |
| APA | Lei, Yao , Ye, Yiqiang , Chen, Zhiyong . Horizontal Wind Effect on the Aerodynamic Performance of Coaxial Tri-Rotor MAV . | APPLIED SCIENCES-BASEL , 2020 , 10 (23) . |
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