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| 1 | Direct Yaw Moment Control for Distributed Drive Electric Vehicle Handling Performance Improvement显示文摘For a distributed drive electric vehicle(DDEV) driven by four in-wheel motors, advanced vehicle dynamic control methods can be realized easily because motors can be controlled independently, quickly and precisely. And direct yaw-moment control(DYC) has been widely studied and applied to vehicle stability control. Good vehicle handling performance: quick yaw rate transient response, small overshoot, high steady yaw rate gain, etc, is required by drivers under normal conditions, which is less concerned, however. Based on the hierarchical control methodology, a novel control system using direct yaw moment control for improving handling performance of a distributed drive electric vehicle especially under normal driving conditions has been proposed. The upper-loop control system consists of two parts: a state feedback controller, which aims to realize the ideal transient response of yaw rate, with a vehicle sideslip angle observer; and a steering wheel angle feedforward controller designed to achieve a desired yaw rate steady gain. Under the restriction of the effect of poles and zeros in the closed-loop transfer function on the system response and the capacity of in-wheel motors, the integrated time and absolute error(ITAE) function is utilized as the cost function in the optimal control to calculate the ideal eigen frequency and damper coefficient of the system and obtain optimal feedback matrix and feedforward matrix. Simulations and experiments with a DDEV under multiple maneuvers are carried out and show the effectiveness of the proposed method: yaw rate rising time is reduced, steady yaw rate gain is increased, vehicle steering characteristic is close to neutral steer and drivers burdens are also reduced. The control system improves vehicle handling performance under normal conditions in both transient and steady response. State feedback control instead of model following control is introduced in the control system so that the sense of control intervention to drivers is relieved. | YU Zhuoping LENG Bo XIONG Lu FENG Yuan SHI Fenmiao | 2016 | Chinese Journal of Mechanical Engineering2016，29，3： | 28 |
| 2 | Recursive Least Square Vehicle Mass Estimation Based on Acceleration Partition显示文摘Vehicle mass is an important parameter in vehicle dynamics control systems.Although many algorithms have been developed for the estimation of mass,none of them have yet taken into account the different types of resistance that occur under different conditions.This paper proposes a vehicle mass estimator.The estimator incorporates road gradient information in the longitudinal accelerometer signal,and it removes the road grade from the longitudinal dynamics of the vehicle.Then,two different recursive least square method(RLSM)schemes are proposed to estimate the driving resistance and the mass independently based on the acceleration partition under different conditions.A 6 DOF dynamic model of four In-wheel Motor Vehicle is built to assist in the design of the algorithm and in the setting of the parameters.The acceleration limits are determined to not only reduce the estimated error but also ensure enough data for the resistance estimation and mass estimation in some critical situations.The modification of the algorithm is also discussed to improve the result of the mass estimation.Experiment data on a sphalt road,plastic runway,and gravel road and on sloping roads are used to validate the estimation algorithm.The adaptability of the algorithm is improved by using data collected under several critical operating conditions.The experimental results show the error of the estimation process to be within 2.6%,which indicates that the algorithm can estimate mass with great accuracy regardless of the road surface and gradient changes and that it may be valuable in engineering applications.This paper proposes a recursive least square vehicle mass estimation method based on acceleration partition. | FENG Yuan XIONG Lu YU Zhuoping QU Tong | 2014 | Chinese Journal of Mechanical Engineering2014，27，3： | 5 |
| 3 | Method of Electric Powertrain Matching for Battery-powered Electric Cars显示文摘The current match method of electric powertrain still makes use of longitudinal dynamics, which can't realize maximum capacity for on-board energy storage unit and can't reach lowest equivalent fuel consumption as well. Another match method focuses on improving available space considering reasonable layout of vehicle to enlarge rated energy capacity for on-board energy storage unit, which can keep the longitudinal dynamics performance almost unchanged but can't reach lowest fuel consumption. Considering the characteristics of driving motor, method of electric powertrain matching utilizing conventional longitudinal dynamics for driving system and cut-and-try method for energy storage system is proposed for passenger cars converted from traditional ones. Through combining the utilization of vehicle space which contributes to the on-board energy amount, vehicle longitudinal performance requirements, vehicle equivalent fuel consumption level, passive safety requirements and maximum driving range requirement together, a comprehensive optimal match method of electric powertrain for battery-powered electric vehicle is raised. In simulation, the vehicle model and match method is built in Matlab/simulink, and the Environmental Protection Agency (EPA) Urban Dynamometer Driving Schedule (UDDS) is chosen as a test condition. The simulation results show that 2.62% of regenerative energy and 2% of energy storage efficiency are increased relative to the traditional method. The research conclusions provide theoretical and practical solutions for electric powertrain matching for modern battery-powered electric vehicles especially for those converted from traditional ones, and further enhance dynamics of electric vehicles. | NING Guobao XIONG Lu ZHANG Lijun YU Zhuoping | 2013 | Chinese Journal of Mechanical Engineering2013，26，3： | 3 |
| 4 | ITDP-Robot: Design of An Intelligent Transport Dispatch Parking Robot显示文摘In this paper, a novel electric autonomous parking robot prototype was proposed, which aims to address the parking hassle caused by the imbalance between the vehicle ownership and the amount of the parking spaces. The mechanical structure was elaborately designed to allow the parking robot to adapt to vehicles with different wheelbases and tracks. The electrical structure was constructed with the aim of X-by-wire and distributed component-based control concept. To be capable of autonomous driving, the parking robot software system based on ROS was designed with the capability of environment perception, self-localization and path planning. Furthermore, a simulation environment based on Gazebo was built in order to simplify the development of the parking robot’s autonomous driving algorithms and validate those algorithms’ robustness. Though this parking robot is under the prototype stage, the dispatch strategy and the convenience for parking were also considered. Compared with the state-of-art parking robot, this parking robot is not only capable of working indoor parking lots but also the complex outdoor environments. | Sanqing Qu Zhongcong Xu Fan Lu Guang Chen Zhuoping Yu | 2019 | 智能城市应用2019，2，5： | 2 |
| 5 | Vehicle dynamics control of four in-wheel motor drive electric vehicle using gain scheduling based on tyre cornering stiffness estimation显示文摘 | XIONG Lu YU Zhuoping WANG Yang | | Vehicle System Dynamics0，50，6： | 1 |
| 6 | Control Allocation of Ve- hicle Dynamics Control for a 4 In-wheel-motored EV显示文摘 | Liu Xiong Yu Zhuoping | 2009 | IEEE Control Systems2009，13，2： | 1 |
| 7 | Concept Eval- uation of A Novel Gear Selector for Automated Manual Transmis- sions 显示文摘 | Zhong Zaimin Kong Guoling Yu Zhuoping | 2012 | Mechanical Systems and Signal Processing2012，31，： | 1 |
| 8 | Vehicle dynamics control of four in-wheel motor drive electric vehicle using gain scheduling based on tyre cornering stiffness estimation显示文摘 | XIONG Lu YU Zhuoping WANG Yang | | 0，，： | 1 |
| 9 | Mass estimation method for intelligent vehicles based on fusion of machine learning and vehicle dynamic model显示文摘Vehicle mass is an important parameter for motion control of intelligent vehicles,but is hard to directly measure using normal sensors.Therefore,accurate estimation of vehicle mass becomes crucial.In this paper,a vehicle mass estimation method based on fusion of machine learning and vehicle dynamic model is introduced.In machine learning method,a feedforward neural network(FFNN)is used to learn the relationship between vehicle mass and other state parameters,namely longitudinal speed and acceleration,driving or braking torque,and wheel angular speed.In dynamics-based method,recursive least square(RLS)with forgetting factor based on vehicle dynamic model is used to estimate the vehicle mass.According to the reliability of each method under different conditions,these two methods are fused using fuzzy logic.Simulation tests under New European Driving Cycle(NEDC)condition are carried out.The simulation results show that the estimation accuracy of the fusion method is around 97%,and that the fusion method performs better stability and robustness compared with each single method. | Zhuoping Yu Xinchen Hou Bo Leng Yuyao Huang | 2022 | Autonomous Intelligent Systems2022，2，1： | 0 |
| 10 | A unified optimal planner for autonomous parking vehicle显示文摘In order to reduce the controlling difficulty caused by trajectory meandering and improve the adaptability to parking into regular lots,a versatile optimal planner(OP)is proposed.Taking advantage of the low speed specificity of parking vehicle,the OP algorithm was modeled the planning problem as a convex optimization problem.Collision-free constraints were formalized into the shortest distance between convex sets by describing obstacles and autonomous vehicle as affine set.Since employing Lagrange dual function and combining KKT conditions,the collision-free constraints translated into convex functions.Taking the national standard into account,5 kinds of regular parking scenario,which contain 0°,30°,45°,60°and 90°parking lots,were designed to verify the OP algorithm.The results illustrate that it is benefit from the continuous and smooth trajectory generated by the OP method to track,keep vehicle's stability and improve ride comfort,compared with A*and hybrid A*algorithms.Moreover,the OP method has strong generality since it can ensure the success rate no less than 82%when parking planning is carried out at the start node of 369 different locations.Both of evaluation criteria,as the pear error and RMSE in x direction,y axis and Euclidean distance d,and heading deviation 6,are stable and feasible in real tests,which illustrates that the OP planner can satisfy the requirements of regular parking scenarios. | Dequan ZENG Zhuoping YU Lu XIONG Peizhi ZHANG Zhiqiang FU | 2019 | Control Theory and Technology2019，17，4： | 0 |
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