Real-time model predictive control of connected electric vehicles

ABSTRACT

Electric Vehicles (EVs) motors develop high torque at low speeds, resulting in a high rate of acceleration with the added advantage of being fitted with smaller gearboxes. However, a rapid rise of torque in EVs fitted with central drive powertrains can create undesired torsional oscillations, which are influenced by wheel slip and flexibility in the halfshaft. These torsional oscillations in the halfshaft lead to longitudinal oscillations in the vehicle, thus creating problems with regard to comfort and drivability. The significance of using wheel slip in addition to halfshaft torsion for design of anti-jerk controllers for EVs has already been highlighted in our previous research. In this research, we have designed a look-ahead model predictive controller (LA-MPC) that calculates the required motor torque demand to meet the dual objectives of increased traction and anti-jerk control. The designed LA-MPC will improve drivability and energy consumption in connected EVs. The real-time capability of the LA-MPC has been demonstrated through hardware-in-the-loop experiments. The performance of the LA-MPC has been compared to other controllers presented in the literature. A validated high-fidelity longitudinal-dynamics model of the Rav4EV, which is the test vehicle of our research has been used to evaluate the controller.     

电动汽车整车运行性能检测试验技术

电动汽车的生产和制造需要符合相关的标准,并且还要对整车的性能进行测试,只有这样才能更好的符合汽车上路的条件。本文主要是将国标要求作为基础来对电动汽车的最高车速、滑行能力、加速性能、制动性能和爬坡能力和能量消耗率和续航里程进行测试,通过实施一定的检测试验方法和配套的相关试验设备来做好检测,并且要对测试的结果进行分析,对电动汽车的运行性能进行进一步的优化,从而推进我国电动汽车行业的更好发展和进步。     

Flow-field guided steering control for rigid autonomous ground vehicles in low-speed manoeuvring

ABSTRACT

This paper studies the low-speed manoeuvring problem for autono-mous ground vehicles operating in complex static environments. Making use of the intrinsic property of a fluid to naturally find its way to an outflow destination, a novel guidance method is proposed. In this approach, a reference flow field is calculated numerically through Computational Fluid Dynamics, based on which both the reference path topology and the steering reference to achieve the path are derived in a single process. Steering control considers three constraints: obstacle and boundary avoidance, rigidity of the vehicle, plus the non-holonomic velocity constraints due to the steering system. The influences of the parameters used during the flow field simulation and the control algorithm are discussed through numerical cases. A divergency field is defined to evaluate the quality of the flow field in guiding the vehicle. This is used to identify any problematic branching features of the flow, and control is adapted in the neighbourhood of such branching features to resolve possible ambiguities in the control reference. Results demonstrate the effectiveness of the method in finding smooth and feasible motion paths, even in complex environments.     

人机混驾环境下基于LSTM的无人驾驶车辆换道行为模型

道路系统中的人机混驾交通环境是指人工驾驶车辆与自动驾驶车辆混合运行的交通环境，其中换道行为建模是人机混驾环境下无人驾驶车辆行为研究的热点。基于深度学习理论，构建人机混驾环境下基于长短期记忆神经网络的无人驾驶车辆换道行为模型（Long-short-term-memory-based Autonomous Vehicles Lane Changing，LSTM-LC）。通过研究人工驾驶车辆在换道过程中与周边车辆的相互作用，对换道行为影响因素进行分析；同时，为了提升模型的迁移性，引入道路横向偏移量信息。结合LSTM神经网络的输入要求，使用美国公开交通数据集Next Generation SIMulation（NGSIM）构建换道行为样本库。针对LSTM-LC模型，以均方差MSE作为损失函数，使用RMSprop优化方法进行训练，对LSTM网络结构、历史序列长度N及训练样本量3个重要参数进行标定。最后，针对道路横向偏移量M对LSTM-LC模型性能的影响进行对比试验。研究结果表明：相比GRU-LC模型，LSTM-LC模型对换道行为的表征更准确，在模型的精度和迁移性上有着显著的提升；GRU-LC模型的均方差为4.64 m²，迁移性均方差为119.82 m²，而LSTM-LC模型的均方差为3.18 m²，迁移性均方差为79.58 m²，分别优化了31.5%和39.71%；通过引入道路横向偏移量M，可将LSTM-LC模型精度和迁移性提升约10%，且模型稳定性更强。     

城市轨道交通单向导通装置智能消弧研究

介绍轨道交通单向导通装置常用的2种消弧方式:放电间隙消弧和大功率GTO消弧。放电间隙消弧的方式存在受环境影响大,无法精确控制导通的缺点;大功率GTO消弧的方式可以精确控制导通,但是无法可控关断,导致单向导通装置长期反向导通,不利于杂散电流防护。因此,提出一种可控关断GTO智能消弧型单向导通装置,对其触发导通、关断过程及等效电路进行分析,可实现单向导通装置的可控可靠关断,避免单向导通装置长期反向导通,完善设备功能。     

路网环境下考虑大型车混入率的事故疏散诱导模型

大型车的混入对高速公路交通流产生了较大的影响，尤其是在交通事故情景下。为了引导事故条件下驾驶人和组织者做出高效准确的决断，将考虑了大型车混入率的动态空间占有率模型引入到交通波模型，构建干涉与非干涉情景下的交通事故影响模型。以郑尧高速为例，对模型的准确性和可行性进行了验证，分别对干涉情景下的疏散时间、疏散量以及事故发生的位置，车辆数等指标与事故影响程度的指标（包含事故最远排队长度，事故持续时间）关系进行分析。研究结果表明：疏散时间与事故影响程度成正相关关系，疏散量与事故影响程度成负相关关系，而事故发生点与上游匝道之间的距离与其关系不大；道路服务水平为0.456，车辆数为1 321 veh·h^-1时，为了使得分合流区不受影响，在不采取任何措施的情景下，应将大型车混入率控制在50.1%以下，使得最远排队长度在10 km内；当大型车混入率大于58%时，将很难通过干涉引导避免对上游分合流区产生影响；在35 min以内采取干涉措施的效果最为明显，而大于35 min时，事故持续时间会发生一个急剧的增加，不利于路网恢复，之后事故恢复时间将趋于平稳；对道路交通量进行模拟可知交通量每增加50 veh，疏散时间和距离增加的范围为[1.5 min，3.6 min]和[1.209 km，1.543 km]。研究结果可为高速公路事故诱导策略制定和疏散效果提升提供参考。     

道路交通事故痕迹鉴定确定肇事车辆

道路交通事故检验鉴定为公安机关交通管理部门的事故处理提供科学、专业的技术支持。检查和核实道路交通事故的影响是对道路交通事故技术评估的基本方法。已成为解决许多事故问题的重要依据。本文根据道路交通事故痕迹鉴定的实际情况,针对公安机关交通管理部门涉及复杂、疑难、特殊技术问题的痕迹鉴定情况,提出了针对性的解决方案。     

电控化油器技术在摩托车上的应用(1)

湛江德利化油器有限公司研制和开发的电控化油器系统,是一款闭环控制燃油供油系统,适应性较强、可靠性高,由于采用主动控制和被动供油方式相结合,在燃油经济、排放等方面都显著优于传统的化油器,在国Ⅲ应用上具有有效性,在国Ⅳ应对上具有可行性。     

A design methodology for mechatronic vehicles: application of multidisciplinary optimization,multibody dynamics and genetic algorithms

A design methodology for mechatronic vehicles is presented. With multidisciplinary optimization (MDO) methods, strongly coupled mechanical, control and other subsystems are integrated as a synergistic vehicle system. With genetic algorithms (GAs) at the system level, the mechanical, control and other relevant parameters can be optimized simultaneously. To demonstrate the feasibility and efficacy of the proposed design methodology for mechatronic vehicles, it is used to resolve the conflicting requirements for ride comfort, suspension working spaces and unsprung mass dynamic loads in the optimization of half-vehicle models with active suspensions. Both deterministic and random road excitations, both rigid and flexible vehicle bodies and both perfect measurement of full state variables and estimated limited state variables are considered. Numerical results show that the optimized vehicle systems based on the methodology have better overall performance than those using the linear quadratic Gaussian (LQG) controller. It is shown that the methodology is suitable for complex design optimization problems where: (1) there is interaction between different disciplines or subsystems; (2) there are multiple design criteria; (3) there are multiple local optima; (4) there is no need for sensitivity analysis for the optimizer at the system level; and (5) there are multiple design variables.