Chassis integrated control for 4WIS distributed drive EVs with model predictive control based on the UKF observer

Four-wheel independent steering(4 WIS) system and direct yaw moment control(DYC) have an important influence on vehicle lateral stability. However, DYC has a great effect on the longitudinal velocity, and the capability of 4 WIS is limited to stability.To decrease the influence on the longitudinal velocity and improve the stability of electrical vehicles, a chassis controller integrated with a 4 WIS system and a DYC system with model predictive control(MPC) is designed. The framework consists of an unscented Kalman filter(UKF) observer and an MPC that contains three blocks: supervisor blocks, upper blocks and lower blocks. First, the sideslip angle, longitudinal velocity and lateral tire forces are estimated by the UKF observer; second, a bicycle model is utilized in the supervisor to calculate the desired values; third, the upper blocks are designed with the MPC to optimize the target steering angles and longitudinal tire forces under the constraints of subsystems; to facilitate the design of the MPC, a nonlinear tire is simplified based on the Taylor expansion method; finally, the target steering angles and longitudinal tire forces are achieved by the lower blocks. The integrated controller is simulated on the co-simulation platform of MATLAB-Carsim. The results show that the proposed integrated controller has less impact on longitudinal velocity and could effectively improve vehicle stability.     

系统效率最优的功率分流式混合动力汽车非线性预测控制

针对一种基于双行星排构型的功率分流式混合动力汽车,建立系统动态模型,准确描述其转速转矩耦合关系,通过建立各部件的效率模型,分析不同模式下系统的工作效率. 设计控制器结构框架,以系统工作效率和电池充放电平衡为目标,构建基于模型预测控制的优化问题,采用一步马尔科夫链模型预测驾驶员需求转矩及车速,将有限时域内的优化问题转化为非线性规划问题,基于序列二次规划算法实现优化求解. 仿真研究表明,基于系统效率最优的预测控制器能够维持电池的充放电平衡,在美国城市驾驶循环（UDDS）下,当电池初始电池荷电状态（SOC）分别为0.50、0.55和0.60时,相较于以发动机燃油消耗最优为目标,车辆等效燃油经济性分别提高了7.17%、5.73%和10.11%,验证了控制器的有效性和优越性.     

MPC-based energy management with adaptive Markov-chain prediction for a dual-mode hybrid electric vehicle

The and energy to management strategy battery is state an important part of a hybrid electrical vehicle design.It is used to improve various fuel economy sustain a proper of charge an by controlling control the power components is while satisfying to constraints and driving demands.However,achieving optimal and performance challenging due the nonlinearities of the hybrid powertrain,conflicting vehicle the time varying constraints,the dilemma capable in which controller control complexity and real-time capability are generally objectives.In this paper,a of real-time cascaded complies strategy is proposed for a dual-mode hybrid electric that considers controller based nonlinearities based the system model and with all time-varying with constraints.sampling The strategy consists of a supervisory controller on a non-linear predictive control short(MPC)sampling a long time with future strategy interval and a coordinating on linear model predictive based control with a time interval to deal different load dynamics of the system.The Additionally,a novel data methodology using adaptive Markov chains to predict demand is introduced.predictive future information is used to improve controller cycles performance.conducted.The The proposed is implemented validity on a real test-bed approach and experimental trials using economy unknown is driving are results other demonstrate the of the proposed and show that fuel significantly improved compared with methods.     

Design and optimization of equivalent consumption minimization strategy for 4WD hybrid electric vehicles incorporating vehicle connectivity

This paper presents an optimized equivalent consumption minimization strategy(ECMS) for four-wheel-drive(4 WD) hybrid electric vehicles(HEVs) incorporating vehicle connectivity. In order to be applicable to the 4 WD architecture, the ECMS is designed based on a rule-based strategy and used under the condition that a certain propulsion mode is activated. Assuming that a group of 4 WD HEVs are connected and position information can be shared with each other, we formulate a decentralized model predictive control(MPC) framework that compromises fuel efficiency, mobility, and inter-vehicle distance to optimize the velocity profile of each individual vehicle. Based on the optimized velocity profile, an optimization problem considering both fuel economy and battery state of charge(SOC) sustainability is formulated to optimize the equivalent factors(EFs) of the ECMS for HEVs over an appropriate time window. MATLAB User Datagram Protocol(UDP) is used in the codes run on multiple computers to simulate the wireless communication among vehicles, which share position information via UDP-based communication, and dSPACE is used as a software-in-the-loop platform for the simulation of the optimized ECMS. Simulation results validate the control effectiveness of the proposed method.     

用变步长法研究电力机车蛇形振动

将机车作为一个整体系统,用17个自由度描述机车在一般情况下横向平面的空间位置.考虑到一系、二系悬挂装置的弹性支承以及减振器的阻尼特性,建立了四轴转向架式车辆蛇形运动力学模型,列出运动微分方程.用变步长法求出的特征值研究车辆蛇形运动稳定性及机车临界速度,结论为增大轮对的横向及纵向刚度和阻尼,可以减小转向架和车体的蛇形运动.得出了合理的优化参数,并提出了改进措施将液压减振器改为电磁液压减振器或蛇形减振器,用锥式弹簧代替原来的柱式弹簧.     

Investigation on hierarchical control for driving stability and safety of intelligent HEV during car-following and lane-change process

The longitudinal and lateral coordinated control for autonomous vehicles is fundamental to achieve safe and comfortable driving performance. Aiming at this for hybrid electric vehicles (HEV) during the car-following (CF) and lane-change (LC) process while accelerating, a hierarchical control strategy for vehicle stability control is proposed. This new approach is different from the conventional hierarchical control. On the basis of model predictive control (MPC) theory, a two-layer MPC controller is designed at the top level of the control structure. The upper layer is a linear time-varying MPC (LTV-MPC), while the lower layer is a hybrid MPC (HMPC). For the LTV-MPC controller, a control-oriented linear discrete model for HEV is established, which integrates the dynamic model with three degrees of freedom (DOF) and the car-following model. The lower-layer HMPC controller is designed on the basis of the analysis for HEV hybrid characteristics and the modelling for the mixed logic dynamic (MLD) model of the HEV powertrain. As for the bottom level, a control plant including the HEV powertrain model and the 7 DOF nonlinear dynamics of the vehicle body is established. In addition, the system stability is proven. A deep fusion of vehicle dynamics control and energy management is achieved. Compared with LC-ACC control and conventional ACC control, the simulation and the hardware-in-the-loop (HIL) test results under different driving scenarios show that the proposed hierarchical control strategy can effectively maintain lateral stability and safety under severe driving conditions. Additionally, the HEV powertrain output torque and the gear-shift point are coordinated and controlled by the HMPC controller.

     

Velocity forecasts using a combined deep learning model in hybrid electric vehicles with V2V and V2I communication

Vehicle velocity forecast is an important clue in improving the performance of energy management in hybrid electric vehicles(HEV). This paper presents a new combined model for predicting vehicle's velocity time series. The main features of the model are to combine the feature extraction capability of deep restricted Boltzmann machines(DBM) and sequence pattern predicting capability of bidirectional long short-term memory(BLSTM). Hence, the model is named as DBMBLSTM. In addition, the DRMBLSTM model utilizes the vehicle driving information and roadside infrastructure information provided respectively through vehicle-to-vehicle(V2V) and vehicle-to-infrastructure(V2I) communication channels to predict vehicle velocity at various length of prediction horizon. Furthermore, the predictions results of this study are compared with the state of the art of vehicle velocity forecasts. The root mean square error(RMSE) is used as an evaluation criteria of predictions accuracy. Finally,these compared prediction model are applied in model predictive control(MPC) energy management strategy for the verifications of fuel economy improvement of a HEV. Simulation results confirm that the proposed combined deep learning model performs better than other five prediction methods. Therefore, it is a means of arriving at a reliable forecast model for HEV.     

舵桨联合操纵的自主式水下机器人运动控制

针对水下机器人舵桨切换操作时,由于控制输出突变而导致系统振荡甚至发散的现象,提出一种切换函数平滑指令输出;同时考虑到高速航态下,纵向速度对其他自由度运动的耦合影响,提出一种基于运动补偿的改进S面控制方法.该方法S面控制器结构简单、参数易于调整,解决了S面控制器在水下机器人高速航行下控制效果较差的问题.利用李雅普诺夫函数对该控制器进行了稳定性分析,将其应用于舵桨联合操纵的水下机器人的运动控制.速度控制、艏向控制及深度控制,试验结果验证了该方法在水下机器人运动控制中应用的可行性.     

Vehicle Active Damping Control with Considering Time Delay

A model predictive control (MPC) based active damping controller for automotive driveline oscillations with time-delay consideration is proposed.A simplified driveline model considering time delay is modeled and converted to a linear parameter varying state space equation.Based on the model and model predictive control theory,an active damping controller is designed for drivability and comfortability improvement.In order to verify the designed controller,a driveline with engine is modeled to simulate the tip-in/out driving operation.An MPC active damping controller without considering time delay is simulated together with the proposed controller.The simulation results show that,by adopting the new MPC active damping controller,the vibration of the vehicle is reduced and the drivability and comfortability are improved.     

行星式混合动力客车的模型预测动态协调控制

行星式混合动力客车在驱动模式切换时会产生较大冲击度,以往基于PID的控制器在模式切换过程中无法有效保证车辆驾驶平顺性.基于此问题,利用模型预测控制(Model Predictive Control,MPC)可以在线滚动优化获得最优控制序列的特点,提出了一种基于MPC的动态协调控制方法,实现发动机的启动控制.依据整车动力学方程和实车历史数据,在Matlab/Simulink平台中搭建基于数据驱动的发动机模型和车辆闭环仿真模型,并将发动机启动过程视为受约束的多目标优化问题,根据系统状态空间方程和优化问题设计基于数据驱动的模型预测控制器,在纯电动模式向混合动力模式切换过程中,与传统基于PID的控制方法以及被动切换展开对比.仿真结果表明,在保证车辆动力性的前提下,相比于PID控制方法和被动切换,在模式切换过程中,基于MPC的动态协调控制方法不仅可以实现发动机的正常启动,还能大幅度降低峰值冲击度,同时使车辆良好地跟随目标车速.本文提出的模型预测控制器可以降低整车冲击度,保证车辆模式切换时的平顺性.     

汽车主动悬架电子控制单元设计

使用AT89C52芯片作为控制芯片,以车身垂向加速度作为控制目标,设计了一种以1/4车辆振动模型为基础的主动悬架电子控制单元。控制单元采集垂向加速度及变化率作为输入信号,经过模糊控制算法处理,输出控制信号实现对车身振动的控制。实验结果表明,该控制单元能够很好的抑制车身的振动。悬架的控制单元的研究设计,对缩小与国际水平的差距,有着非常现实的意义。     

考虑电机参振的电动汽车平顺性频域分析模型

针对某型电动乘用车,建立考虑电机参振的11自由度电动汽车平顺性频域分析模型,分析车辆典型响应的传递函数和功率谱密度基本特征,计算不同行车速度和路面等级下各响应的均方根值;从降低电机振动和提高整车平顺性角度,分析不同悬置刚度和阻尼、电机总成质量对上述特性的影响。结果表明:建立的分析模型与传统模型相比,不仅可分析整车平顺性,还能用于研究电机振动状态及其对整车平顺性的影响;电机总成参数对整车平顺性的影响较小,但对电机自身振动影响明显,增大电机总成悬置刚度和阻尼、减小质量有助于降低电机振动,悬置刚度增大会略微降低整车平顺性。     

基于串级MPC和EPS的集成驾驶员转向车道线保持

针对电动助力转向( EPS)作为转向执行机构的车道线保持的控制系统设计及保留驾驶员对车辆操控问题,提出基于串级模型预测控制( MPC)和EPS集成驾驶员转向的车道线保持系统. 在车道线识别视觉系统空间,建立车道线保持状态空间模型,设计基于MPC的车道线保持控制器( LMPC) . 建立EPS状态空间模型,设计基于MPC的EPS车辆前轮转角控制器( EMPC) . LMPC与EMPC经逆转向机构模型组成串级控制结构. 分析驾驶员转向对车道线保持控制的影响,进而通过保留驾驶员对车辆控制来提高处理紧急事件的能力. 仿真结果表明:在不同车速和不同曲率道路下,该控制策略均能快速消除横向位置偏差和航向角偏差,保证车辆沿着车道线行驶,具有较好的适应性和鲁棒性. 驾驶员转向可以改善车道线保持和提高车辆主动安全性.     

某城市地铁振源系统参数影响预测

为了研究正在建设的某城市地铁振源系统,根据地铁拟采用的车型、块下胶垫和扣件等参数,建立车轨垂向耦合振动模型。计算车体、轮对和道床的垂向最大振动加速度,分析车体运行的平稳性及振源的减振效能。结果表明:该城市地铁拟采用的振源系统具有良好的减振效能;A型车车体运行平稳性优于B型车;低速时,参数的选取对振源系统的减振效能没有明显影响,随着车速的增加,减振效能呈降低趋势且不同参数间差距扩大,但当车速大于60 km/h时,减振效能趋于稳定;对减振效能的贡献,扣件最大,块下胶垫的阻尼其次,而A、B型车体和块下胶垫的刚度影响则不明显。     

带增程器的纯电动汽车整车控制器设计

主要介绍带增程器纯电动整车控制器的设计流程．首先定义整车控制器的功能．通过动力蓄电池荷电状态SOC逻辑门限控制策略,当电池SOC低于门限值后,燃料电池开启,起到增程器的作用．通过仿真软件和数学计算评价整车的经济性和动力性,设定SOC门限值为0．4．使用MPC555芯片作为整车控制器的硬件平台．利用Matlab／Simulink及其子模块Stateflow建立控制模型,通过RTW、TLC等语言编译器生成机器码,使用CANape和MPC555的CAN标定线下载到芯片．最终完成整车调试和标定．     

Cloud Computing Based Optimal Driving for a Parallel Hybrid Electric Vehicle

A cloud computing based optimal driving method is proposed and its feasibility is validated through a real-world scenario simulation. Based on principles of vehicle dynamics, the driving optimization problem has been formulated into an optimal control problem constrained by traffic rules, directed at achieving lower equivalent fuel consumption and shorter travel time. In order to conveniently specify the constraints and facilitate the application of the dynamic programming (DP) algorithm, the driving optimization problem is transformed into spatial domain and discretized properly. Considering the heavy computational costs of the DP algorithm, a cloud computing based platform structure is proposed to solve the optimal driving problem in real-time. A case study is simulated based on a real-world traffic scenario in Matlab. Simulation results demonstrate that the cloud computing framework is promising toward realizing the real-time energy management for hybrid electric vehicles.     

基于径向基函数神经网络PID与模型预测控制的车辆轨迹跟踪控制

为提高无人驾驶车辆的稳定性和鲁棒性,提出一种基于径向基函数神经网络自适应比例积分微分（RBFNN-PID）算法和模型预测控制（MPC）算法相结合的车辆轨迹跟踪控制方法.基于自适应RBFNN-PID算法、MPC算法以及车辆动力学模型,建立智能车辆纵向速度控制和横向控制的仿真模型并将其结合起来.在此基础上,以横向MPC控制和LQR-PID控制算法为基准,验证所提出的控制方法在轨迹跟踪方面的优越性.仿真结果表明,新方法比对照组具有更高的精度.最后,对新控制方法的硬件在环验证表明,该轨迹跟踪控制算法在轨迹跟踪精度和稳定性方面具有一定的有效性和先进性.     

汽车平顺性研究中的线性模型建立

本文介绍了利用多刚体系统动力学理论建立各种车型整车线性模型的原理和方法.提出了一套动力学方程的推导方法,从而完善了车辆振动方程的求解方法和动态优化设计理论,具有较强的通用性和实用性.     

车辆半主动悬架的仿真研究

提出了一种根据车速，路谱和载荷实时调节悬架参数的产主动悬架设计方案。将车辆模化为七自由度振动系统，采用频域法进行随机响应计算，仿真结果表明同传统的被动悬架相比这种半主动悬架可使车身振动显著减小，提高了平顺性和操纵稳定性。     

输流管道流固耦合非线性动力学分析

利用有限元软件，采用ALE法对输流管道流固耦合非线性动力学特性进行计算分析．得到耦合处及其附近流体各向速度随时间变化曲线，在某一时刻管内任意横截面处的速度分布和管子在垂直流速方向上的振型，及管子在耦合作用下的振动频率．为提高设计阶段对振动特性预测的精度、保证管路系统的运行可靠性提供了参考．