Variable Parameter Self-Adaptive Control Strategy Based on Driving Condition Identification for Plug-In Hybrid Electric Bus

A variable parameter self-adaptive control strategy based on driving condition identification is proposed to take full advantage of the fuel saving potential of the plug-in hybrid electric bus(PHEB).Firstly,the principal component analysis(PCA)and the fuzzy c-means clustering(FCM)algorithm is used to construct the comprehensive driving cycle,congestion driving cycle,urban driving cycle and suburban driving cycle of Chinese urban buses.Secondly,an improved particle swarm optimization(IPSO)algorithm is proposed,and is used to optimize the control parameters of PHEB under different driving cycles,respectively.Then,the variable parameter self-adaptive control strategy based on driving condition identification is given.Finally,for an actual running vehicle,the driving condition is identified by relevance vector machine(RVM),and the corresponding control parameters are selected to control the vehicle.The simulation results show that the fuel consumption of using the variable parameter self-adaptive control strategy is reduced by 4.2% compared with that of the fixed parameter control strategy,and the feasibility of the variable parameter self-adaptive control strategy is verified.

Particle Swarm Optimization Algorithm Based on Chaotic Sequences and Dynamic Self-Adaptive Strategy

To deal with the problems of premature convergence and tending to jump into the local optimum in the traditional particle swarm optimization, a novel improved particle swarm optimization algorithm was proposed. The self-adaptive inertia weight factor was used to accelerate the converging speed, and chaotic sequences were used to tune the acceleration coefficients for the balance between exploration and exploitation. The performance of the proposed algorithm was tested on four classical multi-objective optimization functions by comparing with the non-dominated sorting genetic algorithm and multi-objective particle swarm optimization algorithm. The results verified the effectiveness of the algorithm, which improved the premature convergence problem with faster convergence rate and strong ability to jump out of local optimum.

NEW HYDRAULIC ACTUATOR'S POSITION SERVOCONTROL STRATEGY

A new hydraulic actuator-hydraulic muscle （HM） is described, and the actuator＇s features and applications are analyzed, then a position servocontrol system in which HM is main actuator is set up. The mathematical model of the system is built up and several control strategies are discussed. Based on the mathematical model, simulation research and experimental investigation with subsection PID control, neural network self-adaptive PID control and single neuron self-adaptive PID control adopted respectively are carried out, and the results indicate that compared with PID control, neural network self-adaptive PID control and single neuron self-adaptive PID control don＇t need controlled system＇s accurate model and have fast response, high control accuracy and strong robustness, they are very suitable for HM position servo control system.

改善湿式双离合自动变速器换挡品质方法研究

简要介绍了湿式双离合自动变速器的工作原理,分析了换挡品质的影响因素,提出了改善换挡品质的方法,制定了换挡控制策略,并以斯柯达某型车为目标车进行了实车试验,试验结果表明所制定的换挡控制策略能够较好改善换挡品质。

自动变速车辆坡道行驶自适应换挡策略

在分析汽车纵向动力学模型的基础上,运用改进型最小二乘法对整车质量与广义坡度进行实时辨识,以克服采用单一遗忘因子的最小二乘辨识法对变化不同步的参数辨识效果不佳的缺点。制定了自适应坡道换挡策略并进行了实车道路试验。试验结果证明,新型换挡策略能有效地解决了传统换挡策略在坡道行驶时的问题,能够更好地满足自动变速器坡道行驶的要求。

并联式混合动力汽车机械式自动变速器换档策略

并联式混合动力汽车(Parallel Hybrid Electric Vehicle,PHEV)档位决策作为能量管理策略的一部分,对整车动力性、经济性及排放性能有较大影响.混合动力汽车换档策略不仅要考虑发动机,还要考虑电机和电池系统的影响.基于电池电能的等效燃油概念,通过考虑电池充、放电过程中的能量损失,将充、放电生成或消耗的电能折算为等效燃油,由此得到不同档位时整车的综合燃油消耗,进而选取燃油消耗较小时的档位使整车经济性能指标达到最优.同时,该方法也通用于装备液力自动变速器(Automatic Transmission,AT)等有级式自动变速器的混合动力汽车换档策略制定.

基于载荷和坡道识别的纯电动客车自适应换挡策略研究

针对车辆载荷和道路坡度难以实时准确测量的问题,运用无迹卡尔曼滤波算法设计了整车质量和道路坡度联合估计算法,快速有效地估计整车质量和道路坡度,并根据实时辨识结果,采用线性插值的方式构建自适应换挡策略实现了电动汽车换挡规律的动态修正,实车试验结果表明,该算法能够有效地避免因车辆载荷或道路坡度变化带来的车辆意外换挡和换挡循环问题。

基于模型的自动变速器换挡策略优化方法

介绍了整车纵向动力学的建模方法和换挡策略计算方法。通过仿真分析城市工况下各挡位的燃油消耗率,并从整车经济性角度对换挡曲线进行优化。针对城市工况下油门开度反复变化导致的频繁换挡现象,采用基于人工经验的模糊修正方法,根据油门开度变化率对换挡曲线进行修正。测试结果表明,该方法能够有效降低频繁换挡次数,提高整车在城市工况下的驾驶性。

湿式双离合自动变速器换挡控制策略的试验研究

简要介绍了湿式双离合自动变速器的结构和工作原理,提出了换挡品质的定义和评价指标。阐述分析了湿式双离合自动变速器的换挡策略,并结合以大众DQ250为目标变速器的换挡策略试验,比较验证,得出更为理想的换挡策略。

工程车辆自动变速箱电液控制系统的设计及仿真研究

分析了国内外现有工程车辆换档技术现状,采用插装式电液比例阀加离合器构成了自动换档变速箱控制系统,并对其结构合理性进行了校验和分析。在此基础上提出了工程车辆的模糊智能换档策略。通过仿真模型验证了在典型工况下车辆车速和档位变化情况,从而验证了模型和模糊换档策略的正确性。为解决我国现有其它大、中功率工程车辆行星传动变速箱离合器的电控换档问题探索出了一条新路。

机器人驾驶车辆深度强化学习换挡策略

为提高机器人驾驶车辆的动力性能,提出了一种基于深度神经网络强化学习的机器人驾驶车辆偏向动力性的换挡策略。首先建立了车辆纵向动力学模型、驾驶机器人运动学模型、用马尔可夫过程描述的机器人驾驶车辆换挡策略强化学习模型,并以车速、加速度和油门开度为状态变量、挡位为动作变量,设计了状态空间、动作空间和奖惩机制。然后通过深度神经网络强化学习算法求解了机器人驾驶车辆动力性换挡策略。最后对比本文中提出策略与其他换挡策略的仿真与试验结果,验证了提出策略的有效性。

综合考虑CVT动力性和经济性的整车控制策略集成优化

传统的无级变速器(Continuously Variable Transmission,CVT)的策略优化集中在CVT换挡策略优化上,因此在提升燃油经济性的同时往往损害动力性。针对这一问题,在分析换挡策略和踏板扭矩策略对整车动力性和燃油经济性影响的基础上,提出集成优化换挡策略和踏板扭矩策略的方法;设计优化流程,得到不同车速、不同加速踏板开度下换挡策略和踏板扭矩策略的优化结果。搭建整车虚拟仿真模型,并进行仿真与试验对比分析,结果表明:集成优化可在保证动力性不衰减甚至提升的条件下,燃油效率提高约4%。

基于MC9S12XEP100的DCT控制器设计

基于飞思卡尔公司的高性能16位MC9S12XEP100微处理器,采用模块化设计方法设计了DCT(双离合自动变速器)电控单元,包括电源模块、微控制系统模块、输入模块、控制模块及通信模块等的设计。设计中考虑了控制系统的功能性、可靠性及经济性等,并简单介绍了底层驱动及程序设计。

半挂牵引车自动变速器换档策略研究

为了在装有多档变速器的东风半挂牵引车EQ4195上实现变速操纵的自动化,减轻驾驶员换档的疲劳程度,提高车辆的综合性能,利用理论分析和实车试验相结合的方法,根据车辆动力传动系统自身的特点提出了牵引车多档变速器综合换档控制策略.试验结果表明,针对多档变速器所设计的换档控制方法和换档规律,能够满足车辆在不同工况条件下行驶的要求,达到了预期的目标。

Application of clutch to clutch gear shift technology for a new automatic transmission

For the purpose of engineering development for a new 8-step speed automatic transmission,a simplified dynamic model for this gearbox was established and key parameters which affected the shift quality were analyzed.Aiming at four different shift types,the ideal characteristics of shift clutch and engine control were set up.By using torque estimation method,PI slip control algorithm and engine coordinated control principle,the control model and transmission controller were well developed for three shift phases which included rapid-fill phase,torque phase and inertia phase.The testing environment on the rig and prototype vehicle level was built and the testing results obtained in ultimate condition could verify the accuracy and feasibility of this shift control strategy.The peak jerk during shift process was controlled within ±2 g/s where the smooth gearshift was obtained.The development proposal and algorithm have a high value for engineering application.

Multi-objective microgrid optimal dispatching based on improved bird swarm algorithm

Multi-objective optimal dispatching schemes with intelligent algorithms are recognized as effective measures to promote the economics and environmental friendliness of microgrid applications.However,the low accuracy and poor convergence of these algorithms have been challenging for system operators.The bird swarm algorithm(BSA),a new bio-heuristic cluster intelligent algorithm,can potentially address these challenges;however,its computational iterative process may fall into a local optimum and result in premature convergence when optimizing small portions of multi-extremum functions.To analyze the impact of a multi-objective economic-environmental dispatching of a microgrid and overcome the aforementioned problems of the BSA,a self-adaptive levy flight strategy-based BSA(LF-BSA)was proposed.It can solve the dispatching problems of microgrid and enhance its dispatching convergence accuracy,stability,and speed,thereby improving its optimization performance.Six typical test functions were used to compare the LF-BSA with three commonly accepted algorithms to verify its excellence.Finally,a typical summer-time daily microgrid scenario under grid-connected operational conditions was simulated.The results proved the feasibility of the proposed LF-BSA,effectiveness of the multi-objective optimization,and necessity of using renewable energy and energy storage in microgrid dispatching optimization.

Design of an eco-gearshift control strategy under a logic system framework

Good access to traffic information provides enormous potential for automotive powertrain control.We propose a logical control approach for the gearshift strategy,aimed at improving the fuel efficiency of vehicles.The driver power demand in a specific position usually exhibits stochastic features and can be statistically analyzed in accordance with historical driving data and instant traffic conditions;therefore,it offers opportunities for the design of a gearshift control scheme.Due to the discrete characteristics of a gearshift,the control design of the gearshift strategy can be formulated under a logic system framework.To this end,vehicle dynamics are discretized with several logic states,and then modeled as a logic system with the Markov process model.The fuel optimization problem is constructed as a receding-horizon optimal control problem under the logic system framework,and a dynamic programming algorithm with algebraic operations is applied to determine the optimal strategy online.Simulation results demonstrate that the proposed control design has better potential for fuel efficiency improvement than the conventional method.

Design of Energy Efficient WSN Using a Noble SMOWA Algorithm

In this paper,the establishment of efficientWireless Sensor Network(WSN)networks has been projected to minimize the consumption of energy using a new Self-adaptive Multi-Objective Weighted Approach(SMOWA)algorithm for solving a multi-objective problem.The Different WSN nodes deployment policies have been proposed and applied in this paper to design an efficientWireless Sensor Network to minimize energy consumption.After that,the cluster head for each cluster has been selected with the help of the duty cycle.After configuring the WSN networks,the SMOWA algorithms have been developed to obtain the minimum energy consumption for the networks.Energy minimization,as well as the amount of day-saving,has been calculated for the differentWSNswhich has been configured through different deployment policies.The major finding of the research paper is to improve the durability of Wireless Sensor Network(i)applying different deployment strategies:(Random,S pattern and nautilus shell pattern),and(ii)using a new Meta-heuristic algorithm(SMOWA Algorithm).In this research,the lifetime of WSN has been increased to a significant level.To choose the best result set from all the obtained results set some constraints such as“equivalent distribution”,“number of repetitions”,“maximum amount energy storage by a node”has been set to an allowable range.

动力换挡自动变速器在拖拉机上的应用技术分析

动力换挡变速器具有换挡进程中动力输出不中断的优点,在拖拉机和工程机械上得到广泛应用。分析了拖拉机动力换挡自动变速器工作原理,对其在国内外的应用进行了综述。并根据当前变速器换挡控制技术的发展趋势,研究了动力换挡自动变速器传动系统、电液技术、换挡规律以及所采用的换挡控制策略。本文的研究可为动力换挡变速器在农业机械上的应用提供理论参考。

Computational intelligence:From mathematical point of view

A simple but illustrative survey is given on various approaches of computational intelligence with their features, applications and the mathematical tools involved, among which the simulated annealing, neural networks, genetic and evolutionary programming, self-organizing learning and adapting algorithms, hidden Markov models are recommended intensively. The common mathematical features of various computational intelligence algorithms are exploited.Finally, two common principles of concessive strategies implicated in many computational intelligence algorithms are discussed.