Implementation of Radial Basis Function Artificial Neural Network into an Adaptive Equivalent Consumption Minimization Strategy for Optimized Control of a Hybrid Electric Vehicle

Continued increases in the emission of greenhouse gases by passenger vehicles have accelerated the production of hybrid electric vehicles. With this increase in production, there has been a parallel demand for continuously improving strategies of hybrid electric vehicle control. The goal of an ideal control strategy is to maximize fuel economy while minimizing emissions. Methods exist by which the globally optimal control strategy may be found. However, these methods are not applicable in real-world driving applications since these methods require a priori knowledge of the upcoming drive cycle. Real-time control strategies use the global optimal as a benchmark against which performance can be evaluated. The goal of this work is to use a previously defined strategy that has been shown to closely approximate the global optimal and implement a radial basis function (RBF) artificial neural network (ANN) that dynamically adapts the strategy based on past driving conditions. The strategy used is the Equivalent Consumption Minimization Strategy (ECMS), which uses an equivalence factor to define the control strategy and the power train component torque split. An equivalence factor that is optimal for a single drive cycle can be found offline with a priori knowledge of the drive cycle. The RBF-ANN is used to dynamically update the equivalence factor by examining a past time window of driving characteristics. A total of 30 sets of training data (drive cycles) are used to train the RBF-ANN. For the majority of drive cycles examined, the RBF-ANN implementation is shown to produce fuel economy values that are within ±2.5% of the fuel economy obtained with the optimal equivalence factor. The advantage of the RBF-ANN is that it does not require a priori drive cycle knowledge and is able to be implemented in real-time while meeting or exceeding the performance of the optimal ECMS. Recommendations are made on how the RBF-ANN could be improved to produce better results across a greater array of driving conditions.

Simulation research of energy management strategy for dual mode plug-in hybrid electrical vehicles

In this paper, a plug-in hybrid electrical vehicle(PHEV) is taken as the research object, and its dynamic performance and economic performance are taken as the research goals. Battery charge-sustaining(CS) period is divided into power mode and economy mode. Energy management strategy designing methods of power mode and economy mode are proposed. Maximum velocity, acceleration performance and fuel consumption are simulated during the CS period in the AVL CRUISE simulation environment. The simulation results indicate that the maximum velocity and acceleration time of the power mode are better than those in the economy mode. Fuel consumption of the economy mode is better than that in the power mode. Fuel consumption of PHEV during the CS period is further improved by using the methods proposed in this paper, and this is meaningful for research and development of PHEV.

A Novel Braking Control Strategy for Hybrid Electric Buses Based on Vehicle Mass and Road Slope Estimation

Proper braking force distribution strategies can improve both stability and economy performance of hybrid electric vehicles,which is prominently proved by many studies.To achieve better dynamic stable performance and higher energy recovery efficiency,an effective braking control strategy for hybrid electric buses(HEB)based on vehicle mass and road slope estimation is proposed in this paper.Firstly,the road slope and the vehicle mass are estimated by a hybrid algorithm of extended Kalman filter(EKF)and recursive least square(RLS).Secondly,the total braking torque of HEB is calculated by the sliding mode controller(SMC),which uses the information of brake intensity,whole vehicle mass,and road slope.Finally,comprehensively considering driver’s braking intention and regulations of the Economic Commission for Europe(ECE),the optimal proportional relationship between regenerative braking and pneumatic braking is obtained.Furthermore,related simulations and experiments are carried out on the hardware-in-the-loop test bench.Results show that the proposed strategy can effectively improve the braking performance and increase the recovered energy through precise control of the braking torque.

Series-parallel Hybrid Vehicle Control Strategy Design and Optimization Using Real-valued Genetic Algorithm

Despite the series-parallel hybrid electric vehicle inherits the performance advantages from both series and parallel hybrid electric vehicle, few researches about the series-parallel hybrid electric vehicle have been revealed because of its complex co nstruction and control strategy. In this paper, a series-parallel hybrid electric bus as well as its control strategy is revealed, and a control parameter optimization approach using the real-valued genetic algorithm is proposed. The optimization objective is to minimize the fuel consumption while sustain the battery state of charge, a tangent penalty function of state of charge（SOC） is embodied in the objective function to recast this multi-objective nonlinear optimization problem as a single linear optimization problem. For this strategy, the vehicle operating mode is switched based on the vehicle speed, and an ＂optimal line＂ typed strategy is designed for the parallel control. The optimization parameters include the speed threshold for mode switching, the highest state of charge allowed, the lowest state of charge allowed and the scale factor of the engine optimal torque to the engine maximum torque at a rotational speed. They are optimized through numerical experiments based on real-value genes, arithmetic crossover and mutation operators. The hybrid bus has been evaluated at the Chinese Transit Bus City Driving Cycle via road test, in which a control area network-based monitor system was used to trace the driving schedule. The test result shows that this approach is feasible for the control parameter optimization. This approach can be applied to not only the novel construction presented in this paper, but also other types of hybrid electric vehicles.

The Combination of Two Control Strategies for Series Hybrid Electric Vehicles

With most countries paying attention to the environment protection, hybrid electric vehicles have become a focus of automobile research and development due to the characteristics of energy saving and low emission. Power follower control strategy(PFCS) and DC-link voltage control strategy are two sorts of control strategies for series hybrid electric vehicles(HEVs). Combining those two control strategies is a new idea for control strategy of series hybrid electric vehicles. By tuning essential parameters which are the defined constants under DClink voltage control and under PFCS, the points of minimum mass of equivalent fuel consumption(EFC) corresponding to a series of variables are marked for worldwide harmonized light vehicles test procedure(WLTP). The fuel economy of series HEVs with the combination control schemes performs better compared with individual control scheme. The results show the effects of the combination control schemes for series HEVs driving in an urban environment.

ENERGY MANAGEMENT STRATEGY FOR PARALLEL HYBRID ELECTRIC VEHICLES

Energy management strategy (EMS) is the core of the real-time controlalgorithm of the hybrid electric vehicle (HEV). A novel EMS using the logic threshold approach withincorporation of a stand-by optimization algorithm is proposed. The aim of it is to minimize theengine fuel consumption and maintain the battery state of charge (SOC) in its operation range, whilesatisfying the vehicle performance and drivability requirements. The hybrid powertrain bench testis carried out to collect data of the engine, motor and battery pack, which are used in the EMS tocontrol the powertrain. Computer simulation model of the HEV is established in the MATLAB/Simulinkenvironment according to the bench test results. Simulation results are presented for behaviors ofthe engine, motor and battery. The proposed EMS is implemented for a real parallel hybrid carcontrol system and validated by vehicle field tests.

Electro-mechanical Braking Method in Hybrid Electric Vehicles Based on Feedback Control Theory

In this paper, the hybrid electric vehicle braking process is researched, by using variables consists of HEV speed, motor speed, and state of charge established, fimctions of mechanical braking force, regenerative braking force and efficiency of energy recovery are constructed, and the control goal is to maximization the energy recovery efficiency. Under the feedback control strategy, with the constrain condition of braking strength and braking stability, combining experiments in ADVISOR, in different experiments of different working conditions, we can see that in UDDS Cycle, the regenerative braking efficiency is the best. What＇s more, compared with strategies in ADVISOR, strategy proposed in this paper is obviously better.

基于ADVISOR2002混合动力汽车控制策略模块开发

介绍了混合动力汽车仿真软件ADVISOR中的并联控制策略 ,结合课题的实际情况 ,对ADVI SOR2 0 0 2的并联控制策略进行了二次开发 ,并针对某一车型进行了仿真对比 ,结果表明所提出的控制策略不仅能够鲁棒地在ADVISOR2 0 0 2界面中运行 ,而且合理、可靠 。

基于ADVISOR软件的双轴驱动混合动力汽车性能仿真模块开发

介绍了混合动力汽车仿真软件ADVISOR的主要功能 ,并针对其局限性对该软件进行了二次开发 ,使之能进行双轴四轮驱动的混合动力汽车的性能仿真。仿真结果表明采用文中提出的方法可有效扩展ADVISOR软件的应用范围 。

正向仿真模型与反向软件ADVISOR的集成开发

混合动力汽车仿真软件ADVISOR是一种反向仿真软件,介绍在ADVISOR2002平台下如何通过修改进行正向仿真软件的开发,并嵌入到其GUI平台下进行仿真。仿真结果表明,在ADVISOR平台下可以进行正向模型的仿真,这对实际混合动力控制策略的开发具有重要意义。

基于ADVISOR二次开发的复杂混合动力汽车仿真系统

基于ADVIOSR软件,对混合动力汽车仿真系统进行二次开发,建立某特定布置形式的复杂混合动力汽车整车仿真模型和相关控制策略模型,并通过循环工况仿真分析汽车的燃油经济性和动力性。

基于ADVISOR的双能量源纯电动汽车仿真

以蓄电池-超级电容双能量源纯电动汽车为研究对象,在分析双能量源存储系统的结构和工作模式的基础上,建立了相应的双能量源功率分配模糊控制策略.针对电动汽车仿真软件ADVI-SOR,通过双能量源模糊控制建模与关键程序二次开发,实现了适合于蓄电池-超级电容双能量源纯电动汽车仿真的专用软件平台.利用该平台对双能量源纯电动汽车进行了整车动力性能仿真.结果表明:蓄电池-超级电容双能量源存储系统能够同时满足纯电动汽车对能量和功率的双重要求,车辆的经济性和动力性都得到了提高.

基于Advisor的同轴式混联混合动力汽车动力系统仿真

以同轴式混联混合动力汽车为对象,在分析其动力系统运行模式及控制策略的基础上,进行了动力部件参数匹配计算,基于Advisor软件的二次开发建立了动力性能仿真系统.在选定的城市循环工况下,对车辆动力性能和燃油经济性进行仿真.通过分析SOC值变化和动力部件转矩关系,验证动力系统的可行性及仿真系统的准确性,为同轴式混联混合动力汽车开发提供技术支持.

基于ADVISOR的矿用无轨胶轮电动车再生制动控制策略的仿真

将纯电动汽车的概念引入煤矿井下无轨胶轮车。针对汽车仿真软件ADVISOR中原有再生制动控制策略的不足,以提高再生制动能量回收效果为目的,建立了基于模糊控制策略的再生制动模型,并对ADVISOR二次开发,将新再生制动模型嵌入ADVISOR原模型库中。用ADVISOR原有的再生制动控制策略与新策略进行仿真对比。通过对比电池荷电状态SOC图,验证了新策略优于原策略的结论。

汽车仿真软件ADVISOR

ADVISOR软件能够快速分析传统汽车、纯电动汽车和混合动力汽车的动力性、燃油经济性以及排放性等各种性能。本文系统地介绍了ADVISOR2002软件的功能特点、仿真策略和操作方法,并对该软件的应用以及汽车性能仿真软件的进一步研制作了展望。

采用Advisor的串联式混合动力客车控制策略研究

以串联式混合动力客车为研究对象,介绍混合动力汽车的结构及控制原理,在Advisor平台下完成参数计算匹配。对常用的功率跟随和恒温式两种控制策略进行分析对比,提出在功率跟随控制策略基础上融入发动机恒温开关控制的复合控制策略,并在ADVISOR中完成二次开发和仿真对比。结果表明,控制结果鲁棒性好,结合了两种控制策略的优点,能合理稳定的满足实际动力性指标,并获得良好的燃油经济性能和排放性能。

Co-simulation platform for single-shaft parallel HEVs based on Cruise+Matlab/Simulink

We reversely analyzed the energy management strategy （EMS） for a single-shaft parallel hybrid electric vehicle （HEV）, and build a forward co-simulation platform based on Cruise and Matlab. The vehicle dynamics model is built with Cruise, and control model is set up with Matlab/Simulink environment. The data between the two models are transferred by the Matlab API interface in Cruise. After mechanical and signal connections are completed, we establish the computing tasks and take the simulations of vehicle＇ s power performance, economy, and emission performance. The simulation results match the actual measurement results, which show that the co-simulation platform is correct and feasible. The platform can be used not only for a basic simulation platform to optimize further EMS, but also for the development of actual control system.

基于ADVISOR 2004的双电机纯电动汽车仿真研究

以双电机纯电动汽车为研究对象,设计了一种电机-驱动桥整体式动力系统结构,以及双电机驱动控制策略,并对汽车仿真软件ADVISOR2004原有EV模型进行二次开发,构建了双电机纯电动汽车仿真专用软件平台,满足了国内对双电机仿真分析的需求,并利用该平台进行了双电机纯电动汽车整车性能的仿真.结果表明:所设计的结构和控制策略可靠,且车辆的经济性能和动力性能都满足要求.

基于ADVISOR二次开发的Plug-in HEV模糊控制研究

针对插电式混合动力电动汽车(PHEV)运行特点,提出了PHEV最优模糊控制策略。模糊控制器将发动机的工作点控制在发动机最小燃油消耗的高效率区间内,使发动机具有较好的燃油经济性。根据路况功率需求和蓄电池SOC,调节发动机给定功率,并对能量回收不足SOC下降的情况进行改进,实现电池SOC平衡控制。将建立的模糊控制模型嵌入ADVISOR仿真软件,进行二次开发,在不同工况下进行仿真实验。结果表明:PHEV最优模糊逻辑控制具有较好的鲁棒性,在能量回收不足工况下实现电池SOC平衡,且具有较好的燃油经济性。

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.