Modeling and Control of Parallel Hybrid Electric Vehicle Using Sea-Lion Optimization

This paper develops a parallel hybrid electric vehicle(PHEV)propor-tional integral controller with driving cycle.To improve fuel efficiency and reduce hazardous emissions in hybrid electric vehicles(HEVs)combine an electric motor(EM),a battery and an internal combustion engine(ICE).The electric motor assists the engine when accelerating,driving longer highways or climbing hills.This enables the use of a smaller,more efficient engine.It also makes use of the concept of regenerative braking to maximize energy efficiency.In a Hybrid Electric Vehicle(HEV),energy dissipated while braking is utilized to charge the battery.The proportional integral controller was used in this paper to analyze engine,motor performance and the New European Driving Cycle(NEDC)was used in the vehicle driving test using Matlab/Simulink.The proportional integral controllers were designed to track the desired vehicle speed and manage the vehi-cle’s energyflow.The Sea Lion Optimization(SLnO)methods were created to reduce fuel consumption in a parallel hybrid electric vehicle and the results were obtained for the New European Driving Cycle.

Parameters matching and optimization of parallel hybrid electric vehicle powertrain

Aiming at the development of parallel hybrid electric vehicle （PHEV） powertrain, parameter matching and optimization are presented, According to the performance of PHEV, the optimization range of engine, motor, driveline gear ratio and battery parameters are determined. And then a two-level optimization problem is formulated based on analytical target cascading （ATC）. At the system level, the optimization of the whole vehicle fuel economy is carried out, while the tractive performance is defined as the constraints. The optimized parameters are cascaded to the subsystem as the optimization targets. At the subsystem level, the final drive and transmission design are optimized to make the ratios as close to the targets as possible. The optimization result shows that the fuel economy had improved significantly, while the tractive performance maintains the former level.

OPTIMAL TORQUE CONTROL STRATEGY FOR PARALLEL HYBRID ELECTRIC VEHICLE WITH AUTOMATIC MECHANICAL TRANSMISSION

In parallel hybrid electrical vehicle （PHEV） equipped with automatic mechanical transmission （AMT）, the driving smoothness and the clutch abrasion are the primary considerations for powertrain control during gearshift and clutch operation. To improve these performance indexes of PHEV, a coordinated control system is proposed through the analyzing of HEV powertrain dynamic characteristics. Using the method of minimum principle, the input torque of transmission is optimized to improve the driving smoothness of vehicle. Using the methods of fuzzy logic and fuzzy-PID, the engaging speed of clutch and the throttle opening of engine are manipulated to ensure the smoothness of clutch engagement and reduce the abrasion of clutch friction plates. The motor provides the difference between the required input torque of transmission and the torque transmitted through clutch plates. Results of simulation and experiments show that the proposed control strategy performs better than the contrastive control system, the smoothness of driving and the abrasion of clutch can be improved simultaneously.

Study on load performance of electric motor system used in hybrid electric vehicle

The performance of the power assist, global optimization solved by dynamic programming （DP） method, Chery and Insight control strategies are analyzed using the mild parallel hybrid electric vehicle （PHEV） model based on Insight structure. The influence of the four control strategies to the load power of the electric motor system used on parallel hybrid electric vehicle is studied. It is found that 80 percent of the motor load power points are under 1/5 of the electric peak power. The motor load power of the power assist control strategy is distributed in the widest range during generating operation, and the motor load power of the global optimization control strategy has the smallest one.

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.

City-Bus-Route Demand-based Efficient Coupling Driving Control for Parallel Plug-in Hybrid Electric Bus

Recently,plug?in hybrid electric bus has been one of the energy?e cient solutions for urban transportation. However,the current vehicle e ciency is far from optimum,because the unpredicted external driving conditions are di cult to be obtained in advance. How to further explore its fuel?saving potential under the complicated city bus driving cycles through an e cient control strategy is still a hot research issue in both academic and engineering area. To realize an e cient coupling driving operation of the hybrid powertrain,a novel coupling driving control strategy for plug?in hybrid electric bus is presented. Combined with the typical feature of a city?bus?route,the fuzzy logic inference is employed to quantify the driving intention,and then to determine the coupling driving mode and the gear?shifting strategy. Considering the response deviation problem in the execution layer,an adaptive robust controller for electric machine is designed to respond to the transient torque demand,and instantaneously compensate the response delay and the engine torque fluctuation. The simulations and hard?in?loop tests with the actual data of two typical driving conditions from the real?world city?bus?route are carried out,and the results demonstrate that the pro?posed method could guarantee the hybrid powertrain to track the actual torque demand with 10.4% fuel economy improvement. The optimal fuel economy can be obtained through the optimal combination of working modes. The fuel economy of plug?in hybrid electric bus can be significantly improved by the proposed control scheme without loss of drivability.

Energy management strategy for a parallel hybrid electric vehicle equipped with a battery/ultra-capacitor hybrid energy storage system

To solve the low power density issue of hybrid electric vehicular batteries, a combination of batteries and ultracapacitors (UCs) could be a solution. The high power density feature of UCs can improve the performance of battery/UC hybrid energy storage systems (HESSs). This paper presents a parallel hybrid electric vehicle (HEV) equipped with an internal combus- tion engine and an HESS. An advanced energy management strategy (EMS), mainly based on fuzzy logic, is proposed to improve the fuel economy of the HEV and the endurance of the HESS. The EMS is capable of determining the ideal distribution of output power among the internal combustion engine, battery, and UC according to the propelling power or regenerative braking power of the vehicle. To validate the effectiveness of the EMS, numerical simulation and experimental validations are carried out. The results indicate that EMS can effectively control the power sources to work within their respective efficient areas. The battery load can be mitigated and prolonged battery life can be expected. The electrical energy consumption in the HESS is reduced by 3.91% compared with that in the battery only system. Fuel consumption of the HEV is reduced by 24.3% compared with that of the same class conventional vehicles under Economic Commission of Europe driving cycle.

Design and Strategy of Series-Parallel Hybrid System Based on BSFC

In this paper,a drive control strategy is developed based on the characteristics of series-parallel plug-in hybrid system.Energy management strategies in various modes are established with the basis on the minimum brake specific fuel consumption(BSFC)curve of engine.The control strategy,which is based on rules and system efficiency,is adopted to determine the entry/exit mechanisms of various modes according to battery state of charge(SOC),required power and required speed.The vehicle test results verify that the proposed control strategy can improve vehicle economy efficiently and makes a good effect on engine control.

面向混合动力汽车液压再生能量控制策略研究

针对混合动力汽车制动能量回收控制,提出了一种基于模糊Q学习的液压再生制动系统能量回收效率优化算法。建立了基于并联液压混合动力系统的汽车动力学模型,通过模糊Q学习法对汽车再生制动控制策略进行了优化。在此基础上建立了试验平台,验证液压再生制动仿真模型的准确性。与基于专家经验的模糊控制策略、动态规划算法相比,结果表明,经模糊Q学习算法优化的汽车节能率分别提高了9.62%和8.91%。

基于工况识别的PHEV能量管理策略

为提升并联式混合动力汽车(parallel hybrid electric vehicle,PHEV)的燃油经济性,针对等效燃油消耗最小控制策略(equivalent fuel consumption minimum strategy,ECMS)在不同工况下适应性差的问题,以优化整车等效燃油消耗量为目标,设计基于工况识别算法的变等效因子ECMS能量管理策略。选取3类典型工况建立支持向量机分类模型,通过递归特征消除法对样本特征进行选择,采用鲸鱼算法对支持向量机进行参数优化,使用模拟退火算法分别对3类工况的ECMS等效因子进行离线全局最优求解,并分别存储于等效因子库中,通过训练好的支持向量机分类器对目标优化工况进行工况识别,不同类型的工况片段采用不同的等效因子进行转矩分配。仿真结果显示:相比于逻辑门限能量管理策略,基于工况识别算法的变等效因子ECMS能量管理策略的电池荷电状态(state of charge,SOC)变化量减少8.67%,节油率为13.11%;相比于优化前的ECMS策略电池SOC变化量减少3.47%,节油率约为6.63%。本文提出的基于工况识别算法的变等效因子ECMS能量管理策略可以有效地减少燃油消耗量,提升PHEV的整车经济性。

电加热催化器降低混合动力车辆排放的应用研究

为了应对全球越来越严苛的汽车排放法规,例如欧7、北美Tier3Bin30以及未来的国7,降低整车排放势在必行,而缩短发动机冷起动阶段的催化器起燃时间将是关键点。将着重研究电加热催化器在混合动力车辆上的应用开发,以达到更好的排放结果。

基于改进深度强化学习的HEV能量分配策略研究

以并联式混合动力汽车(HEV)为研究对象,建立整车需求功率及动力系统模型,提出一种基于改进深度强化学习(DRL)的能量分配策略。通过改进DRL中的双延迟深度确定性策略梯度(TD3)算法,引入双重回放缓冲区,提出DRB-TD3算法以提升原算法的采样效率。设计了基于规则的约束控制器并嵌入到DRL结构中,以消除不合理的转矩分配。在UDDS行驶工况下,以基于动态规划(DP)的能量分配策略性能作为基准进行仿真实验。实验结果表明,与深度确定性策略梯度(DDPG)算法以及传统TD3算法相比,DRB-TD3算法收敛性能最佳,收敛效率分别提高了61.2%和31.6%;所提出的能量分配策略相比于基于DDPG和基于TD3的能量分配策略,平均燃油消耗分别降低了3.3%和2.3%,燃油经济性达到基于DP的95.2%,效果最佳,且电池荷电状态(SOC)能够保持在一个较好的水平,有助于延长电池的使用寿命。

并联混合动力汽车扭转减振器性能仿真分析

为改善并联混合动力汽车传动系统的扭振特性,开展了扭转减振器的结构及仿真分析研究。对并联混合动力汽车传动系现有扭转减振器进行分析,提出了一种具有新型结构的弧形弹簧式从动盘扭转减振器;针对某款车型建立8自由度集中质量模型,采用AMESim仿真软件搭建仿真模型;通过对离合器从动盘扭转减振器、双质量飞轮和弧形弹簧式从动盘扭转减振器3种不同结构减振器的扭振特性进行仿真对比,分析了它们在典型工况下的扭振特性,并对扭转刚度和迟滞力矩进行了灵敏度分析。结果表明,弧形弹簧式从动盘扭转减振器能保证较短的发动机启动时间,且拥有较好的减振特性;在混合驱动行驶工况下扭转减振器的减振效果与扭转刚度及迟滞力矩的大小呈负相关。

面向城市信号灯环境的多模混合动力汽车经济性驾驶研究

以智能网联环境下的多模混合动力汽车作为研究对象,开展了面向城市道路信号交叉口的经济性驾驶研究。首先,基于车联网信息获取信号灯相位与计时数据,根据信号灯位置与道路限速等约束搭建多个信号交叉口路网模型,建立了面向控制的多模混合动力系统部件模型与整车能耗模型;其次,基于庞特里亚金极小值原理解决单车经济性驾驶问题,针对红绿灯路口进行场景分析,规划车辆在不停车通过条件下的有效速度范围,分析了连续信号交叉口的经济性速度优化问题;最后,基于模型预测控制求解多模混合动力汽车的最优能量管理问题,同时引入模式切换惩罚项以提高多模混合动力系统的运行平顺性。与全局最优的动态规划型能量管理相比,新控制策略取得了3.767 L/100 km的近优燃油经济性。

并联混合动力电动汽车动力系统的参数匹配

针对典型的转矩复合结构并联混合动力电动汽车(PHEV),提出了用工程分析与仿真校验相结合的方法,对其动力系统中发动机功率、电动机参数、传动系速比和电池参数等主要参数进行匹配的原则、步骤,并通过实例加以说明和验证。

混联式混合动力车多能源动力控制系统的开发

提出一种混联式混合动力车的控制系统,它在结构上有以下特点:双电动机方案、动力在自动变速箱后端耦合、基于CAN网络的整车控制系统。在能量管理策略方面采用基于最优动力系统效率的优化ICE曲线控制策略,用“九点定义法”确定驱动需求转矩,在自动变速换挡过程中的采用动力协调控制策略,并在制动能量回收控制中考虑了滑行制动和强制制动两种控制模式。在此基础上开发多能源动力控制系统,并进行硬件在环仿真测试和实车匹配调试。实车测试结果表明,该多能源动力总成控制器能有效地实现对多能源动力的控制,使车辆的经济性和动力性都有很大提高。

并联混合动力汽车模式切换过程的协调控制

结合解放牌混合动力城市客车的实车调试,对不同模式之间的切换过程进行了分析,针对其切换过程易导致转矩波动,提出了基于电机辅助的协调控制策略,并利用AVL/Cruise及MATLAB联合仿真平台进行了建模仿真研究。仿真结果表明,协调控制策略保证了模式切换过程中动力传递的平稳性。

并联式混合动力汽车遗传模糊控制策略的研究

为带双离合器和单轴转矩耦合的某并联混合动力汽车开发了模糊控制策略,采用遗传算法对转矩分配模糊控制器进行了优化。在Matlab/Simulink和ADVISOR环境下基于试验数据建立了仿真模型,并进行了NEDC循环下只考虑经济性的控制策略优化和综合考虑经济性和排放性能的多目标控制策略优化。结果表明,应用遗传算法对模糊控制策略进行多目标优化后,油耗降低了3.65%,同时整车排放也有明显降低。

基于动态规划算法的混联混合动力汽车控制策略

针对某混联型混合动力汽车的布置形式,构建了整车控制数学模型。利用动态规划方法,提出了以控制整个循环发动机燃油消耗最低和电池SOC维持在理想值为目标的优化函数。提出了动态限制SOC区域模型。设计了动态规划全局搜索算法。进行了换档逻辑的优化。仿真对比了基于功率跟随的混联汽车控制策略、动态规划方法以及改进的功率跟随策略。结果显示,动态规划方法可以作为其他控制策略的评价参考,可以改善功率跟随控制策略控制效果。

基于模糊在线识别的并联混合动力客车自适应控制策略

针对一款并联混合动力客车提出了一种基于模糊在线识别的自适应控制策略.基于自主研发的混合动力车数据采集监控系统构建符合本地车辆实际行驶道路特点的典型工况,设计模糊工况识别算法对车辆实际行驶的工况类型进行在线识别.根据最小等效燃油消耗控制算法和电池电量平衡控制方法,结合工况识别的结果调用相应最优控制参数,对发动机和电池的功率分配进行实时优化计算,实现对整车的控制.实验结果表明,所设计的模糊识别方法能够较好地完成行驶工况类型的识别.基于此所提出的自适应控制方法能够在满足车辆需求功率和电池SOC维持在有效工作区间内的前提下完成发动机和电池的最优功率分配,显著提高整车的燃油经济性.