Design of genetic-fuzzy control strategy for parallel hybrid electric vehicles

Hybrid Electric Vehicles (HEVs) generate the power required to drive the vehicle via a combination of internal combustion engines and electric generators. To make HEVs as efficient as possible, proper management of the different energy elements is essential. This task is performed using the HEV control strategy. The HEV control strategy is the algorithm according to which energy is produced, used and saved. This paper describes a genetic-fuzzy control strategy for parallel HEVs. The genetic-fuzzy control strategy is a fuzzy logic controller that is tuned by a genetic algorithm. The objective is to minimize fuel consumption and emissions, while enhancing or maintaining the driving performance characteristics of the vehicle. The tuning process is performed over three different driving cycles including NEDC, FTP and TEH-CAR. Results from the computer simulation demonstrate the effectiveness of this approach in reducing fuel consumption and emissions without sacrificing vehicle performance.     

负载影响下纯电动汽车配送路径规划及充电策略

由于纯电动汽车行驶里程的限制,在满足商用要求的前提下,纯电动汽车用于长途运输服务在短期内难以实现。不过,城市物流因其配送区域较小、货物的批量较小、批次较多的特点,可以考虑使用纯电动汽车来完成城市的配送任务。为满足车辆当天多次配送任务的要求以及考虑车辆负载对实时能耗的具体影响,建立了考虑车辆负载对实时能耗影响的配送模型,以及时满足客户的服务时间要求。并以城市A为例,设计了蚁群算法对模型进行求解,为纯电动汽车的配送任务进行合理的路径规划与充电策略的安排。最后,通过与使用燃油车辆运营相比较,分析未来纯电动汽车在城市配送物流中的可行性。     

负载影响下纯电动汽车配送路径规划及充电策略

由于纯电动汽车行驶里程的限制，在满足商用要求的前提下，纯电动汽车用于长途运输服务在短期内难以实现。不过，城市物流因其配送区域较小、货物的批量较小、批次较多的特点，可以考虑使用纯电动汽车来完成城市的配送任务。为满足车辆当天多次配送任务的要求以及考虑车辆负载对实时能耗的具体影响，建立了考虑车辆负载对实时能耗影响的配送模型，以及时满足客户的服务时间要求。并以城市A为例，设计了蚁群算法对模型进行求解，为纯电动汽车的配送任务进行合理的路径规划与充电策略的安排。最后，通过与使用燃油车辆运营相比较，分析未来纯电动汽车在城市配送物流中的可行性。     

基于改进型滤波器功率分流控制的HEV复合电源

针对混合动力汽车能量存储系统需要满足可变的负载功率需求以及吸收制动时的可再生功率这一问题,引入蓄电池-超级电容器复合电源储能系统。对主动式结构复合电源进行分析与研究,采用改进型滤波器功率分流控制策略,在MATALB 7仿真环境下对其进行建模和仿真,结果表明:由于超级电容器的加入,复合电源的功率输出能力大大提高了;改进型滤波器功率分流控制策略使得蓄电池的放电过程得到优化。     

Predictive planning of optimal velocity and state of charge trajectories for hybrid electric vehicles

The combination of electric motors and internal combustion engines in hybrid electric vehicles (HEV) can considerably improve the fuel efficiency compared to conventional vehicles. In order to use its full potential, a predictive intelligent control system using information about impending driving situations has to be developed, to determine the optimal gear shifting strategy and the torque split between the combustion engine and the electric motor. To further increase fuel efficiency, the vehicle velocity can be used as an additional degree of freedom and the development of a predictive algorithm calculating good choices for all degrees of freedom over time is necessary.In this paper, an optimization-based algorithm for combined energy management and economic driving over a limited horizon is proposed. The results are compared with results from an offline calculation, which determine the overall fuel savings potential through the use of a discrete dynamic programming algorithm.     

Simulation and control of hybrid electric vehicles

This research develops a typical model for a parallel hybrid electric vehicle. Model predictive controllers and simulations for this model have been built to verify the ability of the system to control the speeds and to handle the transitional period for the clutch engagement from the pure electrical driving to the hybrid driving. If the output constraints are the measured speeds and the unmeasured torques which are not strictly imposed and can be violated somewhat during the clutch engagements, a modified model predictive controller with soften output constraints has been developed. Simulations show that the new model predictive controller can control the speeds very well for rapid clutch engagements, which enhance the driving comfort and reduce the jerk on the parallel hybrid electric vehicles.     

Initial position estimation strategy for a surface permanent magnet synchronous motor used in hybrid electric vehicles

A novel nonlinear model for surface permanent magnet synchronous motors (SPMSMs) is adopted to estimate the initial rotor position for hybrid electric vehicles (HEVs). Usually, the accuracy of initial rotor position estimation for SPMSMs relies on magnetic saturation. To verify the saturation effect, the transient finite element analysis (FEA) model is presented first. Hybrid injection of a static voltage vector (SVV) superimposed with a high-frequency rotating voltage is proposed. The magnetic polarity is roughly identified with the aid of the saturation evaluation function, based on which an estimation of the position is performed. During this procedure, a special demodulation is suggested to extract signals of iron core saturation and rotor position. A Simulink/MATLAB platform for SPMSMs at standstill is constituted, and the effectiveness of the proposed strategy is verified. The proposed method is also validated by experimental results of an SPMSM drive.     

Real-time energy management strategy based on predictive cruise control for hybrid electric vehicles

With the help of traffic information of the connected environment, an energy management strategy (EMS) is proposed based on preceding vehicle speed prediction, host vehicle speed planning, and dynamic programming (DP) with PI correction to improve the fuel economy of connected hybrid electric vehicles (HEVs). A conditional linear Gaussian (CLG) model for estimating the future speed of the preceding vehicle is established and trained by utilizing historical data. Based on the predicted information of the preceding vehicle and traffic light status, the speed curve of the host vehicle can ensure that the vehicle follows safety and complies with traffic rules simultaneously as planned. The real-time power allocation is composed of offline optimization results of DP and the real-time PI correction items according to the actual operation of the engine. The effectiveness of the control strategy is verified by the simulation system of HEVs in the interconnected environment established by E-COSM 2021 on the MATLAB/Simulink and CarMaker platforms.     

电动汽车混合储能系统拓扑结构与控制策略综述

混合储能系统兼具高功率密度和高能量密度,可有效提升电动汽车动力性能和续驶里程.围绕电动汽车混合储能系统拓扑结构和控制策略,本文首先对混合储能系统及其典型储能装置进行概述,并对混合储能系统技术难点进行分析.之后在分析隔离型双向DC/DC变换器和非隔离型双向DC/DC变换器拓扑结构、工作原理和基本特性的基础上,综述分析了被动、半主动和全主动3种混合储能系统的工作原理和优缺点.然后,重点分析了基于规则的控制策略、基于优化的控制策略和混合控制策略3大类混合储能系统控制策略的工作原理.最后对混合储能系统的发展趋势进行了展望.     

An optimal regulation strategy with disturbance rejection for energy management of hybrid electric vehicles

The energy management problem of finding the optimal split between the different sources of energy in a charge-sustaining parallel HEV, ensuring stability and optimality with respect to a performance objective (fuel consumption minimization over a driving cycle), is addressed in this paper. The paper develops a generic stability and optimality framework within which the energy management problem is cast in the form of a nonlinear optimal regulation (with disturbance rejection) problem and a control Lyapunov function is used to design the control law. Two theorems ensuring optimality and asymptotic stability of the energy management strategy are proposed and proved. The sufficient conditions for optimality and stability are used to derive an analytical expression for the control law as a function of the battery state of charge/state of energy and system parameters. The control law is implemented in a simplified backward vehicle simulator and its performance is evaluated against the global optimal solution obtained from dynamic programming. The strategy performs within 4% of the benchmark solution while guaranteeing optimality and stability for any driving cycle.     

基于多指标正交实验的并联混合动力汽车控制策略参数分析

针对并联式混合动力汽车电辅助控制策略的参数优化问题,基于多指标正交优化设计理论,以混合动力汽车的燃油消耗、CO排放量、HC和NOx的总排放量为实验指标,设计了正交优化实验表。运用直观分析法分析了18组实验结果,量化研究了控制策略参数对并联式混合动力汽车整车性能的影响,找出了各个指标的显著性影响因素。     

Editorial: Special issue on on-board optimization strategy designmethods for connected hybrid electric vehicles

As a trend in the innovation of automotive engineering, connectivity provides new opportunities and challenging issuesfor vehicular powertrain control due to big potential in the use of the connected information for improving energy efficiencyand reducing CO2 emission. In real world driving situation, a bottleneck for achieving optimal energy efficiencyvia control of the power sources in the powertrain is the uncertainty in power demand, since the power demand isdelivered by the driver according to the driving environment which is always with stochasticity and un-detectable eventin the environment. The connectivity enables us to predict the power demand in advance by using the real-time informationof vehicle-to-vehicle, vehicle-to-infrastructure, and vehicle-to-cloud etc., this new configuration for hybrid powertraincontrol excites innovative research for optimization of connected vehicles. This special issue focuses on the latestdevelopment, trends, and novel techniques for the design of on-board optimization strategies for hybrid electric vehicles(HEVs) under the connected environment. Especially, a special section is included that corrects several paperson new challenging solutions of the 6th IFAC Conference on Engine and Powertrain Control, Simulation and Modeling(IFAC ECOSM 2021) Benchmark Competition, where a real-time energy management problem with vehicle-toeverything(V2X) information is targeted for a typical HEV.There are eleven papers collected in this special issue.The collections are divided into three groups. The first group,including seven papers, focuses on the solution of benchmark challenging problem of IFAC ECOSM 2021. F. Xu et al.,proposing this benchmark problem of developing real-time optimization algorithm for HEVs in V2X communicationenvironment, describe the detail information of benchmark challenging problem, including the simulator, the challengingissue and the evaluating standard. It is concluded that the solutions from the challengers are hierarchical optimizationarchitectures. In the paper from X. Jiao et al., the conditional linear Gaussian is employed to predict future preceding vehicle’sspeed and a DP-based energy management strategy is designed. B. Zhang et al. propose a rule-based controller tomaintain safe driving and the Gaussian process regression is used to predict preceding vehicle speed, which is used forthe calculation of ego demand torque in the HEV powertrain control. J. Gao et al. also develop a layered optimizationframework, where ECMS is used for the torque distribution control ofHEVpowertrain. The solution fromY.Yamasaki etal. is designing a rule-based controller to determine ego vehicle’s speed and proposing the equivalent fuel consumptionsin motors for HEV powertrain control. In the proposal from S. Dong et al., model predictive control (MPC) is used forspeed planning and an explicit solution is obtained for HEV powertrain control. T. Namerikawa et al. develop a hierarchicalMPC framework for the NOx emission reduction and fuel economy improvement.     

Shortest path stochastic control for hybrid electric vehicles

When a hybrid electric vehicle (HEV) is certified for emissions and fuel economy, its power management system must be charge sustaining over the drive cycle, meaning that the battery state of charge (SOC) must be at least as high at the end of the test as it was at the beginning of the test. During the test cycle, the power management system is free to vary the battery SOC so as to minimize a weighted combination of fuel consumption and exhaust emissions. This paper argues that shortest path stochastic dynamic programming (SP‐SDP) offers a more natural formulation of the optimal control problem associated with the design of the power management system because it allows deviations of battery SOC from a desired setpoint to be penalized only at key off. This method is illustrated on a parallel hybrid electric truck model that had previously been analyzed using infinite‐horizon stochastic dynamic programming with discounted future cost. Both formulations of the optimization problem yield a time‐invariant causal state‐feedback controller that can be directly implemented on the vehicle. The advantages of the shortest path formulation include that a single tuning parameter is needed to trade off fuel economy and emissions versus battery SOC deviation, as compared with two parameters in the discounted, infinite‐horizon case, and for the same level of complexity as a discounted future‐cost controller, the shortest‐path controller demonstrates better fuel and emission minimization while also achieving better SOC control when the vehicle is turned off. Linear programming is used to solve both stochastic dynamic programs. Copyright © 2007 John Wiley & Sons, Ltd.     

A genetic-based methodology for hybrid electric vehicles sizing

 As private transport concerns, the global challenge of this millennium is the reduction of carbon dioxide emissions from passenger cars by improving fuel economy without sacrificing the vehicle performance. Hybrid electric vehicles powertrain, combining electric motor with an auxiliary power unit, can improve effectively the vehicle performance and fuel economy, reducing at the same time the effects of the use of private cars on the air quality of the cities. These advantages can be achieved only if the design of the powertrain is inspired to the minimisation of the main figures of merit holding in consideration many general aspects and variables. As supporting methodology in developing this difficult activity, a genetic-based sizing methodology will be presented. It will be aimed to minimise a function objective which takes into account not only technical specifications but also environmental, social, and economic aspects. Some interesting simulation results will be reported to prove the validity of the methodology, which will contribute to a substantial reduction of the pollutant emissions from hybrid electric vehicles.     

Least costly energy management for series hybrid electric vehicles

Energy management of plug-in hybrid electric vehicles (HEVs) has different challenges from non-plug-in HEVs, due to bigger batteries and grid recharging. Instead of tackling it to pursue energetic efficiency, an approach minimizing the driving cost incurred by the user – the combined costs of fuel, grid energy and battery degradation – is here proposed. A real-time approximation of the resulting optimal policy is then provided, as well as some analytic insight into its dependence on the system parameters. The advantages of the proposed formulation and the effectiveness of the real-time strategy are shown by means of a thorough simulation campaign.     

A stochastic method for the energy management in hybrid electric vehicles

There are many approaches addressing the problem of optimal energy management in hybrid electric vehicles; however, most of them optimise the control strategy for particular driving cycles. This paper takes into account that the driving cycle is not a priori known to obtain a near-optimal solution. The proposed method is based on analysing the power demands in a given receding horizon to estimate future driving conditions and minimise the fuel consumption while cancelling the expected battery energy consumption after a defined time horizon. Simulations show that the proposed method allows charge sustainability providing near-optimal results.     

单轴联结式并联混合动力汽车分层切换控制设计

为提高整车燃油经济性,降低尾气排放,本文针对单轴联结式并联混合动力汽车(parallel hybrid electric vehicle,PHEV)提出了一种分层切换控制方法.首先,在分析发动机稳态效率和电池充放电内阻变化规律基础上,采用分层切换思想,制定了PHEV各运行模式间切换规则.然后,研究了不同目标运行模式下的能量分配策略,针对单一驱动模式和3种制动模式,设计了基于规则的转矩分配策略;针对混合模式,分别设计了行车充电/混合驱动模式下的Lyapunov优化功率分配策略以及驻车充电模式下的Willans line模型极值法功率分配策略.最后,仿真结果表明,所提出方法可确保发动机和电池工作在高效区.在UDDS+HWFET工况下,与电辅助策略相比,百公里油耗降低了40.82%,CH,CO和NOx的排放量分别减少了2.86%,4.41%和8.02%;与基于庞特里亚金最小值原理(Pontryagin’s minimum principle,PMP)的全局优化策略相比,百公里油耗降低了9.37%.     

插电式混合动力汽车车速预测及整车控制策略

本文针对插电式混合动力汽车(plug-in hybrid electric vehicle,PHEV)这一典型混杂系统,提出了一种基于车速预测的混合逻辑动态(mixed logical dynamical,MLD)模型预测控制策略.首先,通过对发动机和电动机能量消耗模型进行线性化,建立双轴并联插电式混合动力城市公交车的动力传动系统数学模型;其次,运用模糊推理进行驾驶意图分析,提出基于驾驶意图识别和历史车速数据相结合的非线性自回归(nonlinear auto-regressive models,NAR)神经网络车速预测方法进行未来行驶工况预测.然后,以最小等效燃油消耗为目标建立PHEV的混合逻辑动态模型,运用预测控制思想对车速预测时域内最优电机转矩控制序列进行求解.最后,通过仿真实验验证了本文所提出控制策略在特定的循环工况下与电动助力策略相比,能够提高燃油经济性.