Socially optimal electric driving range of plug-in hybrid electric vehicles

This study determines the optimal electric driving range of plug-in hybrid electric vehicles (PHEVs) that minimizes the daily cost borne by the society when using this technology. An optimization framework is developed and applied to datasets representing the US market. Results indicate that the optimal range is 16 miles with an average social cost of $3.19 per day when exclusively charging at home, compared to $3.27 per day of driving a conventional vehicle. The optimal range is found to be sensitive to the cost of battery packs and the price of gasoline. When workplace charging is available, the optimal electric driving range surprisingly increases from 16 to 22 miles, as larger batteries would allow drivers to better take advantage of the charging opportunities to achieve longer electrified travel distances, yielding social cost savings. If workplace charging is available, the optimal density is to deploy a workplace charger for every 3.66 vehicles. Moreover, the diversification of the battery size, i.e., introducing a pair and triple of electric driving ranges to the market, could further decrease the average societal cost per PHEV by 7.45% and 11.5% respectively.     

某PHEV车型EMC试验方法、超标分析与解决

应对EMC国家标准GB/T 18387—2017和GB 34660—2017,具体针对某款混合动力汽车电磁兼容超标问题进行分析,通过排除法和对比法,对超标零部件进行具体定位,并提出相关EMC的解决方案,使整车试验通过国标测试,满足开发要求。     

中美PHEV能耗评价方法对比研究

研究了中国和美国对于插电式混合动力乘用车( PHEV)油耗与电耗的评价方法。通过从测试标准与指标计算两方面进行对比,指出了我国现行PHEV能耗评价方法中的不足,包括PHEV能耗标签的缺失、不能获得混合式PHEV的续驶里程和未考虑出行特征对PHEV能耗的影响等。最后提出了加强出行特征研究以完善我国PHEV能耗评价方法的建议。     

并联式混合动力电动汽车动力总成控制策略的仿真研究

分析了并联式混合动力电动汽车(PHEV)动力总成的构成及主要传递参数,阐述了电力辅助控制策略以及控制逻辑的具体实现,建立了PHEV动力总成各子系统包括发动机、电机和电池等的仿真模型,利用建立的仿真软件进行了控制策略及其控制参数的仿真研究。仿真结果表明,电池特性及发动机运行参数对整车的性能有较大的影响,根据不同的控制要求,调整相应的控制参数值,可以达到改善整车性能的目的。     

混合动力重型商用汽车驱动系统设计

首先介绍了混合动力汽车的基本概念及核心驱动系的构造。通过对比分析国外Volvo-FE混合动力商用汽车结构,对我公司某款车型进行了并联式混合动力驱动系统结构方案设计、驱动策略设计及主要动力总成部件参数的确定。     

基于遗传算法系统效率优化的PHEV电量保持模式控制策略

为了提高插电式混合动力汽车（PHEV）在电量保持下的燃油经济性,并解决插电式混合动力汽车在运行过程中动力元件效率对系统能量利用率影响的问题,制定了系统效率最优的控制策略。以PHEV关键动力部件的测试数据为基础,建立发动机、驱动电机、无级变速器（CVT）以及动力电池等关键部件的效率数值模型,并考虑了温度及荷电状态（SOC）对动力电池充放电功率的影响。设计以混合动力系统效率最优为适应度评价函数,将CVT速比、发动机转矩作为优化变量,以车速、加速度和SOC为状态变量,在动力性指标的约束下,运用遗传算法进行迭代寻优,PHEV的系统效率在第20代左右收敛于全局最优值。同时发动机转矩和CVT速比通过多代遗传进化,较快收敛于最佳值。将相关优化结果与车速、加速度拟合成相应的三维控制数表,综合数值建模和试验测试数据建模的方法,基于MATLAB/Simulink搭建插电式混合动力汽车整车控制策略仿真模型,采用新欧洲行驶循环工况进行仿真验证。结果表明：插电式混合动力汽车在电量保持模式下,利用遗传算法优化的系统效率最优控制策略相比优化前,动力电池SOC运行更为平稳,CVT效率有所提升,驱动电机及发动机转矩分配更为合理;百公里燃油消耗量从优化前的5.2 L降至4.5 L,燃油经济性提升了13.5%。     

Electrification of the two-car household: PHEV or BEV?

In previous works, we have shown two-car households to be better suited than one-car households for leveraging the potential benefits of the battery electric vehicle (BEV), both when the BEV simply replaces the second car and when it is used optimally in combination with a conventional car to overcome the BEV’s range limitation and increase its utilization. Based on a set of GPS-measured car movement data from 64 two-car households in Sweden, we here assess the potential electric driving of a plug-in hybrid electric vehicle (PHEV) in a two-car household and compare the resulting economic viability and potential fuel substitution to that of a BEV.Using estimates of near-term mass production costs, our results suggest that, for Swedish two-car households, the PHEV in general should have a higher total cost of ownership than the BEV, provided the use of the BEV is optimized. However, the PHEV will increasingly be favored if, for example, drivers cannot or do not want to optimize usage. In addition, the PHEV and the BEV are not perfect substitutes. The PHEV may be favored if drivers require that the vehicle be able to satisfy all driving needs (i.e., if drivers don’t accept the range and charge-time restrictions of the BEV) or if drivers requires an even larger battery in the BEV to counter range anxiety.We find that, given a particular usage strategy, the electric drive fraction (EDF) of the vehicle fleet is less dependent on whether PHEVs or BEVs are used to replace one of the conventional cars in two-car households. Instead, the EDF depends more on the usage strategy, i.e., on whether the PHEV/BEV is used to replace the conventional car with the higher annual mileage (“the first car”), the less used car (“the second car”), or is used flexibly to substitute for either in order to optimize use. For example, from a fuel replacement perspective it is often better to replace the first car with a PHEV than to replace the second with a BEV.     

新能源插电式混合动力汽车馈电时控制逻辑的分析与驾驶体验优化——不同混动架构与驾驶体验以及与能耗之间的关系分析

时代的发展使得环境污染问题愈发严重,机动车成为排放污染物的重要贡献者。因此,发展新能源汽车具有很强的战略性必要性。常见的混合动力汽车可粗略分为两大类:HEV 普通型混合动力汽车;PHEV 插电式混合动力汽车。前者的代表性车型为:丰田普锐斯、一汽丰田卡罗拉双擎。后者的代表车型为:比亚迪秦、荣威Ei6等国产车型。特殊车型为传祺GA5插电增程式电动车。本文重点介绍PHEV插电式混合动力汽车根据不同混动架构,在馈电时对驾驶质感以及能耗的影响做出分析、以及探讨可采取对应结构的改进方法及手段的可行性分析。PHEV插电式混合动力汽车馈电驾驶质感以及能耗的改变最终通过P0位置加装BSG电机或者P1位置加装ISG电机(适用于P3结构混合动力车型、代表车型比亚迪秦)、采用能量分流的混动架构(例如一汽丰田卡罗拉双擎)以及双电机增程式架构(例如传祺GS4 GA5新能源前轴发动机带动发电机发电输出动力的任务完全交给大功率的独立电机)等。通过该方式使得在馈电状态时,发动机与传动系脱藕,于最佳经济转速只用于发电。从而使能量得到最合理的利用、实现馈电油耗低、以及馈电行驶质感提升的目的。     

To save money or to save time: Intelligent routing design for plug-in hybrid electric vehicle

Electric vehicles (EVs) are promising alternative to conventional vehicles, due to their low fuel cost and low emissions. As a subset of EVs, plug-in hybrid electric vehicles (PHEVs) backup batteries with combustion engines, and thus have a longer traveling range than battery electric vehicles (BEVs). However, the energy cost of a PHEV is higher than a BEV because the gasoline price is higher than the electricity price. Hence, choosing a route with more charging opportunities may result in less fuel cost than the shortest route. Different with the traditional shortest-path and shortest-time routing methods, we propose a new routing choice with the lowest fuel cost for PHEV drivers. Existing algorithms for gasoline vehicles cannot be applied because they never considered the regenerative braking which may result in negative energy consumption on some road segments. Existing algorithms for BEVs are not competent too because PHEVs have two power sources. Thus, even if along the same route, different options of power source will lead to different energy consumption. This paper proposes a cost-optimal algorithm (COA) to deal with the challenges. The proposed algorithm is evaluated using real-world maps and data. The results show that there is a trade-off between traveling cost and time consumed when driving PHEVs. It is also observed that the average detour rate caused by COA is less than 14%. Significantly, the algorithm averagely saves more than 48% energy cost compared to the shortest-time routing.     

Consumers’ willingness to pay for alternative fuel vehicles: A comparative discrete choice analysis between the US and Japan

This paper conducts a comparative discrete choice analysis to estimate consumers’ willingness to pay (WTP) for electric vehicles (EVs) and plug-in hybrid electric vehicles (PHEVs) on the basis of the same stated preference survey carried out in the US and Japan in 2012. We also carry out a comparative analysis across four US states. We find that on average US consumers are more sensitive to fuel cost reductions and alternative fuel station availability than are Japanese consumers. With regard to the comparative analysis across the four US states, consumers’ WTP for a fuel cost reduction in California is considerably greater than in the other three states. We use the estimates obtained in the discrete choice analysis to examine the EV/PHEV market shares under several scenarios. In a base case scenario with relatively realistic attribute levels, conventional gasoline vehicles still dominate both in the US and Japan. However, in an innovation scenario with a significant purchase price reduction, we observe a high penetration of alternative fuel vehicles both in the US and Japan. We illustrate the potential use of a discrete choice analysis for forward-looking policy analysis, with the future opportunity to compare its predictions against actual revealed choices. In this case, increased purchase price subsidies are likely to have a significant impact on the market shares of alternative fuel vehicles.