A decision support model for buying battery electric vehicles considering consumer learning and psychological behavior

Developments in battery electric vehicles (BEVs) have received more and more attentions in the last decades due to alleviating carbon emissions and energy crisis. Consequently, how to rank alternative BEVs to assist consumers make better purchasing decisions is a worthy research study. However, there are still some defects in the existing studies for ranking of BEVs: 1) the evaluation index system of BEVs is not comprehensive; 2) the determination of criteria weights cannot be well applied to the actual purchase scenarios; and 3) the psychological behavior of consumers is ignored. To address those shortcomings, this paper proposes a decision support model to assist with consumers to buy BEVs. First, a systematic evaluation criteria system of BEVs including quantitative and qualitative indicators from parameter configurations and online reviews is constructed. Then, a weight algorithm considering consumer learning is proposed to determine the criteria weights. Furthermore, a decision support process considering consumers' regret avoidance behavior is proposed. Finally, an actual BEV purchase case is given to illustrate the practicability of the decision support model. This can be seen in case studies the proposed support model can be well applied to consumers with different regret avoidance behaviours.     

Analysis of the effect of charging infrastructure design on electric taxi driving profiles: A case study approach on the example of Singapore

Replacing conventional vehicle taxis with electric vehicles would be an efficient measure to reduce greenhouse gas emissions. Due to the limited range and long charging times of current battery electric vehicles, it is of utmost importance to provide sufficient charging facilities. This article analyses the impact of the placement and charging power of charging stations on potential mileage and revenue of electric taxis on the example of Singapore. Therefore, we developed an agent-based electric taxi simulation model to investigate electric taxis’ driving profiles with respect to different vehicle types and charging infrastructure designs. This model is also capable of simulating conventional taxi driving profiles. The validation of these simulation results with real taxi data showed that the model is reproducing taxi driving profiles with high accuracy in great detail. We found out that electric taxis could reach the same mileage and revenue as conventional taxis if charging with a power of 160?kW is possible. Furthermore, we discovered that waiting times for available charging stations have a stronger effect on revenue than the length of detours to reach charging stations. Based on these findings, we concluded that it is more important to reduce waiting times by placing sufficient numbers of charging stations at each location before expanding the charging network by installing small numbers of charging stations at many locations.     

Fuel consumption for various driving styles in conventional and hybrid electric vehicles: Integrating driving cycle predictions with fuel consumption optimization*

Improving fuel economy and lowering emissions are key societal goals. Standard driving cycles, pre-designed by the US Environmental Protection Agency (EPA), have long been used to estimate vehicle fuel economy in laboratory-controlled conditions. They have also been used to test and tune different energy management strategies for hybrid electric vehicles (HEVs). This paper aims to estimate fuel consumption for a conventional vehicle and a HEV using personalized driving cycles extracted from real-world data to study the effects of different driving styles and vehicle types on fuel consumption when compared to the estimates based on standard driving cycles. To do this, we extracted driving cycles for conventional vehicles and HEVs from a large-scale U.S. survey that contains real-world GPS-based driving records. Next, the driving cycles were assigned to one of three categories: volatile, normal, or calm. Then, the driving cycles were used along with a driver-vehicle simulation that captures driver decisions (vehicle speed during a trip), powertrain, and vehicle dynamics to estimate fuel consumption for conventional vehicles and HEVs with power-split powertrain. To further optimize fuel consumption for HEVs, the Equivalent Consumption Minimization Strategy (ECMS) is applied. The results show that depending on the driving style and the driving scenario, conventional vehicle fuel consumption can vary widely compared with standard EPA driving cycles. Specifically, conventional vehicle fuel consumption was 13% lower in calm urban driving, but almost 34% higher for volatile highway driving compared with standard EPA driving cycles. Interestingly, when a driving cycle is predicted based on the application of case-based reasoning and used to tune the power distribution in a hybrid electric vehicle, its fuel consumption can be reduced by up to 12% in urban driving. Implications and limitations of the findings are discussed.     

Is the environment‐friendly factor attractive to customers when purchasing electric vehicles? Evidence from South Korea

In a bid to reduce greenhouse gas emissions, several countries worldwide are implementing policies to promote electric vehicles (EVs). However, contrary to expectations, the diffusion speed of EVs has been rather slow in South Korea. This study analyzes consumer preferences for the technological and environmental attributes of EVs and derives policy and environmental implications to promote market diffusion of EVs in South Korea. We conduct a choice‐based conjoint survey of 1,008 consumers in South Korea and estimate the consumer utility function using a mixed logit model considering consumer heterogeneity. Based on the consumer utility function, we analyze consumers' willingness‐to‐pay (WTP) for EV attributes such as driving range, charging method, charging time, autonomous driving function, carbon dioxide (CO₂) reduction rate, and purchase price. The results indicate that the current low acceptance of EVs is due to their relatively high price and lack of a battery charging technology that satisfies consumers' expectations of the charging method and time. One interesting finding is that Korean consumers have a relatively higher WTP for the CO₂ reduction rate of EVs than consumers in other countries; however, they do not consider CO₂ reduction over other technological attributes when choosing EVs. This implies that the rate of CO₂ reduction of EVs is not an important factor for South Korean consumers when buying EVs. We also calculate the effect of CO₂ reduction with the market penetration of EVs and find that CO₂ reduction through the diffusion of EVs depends on the country's electricity generation mix.     

增程式电动汽车产业浅析

新能源汽车是未来汽车产业转型发展的重要方向。我国新能源汽车前期发展取得了突出的成绩,产销量位居世界首位。但目前产业发展正面临新形势,未来一段时期内将由“政策驱动”转向“政策+市场双驱动”。到2021年购置补贴完全退出后的后补贴时代,如何保障新能源汽车产业的健康、可持续发展,值得人们共同思考,开发什么类型的产品才能更好地满足消费者的需求,值得行业企业考虑。     

双离合自动变速器电控电动换挡执行机构仿真优化与试验对比

为了提高双离合变速器在车辆上的换挡品质,提升电气化换挡执行机构的工作效率,给出执行机构性能与换挡品质评价准则;通过两种不同双离合变速器电控电动执行机构的工作原理与性能分析,基于MATLAB数值仿真方法优化出最高效率进行试验.基于车辆6个维度的换挡工况对二者所搭载的变速器分别进行试验与CAN线数据采集,对比研究两种机构优缺点,通过雷达图的方式进行优劣说明.结果表明,两种执行机构传动效率可达70％且换挡品质相当.可见Tip in tip out工况对二者要求最为严格.     

Modeling green vehicle adoption: An integrated approach for policy evaluation

This article employs an integrated discrete-continuous car ownership model to jointly forecast households’ future preferences on vehicle type, quantity and use, and to estimate greenhouse gas (GHG) emissions. The model system is estimated on a dataset collected from a web-based stated preference survey conducted in Maryland in 2014. The data contain vehicle purchase decisions and sociodemographic information of 456 households who were requested to state their future preferences over a 9-year period (2014–2022). In each time period, a respondent is faced to four alternatives that include the current vehicle, a new gasoline vehicle, a new hybrid electric vehicle, and a new battery electric vehicle. Intertemporal choices between conventional and “green” vehicles such as hybrid and electric cars capture dynamics in vehicle purchase decisions. Short run and medium-long run situations were predicted and compared based on the first 4-year data and the entire 9-year data of the dynamic panel. Vehicle GHG emissions were calculated correspondingly. We find the introduction of “green” vehicles makes a positive impact on car ownership and use, especially in a medium-long run. Two “green” taxation policies, gasoline tax and ownership tax, were proposed and their impact on vehicle use and emission reductions was evaluated. Results indicate that: (a) gasoline tax is a more effective way to reduce vehicle miles traveled and GHG emissions and (b) gasoline tax makes a higher impact on car use and emission reductions in the medium-long run, while ownership tax makes a higher impact in the short run.     

价值网络视角下纯电动汽车的破坏性创新研究

随着全球化的迅速发展,各行各业的竞争越来越激烈,为寻求自身竞争力的提高,后发企业凭借新的价值网络进行破坏性创新,从而实现对原有企业的冲击或替代。通过市场所要求的性能改善轨线与纯电动汽车技术性能改善轨线的有无交点的方法对我国纯电动汽车开展破坏性创新进行分析,研究发现,虽然充电时间是制约纯电动汽车的最大障碍,但是由于纯电动汽车整个价值网络的支撑和燃油汽车市场的开始衰落,纯电动汽车的破坏性技术已经对燃油汽车市场形成初步的冲击。     

不同调制方式下高斯信道容量的计算

计算不同调制方式下的信道容量对实际通信系统的信道编译码设计具有重要的理论意义。首先从Shannon信息论出发,介绍了平稳无记忆连续信道与加性白高斯噪声信道容量的定义。接着利用互信息与微分熵的相互关系,推导了几种常用的不同调制方式下高斯信道的容量,并给出了相应的信道容量闭合计算表达式。通过将电力卫星通信信道模拟为高斯信道,对采用无速率信道编码联合不同调制方式的卫星通信进行了数值仿真。仿真结果显示,通过推导得到的闭合表达式计算信道容量,能对实际卫星通信的编码速率、译码启动条件的确定起到很好的理论指导作用。