Energy consumption estimation model for dual-motor electric vehicles based on multiple linear regression

ABSTRACT

The drive range of electric vehicle (EV) is one of the major limitations that impedes its universalism. A great deal of research has been devoted to drive range improvement of EV, an accurate and efficiency energy consumption estimation plays a crucial role in these researches. However, the majority of EV’s energy consumption estimation models are based on single motor EV, these models are not suitable for dual-motor EVs, which are composed of more complex transmission mechanisms and multiple operating modes. Thus, an energy consumption estimation model for dual-motor EV is proposed to estimate battery power. This article focuses on studying the operating modes and system efficiency in each operating mode. The limitation of working area of each mode ensures the vehicle dynamic performance, then PSO algorithm is adopted to optimize the torque (speed) distribution between two motors to improve the system efficiency in the coupled driving mode. Finally, the energy consumption estimation model is established by multiple linear regression (MLR). The result shows that the proposed model has a high precision in energy consumption estimation of dual-motor EV.     

Risk-based optimal operation of hybrid power system using multiobjective optimization

ABSTRACT

This paper solves an optimal generation scheduling problem of hybrid power system considering the risk factor due to uncertain/intermittent nature of renewable energy resources (RERs) and electric vehicles (EVs). The hybrid power system considered in this work includes thermal generating units, RERs such as wind and solar photovoltaic (PV) units, battery energy storage systems (BESSs) and electric vehicles (EVs). Here, the two objective functions are formulated, i.e., minimization of operating cost and system risk, to develop an optimum scheduling strategy of hybrid power system. The objective of proposed approach is to minimize operating cost and system risk levels simultaneously. The operating cost minimization objective consists of costs due to thermal generators, wind farms, solar PV units, EVs, BESSs, and adjustment cost due to uncertainties in RERs and EVs. In this work, Conditional Value at Risk (CVaR) is considered as the risk index, and it is used to quantify the risk due to intermittent nature of RERs and EVs. The main contribution of this paper lies in its ability to determine the optimal generation schedules by optimizing operating cost and risk. These two objectives are solved by using a multiobjective-based nondominated sorting genetic algorithm-II (NSGA-II) algorithm, and it is used to develop a Pareto optimal front. A best-compromised solution is obtained by using fuzzy min-max approach. The proposed approach has been implemented on modified IEEE 30 bus and practical Indian 75 bus test systems. The obtained results show the best-compromised solution between operating cost and system risk level, and the suitability of CVaR for the management of risk associated with the uncertainties due to RERs and EVs.     

Effectiveness of primary enforcement safety belt laws and enhanced enforcement of safety belt laws: a summary of the Guide to Community Preventive Services systematic reviews

BACKGROUND: The use of safety belts is the single most effective means of reducing fatal and nonfatal injuries in motor-vehicle crashes. This paper summarizes the systematic reviews of two interventions to increase safety belt use: primary enforcement safety belt laws and enhanced enforcement of safety belt laws. The reviews were previously published in the American Journal of Preventive Medicine. METHODS: We conducted the systematic reviews using the methodology developed for the Guide to Community Preventive Services. RESULTS: These reviews provide strong evidence that primary laws are more effective than secondary laws in increasing safety belt use and decreasing fatalities and that enhanced enforcement is effective in increasing safety belt use. Increases in belt use are generally highest in states with low baseline rates of belt use. DISCUSSION: Primary safety belt laws and enhanced enforcement programs tend to result in greater increases in usage rates for target groups with lower baseline rates. Concerns regarding public opposition to these interventions may impede their implementation in some jurisdictions. However, surveys indicate that a substantial majority of the public supports implementation of both primary laws and enhanced enforcement programs. CONCLUSION: Based on the strong evidence for effectiveness of primary safety belt laws and enhanced enforcement programs, the Task Force on Community Preventive Services recommended that all states enact primary safety belt laws and that communities implement enhanced enforcement programs.     

The connecticut indirect source review: A methodology and model for evaluating CO concentrations

The body of information presented in this paper is directed to air pollution engineers who are concerned with the effect of indirect sources on ambient concentrations of carbon monoxide (CO). Data taken under controlled conditions are used to empirically derive and calibrate a model for predicting CO concentrations in the vicinity of roadway intersections and other points of possible vehicular congestion. Since the predicted free flow CO contribution of vehicles traveling at normal road speeds is relatively low, it is concluded that idling vehicles at points of congestion are the major cause of CO violations, and that state and federal programs should place more emphasis on relieving congestion and reducing idling emission rates in new vehicles.     

利用IVE模型建立成都市轻型汽油客车排放清单

城市机动车污染物排放清单的建立是控制机动车污染的关键.本研究以2012年为基准年,通过对成都市轻型汽油客车技术水平分布、活动水平和保有量等数据的调查,将IVE模型本地化,计算了成都市2012年轻型汽油客车VOCs、PM、NOx、CO的排放清单,并分析了清单的不确定性.结果表明:成都市2012年轻型汽油客车排放的VOCs、PM、NOx和CO分别为2.23×104t、1.6×102t、1.26×104t和2.03×105t;轻型汽油客车中黄标车VOCs、PM、NOx、CO的排放量分别占排放总量的27.5%、18.1%、37.2%和42.5%,表明黄标车是轻型汽油客车污染物排放的主要来源;排放清单的不确定性主要来自于排放因子,VOCs、PM、NOx和CO清单的不确定性分别为-31.67%~32.35%、-54.75%~55.09%、-6.56%~6.76%和-12.22%~12.51%.     

面向2035的节能与新能源汽车全生命周期碳排放预测评价

发展节能与新能源汽车是降低交通运输行业碳排放的重要技术路径.为量化预测节能与新能源汽车的全生命周期碳排放,利用全生命周期评价方法,以汽车相关技术路线和政策为参考,选取燃油经济性、整车轻量化水平、电力结构碳排放因子和氢能碳排放因子为关键参数,构建传统燃油汽车(ICEV)、轻度混合动力汽车(MHEV)、重度混合动力汽车(HEV)、纯电动汽车(BEV)和燃料电池汽车(FCV)的数据清单并对其全生命周期碳排放进行量化预测评价,对电力结构碳排放因子和不同制氢方式碳排放因子进行了敏感性分析和讨论.结果发现,2022年ICEV、 MHEV、 HEV、 BEV和FCV的全生命周期碳排放量(以CO₂-eq计)分别为208.0、 195.5、 150.0、 113.5和205.0 g·km^-1.到2035年,BEV和FCV相比于ICEV具有较为显著的减碳效益,分别降低69.1%和49.3%.电力结构的碳排放因子对BEV的全生命周期碳排放的影响最显著.关于燃料电池汽车的不同制氢方式,短期应以工业副产氢提纯为主供应FCV氢能需求,长期以可再生能源电解水制氢和化石能源...     

装甲车辆沙漠戈壁地区环境适应性试验设计

针对我军当前装甲车辆鉴定定型试验中，沙漠戈壁地区适应性试验考核试验覆盖度较低、考核强度不足、试验标准老旧等问题，导致作战效能大幅下降，甚至无法满足作战使用要求。在参照当前传统的严寒、湿热、高原地区适应试验组织方法基础上，结合当前复杂环境试验及边界条件考核要求，分析了沙漠戈壁地区敏感环境因素，提出了2种新型试验方法，一是“基础+复杂”组合的沙漠戈壁地区适应性试验方法，二是基于作战使用任务剖面循环的试验方法，进一步提升试验考核强度及深度，为装甲车辆沙漠戈壁地区适应性试验开展提供了设计新思路。     

Evaluation of the emission characteristics of PCDD/Fs from electric arc furnaces

Distribution of PCDD/F (polychlorinated dibenzo-p-dioxin and polychlorinated dibenzofuran) congeners at two electric arc furnaces (EAFs) in Taiwan is evaluated via intensive stack sampling and analysis. Two kinds of exhaust system in EAFs including stack system and shutter system are selected for measuring dioxin emissions. In addition, dioxin emissions during oxidation and reduction stages at EAF-A were characterized. Results indicate that the PCDD/F concentration of stack gas in EAF-A was 4.39 ng/N m³ while total Toxic Equivalent Quantity (TEQ) concentration was 0.35 ng I-TEQ/N m³. The PCDD/F concentration of stack gas in EAF-B was 2.20 ng/N m³ and the TEQ concentration was 0.14 ng I-TEQ/N m³. 1,2,3,4,6,7,8-H_pCDF, OCDD and OCDF are the major contributors of the dioxin concentrations for two EAFs investigated and the percentage of PCDD/F in particulate phase increases as the chlorination level of the PCDD/F congener increases. The results obtained on gas/particulate partitioning of PCDD/Fs in flue gases prior to the APCD in EAFs indicate that more than 90% exists in particulate phase. In EAF-A, the PCDD/F concentration during oxidation stage is slightly higher than that measured during reduction stage, including the sampling points of CO converter outlet, prior to bag filter and stack. Majority of PCDD/Fs emitted from steel-making processes exists in particulate-phase (about 60–70%) at both EAFs investigated.     

E-bikers’ braking behavior: Results from a naturalistic cycling study

Abstract

Objective: The number of e-bike users has increased significantly over the past few years and with it the associated safety concerns. Because e-bikes are faster than conventional bicycles and more prone to be in conflict with road users, e-bikers may need to perform avoidance maneuvers more frequently. Braking is the most common avoidance maneuver but is also a complex and critical task in emergency situations, because cyclists must reduce speed quickly without losing balance. The aim of this study is to understand the braking strategies of e-bikers in real-world traffic environments and to assess their road safety implications. This article investigates (1) how cyclists on e-bikes use front and rear brakes during routine cycling and (2) whether this behavior changes during unexpected conflicts with other road users.

Methods: Naturalistic data were collected from 6 regular bicycle riders who each rode e-bikes during a period of 2 weeks, for a total of 32.5?h of data. Braking events were identified and characterized through a combined analysis of brake pressure at each wheel, velocity, and longitudinal acceleration. Furthermore, the braking patterns obtained during unexpected events were compared with braking patterns during routine cycling.

Results: In the majority of braking events during routine cycling, cyclists used only one brake at a time, favoring one of the 2 brakes according to a personal pre-established pattern. However, the favored brake varied among cyclists: 66% favored the rear brake and 16% the front brake. Only 16% of the cyclists showed no clear preference, variously using rear brake, front brake, or combined braking (both brakes at the same time), suggesting that the selection of which brake to use depended on the characteristics of the specific scenario experienced by the cyclist rather than on a personal preference. In unexpected conflicts, generally requiring a larger deceleration, combined braking became more prevalent for most of the cyclists; still, when combined braking was not applied, cyclists continued to use the favored brake of routine cycling. Kinematic analysis revealed that, when larger decelerations were required, cyclists more frequently used combined braking instead of single braking.

Conclusions: The results provide new insights into the behavior of cyclists on e-bikes and may provide support in the development of safety measures including guidelines and best practices for optimal brake use. The results may also inform the design of braking systems intended to reduce the complexity of the braking operation.     

Pediatric electric bicycle injuries and comparison to other pediatric traffic injuries

Objective: The objective of this study was to conduct a comprehensive analysis of demographics, injury characteristics and hospital resource utilization of significant pediatric electric bicycle (e-bike) injuries leading to hospitalization following an emergency department visit in comparison to pediatric injuries caused by other traffic related mechanisms.

Methods: A retrospective review of all pediatric traffic injury hospitalizations following an emergency department visit to a level I trauma center between October 2014 and September 2016 was conducted. Data regarding age, sex, number of computed tomography (CT) scans obtained, number of major procedures, length of hospital stay (LOS), Injury Severity Score (ISS), and number of injuries per patient were collected and compared between e-bike injuries and other traffic injuries.

Results: Three hundred thirty-seven admissions were analyzed: 46 (14%) were due to e-bike injuries (29% of patients >12 years). Age, proportion of brain injuries, and use of CT were significantly increased compared to mechanical bicycle injuries (13.1?±?3.4 vs. 10.6?±?3.6, 13% vs. 3%, 1 [0–3] vs. 1 [0–1], P < .01, P = .03, P = .05). Age, LOS, and use of CT were significantly increased compared to injuries caused to automobile passengers (13.1?±?3.4 vs. 7.4?±?5.3, 1 [1–3] vs. 1 [1–2], 1 [0–3] vs. 0 [0–1], P < .01, P = .03, P = .01), as well as ISS and number of injuries per patient (P = .04, P < .01). Injuries caused by e-bikes were similar to injuries caused to pedestrians, except for age (13.1?±?3.4 vs. 8.5?±?3.7, P < .01). Multivariable analysis revealed a significant association between mechanism of injury and ISS, with increased ISS among e-bike injuries compared to mecahnical bike injuries (OR 2.56, CI 1.1–5.88, P = 0.03) and automobile injuries (OR 4.16, CI 1.49–12.5, (P < .01).

Conclusion: E-bikes are a significant cause of severe injury in children compared to most other traffic injuries, particularly in older children.