An improved adaptive unscented Kalman filter for estimating the states of in‐wheel‐motored electric vehicle

Vehicle state is essential for active safety stability control. However, the accurate measurement of some vehicle states is difficult to achieve without the use of expensive equipment. To improve estimation accuracy in real time, this paper proposes an estimator of vehicle velocity based on the adaptive unscented Kalman filter (AUKF) for an in‐wheel‐motored electric vehicle (IWMEV). Given the merits of an independent drive structure, the tire forces of the IWMEV can be directly calculated through a vehicle dynamic model. Additionally, by means of the normalized innovation square, the validity of vehicle velocity estimation can be detected, and the sliding window length can be adjusted adaptively; thus, the steady‐state error and the dynamic performance of the IWMEV are demonstrated to be simultaneously improved over an alternative approach in comparisons. Then, an adaptive adjustment strategy for the noise covariance matrices is introduced to overcome the impact of parameter uncertainties. The numerically simulated and experimental results prove that the proposed vehicle velocity estimator based on AUKF not only improves estimation accuracy but also possesses strong robustness against parameter uncertainties. The deployment of the estimation algorithm by using a single‐chip microcomputer verifies the strong real‐time performance and easy‐to‐implement characteristics of the proposed algorithm.     

一种新颖的SPMSM驱动系统自适应死区补偿方法

逆变器死区效应将造成电机电流畸变,为提高表贴式永磁同步电机电流控制精度,提出一种自适应死区电压补偿策略。该方法利用参考电流建立d、q轴死区电压核函数,能够在表贴式永磁同步电机电阻、电感参数未知的情况下,对d、q轴死区电压的幅值进行辨识,将辨识的电压幅值与其核函数相乘得到所需补偿的死区电压。从而解决了输死区电压造成的相电流畸变难题。仿真结果表明,所提出的电流控制策略能够有效减少相电流谐波含量、提高电流控制精度。     

Passive fuel cell/lithium-ion capacitor hybridization for vehicular applications

Electric recreational vehicles represent a new challenge in terms of power supply systems compared to the current light-duty electric vehicles, which achieve high performance and long-range. The recreational vehicles need to heed the limited dimension requirements while assuring the high requested power. This paper proposes an integration of Lithium-Ion Capacitor (LIC) with Fuel Cell (FC) without any power electronic device for a three-wheel electric motorcycle. Unlike other hybrid power supply systems, the proposed FC-LIC passive configuration is lighter, compact, more efficient, and simpler to implement. Due to the different impedance of the components the system is self-management, in which FC supplies the average power component and LIC operates as a low-pass filter. In this respect, a simulator is built based on experimental tests to study the system performance in terms of hydrogen consumption and FC degradation. Subsequently, the system is tested under three standard motorcycle driving cycles at three different FC system lifespan stages. The obtained results demonstrate that a passive topology can supply the requested power along different FC stages of life and reported just an increment of 12% of hydrogen consumption at the oldest condition compared to the new condition.     

Impact of vehicle charging on Portugal's national electricity load profile in 2030

This study aims to explore the impact of light-duty passenger (LdP) electric vehicle (EV) charging on the Portuguese national load profile (LP) in 2030. The goal is to identify EV charging strategies that enable a sustainable configuration of the Portuguese LP under different potential levels of LdP EV penetration in 2030. The research offers Portuguese utilities and policymakers information regarding potential threats and solutions of different EV charging strategies on the national grid infrastructure in 2030. Furthermore, it proposes a methodology that can also be adopted in other countries to analyse similar problems. Low, medium, and high penetration scenarios were designated based on the number of LdP EVs. The low and medium penetration scenarios indicate that an intelligent grid is not necessary to perform charging activities. However, coordinating EV charging in the evening via a smart grid (SG) is imperative in the high penetration scenario, as unsustainable levels of power demand will otherwise be reached, compared with the current production and distribution capacity. Moreover, morning charging sessions must also be addressed, as they may induce new peaks of daily consumption given the significant amount of charging activity taking place within that period.     

基于混合蚁群算法的异质车队低碳VRP研究

针对货运车辆在配送调度过程中产生大量碳排放的问题，建立模型将多种影响碳排放量的因素协同优化。模型中考虑了不同载重量的异质车队，两个节点之间有多条道路的柔性路径，以及车辆重量随卸货而减少的动态负载等因素，以碳排放量、行驶时间和行驶路程为优化目标，并加入了节点需求时间窗、根据速度变化划分路段、交接和卸货时间的约束。提出了一种混合蚁群算法，利用蚁群算法信息素强度更新方式保持群体记忆性，利用粒子群算法的快速收敛特性增加计算效率。通过随机数值算例的仿真优化与对比分析，验证了算法和模型的有效性。     

Experience-Based Cognition for Driving Behavioral Fingerprint Extraction

Abstract

With the rapid progress of information technologies, cars have been made increasingly intelligent. This allows cars to act as cognitive agents, i.e., to acquire knowledge and understanding of the driving habits and behavioral characteristics of drivers (i.e., driving behavioral fingerprint) through experience. Such knowledge can be then reused to facilitate the interaction between a car and its driver, and to develop better and safer car controls. In this paper, we propose a novel approach to extract the driver’s driving behavioral fingerprints based on our conceptual framework Experience-Oriented Intelligent Things (EOIT). EOIT is a learning system that has the potential to enable Internet of Cognitive Things (IoCT) where knowledge can be extracted from experience, stored, evolved, shared, and reused aiming for cognition and thus intelligent functionality of things. By catching driving data, this approach helps cars to collect the driver’s pedal and steering operations and store them as experience; eventually, it uses obtained experience for the driver’s driving behavioral fingerprint extraction. The initial experimental implementation is presented in the paper to demonstrate our idea, and the test results show that it outperforms the Deep Learning approaches (i.e., deep fully connected neural networks and recurrent neural networks/Long Short-Term Memory networks).     

A case study of socio-cultural and technical factors in automobile design: Discourses between designers and potential users on a new electric vehicle in Africa

Technological artifacts emerge and develop within social contexts, inextricably interconnecting technology and society. This article addresses this interconnection empirically, analyzing the implementation of an electric vehicle designed and built by students of automotive engineering at the Technical University of Munich (TUM) in Germany. The students created this vehicle to address problems of mobility for rural populations in Africa, aiming to provide local residents with better access to necessities of life such as healthcare, education, and transportation. The article examines African perceptions of and responses to the vehicle, as well as specific suggestions for future improvement. It discusses not only African reactions to the vehicle and its potential social impact on African societies, but also the concepts and ideas of the designers behind this automotive construction. It also acknowledges the ways in which the engineers based their work on assumptions about African needs rather than on substantial ethnographic research, which diminished their ability to succeed.     

Steel slag waste combined with melamine pyrophosphate as a flame retardant for rigid polyurethane foams

To explore the potential application of industrial waste, steel slag powder in combination with melamine pyrophosphate (MPP) was adopted to improve the flame retardancy of rigid polyurethane foam (RPUF). The incorporation of steel slag slightly reduced the thermal conductivity of the resulting flame-retardant RPUF samples. The addition of MPP and/or steel slag did not significantly alter the thermal stability in terms of T_-10% and T_max but did obviously increase the T_-50% value, suggesting the improved thermal resistance of the residues. The coaddition of MPP and steel slag into RPUF resulted in higher LOI values and lower peak heat release rates than the samples incorporating either MPP or steel slag alone. The superior flame retardancy could be attributed to MPP promoting char formation, which then acted as a barrier at the beginning of RPUF thermal decomposition; simultaneously, the thermally stable inorganics in the steel slag powder strengthened the thermal resistance of this char layer.     

The control of paracetamol particle size and surface morphology through crystallisation in a spray dryer

The parameters governing the crystallisation of paracetamol using various conventional techniques has been extensively studied, however the factors influencing the drug crystallisation using spray drying is not as well understood. The aim of this work was to investigate the crystallisation of an active pharmaceutical ingredient through evaporative crystallisation using a spray dryer to study the physicochemical properties of the drug and to use semi-empirical equations to gain insight into the morphology and particle size of the dried powder. Paracetamol solutions were spray dried at various inlet temperatures ranging from 60 °C to 120 °C and also from a series of inlet feed solvent compositions ranging from 50/50% v/v ethanol/water to 100% ethanol and solid-state characterisation was done. The size and morphology of the dried materials were altered with a change in spray drying parameters, with an increase in inlet temperature leading to an increase in particle Sauter mean diameter (from 3.0 to 4.4 µm) and a decrease in the particle size with an increase in ethanol concentration in the feed (from 4.6 to 4.4 µm) as a result of changes in particle density and atomised droplet size. The morphology of the dried particles consisted of agglomerates of individual crystallites bound together into larger semi-spherical agglomerates with a higher tendency for particles having crystalline ridges to form at higher ethanol concentrations of the feed.