The safety benefits of torque-vectoring control of electric vehicles with multiple drivetrains are well known and extensively discussed in the literature. Also, several authors analyze wheel torque control allocation algorithms for reducing the energy consumption while obtaining the wheel torque demand and reference yaw moment specified by the higher layer of a torquevectoring controller. Based on a set of novel experimental results, this study demonstrates that further significant energy consumption reductions can be achieved through the appropriate tuning of the reference understeer characteristics. The effects of drivetrain power losses and tire slip power losses are discussed for the case of identical drivetrains at the four vehicle corners. Easily implementable yet effective rule-based algorithms are presented for the setup of the energy-efficient reference yaw rate, feedforward yaw moment and wheel torque distribution of the torque-vectoring controller.

The paper discusses novel computationally efficient torque distribution strategies for electric vehicles with individually controlled drivetrains, aimed at minimizing the overall power losses while providing the required level of wheel torque and yaw moment. Analytical solutions of the torque control allocation problem are derived and effects of load transfers due to driving/braking and cornering are studied and discussed in detail. Influences of different drivetrain characteristics on the front and rear axles are described. The results of an analytically derived algorithm are contrasted with those from two other control allocation strategies, based on the offline numerical solution of more detailed formulations of the control allocation problem (i.e., a multiparametric nonlinear programming (mp-NLP) problem). The control allocation algorithms are experimentally validated with an electric vehicle with four identical drivetrains along multiple driving cycles and in steady-state cornering. The experiments show that the computationally efficient algorithms represent a very good compromise between low energy consumption and controller complexity.

Abstract-Electric Vehicles (EVs) with multiple motors permit to design the steady-state cornering response by imposing reference understeer characteristics according to expected vehicle handling quality targets. To this aim a direct yaw moment is generated by assigning different torque demands to the left and right vehicle sides. The reference understeer characteristic has an impact on the drivetrain input power as well. In parallel, a Control Allocation (CA) strategy can be employed to achieve an energy-efficient wheel torque distribution generating the reference yaw moment and wheel torque. To the knowledge of the authors, for the first time this paper experimentally compares and critically analyses the potential energy efficiency benefits achievable through the appropriate set-up of the reference understeer characteristics and wheel torque CA. Interestingly, the experiments on a fourwheel-drive EV demonstrator show that higher energy savings can be obtained through the appropriate tuning of the reference cornering response rather than with an energyefficient CA.

During the years 2014–2016 the University of Salento performed the “Impact of Air Quality on Health of Residents in the Municipalities of Cutrofiano, Galatina, Sogliano Cavour, Soleto and Sternatia” (IMP.AIR) study, an epidemiological-molecular research project aiming to evaluate early DNA damage in children living in an area of Salento with high incidence of lung cancer among the male population. One hundred and twenty-two children aged 6–8 years attending primary school were enrolled and the frequency of micronucleated cells (MNC) in oral mucosa was evaluated. In addition, a questionnaire was administered to parents to obtain information about personal data, anthropometric characteristics and lifestyles (physical activity, food habits, family context) of the children and perform a multivariate analysis to detect any factors associated with MNC occurrence. Data on airborne pollutants detected in the study area were acquired by the Regional Agency for the Environmental Protection. The presence of MNC was highlighted in about 42% of children with a mean MNC frequency of 0.49‰. The frequency of MNC was associated to obesity, consumption of red or processed meat and having a mother who smokes. Moreover, the prevalence of biomarkers was higher than in another area of Salento not included in the cluster area.

The number of children diagnosed with Autism Spectrum Disorder (ASD) has rapidly increased globally. Genetic and environmental factors both contribute to the development of ASD. Several studies showed linkage between prenatal, early postnatal air pollution exposure and the risk of developing ASD. We reviewed the available literature concerning the relationship between early-life exposure to air pollutants and ASD onset in childhood. We searched on Medline and Scopus for cohort or case-control studies published in English from 1977 to 2020. A total of 20 articles were selected for the review. We found a strong association between maternal exposure to particulate matter (PM) during pregnancy or in the first years of the children’s life and the risk of the ASD. This association was found to be stronger with PM2.5 and less evident with the other pollutants. Current evidence suggest that pregnancy is the period in which exposure to environmental pollutants seems to be most impactful concerning the onset of ASD in children. Air pollution should be considered among the emerging risk factors for ASD. Further epidemiological and toxicological studies should address molecular pathways involved in the development of ASD and determine specific cause–effect associations.