Battery Management System (BMS) design for Lithium-ion batteries State of Charge (SoC) prediction has a crucial role in Electric Vehicles (EVs) and smart grids development. The need to design compact, light and fast devices requires finding a suitable trade-off between effectiveness and efficiency. In the literature, it is well emphasized that the application of electrochemical-based methods such as the Pseudo-Two-Dimensional (P2D) model is computationally prohibitive and requires significant simplifications. Conversely, plain Equivalent Circuit Models (ECM) are too simple and unable to represent the cell dynamics. The application of an Ensemble Neural Network (ENN) as Equivalent Neural Network Circuit (ENNC) emerged as a promising solution able to synthesize expressive and computationally efficient models. Indeed, with the support of a suitable dataset, an ENN can be configured to represent a given ECM, modeling each lumped parameter through an assigned Neural Network (NN). Accordingly, the ENNC system is able to keep a physical description of the battery cell while approximating the non-linear dynamic of each component. The paper proposes a novel ENNC battery named Physical Inspired-Equivalent Neural Network Circuit (PI-ENNC) whose ensemble architecture relies on a fractional-order Extended Single Particle (ESP) Lithium-ion cell formulation. The PI-ENNC is designed to approximate the ESP transfer functions referred to the ohmic effects, the electrolyte diffusion and the non-uniform charge distribution in the cell. The proposed model has been tested with three publicly available datasets, investigating the model behavior according to two different training strategies and with different input configurations. In order to prove its effectiveness, results have been compared with a simpler version proposed in a previous work. Results highlight the effectiveness of PI-ENNC in SoC prediction, underlining the importance of designing an ENN architecture that leverages on equations and constraints that reflect the physical phenomena of the cell.

Granular Computing is a powerful information processing paradigm, particularly useful for the synthesis of pattern recognition systems in structured domains (e.g., graphs or sequences). According to this paradigm, granules of information play the pivotal role of describing the underlying (possibly complex) process, starting from the available data. Under a pattern recognition viewpoint, granules of information can be exploited for the synthesis of semantically sound embedding spaces, where common supervised or unsupervised problems can be solved via standard machine learning algorithms. In this work, we show a comparison between different strategies for the automatic synthesis of information granules in the context of graph classification. These strategies mainly differ on the specific topology adopted for subgraphs considered as candidate information granules and the possibility of using or neglecting the ground-truth class labels in the granulation process. Computational results on 10 different open-access datasets show that by using a class-aware granulation, performances tend to improve (regardless of the information granules topology), counterbalanced by a possibly higher number of information granules.

The measure of Quality of Life (QoL) has become one of the most important criteria used to assess the impact of chronic illness, such as asthma, on the patient's daily life, in adults and children alike. The objective of our open observational study was to measure the QoL and analyze several factors that potentially affect QoL, such as symptoms and functional respiratory parameters, in a cohort of children with asthma. One hundred and twenty-seven children with asthma, 6 to 14 years of age, living in the city of Rome, were enrolled as outpatients. They were subjected to Skin Prick Tests (SPT), underwent spirometry and filled out the Pediatric Asthma Quality of Life Questionnaire (PAQLQ). One hundred and eleven children were diagnosed with intermittent asthma, 12 (10%) with mild asthma, and four with moderate persistent asthma. Ninety-six children had a positive SPT. The mean total score of QoL, obtained from the questionnaire, was 5.4 (±1.2 SD). Two QoL groups were created. Children with total QoL score <5.5 were included in the “Lower QoL” score group while children with total QoL score ≥ 5.5 were included in the “Higher QoL” score group. Children in the Higher group and their mothers had a higher mean age, suffered from fewer asthma exacerbations during the year preceding the study, and showed a higher mean value of forced expiratory volume (FEV1) compared to the children in the Lower category. Using Logistic regression we identified the main factors that may affect QoL as FEV1, symptoms in the previous year and mother's age. QoL is correlated with the frequency of asthma exacerbations and FEV1 values. Furthermore, our research shows that a significant impairment of QoL may also occur in patients with normal lung function, pointing out the importance of evaluating QoL in all children with asthma.