Big data analytics for proactive and predictive maintenance in electric car battery packs

The idea behind the project is to develop a methodology for analyzing and developing techniques for the diagnosis and the prediction of the state of charge and health of lithium-ion batteries for automotive applications. For lithium-ion batteries, residual functionality is measured in terms of state of health; however, this value cannot be directly associated with a measurable value, so it must be estimated. The development of the algorithms is based on the identification of the causes of battery degradation, in order to model and predict the trend. Therefore, models have been developed that are able to predict the electrical, thermal and aging behavior. In addition to the model, it was necessary to develop algorithms capable of monitoring the state of the battery, online and offline. This was possible with the use of algorithms based on Kalman filters, which allow the estimation of the system status in real time. Through machine learning algorithms, which allow offline analysis of battery deterioration using a statistical approach, it is possible to analyze information from the entire fleet of vehicles. Both systems work in synergy in order to achieve the best performance. Validation was performed with laboratory tests on different batteries and under different conditions. The development of the model allowed to reduce the time of the experimental tests. Some specific phenomena were tested in the laboratory, and the other cases were artificially generated.

In-car sound linear simulation

In recent years, vehicle acoustics have gained significant importance in new car development: increasingly advanced infotainment systems for spatial audio and sound enhancement algorithms have become the norm in modern vehicles. In the past, car manufacturers had to build numerous prototypes to study the sound behaviour inside the car cabin or the effect of new algorithms under development. Nowadays, advanced simulation techniques can reduce development costs and time. In this work, after selecting the reference test vehicle, a modern luxury sedan equipped with a high-end sound system, two independent tools were developed: a simulation tool created in the Comsol Multiphysics environment and an auralization tool developed in the Cycling ‘74 MAX environment. The simulation tool can calculate the impulse response and acoustic spectrum at a specific position inside the cockpit. Its input data are the vehicle’s geometry, acoustic absorption parameters of materials, the acoustic characteristics and position of loudspeakers, and the type and position of virtual microphones (or microphone arrays). The simulation tool can also provide binaural impulse responses thanks to Head Related Transfer Functions (HRTFs) and an innovative algorithm able to compute the HRTF at any distance and angle from the head. Impulse responses from simulations or acoustic measurements inside the car cabin are processed and fed into the auralization tool, enabling real-time interaction by applying filters, changing the channels gain or displaying the acoustic spectrum. Since the acoustic simulation of a vehicle involves multiple topics, the focus of this work has not only been the development of two tools but also the study and application of new techniques for acoustic characterization of the materials that compose the cockpit and the loudspeaker simulation. Specifically, three different methods have been applied for material characterization through the use of a pressure-velocity probe, a Laser Doppler Vibrometer (LDV), and a microphone array.

Analysis of efficacy and safety of CAR T-cell therapy in relapsed/refractory lymphomas: current indications and future perspectives

In the last few years, the introduction of chimeric antigen receptor (CAR) T-cell therapy into clinical practice has revolutionized the approach to patients with relapsed/refractory (R/R) large B-cell lymphoma (LBCL), whose outcome used to be dismal with median overall survival (OS) of approximately 6 months with standard salvage therapy. At our Institute, we started treating diffuse large B-cell lymphoma (DLBCL) patients with CAR T-cell products in August 2019 and they received either axicabtagene ciloleucel (axi-cel) and tisagenlecleucel (tisa-cel) as per regulatory indications. This research project presents the 2-year follow-up of the first 53 treated patients. Our first aim is to investigate the feasibility of this treatment strategy in a real-world setting, although the reimbursement criteria set by the Italian Medicines Agency (Agenzia Italiana del Farmaco, AIFA) are very similar to the inclusion criteria of clinical trials and stricter than those established by the regulatory authorities of many foreign countries. One month after infusion, the ORR was 66% with 19 patients already in CR (38%). Restaging at 3, 6 and 12 months post-infusion shows that early CRs tend to be maintained over time and, moreover, that a considerable number of PRs and a few SDs can improve into a CR. The safety data were consistent with what is reported in the literature; toxicity was generally manageable, largely due to the increasing expertise in handling the specific adverse events related to CAR T-cell therapy. Our results confirms that CAR T-cell therapy is both safe and effective in a real-life setting and that it represents a crucial weapon in a subset of patients who were previously doomed to an inevitably severe prognosis.