Modelling and Optimization of Energy Management Strategies for Hybrid Vehicles

In the last decades the automotive sector has seen a technological revolution, due mainly to the more restrictive regulation, the newly introduced technologies and, as last, to the poor resources of fossil fuels remaining on Earth. Promising solution in vehicles’ propulsion are represented by alternative architectures and energy sources, for example fuel-cells and pure electric vehicles. The automotive transition to new and green vehicles is passing through the development of hybrid vehicles, that usually combine positive aspects of each technology. To fully exploit the powerful of hybrid vehicles, however, it is important to manage the powertrain’s degrees of freedom in the smartest way possible, otherwise hybridization would be worthless. To this aim, this dissertation is focused on the development of energy management strategies and predictive control functions. Such algorithms have the goal of increasing the powertrain overall efficiency and contextually increasing the driver safety. Such control algorithms have been applied to an axle-split Plug-in Hybrid Electric Vehicle with a complex architecture that allows more than one driving modes, including the pure electric one. The different energy management strategies investigated are mainly three: the vehicle baseline heuristic controller, in the following mentioned as rule-based controller, a sub-optimal controller that can include also predictive functionalities, referred to as Equivalent Consumption Minimization Strategy, and a vehicle global optimum control technique, called Dynamic Programming, also including the high-voltage battery thermal management. During this project, different modelling approaches have been applied to the powertrain, including Hardware-in-the-loop, and diverse powertrain high-level controllers have been developed and implemented, increasing at each step their complexity. It has been proven the potential of using sophisticated powertrain control techniques, and that the gainable benefits in terms of fuel economy are largely influenced by the chose energy management strategy, even considering the powerful vehicle investigated.

The RAS-Lung project: implementing blood and tissue genotyping in KRAS-Positive non-small cell lung cancer treatment

Background: The frontline management of non-oncogene addicted non-small cell lung cancer (NSCLC) involves immunotherapy (ICI) alone or combined with chemotherapy (CT-ICI). As therapeutic options expand, refining NSCLC genotyping gains paramount importance. The dynamic landscape of KRAS-positive NSCLC presents a spectrum of treatment options, including ICI, targeted therapy, and combination strategies currently under investigation. Methods: The two-year RASLUNG project, featuring both retrospective and prospective cohorts, aimed to analyze the predictive and prognostic impact of KRAS mutations on tumor tissue and circulating DNA (ctDNA). Secondary objectives included assessing the roles of co-mutations and longitudinal changes in KRAS mutant copies concerning treatment response and survival outcomes. An external validation study confirmed the prognostic or predictive significance of co-mutations. Results: In the prospective cohort (n=24), patients with liver metastases exhibited significantly elevated ctDNA levels(p=0.01), while those with >3 metastatic sites showed increased Allele Frequency (AF) (P=0.002). Median overall survival (OS) was 7.5 months, progression-free survival (PFS) was 4.0 months, and the objective response rate (ORR) was 33.3%. Higher AF correlated with an increased risk of death (HR 1.04, p = 0.03), though not progression. Notably, a reduction in plasma DNA levels was significantly associated with objective response(p=0.01). In the retrospective cohort, KRAS and STK11 mutations co-occurred in 14/21 patients (p=0.053). STK11 mutations were independently detrimental to OS (HR 1.97, p=0.025) after adjusting for various factors. KRAS tissue AF did not correlate with OS or PFS. Within the validation dataset, STK11 mutations were significantly associated with an increased risk of death in univariate (HR 2.01, p<0.001) and multivariate models (HR 1.66, p=0.001) after adjustments. Conclusion: The RAS-Lung Project, employing innovative genotyping techniques, underscores the significance of comprehensive NSCLC genotyping. Tailored next-generation sequencing (NGS) and ctDNA monitoring may offer potential benefits in navigating the evolving landscape of KRAS-positive NSCLC treatment.