Design of alternative energy storage systems for hybrid vehicles based on statistical processing of driving cycles information

Hybrid vehicles represent the future for automakers, since they allow to improve the fuel economy and to reduce the pollutant emissions. A key component of the hybrid powertrain is the Energy Storage System, that determines the ability of the vehicle to store and reuse energy. Though electrified Energy Storage Systems (ESS), based on batteries and ultracapacitors, are a proven technology, Alternative Energy Storage Systems (AESS), based on mechanical, hydraulic and pneumatic devices, are gaining interest because they give the possibility of realizing low-cost mild-hybrid vehicles. Currently, most literature of design methodologies focuses on electric ESS, which are not suitable for AESS design. In this contest, The Ohio State University has developed an Alternative Energy Storage System design methodology. This work focuses on the development of driving cycle analysis methodology that is a key component of Alternative Energy Storage System design procedure. The proposed methodology is based on a statistical approach to analyzing driving schedules that represent the vehicle typical use. Driving data are broken up into power events sequence, namely traction and braking events, and for each of them, energy-related and dynamic metrics are calculated. By means of a clustering process and statistical synthesis methods, statistically-relevant metrics are determined. These metrics define cycle representative braking events. By using these events as inputs for the Alternative Energy Storage System design methodology, different system designs are obtained. Each of them is characterized by attributes, namely system volume and weight. In the last part the work, the designs are evaluated in simulation by introducing and calculating a metric related to the energy conversion efficiency. Finally, the designs are compared accounting for attributes and efficiency values. In order to automate the driving data extraction and synthesis process, a specific script Matlab based has been developed. Results show that the driving cycle analysis methodology, based on the statistical approach, allows to extract and synthesize cycle representative data. The designs based on cycle statistically-relevant metrics are properly sized and have satisfying efficiency values with respect to the expectations. An exception is the design based on the cycle worst-case scenario, corresponding to same approach adopted by the conventional electric ESS design methodologies. In this case, a heavy system with poor efficiency is produced. The proposed new methodology seems to be a valid and consistent support for Alternative Energy Storage System design.

Are there genetic breaks between Atlantic and Pacific Yellowfin tuna (Thunnus albacares) populations? A preliminary study based on microsatellites gene variation

Yellowfin tuna (Thunnus albacares, YFT, Bonnaterre 1788) is one of the most important market tuna species in the world. The high mortality of juveniles is in part caused by their bycatch. Indeed, if unregulated, it could permanently destabilize stocks health. For this reason investigating and better knowing the stock boundaries represent a crucial concern. Aim of this thesis was to preliminary investigate the YFT population structure within and between Atlantic and Pacific Oceans through the analysis of genetic variation at eight microsatellite loci and assess the occurrence of barriers to the gene flow between Oceans. For this propouse we collected 4 geographical samples coming from Atlantic and Pacific Ocean and selected a panel of 8 microsatellites loci developped by Antoni et al., (2014). Samples 71-2-Y and 77-2-Y, came from rispectively west central pacific ocean (WCPO) and east central pacific ocean (ECPO), instead samples 41-1-Y and 34-2-Y derive from west central atlantic ocean (WCAO) and east central atlantic ocean (ECAO). Total 160 specimens were analyzed (40 per sample) and were carried out several genetic information as allele frequencies, allele number, allelic richness, HWE (using He and Ho) and pairwise Fst genetic distance. Results obtained, may support the panmictic theory of this species, only one of pairwise Fst obtained is statistically significant (Fst= 0.00927; pV= 0.00218) between 41-1-Y and 71-2-Y samples. Results suggest low genetic differentiation and consequent high level of gene flow between Atlantic and Pacific populations. Furthermore, we performed an analysis of molecular taxonomy through the use of ATCO (the flaking region between ATPse6 and cytochrome oxidase subunit III genes mt DNA, to discriminate within the gener Thunnus two of the related species (Yellofin and bigeye tuna) according with their difficult recognition at certain size (<40 cm). ATCO analysis in this thesis, has provided strong discriminate evidence between the target species proving to be one of the most reliable genetic tools capable to indagate within the genus Thunnus. Thus, our study has provided useful information for possible use of this protocol for conservation plans and management of this fish stocks.

Development of an innovative non rule-based energy management strategy for a Hybrid Electric Vehicle, structured for predictive driving controls based on external information knowledge

Recently, the interest of the automotive market for hybrid vehicles has increased due to the more restrictive pollutants emissions legislation and to the necessity of decreasing the fossil fuel consumption, since such solution allows a consistent improvement of the vehicle global efficiency. The term hybridization regards the energy flow in the powertrain of a vehicle: a standard vehicle has, usually, only one energy source and one energy tank; instead, a hybrid vehicle has at least two energy sources. In most cases, the prime mover is an internal combustion engine (ICE) while the auxiliary energy source can be mechanical, electrical, pneumatic or hydraulic. It is expected from the control unit of a hybrid vehicle the use of the ICE in high efficiency working zones and to shut it down when it is more convenient, while using the EMG at partial loads and as a fast torque response during transients. However, the battery state of charge may represent a limitation for such a strategy. That’s the reason why, in most cases, energy management strategies are based on the State Of Charge, or SOC, control. Several studies have been conducted on this topic and many different approaches have been illustrated. The purpose of this dissertation is to develop an online (usable on-board) control strategy in which the operating modes are defined using an instantaneous optimization method that minimizes the equivalent fuel consumption of a hybrid electric vehicle. The equivalent fuel consumption is calculated by taking into account the total energy used by the hybrid powertrain during the propulsion phases. The first section presents the hybrid vehicles characteristics. The second chapter describes the global model, with a particular focus on the energy management strategies usable for the supervisory control of such a powertrain. The third chapter shows the performance of the implemented controller on a NEDC cycle compared with the one obtained with the original control strategy.