This document is the accepted version of the article "Development of driving cycles for Electric Vehicles in the context of the city of Florence", to be used for sharing through Author Institution repository.
The railway system represents one of the most resource-efficient answer to our ever-growing demand for transport service and the development trends for the following years forecast a substantial increase in this sector. Considering the European Union, rolling stock realizes a significant share of both goods and passengers carriage while it is responsible for a derisory quota of environmental impact and energy consumption involved by transportation. Contrary to the low environmental impact, the amount of End-of-Life (EoL) waste generated by rolling stocks in relation to the number of vehicles is notable, much greater than in the case of road vehicles. As railway vehicles are built from many heterogeneous components, the EoL rolling stock is a precious source of materials, whose recycling brings measurable economic benefits and needs to be appropriately debated. The paper performs the calculation of recoverability/recyclability rate for different typologies of representative railway vehicles on the basis of primary data and according to the recyclability and recoverability calculation method issued by UNIFE in the context of Product category Rules (PCR). The typologies of railway vehicles taken into account are electric metro, diesel commuter train and high-speed electric train. The analysis envisages also to repeat the calculation in case innovative materials and manufacturing technologies are adopted in the construction of car-body structure. Results show that recyclability/recoverability rates are abundantly over the quota of 90% for each one of the three trains, these latter being made in major part of metals which benefit from very efficient recovery processes. The adoption of innovative materials and manufacturing technologies for car-body structure involves a scarce reduction of recyclability and recoverability rates (about 2% and 0.2% respectively) due to the introduction of components and materials characterized by critical dismantlability and low efficiency recovery processes; recoverability results less affected by lightweighting because post-shredding thermal recovery treatments are roughly independent with respect to dismantlability. A sensitivity analysis based on different dismantling scenarios reveals that the effectiveness of dismantling has a moderate influence on recyclability/recoverability rate (the variation does not exceed 3%). The low variability of recyclability/recoverability rate can be explained by the following reasons: material composition of trains shows a predominance of metals; the efficiency of metals separation processes is close to 100%; post-shredding recycling processes of metals are characterized by recovery factors equal to the ones of post-dismantling recycling processes.
Keywords:Train, Railway, Rolling stock, Recyclability, Recoverability, End-of-Life, Dismantling
Highlights:Overview on railway vehicles End-of-Life; Assessment of recyclability and recoverability for three trains representative of current railway vehicle categories "urban, high-speed and commuter" ...
On Electrical and Hybrid Vehicles (EVs, HEVs), energy is stored in accumulators, mainly electro-chemical batteries. A reliable and cost effective management of energy storage system is a key point for the development of such devices, their durability and for vehicle performance optimization. This requires the accurate estimation of the battery state over time and in a wide range of operating conditions. The battery state is usually expressed as State Of Charge (SOC) and State Of Health (SOH). Their estimations requires an accurate model to represent the static and dynamic behaviors of the battery. This paper presents a model adaptive Unscented Kalman Filter (UKF) method to estimate online SOC of Li-ion batteries. The proposed approach uses a Recursive Least Squares method to update the UKF model parameters during a discharge period. The effectiveness of the method has been verified based on real data acquired from five LiFePO4 battery packs installed on a working EV.
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