In this study, the life cycle costs of railway projects involving hybrid diesel-electric vehicles are analysed. Specifically, the analysis focuses on the comparison of 3 lithium-ion battery technologies (NMC, LTO and LFP) and 8 energy management strategies (including rule-based and optimization-based strategies). In order to develop this analysis, a methodology that returns the life cycle cost of each proposed case is presented. The methodology includes the optimization of the diesel generator and lithium-ion battery sizing. A scenario based on a real railway line is introduced, and the obtained results are compared to a traditional diesel-electric railway vehicle to develop a techno-economical discussion. The best lithium-ion battery technologies are found to be LTO and NMC, and the most appropriated strategy a state-machine controller optimised by a genetic algorithm approach. The best case obtains a life cycle cost reduction of the 4.0% and diesel savings of the 13.7% compared to a traditional diesel-electric railway vehicle. The proposed analysis is claimed to be potentially helpful for the cost-optimal design and operation definition of powertrains for hybrid railway vehicles.

Lithium-ion (Li-ion) batteries are still the best technology to power the Electric Vehicle (EV), due to their high power and energy density. However, the use of these batteries can be limited in cars with a high demand for peak power and very high energy density. One way to improve the performance of the Li-ion battery and reduce its weight is to associate this battery with another technology of higher specific energy as a second energy source, e.g. Lithium-sulfur (Li-S). The development of Hybrid Energy Storage Systems (HESSs) is a promising solution optimizing the energy management of EVs. In this paper, we present experimental results obtained with a high specific energy and power capability HESS prototype, composed of i) a Lithium-Titanate-Oxide battery to ensure high power capabilities, ii) a Li-S battery to improve specific energy, and iii) a power converter based on Gallium Nitride (GaN) devices to link both battery modules, minimizing at the same time system weight, volume and power losses. The developed GaN-based power converter achieves high efficiency (96.5%) operating at 300 kHz with a reduced size (0.4 L). Besides, the behavior of the developed HESS prototype is experimentally evaluated under standard automotive profiles, for different driving scenarios. The HESS prototype achieves an increase of energy density and specific energy of +5.56% and +28.21%, respectively, compared to a battery system composed only of Li-ion cells. The limitation of the developed system for automotive application are highlighted, and the critical research needs are clearly identified to accelerate the implementation of such systems on commercial EV solutions.

The present paper is focused on the evaluation of the economic influence of a battery lifetime model upon the optimal sizing and energy management strategy of a dual energy storage system (ESS) composed of Lithium-ion batteries and supercapacitors. The operation of a Hybrid Bus is taken as a case study in order to evaluate the effects of battery lifetime models’ accuracy on ESS sizing and operation in a heavy-duty application. For this purpose, two different lifetime models (a Wöhler-curve-based model and a semi-empirical model) were applied in the multi-objective optimisation of a hybrid electric urban bus. Differences up to ca. 8% on the daily operation costs and ca. 25% on the dual ESS costs were estimated depending on the lifetime model considered for the optimisation.

Progress towards a decarbonized economy is dependent on the development of energy storage devices enabling the deployment of new strategies in the transport and energy sectors. Lithium-Ion Batteries (LIBs) play a key role in facilitating the shift from fossil fuels to renewables based on ubiquitous sources of decentralized energy. However, the rapidly increasing demand for batteries has generated environmental concerns regarding recycling, and raw materials depletion and exhaustion. Moreover, the level of extraction and processing of battery raw materials from local resources is almost negligible, inducing geopolitical concerns regarding potential threats to production and the stability of local manufactures. Finally, the storage and processing of hazardous wastes remains a difficult challenge in the circular economy concept. Recycling, therefore, is beneficial to the environment and has a high economic value via strengthening local supply chains. This work presents an alternative, comprehensive, and holistic review of LIB recycling technologies; from traditional metallurgy strategies to innovative, direct recycling methods and pioneering in-production and design-for-recycling approaches. It identifies new directions for making recycling efficient and techno-economically profitable and sustainable - including the vital role of robotics, artificial intelligence, and machine learning. Keywords: Lithium Ion Batteries, Recycling, Design for Recycling, In-production Recycling, Artificial Intelligence

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