Batteries play a crucial role in the domain of energy storage systems and electric vehicles by enabling energy resilience, promoting renewable integration, and driving the advancement of eco-friendly mobility. However, the degradation of batteries over time remains a significant challenge. This paper presents a comprehensive review aimed at investigating the intricate phenomenon of battery degradation within the realm of sustainable energy storage systems and electric vehicles (EVs). This review consolidates current knowledge on the diverse array of factors influencing battery degradation mechanisms, encompassing thermal stresses, cycling patterns, chemical reactions, and environmental conditions. The key degradation factors of lithium-ion batteries such as electrolyte breakdown, cycling, temperature, calendar aging, and depth of discharge are thoroughly discussed. Along with the key degradation factor, the impacts of these factors on lithium-ion batteries including capacity fade, reduction in energy density, increase in internal resistance, and reduction in overall efficiency have also been highlighted throughout the paper. Additionally, the data-driven approaches of battery degradation estimation have taken into consideration. Furthermore, this paper delves into the multifaceted impacts of battery degradation on the performance, longevity, and overall sustainability of energy storage systems and EVs. Finally, the main drawbacks, issues and challenges related to the lifespan of batteries are addressed. Recommendations, best practices, and future directions are also provided to overcome the battery degradation issues towards sustainable energy storage system.

Battery energy storage systems (BESSs) can be very beneficial to power systems and microgrids for various applications. With increasing sales of electric vehicles (EV), the availability of used EV batteries (EVBs) is on the rise, which has received significant attention in recent years. The retired EVBs, after re-purposing, can serve as an alternative option to new batteries in a BESS. In addition, when a microgrid operator desires to install a BESS, the optimal decisions such as installation year, energy and power size, replacement year, and number of cycles-to-failure-that are normally overlooked- have to be determined. Therefore, this paper proposes a comprehensive and novel framework for planning and operation of BESS based on re-purposed EVBs (REVBs). A novel linearized BESS sizing model is proposed that obtains the BESS optimal decisions regarding design and operation. Various new, modified and linearized relationships for BESS have been included in the planning model to ensure replacement year of BESS is optimally determined. Several class of EVs with multiple drive cycles are clustered and integrated within the proposed framework.

Due to the rapid changes in the energy situation on a global scale, the amount of RES installed using clean renewable energy sources such as Photovoltaic (PV) and Wind-power Generators (WGs) is rapidly increasing. As a result, there has been a great deal of research aimed at promoting the adoption of renewable energy. Research on Demand-side Management (DSM) has also been important in promoting the adoption of RES. However, the massive introduction of PV has changed the shape of the demand curve for electricity, which significantly impacts the operational planning of thermal generators. Therefore, this paper proposes an Advanced Direct Load Control (ADLC) model to temporarily shutdown the electric connection between the power grid and Smart Houses (SHs). The most important feature of the proposed model is that it temporarily shuts down the electric connection with the power grid. The shutdown is performed twice to increase the load demand during daytime hours and reduce the peak load during night-time hours. The proposed model also promotes the self-consumption of the generated power during the shutdown period, which is expected to reduce the operating cost. This paper considers six case studies for SH, and the operational costs and carbon dioxide emissions are compared and discussed. The results show that the SH with ADLC successfully reduces the operating costs and carbon dioxide emissions.

Plug-in electric vehicles (PEVs) present a promising solution to mitigate greenhouse gas emissions but on the other hand, their increased penetration can impact power system operation, particularly so in an isolated microgrid. Similarly, demand response (DR) has the potential to provide significant flexibility in the operation of an isolated microgrid with limited generation capacity, by altering the demand and introducing an elasticity effect. This paper proposes a new mathematical model for optimal scheduling of energy resources and smart management of loads which includes smart charging of PEVs, DR, and operation of battery energy storage systems (BESSs), for isolated microgrids. Different case studies are developed to examine isolated microgrid operations when the demand increases, and how the energy management model copes with such increase. The proposed model develops energy management strategies considering the network constraints and different objective functions from the perspective of the microgrid operator as well as from the owners of PEVs and BESS.

Timely prediction of the State of Health (SoH) of lithium-ion batteries is important for battery management and longevity. Traditional centralized deep learning models have shown promising results, but they raise concerns related to data privacy, as data needed to be collected and trained on a single node. This study addresses this challenge by utilizing both centralized (i.e., deep learning) and decentralized (i.e., federated learning) approaches for SoH prediction. The NASA battery dataset, containing charging and discharging cycles, is used for model training and evaluation. Three deep learning architectures 1D Convolutional Neural Networks (CNN), CNN plus Long Short-Term Memory (LSTM), and CNN plus Gated Recurrent Units (GRU) are used in the centralized approach. The 1DCNNmodel outperforms, demonstrating strong predictive capabilities, thus for decentralized learning (i.e., federated learning), the 1D CNN model is utilized with federated averaging technique across five clients, allowing for local training without sharing raw data. Obtained results shows that the highest testing RMSE (0.666) and MAPE (0.980) are observed during decentralized learning, while the centralized approach shows varying performance across different batteries. The decentralized approach effectively balances performance and privacy, highlighting the reliability of federated learning in SoH prediction for lithium-ion batteries.