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Understanding the aging mechanism for lithium-ion batteries (LiBs) is crucial for optimizing the battery operation in real-life applications. This article gives a systematic description of the LiBs aging in real-life electric vehicle (EV) applications. First, the characteristics of the common EVs and the lithium-ion chemistries used in these applications are described. The battery operation in EVs is then classified into three modes: charging, standby, and driving, which are subsequently described. Finally, the aging behavior of LiBs in the actual charging, standby, and driving modes are reviewed, and the influence of different working conditions are considered. The degradation mechanisms of cathode, electrolyte, and anode during those processes are also discussed. Thus, a systematic analysis of the aging mechanisms of LiBs in real-life EV applications is achieved, providing practical guidance, methods to prolong the battery life for users, battery designers, vehicle manufacturers, and material recovery companies.

During recent years, emissions reduction has been tightened worldwide. Therefore, there is an increasing demand for electric vehicles (EVs) that can meet emission requirements. The growing number of new EVs increases the consumption of raw materials during production. Simultaneously, the number of used EVs and subsequently retired lithium-ion batteries (LIBs) that need to be disposed of is also increasing. According to the current approaches, the recycling process technology appears to be one of the most promising solutions for the End-of-Life (EOL) LIBs—recycling and reusing of waste materials would reduce raw materials production and environmental burden. According to this performed literature review, 263 publications about “Recycling of Lithium-ion Batteries from Electric Vehicles” were classified into five sections: Recycling Processes, Battery Composition, Environmental Impact, Economic Evaluation, and Recycling & Rest. The whole work reviews the current-state of publications dedicated to recycling LIBs from EVs in the techno-environmental-economic summary. This paper covers the first part of the review work; it is devoted to the recycling technology processes and points out the main study fields in recycling that were found during this work.

The state of charge (SOC) and state of health (SOH) are two crucial indicators needed for a proper and safe operation of the battery. Coulomb counting is one of the most adopted and straightforward methods to calculate the SOC. Although it can be implemented for all kinds of applications, its accuracy is strongly dependent on the operation conditions. In this work, the behavior of the batteries at different current and temperature conditions is analyzed in order to adjust the charge measurement according to the battery efficiency at the specific operating conditions. The open-circuit voltage (OCV) is used to reset the SOC estimation and prevent the error accumulation. Furthermore, the SOH is estimated by evaluating the accumulated charge between two different SOC using a recursive least squares (RLS) method. The SOC and SOH estimations are verified through an extensive test in which the battery is subjected to a dynamic load profile at different temperatures.

CubeSats and small satellite solutions are increasing in popularity as they enable a fast, cheap, and agile way for satellite applications. An essential component of nearly every satellite is the energy storage device, which is practically equal to a battery. Consequently, an overview of past, present, and future battery technologies for CubeSats is presented. CubeSats use typically commercial off-the-shelf (COTS) batteries. They are not primarily dedicated to space, so their suitability to the space environment needs to be evaluated. Batteries are also considered as potentially dangerous goods. Thus, there are guidelines and standards that specify safety criteria and tests for the batteries in order to be allowed for transportation and launch. Furthermore, the character of satellites’ missions determines their demand on batteries in terms of current rates, depth-of-discharge, and lifetime. Thus, these expectations are discussed. A market survey was also carried out to identify currently available commercial battery solutions and their parameters. This work summarizes the status, requirements, and the market situation of batteries for CubeSats.

Lithium-ion batteries (LIBs) are crucial for consumer electronics, complex energy storage systems, space applications, and the automotive industry. The increasing requirements for decarbonization and CO2 emissions reduction affect the composition of new production. Thus, the entire automotive sector experiences its turning point; the production capacities of new internal combustion engine vehicles are limited, and the demand for electric vehicles (EVs) has continuously increased over the past years. The growing number of new EVs leads to an increasing amount of automotive waste, namely spent LIBs. Recycling appears to be the most suitable solution for lowering EV prices and reducing environmental impacts; however, it is still not a well-established process. This work is the second part of the review collection based on the performed literature survey, where more than 250 publications about “Recycling of Lithium-ion Batteries from Electric Vehicles” were divided into five sections: Recycling Processes, Battery Composition, Environmental Impact, Economic Evaluation, and Recycling and Rest. This paper reviews and summarizes 162 publications dedicated to recycling procedures and their environmental or economic perspective. Both reviews cover the techno-environmental economic impacts of recycling spent LIBs from EVs published until 2021.

As a critical indictor in the Battery Management System (BMS), State of Charge (SOC) is closely related to the reliable and safe operation of lithium-ion (Li-ion) batteries. Model-based methods are an effective solution for accurate and robust SOC estimation, the performance of which heavily relies on the battery model. This paper mainly focuses on battery modeling methods, which have the potential to be used in a model-based SOC estimation structure. Battery modeling methods are classified into four categories on the basis of their theoretical foundations, and their expressions and features are detailed. Furthermore, the four battery modeling methods are compared in terms of their pros and cons. Future research directions are also presented. In addition, after optimizing the parameters of the battery models by a Genetic Algorithm (GA), four typical battery models including a combined model, two RC Equivalent Circuit Model (ECM), a Single Particle Model (SPM), and a Support Vector Machine (SVM) battery model are compared in terms of their accuracy and execution time.

The high variability of solar irradiance, originated by moving clouds, causes fluctuations in Photovoltaic (PV) power generation, and can negatively impact the grid stability. For this reason, grid codes have incorporated ramp-rate limitations for the injected PV power. Energy Storage Systems (ESS) coordinated by ramp-rate (RR) control algorithms are often applied for mitigating these power fluctuations to the grid. These algorithms generate a power reference to the ESS that opposes the PV fluctuations, reducing them to an acceptable value. Despite their common use, few performance comparisons between the different methods have been presented, especially from a battery status perspective. This is highly important, as different smoothing methods may require the battery to operate at different regimes (i.e., number of cycles and cycles deepness), which directly relates to the battery lifetime performance. This paper intends to fill this gap by analyzing the different methods under the same irradiance profile, and evaluating their capability to limit the RR and maintain the battery State of Charge (SOC) at the end of the day. Moreover, an analysis into the ESS capacity requirements for each of the methods is quantified. Finally, an analysis of the battery cycles and its deepness is performed based on the well-established rainflow cycle counting method.

Lithium-ion batteries play an indispensable role, from portable electronic devices to electric vehicles and home storage systems. Even though they are characterized by superior performance than most other storage technologies, their lifetime is not unlimited and has to be predicted to ensure the economic viability of the battery application. Furthermore, to ensure the optimal battery system operation, the remaining useful lifetime (RUL) prediction has become an essential feature of modern battery management systems (BMSs). Thus, the prediction of RUL of Lithium-ion batteries has become a hot topic for both industry and academia. The purpose of this work is to review, classify, and compare different machine learning (ML)-based methods for the prediction of the RUL of Lithium-ion batteries. First, this article summarizes and classifies various Lithium-ion battery RUL estimation methods that have been proposed in recent years. Secondly, an innovative method was selected for evaluation and compared in terms of accuracy and complexity. DNN is more suitable for RUL prediction due to its strong independent learning ability and generalization ability. In addition, the challenges and prospects of BMS and RUL prediction research are also put forward. Finally, the development of various methods is summarized.

With widespread applications for lithium-ion batteries in energy storage systems, the performance degradation of the battery attracts more and more attention. Understanding the battery’s long-term aging characteristics is essential for the extension of the service lifetime of the battery and the safe operation of the system. In this paper, lithium iron phosphate (LiFePO4) batteries were subjected to long-term (i.e., 27–43 months) calendar aging under consideration of three stress factors (i.e., time, temperature and state-of-charge (SOC) level) impact. By means of capacity measurements and resistance calculation, the battery’s long-term degradation behaviors were tracked over time. Battery aging models were established by a simple but accurate two-step nonlinear regression approach. Based on the established model, the effect of the aging temperature and SOC level on the long-term capacity fade and internal resistance increase of the battery is analyzed. Furthermore, the storage life of the battery with respect to different stress factors is predicted. The analysis results can hopefully provide suggestions for optimizing the storage condition, thereby prolonging the lifetime of batteries.

Lithium-ion (Li-ion) batteries have been competitive in Electric Vehicles (EVs) due to their high energy density and long lifetime. However, there are still issues, which have to be solved, related to the fast-charging capability of EVs. The pulsed current charging technique is expected to improve the lifetime, charging speed, charging/discharging capacity, and the temperature rising of Li-ion batteries. However, the impact of the pulsed current parameters (i.e., frequency, duty cycle, and magnitude) on characteristics of Li-ion batteries has not been fully understood yet. This paper summarizes the existing pulsed current modes, which are positive Pulsed Current Mode (PPC) and its five extended modes, and Negative Pulsed Current (NPC) mode and its three extended modes. An overview of the impact of pulsed current techniques on the performance of Li-ion batteries is presented. Then the main impact factors of the PPC strategy and the NPC strategy are analyzed and discussed. The weight of these impact factors on lifetime, charging speed, charging/discharging capacity, and the temperature rising of batteries is presented, which provides guidance to design advanced charging/discharging strategies as well as to determine future research gaps.