Kalman-variant estimators for state of charge in lithium-sulfur batteries

Lithium-sulfur batteries are now commercially available, offering high specific energy density, low production costs and high safety. However, there is no commercially-available battery management system for them, and there are no published methods for determining state of charge in situ. This paper describes a study to address this gap. The properties and behaviours of lithium-sulfur are briefly introduced, and the applicability of ‘standard’ lithium-ion state-of-charge estimation methods is explored. Open-circuit voltage methods and ‘Coulomb counting’ are found to have a poor fit for lithium-sulfur, and model-based methods, particularly recursive Bayesian filters, are identified as showing strong promise. Three recursive Bayesian filters are implemented: an extended Kalman filter (EKF), an unscented Kalman filter (UKF) and a particle filter (PF). These estimators are tested through practical experimentation, considering both a pulse-discharge test and a test based on the New European Driving Cycle (NEDC). Experimentation is carried out at a constant temperature, mirroring the environment expected in the authors' target automotive application. It is shown that the estimators, which are based on a relatively simple equivalent-circuit–network model, can deliver useful results. If the three estimators implemented, the unscented Kalman filter gives the most robust and accurate performance, with an acceptable computational effort

Literature Review, Recycling of Lithium-Ion Batteries from Electric Vehicles, Part I: Recycling Technology

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

Literature Review, Recycling of Lithium-Ion Batteries from Electric Vehicles, Part II:Environmental and Economic Perspective

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

Adaptive configuration of generalized nonlinear ECM of Li-ion batteries based on impedance measurements and DRT analysis

An adaptive approach for configuration of generalized battery nonlinear equivalent-circuit-model (ECM) is proposed. In the approach, the distribution-relaxation-times (DRT) analysis is used to configure and initialize the ECM to be fitted to the impedance data. The performance of the approach is validated and analyzed by using experimental battery impedance measurements

Concurrent real-time estimation of state of health and maximum available power in lithium-sulfur batteries

Lithium-sulfur (Li-S) batteries are an emerging energy storage technology with higher performance than lithium-ion batteries in terms of specific capacity and energy density. However, several scientific and technological gaps need to be filled before Li-S batteries will penetrate the market at a large scale. One such gap, which is tackled in this paper, is represented by the estimation of state-of-health (SOH). Li-S batteries exhibit a complex behaviour due to their inherent mechanisms, which requires a special tailoring of the already literature-available state-of-charge (SOC) and SOH estimation algorithms. In this work, a model of SOH based on capacity fade and power fade has been proposed and incorporated in a state estimator using dual extended Kalman filters has been used to simultaneously estimate Li-S SOC and SOH. The dual extended Kalman filter’s internal estimates of equivalent circuit network parameters have also been used to the estimate maximum available power of the battery at any specified instant. The proposed estimators have been successfully applied to both fresh and aged Li-S pouch cells, showing that they can accurately track accurately the battery SOC, SOH, and power, providing that initial conditions are suitable. However, the estimation of the Li-S battery cells’ capacity fade is shown to be more complex, because the practical available capacity varies highly with the applied current rates and the dynamics of the mission profile