The world’s demand for lithium extraction has grown in recent years—driven by lithium use in new consumer electronic battery technologies and electric cars. Lithium is a highly reactive alkali metal with excellent heat and electrical conductivity, and these properties make it useful for manufacturing glass, high-temperature lubricants, chemicals, pharmaceuticals, and lithium-ion batteries for electric cars and consumer electronics. 

Global lithium output is on track to triple this decade, but sales of electric cars threaten to surpass even the most conservative output estimates. Each battery requires about eight kilograms (17 pounds) of lithium, plus cobalt, nickel, and other metals. As automakers worldwide struggle to meet extraordinarily ambitious electric vehicle production targets, there is growing interest in doing things differently. 

Recently, a feature story on 60 Minutes talked about how companies are developing lithium extraction for batteries in California’s Imperial Valley.


The IEEE Xplore digital library brings you access to advancements and breakthroughs in the electric vehicles field. We have highlighted several recent advancements below:

Real-time Estimation for Charging Lithium Iron Phosphate Batteries 

Online and real-time estimation of the State of Charge (SoC) of batteries is an issue that affects several applications where energy storage systems are used. Among the most effective techniques for estimating the SoC is based on Electrochemical Impedance Spectroscopy (EIS). One of the problems with EIS is that a single frequency sweep can last too long compared to the need to evaluate the SoC online and in real time. In a paper presented at the 2023 IEEE Conference on Electrical Systems for Aircraft, Railway, Ship Propulsion, and Road Vehicles & International Transportation Electrification (ESARS-ITEC), researchers looked at how to reduce the measurement time required to perform EIS for estimating the State of Charge (SoC) of batteries. 

This work aims to minimize the time required to perform EIS through a feature selection technique based on Genetic Algorithms. Specifically, an experimental campaign was conducted on five different Lithium Iron Phosphate batteries to create a dataset, and a feature selection evaluation strategy was implemented. The proposed approach is based on Genetic Algorithms to perform feature selection to choose some frequencies for EIS that minimize measurement time while still providing good performance for SoC estimation. The implemented feature selection procedure made it possible to reduce the measurement time by about 95% with very similar accuracy compared to an EIS performed with all features.

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Rechargeable Battery State Estimation Based on Machine Learning

Electronic systems and their integration into industrial systems and different aspects of modern life (internet of things, electric vehicles, robotics, smart grids) give rise to new challenges related to storage and optimized energy management. Lithium-ion batteries perfectly meet this objective due to having high energy density, small installation size, low self-discharge, and high supply capacity. However, their wide application requires further research on battery failure prediction and health management. 

Intelligent “battery management systems” (BMSs) employ real-time estimation and control algorithms to improve battery safety while enhancing performance. In a paper presented at the 2023 Conference on Advanced Innovations in Smart Cities (ICAISC), researchers present a new approach for efficient prediction of the “Lithiumion” (Li-ion) battery cells capacities by analyzing and exploiting the battery parameters based on the machine learning algorithms and event-based segmentation.

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Improved Prediction Method for Lithium-Ion Batteries

Accurate prediction of the remaining useful life (RUL) of lithium-ion batteries (LIBs) can provide an important reference for the safe operation of LIBs. In an article in IEEE Transactions on Instrumentation and Measurement, researchers propose an RUL prediction method combining kernel principal component analysis (KPCA), improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), whale optimization algorithm (WOA), and extreme learning machine (ELM).

The prediction results are superimposed and input into the capacity prediction model to realize RUL indirect prediction. The proposed method is verified using National Aeronautics and Space Administration (NASA) battery degradation datasets at different temperatures and experimental environments. The results show that the proposed indirect prediction method has better performance, stability, and robustness.

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These are just a few examples of thousands of articles in the IEEE Xplore Digital Library related to lithium batteries for electric vehicles.

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