5 ways to optimize lithium-ion batteries
By Shina Banerjee, SAKSHI GAURAV, SAMIRAN SARKAR, ADITYA AGRAWAL
State of charge estimation of lithium-ion battery based on improved adaptive boosting algorithm
By Xiaobo Zhao, Seunghun Jung, Biao Wang, Dongji Xuan,
State of charge estimation of lithium-ion battery based on improved adaptive boosting algorithm,
Journal of Energy Storage, Volume 71
The research paper proposes a new algorithm called CWAELM for estimating the state of charge (SOC) of lithium-ion batteries used in electric vehicles. The CWAELM algorithm is a combination of three different methods: Adaboost.RT algorithm, CEEMDAN, and WTD.
CEEMDAN is an adaptive data analysis method for dealing with nonlinear and non-stationary signals. It is developed based on the Empirical Mode Decomposition (EMD) and Ensemble Empirical Mode Decomposition (EEMD) methods. CEEMDAN is a two-step process. In the first step, a set of additional noise sequences is generated and added to the original signal. The noise sequences are generated by adding different Gaussian white noise to the original signal, and the noise coefficient controls the signal-to-noise ratio (SNR). In the second step, the signal is decomposed into different frequency components using the EMD method. The EMD method decomposes the signal into a finite number of IMFs, which represent different frequency components of the signal. The CEEMDAN method improves upon the EMD method by adding adaptive noise to the signal during the decomposition process. This adaptive noise helps to separate the signal from the noise and improve the accuracy of the decomposition.
Adaboost.RT is an adaptive boosting algorithm that generates a series of differential weak learners to reduce the model parameters and weaken the overfitting tendency. The algorithm works by iteratively training a series of weak learners on the training data, with each weak learner focusing on the samples that were misclassified by the previous weak learners. The final prediction is made by combining the predictions of all the weak learners, with each weak learner’s prediction weighted according to its accuracy.
WTD is a wavelet threshold denoising method that is used to remove noise from signals. The method works by decomposing the signal into different frequency components using wavelet transform, and then applying a threshold to each component to remove the noise. The threshold is chosen based on the noise level of each component, which is estimated using a statistical method. The denoised signal is then reconstructed by combining the denoised components
The paper shows that the CWAELM algorithm outperforms other state-of-the-art algorithms in terms of SOC estimation accuracy. The accuracy and robustness of SOC estimation are further manifested in two points: the error range of SOC estimation is small and kept within a reasonable interval, and there are no mutation points with large errors during the whole SOC estimation process under different temperatures and working conditions.
The paper provides comparison results of the CWAELM model with other models under four conditions at three temperatures. The results show that the CWAELM model has lower mean absolute error (MAE) and root mean square error (RMSE) values than other models. The CWAELM algorithm has the advantages of high accuracy and good robustness in SOC estimation of lithium-ion batteries for electric vehicles.
In summary, the proposed CWAELM algorithm with the two-step denoising method has improved SOC estimation accuracy compared to regular models. The combination of Adaboost.RT algorithm, CEEMDAN, and WTD has resulted in a more accurate and robust SOC estimation algorithm for lithium-ion batteries used in electric vehicles.
Xiaobo Zhao, Seunghun Jung, Biao Wang, Dongji Xuan,
State of charge estimation of lithium-ion battery based on improved adaptive boosting algorithm,
Journal of Energy Storage, Volume 71
https://doi.org/10.1016/j.est.2023.108047.
AI based predictive model for selecting doped electrode material for organic lithium ion batteries
By Pandey and J. D. Tiwari,
“AI based predictive model for selecting doped eletrode material for organic lithium ion batteries for Electric Vehicles,” 2022 IEEE 3rd Global Conference for Advancement in Technology (GCAT), Bangalore, India, 2022
The adoption of organic molecules represents a significant advancement in battery technology, particularly in terms of improving fire safety, preventing dendrite formation, and minimizing self-discharge issues. Selecting the most suitable organic components for electrode design not only streamlines production processes but also leads to the development of biodegradable, environmentally friendly, high-performance, lightweight batteries. To identify the ideal organic molecules for doping materials and customizing cathodes for truly eco-friendly batteries, a combination of computational quantum mechanics and AI methodologies was employed. Density functional theory (DFT) played a key role in optimizing the molecular properties of the chosen doped organic molecules, while AI modeling aided in the selection of appropriate nanoparticles and organic compounds for the creation of electroactive cathode materials.
The study focuses on optimizing Ferrocyanide and certain organic quinone molecules using DFT (Density Functional Theory) for potential use in organic lithium-ion batteries. Quinone-based organic compounds are preferred due to their ability to facilitate lithiation and lower HUMO energy states, which enhances electrochemical potential and lithium-ion redox chemistry. Open circuit voltage (Voc) values for 22 ferrocyanide-doped organic compounds are presented, ranging from 0.61 to 3.32, making them suitable for organic lithium-ion batteries. Statistical diversity improves the performance of linear models using these voltage datasets. Amino-quinone moieties show the lowest potential values, while other functional groups like fluoro, hydroxy, and vinyl exhibit varying potentials.
The study employs regression analysis to validate Voc values, achieving a good correlation between predictive and experimental voltage values for ferrocyanide-doped organic compounds. Simplified molecular-input line-entry system architecture is chosen for further studies due to its simplicity and robustness. A neural network model is developed using Natural Language Processing to segment the SMIlLES dataset into a unique vocabulary, enabling the neural network to identify individual organic molecule index vocabulary entries and their related molecular properties such as oxidation potential. Molecular descriptors like dielectric constant, oxidation potential, reduction potential, HOMO, and LUMO are obtained through encoding and decoding processes in the neural network.
The study also presents a mean absolute error plot for reduction potential and demonstrates the performance of the neural network model in predicting test reduction potentials. Ultimately, the developed AI predictive model can be used for screening organic moieties for new electrode materials in the development of advanced battery technologies, particularly organic lithium-ion batteries, with an emphasis on small aromatic rings.
Novel battery systems based on two-additive electrolyte systems
By Tesla's Advanced Battery Research division, founded in 2016, is led by Dr. Jeff Dahn
Dalhousie University in Canada
Two-operative, additive electrolyte systems disclosed include 1) vinylene carbonate (VC) combined with 1,3,2-dioxathiolane-2,2-dioxide (DTD, also known as ethylene sulfate) or another sulfur-containing additive (such as methylene methane disulfonate, trimethylene sulfate, 3-hydroxypropanesulfonic acid γ-sultone, glycol sulfite, or another sulfur-containing additive), 2) fluoro ethylene carbonate (FEC) combined with DTD or another sulfur-containing additive, and 3) prop-1-ene-1,3-sultone (PES) combined with DTD or another sulfur-containing additive. Further, because VC and FEC provide similar improvements (and are believed to function similarly), a mixture of VC and FEC may be considered as only a single operative electrolyte. That is, another disclosed two-operative, additive electrolyte system includes a mixture of VC and FEC combined with DTD or another sulfur-containing additive. When used as part of a greater battery system (which includes the electrolyte, electrolyte solvent, positive electrode, and negative electrode), these two-operative, additive electrolyte systems produce desirable properties for energy storage applications, including in vehicle and grid applications.
More specifically, lithium nickel manganese cobalt oxide (NMC) positive electrodes, a graphite negative electrodes, a lithium salt dissolved in an organic or non-aqueous solvent, which may include methyl acetate (MA), and two additives to form a battery system with desirable properties for different applications. The electrolyte solvent may be the following solvents alone or in combination: ethylene carbonate (EC), ethyl methyl carbonate (EMC), methyl acetate, propylene carbonate, dimethyl carbonate, diethyl carbonate, another carbonate solvent (cyclic or acyclic), another organic solvent, and/or another non-aqueous solvent. Solvents are present in concentrations greater than the additives, typically greater than 6% by weight. The solvent may be combined with the disclosed two-additive pairs (such as VC with DTD, FEC with DTD, a mixture of VC and FEC with DTD, or another combination) to form a battery system with desirable properties for different applications. The positive electrode may be coated with a material such as aluminum oxide (Al 2O 3), titanium dioxide (Ti02), or another coating. Further, as a cost savings, the negative electrode may be formed from natural graphite, however depending on the pricing structure, in certain instances artificial graphite is cheaper than natural graphite.”
All-solid-state lithium-ion batteries and their optimization by boron doping
By Zinaida Shakel, Francisco J.A. Loureiro, B.M.G. Melo, D. Pukazhselvan, Sergey M. Mikhalev, Aliaksandr L. Shaula, Duncan P. Fagg
Investigating the grain boundary features of lithium titanium phosphate as an electrolyte for all-solid-state lithium-ion batteries and their optimization by boron doping,Journal of Energy Storage,Volume 65 https://doi.org/10.1016/j.est.2023.107387.
Solid-state batteries are a promising technology for energy storage due to their potential for higher energy density, improved safety, and longer cycle life compared to traditional liquid electrolyte batteries. However, there are still challenges that need to be addressed in order to make solid-state batteries more efficient and practical for widespread use. One of these challenges is improving the conductivity of the solid electrolyte, which is necessary for efficient ion transport between the electrodes.
This paper addresses this challenge by investigating the electrochemical behavior of NASICON-based solid-state Li-electrolytes, which are currently the best commercially available solid Li-electrolytes. The authors use electrochemical impedance spectroscopy (EIS) to study the impedance dispersions of the samples, which show two semicircles belonging to the bulk and the apparent grain boundary responses, respectively. They find that the presence of a highly resistive grain boundary process in series with a bulk process, which is indicative of a perpendicular grain boundary conduction path, can lead to a much higher overall conductivity in the boron-containing sample, despite a finer microstructure
The authors suggest that this composition-driven grain boundary engineering can be an effective strategy for improving the performance of solid-state batteries. By providing a better understanding of the electrochemical behavior of solid-state Li-electrolytes, this paper could help guide the development of more efficient solid-state batteries in the future.
The authors found that the presence of a highly resistive grain boundary process in series with a bulk process, which is indicative of a perpendicular grain boundary conduction path, can lead to a much higher overall conductivity in the boron-containing sample, despite a finer microstructure. This suggests that composition-driven grain boundary engineering can be used to optimize the microstructure of solid-state Li-electrolytes and improve their overall conductivity.
In other words, the authors found that by carefully controlling the composition of the solid-state Li-electrolyte, it is possible to engineer the grain boundaries in such a way that they facilitate more efficient ion transport between the electrodes in solid-state batteries. This is important because the conductivity of the solid electrolyte is a critical factor in determining the efficiency and performance of solid-state batteries. By providing a better understanding of the electrochemical behavior of solid-state Li-electrolytes, this article could help guide the development of more efficient and practical solid-state batteries in the future.
Nanoporous Si composite anodes in all-solid-state lithium-ion batteries by using acetylene black as a conductive additive
By Ryota Okuno, Mari Yamamoto, Atsutaka Kato, Masanari Takahashi.
Performance improvement of nanoporous Si composite anodes in all-solid-state lithium-ion batteries by using acetylene black as a conductive additive, Electrochemistry Communications, Volume 138,2022 https://doi.org/10.1016/j.elecom.2022.107288
The study explores the use of acetylene black as a conductive additive to improve the electrical conductivity and charge capacity of the anodes. The authors report that the use of acetylene black has led to significant improvements in the electrical conductivity and charge capacity of the anodes.
The study also discusses the challenges associated with Si-based anodes, such as electrical disconnection between Si, SE, and CA particles under severe expansion and contraction, which can cause battery failure. To address this issue, the authors propose modifying the Si particles with one-dimensional carbon nanotubes, two-dimensional graphene, or a three-dimensional carbon coating. These modifications can build a hierarchical 3D conductive network and act as structural buffers against large volume changes.
The study provides valuable insights into the development of high-performance anode materials for all-solid-state lithium-ion batteries. The authors discuss the effect of the CA on the electrical conductivity and initial charge capacity of the nanoporous Si composite anodes. They also note that the dependence of the electrochemical characteristics on the CA content after cycling was more complex. The values of both the discharge capacity and capacity retention after 50 cycles fluctuated and peaked at × = 2. The authors attribute this to the structural stress arising from Si, which is buffered by the elastic deformation of Li3PS4
the study reports that the use of acetylene black as a conductive additive has led to significant improvements in the electrical conductivity and charge capacity of the nanoporous Si composite anodes. The authors also note that upon cycling without capacity limitation, a high discharge capacity was maintained at 1800 mAh g-1 after 200 cycles, which is higher than the discharge capacity achieved with other carbon-based anode materials such as CB.
The study also discusses the effect of the CA on the electrical conductivity and initial charge capacity of the nanoporous Si composite anodes. The authors note that the cycling performance was highly sensitive to the active mass loading, with a major decrease in capacity retention with increasing mass loading. They also report that the dependence of the electrochemical characteristics on the CA content after cycling was more complex, with the values of both the discharge capacity and capacity retention after 50 cycles fluctuating and peaking at × = 2.
Overall, the study suggests that the use of acetylene black as a conductive additive can significantly improve the charge capacity of nanoporous Si composite anodes in all-solid-state lithium-ion batteries, and that the performance of the anodes is highly dependent on the active mass loading and the CA content.
