Overdischarging Lithium-Ion Batteries

Hauck, Daniel; Kurrat, Michael

doi:10.1007/978-3-319-70572-9_4

Cited by 11 publications

(7 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Additionally, even for a single battery, excessive use and the difference between the internal local structure and contact can also result in over-discharging. 6 Most studies of this problem have focused on the exploration of the over-discharge mechanism of commercial LIBs (vs Graphite). The over-discharge of LIBs (vs Graphite) significantly influences the anode.…”

mentioning

confidence: 99%

Investigation on the Overlithiation Mechanism of LiCoO₂ Cathode for Lithium Ion Batteries

Tian

Xiao

et al. 2021

J. Electrochem. Soc.

View full text Add to dashboard Cite

Overlithiation of lithium ion batteries often causes a structural transformation of the electrode and capacity degradation and may even lead to severe safety problems. In this study, the electrode structure, surface morphology and compositions at the different overlithiation depths of LiCoO2 cathode material were investigated in detailed by examining the LCO/Li cells and anode-free cells, combined with post-mortem characterizations. When LiCoO2 is found in a slight overlithiation state, the cycle capacity fades slightly and minority of LiCoO2 reduces to CoO and Li2O. After LiCoO2 undergoes a deep overlithiation reaction, a long flat voltage plateau appears at 1.2 V and the cycle performance is greatly deteriorated. SEM and EIS tests show that the single-crystal particles are fractured into small particles with thick-layer formation at the surface and the shape of the EIS curve changes strongly with increasing overlithiation depth. Additionally, as shown by XPS Ar-ion etching and TEM characterization, the LiCoO2 surface is converted into greater amounts of Li2O, CoO and Co metal associated with the severe electrolyte decomposition, and LiF, Li2CO3 and LixPOyFz are the main electrolyte decomposition products. The detailed investigation of the overlithiation mechanism of LiCoO2 provides comprehensive and thorough guidance for understanding the overlithiation mechanism of cathode materials as well as important reference information for practical all-solid-state battery applications.

show abstract

mentioning

confidence: 99%

Investigation on the Overlithiation Mechanism of LiCoO₂ Cathode for Lithium Ion Batteries

Tian

Xiao

et al. 2021

J. Electrochem. Soc.

View full text Add to dashboard Cite

show abstract

“…Hauck and Kurrat reviewed various methods for discharging batteries using electronic techniques and conductive liquids. 49 The most used method is to submerge the battery in a conductive solution, such as a NaCl solution. Although NaCl is the most popular conductive solution, there is ongoing research on the discharge rate and corrosion rate of other conductive liquids.…”

Section: Pre-treatment Methods For Waste Lithium-ion Batterymentioning

confidence: 99%

Technologies of lithium recycling from waste lithium ion batteries: a review

2021

View full text Add to dashboard Cite

show abstract

“…Baterai yang rusak dapat disebabkan oleh penggunaan yang tidak optimal, dimana kapasitas baterai dapat menjadi kosong atau terlalu penuh, sehingga mengakibatkan baterai menjadi terlalu panas (overheating) serta dapat mengurangi umur baterai (Lindgren & Lund, 2016). Oleh karena itu, penting untuk menjaga baterai dari masalah seperti overcharge, undercharge (Hauck & Kurrat, 2018), overcurrent, short circuit (Ouyang, dkk, 2018), serta variasi suhu (Łebkowski, 2017), agar baterai dapat bertahan lama. Untuk mengatasi permasalahan tersebut, diperlukan suatu sistem yang mampu memperkirakan nilai State of Charge (SOC) pada baterai dengan tujuan menjaga performa baterai tetap optimal, serta melindungi baterai dari kelebihan dan kekurangan muatan yang dapat menyebabkan umur baterai menjadi pendek (Afandi, dkk, 2022).…”

Section: Pendahuluanunclassified

Estimasi State of Charge pada Baterai Lead Acid menggunakan Elman Recurrent Neural Network

RAKHMAWATI,

SUTEDJO,

OKTAVIANI

et al. 2023

ELKOMIKA

View full text Add to dashboard Cite

ABSTRAKPenggunaan panel surya sebagai sumber energi terbarukan membutuhkan baterai sebagai tempat penyimpanan energi. Penggunaan baterai secara terus menerus, dapat menyebabkan pengurangan kapasitas dan penurunan performa. Untuk mengatasi permasalahan tersebut, diperlukan sistem estimasi nilai State of Charge (SOC) pada baterai yang berfungsi untuk mengontrol kondisi charge, agar performa baterai tetap optimal. Pada penelitian dikembangan suatu sistem estimasi SOC pada baterai jenis lead acid, dengan metode algoritma Elman Recurrent Neural Network (ERNN). Keunggulan yang terkait dengan metode ERNN meliputi proses iterasi menjadi lebih cepat, peningkatan kecepatan pembaruan parameter, dan pencapaian konvergensi yang lebih cepat. Hasil dari penelitian estimasi SOC pada baterai lead acid 12V, 12Ah dengan menggunakan algoritma ERNN sebesar 0.101% sedangkan dengan algoritma Feedforward Backpropagation sebesar 0.767%. Sehingga dapat disimpulkan bahwa algoritma ERNN lebih efisien dalam mengestimasi nilai SOC pada baterai lead acid.Kata kunci: Baterai, Elman Recurrent Neural Network, Panel Surya, State of Charge; Lead Acid ABSTRACTUsing solar panels as a renewable energy source requires batteries as energy storage. Continuous use of batteries can result in reduced capacity and performance degradation. Based on these problems, a State of Charge (SOC) estimation system is needed for the battery to control charge conditions so that battery performance remains optimal. In this research, a SOC estimation system was developed for lead acid battery using the Elman Recurrent Neural Network (ERNN) algorithm. The advantage of the ERNN method is that the iteration process is faster, the parameter update speed is increased, and convergence is faster. The results of the SOC estimation for a 12V, 12Ah lead acid battery using the ERNN algorithm were 0.101%, while the Feedforward Backpropagation algorithm resulted in 0.767%. The ERNN algorithm is more efficient in estimating the SOC value of a lead acid battery.Keywords: Battery, Elman Recurrent Neural Network, Solar Panel, State of Charge, Lead Acid

show abstract

Overdischarging Lithium-Ion Batteries

Cited by 11 publications

References 1 publication

Investigation on the Overlithiation Mechanism of LiCoO₂ Cathode for Lithium Ion Batteries

Investigation on the Overlithiation Mechanism of LiCoO₂ Cathode for Lithium Ion Batteries

Technologies of lithium recycling from waste lithium ion batteries: a review

Estimasi State of Charge pada Baterai Lead Acid menggunakan Elman Recurrent Neural Network

Contact Info

Product

Resources

About

Overdischarging Lithium-Ion Batteries

Cited by 11 publications

References 1 publication

Investigation on the Overlithiation Mechanism of LiCoO2 Cathode for Lithium Ion Batteries

Investigation on the Overlithiation Mechanism of LiCoO2 Cathode for Lithium Ion Batteries

Technologies of lithium recycling from waste lithium ion batteries: a review

Estimasi State of Charge pada Baterai Lead Acid menggunakan Elman Recurrent Neural Network

Contact Info

Product

Resources

About

Investigation on the Overlithiation Mechanism of LiCoO₂ Cathode for Lithium Ion Batteries

Investigation on the Overlithiation Mechanism of LiCoO₂ Cathode for Lithium Ion Batteries