Overdischarge Detection and Prevention with Temperature Monitoring of Li-ion Batteries and Linear Regression-based Machine Learning

This work focuses on the use of linear regression analysis-based machine learning for the prediction of the end of discharge of a commercial prismatic lithium (Li)-ion cell. The cell temperature was recorded during the cycling of Li-ion cells and the relation between the open circuit voltage and cell temperature was used in the development of the linear regression-based machine learning algorithm. The peak temperature was selected as the indicator of battery end of discharge. A battery management system using a pyboard microcontroller was constructed to monitor the temperature of the cell under test, and was also used to control a MOSFET that acted as a switch to disconnect the cell from the circuit. The method used an initial 10 charge and discharge cycles at a rate of 1C as the training data, then another charge and discharge cycle for the testing data. During the test cycling, the discharge was continued beyond the cutoff voltage to initiate an overdischarge while the temperature of the cell was continuously monitored. The experiment was performed on 3 different cells, and the overdischarge for each was secured within 0.1 V of the cutoff voltage. The results of these experiments show that a linear regression-based analysis can be implemented to detect an overdischarge condition of a cell based on the anticipated peak temperature during discharge.

Silicon/Biogas-Derived Carbon Nanofibers Composites for Anodes of Lithium-Ion Batteries

The electrochemical performance of novel nano-silicon/biogas-derived carbon nanofibers composites (nSi/BCNFs) as anodes in lithium-ion batteries was investigated, focusing on composition and galvanostatic cycling conditions. The optimization of these variables contributes to reduce the stress associated with silicon lithiation/delithiation by accommodating/controlling the volume changes, thus preventing anode degradation and therefore improving its performance regarding capacity and stability. Specific capacities up to 520 mAh g−1 with coulombic efficiency > 95% and 94% of capacity retention are achieved for nSi/BCNFs anodes at electric current density of 100/200 mA g−1 and low cutoff voltage of 80 mV. Among the BCNFs, those no-graphitized with fishbone microstructure, which have a great number of active sites to interact with nSi particles, are the best carbon matrices. Specifically, a nSi:BCNFs 1:1 weight ratio in the composite is the optimal, since it allows a compromise between a suitable specific capacity, which is higher than that of graphitic materials currently commercialized for LIBs, and an acceptable capacity retention along cycling. Low cutoff voltage in the 80–100 mV range is the most suitable for the cycling of nSi/BCNFs anodes because it avoids formation of the highest lithiated phase (Li15Si4) and therefore the complete silicon lithiation, which leads to electrode damage.

Boosting Electrochemical Performances of High-Voltage NCA/LTO Cells by Cathode Electrolyte Interface Nano Film Formation in Ionic Liquid Electrolyte

LiTFSI/EMITFSI ionic liquid was applied in this study as an electrolyte to enhance electrochemical performances of LiNi0.8 Co0.15 Al0.05 O2 /Li4 Ti5 O12 (NCA/LTO) batteries at a high charging cutoff voltage of 3.2 V. Li-EMITFSI electrolyte generated extremely stable and uniform cathode electrolyte interface (CEI) nano film on the cathode surface. This CEI film not only inhibited the continuous decomposition of electrolyte, but also stabilized the high operating voltage of NCA/LTO batteries, resulting in enhanced discharge gravimetric specific capacity (Cg) and cyclic stability of NCA/LTO batteries. With Li-EMITFSI electrolyte, the NCA/LTO batteries achieved higher C g of 172 mAhg –1 at 0.1 C and good capacity retention of 75% over 50 cycles at 1.4 ~ 3.2 V, compared to traditional commercial electrolytes.