Experimental and optimization of material synthesis process parameters for improving capacity of lithium-ion battery

Abstract

Summary New methods for synthesis of active materials have been developed to improve capacity and cycle life performance of lithium‐ion batteries. Past studies have focused on routes of development of materials and new processes, which might not be economical for large‐scale production. In this regard, this study examines a widely employed carbothermal reduction technology for the synthesis of lithium‐iron phosphate (LiFePO4/C) and investigates effects of process conditions during this synthesis on final battery performance. An experimental combined genetic programming approach is used to model the effects of crucial process conditions (sintering time, the carbon content, and the sintering temperature) on the discharge capacity of the assembled battery. Experiments are conducted to collect the discharge capacity data based on varying LiFePO4/C synthesis conditions, and genetic programming is employed to develop a suitable functional relationship between them. The results show that the battery discharge capacity is controlled significantly by adjusting sintering temperature and carbon content, while the effect of sintering time is found to be insignificant. Further, the interaction effect of the sintering time and carbon content is much more obvious than that of the sintering time and the sintering temperature. The findings from the study pave the way for the optimum design of the synthesis process of LiFePO4/C for a higher battery performance.

Active Equalization Strategy for Lithium-Ion Battery Packs Based on Multilayer Dual Interleaved Inductor Circuits in Electric Vehicles

“…ese issues are worsened by the fact that LIBs are very sensitive to deep discharge and overcharge [9]. Numerous studies have been conducted to improve the capacity and life cycle of LIBs, and the safety issues have not been well addressed [10]. Manufacturing differences are inevitable, and thus a proper equalization technology is needed to make each cell operate safely and reliably.…”

Machine learning approach in exploring the electrolyte additives effect on cycling performance of LiNi 0 . 5 Mn 1 . 5 O 4 cathode and graphite anode‐based lithium‐ion cell

“…Among many data‐driven algorithms, artificial neural network (ANN) has appeared to be promising as its ability of robust prediction based on both the external and internal changes of input to determine optimal experimental conditions and eventually achieve best performance 37,38 . The ANN algorithm has been applied in several aspects for enhancing prediction of battery performance followed by a sequence of variants such as the synthesis condition, the reactants, and even open‐circuit voltage (OCV) 35‐37,39‐41 .…”

“…37,38 The ANN algorithm has been applied in several aspects for enhancing prediction of battery performance followed by a sequence of variants such as the synthesis condition, the reactants, and even open-circuit voltage (OCV). [35][36][37][39][40][41] ANN is a self-adaptive information processing artificial intelligence (AI) method, which is comprised of numerous processing units called neurons. All neurons are distributed in three layers, that is, input layer, hidden layer, and output layer (Figure 1).…”

Synthesis and electrochemical performance of Na 3− x Li x V 2 (PO 4 ) 3 /C as cathode materials for Li‐ion batteries

“…With the improvement of human requirements for living environment, energy and environmental issues have become the focus of attention, especially the development and application of clean energy storage materials . Although Li‐ion batteries are widespread used to power devices ranging from small electronics to electric vehicles, the amount of available Li resources is limited, which has inspired the search for new energy storage materials. In particular, sodium‐based energy storage materials have been extensively researched as lithium storage materials as a result of the high natural abundance of Na.…”