The aim of the present research topic is to gather relevant contributions addressed to improve the characterization and the understanding of commercial lithium-ion battery (LIB) cells and LIB packs, composed of serial-parallel combination of cells. For reliable and optimum operation of connected cells, an accurate understanding and control of cells behavior is mandatory. LIB packs performance and durability can be severely affected by cell-to-cell variations (CtCV). Identification, quantification, statistical analysis, modeling and simulation of CtCV is a key issue in battery pack performance optimization both prior and after assembly. Addressing the issue prior assembly will allow proper matching of cells to be placed in series and in parallel and undoubtfully increase pack performance and safety but it will require additional testing and thus time and money. CtCV can relate to the initial performance of the cells but also to their aging pace. Parameters to be considered includes manufacturing and assembly intrinsic variations but also extrinsic variations such as non-uniform thermal conditions. Addressing the issue after assembly will be the job of the Battery Management Systems (BMS) and should therefore not be calculation intensive and be derivable from information from available voltage, current and temperature sensors. This can be very challenging in large LIB packs, owing to the high number of involved cells and the random nature of battery usage in the field. Furthermore, BMS must take into account the CtCV in the charging algorithm, in the algorithm to keep the battery ready to deliver full power when required, and in the definition strategies to extend the life of the battery pack. In this research topic, we aim to collect the most relevant research on commercial LIB characterization, diagnosis, prognosis, and performance optimization, from experimental testing, statistical analysis, thermal modeling, to BMS algorithms.
https://www.frontiersin.org/research-topics/9583/commercial-lithium-ion-battery-performance-analysis-and-optimization-from-experimental-testing-to-bm
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