Abstract
Incremental capacity analysis (ICA) has proven to be an effective tool for determining the state of health (SOH) of Li-ion cells under laboratory conditions. This paper deals with an outstanding challenge of applying ICA in practice: the evaluation of battery series connections. The study uses experimental aging and characterization data of lithium iron phosphate (LFP) cells down to 53% SOH. The evaluability of battery series connections using ICA is confirmed by analytical and experimental considerations for cells of the same SOH. For cells of different SOH, a method for identifying non-uniform aging states on the modules’ IC curve is presented. The findings enable the classification of battery modules with series and parallel connections based on partial terminal data.
Highlights
In view of global initiatives to control the effects of greenhouse gas emissions, there have been considerable efforts to reduce the use of fossil fuels as an energy source and to lower carbon dioxide emissions
These in turn lead to aging modes that are described in the literature as loss of lithium inventory (LLI), loss of active material (LAM), and resistance increase (RI) [3,4]
This study focused on the implementation of Incremental capacity analysis (ICA) as a state of health (SOH)-monitoring method for modules, including series-connected cells or packs
Summary
In view of global initiatives to control the effects of greenhouse gas emissions, there have been considerable efforts to reduce the use of fossil fuels as an energy source and to lower carbon dioxide emissions. Jiang et al [20] examined LFP modules consisting of five cells connected in parallel from an aged electric vehicle battery They found that the height and area of the third IC peak show a linear correlation with the capacity, which was used for the SOH determination of the modules. The aim is to determine the SOH of a module including cells connected in series by only having to evaluate data within a short specific state of charge (SOC) range, thereby reducing processed data and time. The applicability of ICA to modules consisting of cells connected in series was first analytically and experimentally investigated Based on these results, a method to classify modules on the basis of their partial terminal data is presented. All measured data were recorded with a sampling rate of 1 s
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