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Timely identification of early internal short circuit faults, commonly referred to as micro short circuits (MSCs), is essential yet poses significant challenges for the safe and reliable operation of lithium-ion battery (LIB) energy storage systems. This paper introduces an innovative diagnostic method for early internal short circuits in LIB packs, utilizing dynamic time warping (DTW) applied to incremental capacity (IC). Initially, the terminal voltages of all cells within the LIB pack are ordered at any moment to determine the median terminal voltage, which is then used to generate the median IC curve. This curve acts as a reference benchmark that represents the condition of healthy cells in the pack. Subsequently, the DTW algorithm is utilized to measure the similarity between each cell’s IC curve and the median IC curve. Cells exhibiting similarity scores that exceed a specified threshold are identified as having MSC faults. Lastly, for the cells diagnosed with MSC conditions, a method for estimating short-circuit resistance (SR) based on variations in maximum charging voltage is devised to quantitatively evaluate the severity and evolution of the MSC. Experimental findings reveal that the proposed method effectively identifies MSC cells in the LIB pack and estimates their SRs without the necessity of a battery model, thereby affirming the method’s validity.

This paper presents battery aging models based on high-current incremental capacity features in the presence of battery cycling profiles characterized by fast charging conditions. In particular, the main peak area under the incremental capacity graph is proposed as a capacity indicator. A dataset from the Toyota Research Institute is analyzed. Batteries' cycling data are characterized by various single- or double-step fast charges in constant current to reach 80% of the battery state of charge; the remaining charge process is performed by a 1C charge. Depending on the battery, a linear or logarithmic model was identified as the best suitable for representing the capacity–peak area relationship. The generalization capabilities of the proposed models are evaluated by performing an inference analysis of the fitting results over groups of batteries. Finally, we evaluated the prediction performance of the models by adopting a cross-validation approach. [ABSTRACT FROM AUTHOR]

An incremental capacity parametric model for batteries is proposed. The model is based on Verhulst's logistic equations and distributions in order to describe incremental capacity peaks. The model performance is compared with polynomial models and is demonstrated on a commercial lithium-ion cell. Experimental data features low-current discharges performed at temperatures ranging from −20 °C to 55 °C. The results demonstrate several advantages of the model compared to empirical models. The proposed model enables a clear description of the geometric features of incremental capacity peaks. It also doubles as an open circuit voltage model as the voltage curve can be fully recovered from parameterization on incremental capacity curves. The study of temperature sensitivity show that peak geometric parameters can be modelled as a function of temperature. An example of practical application is then displayed by using the model to estimate battery state-of-charge from voltage and temperature measurements. This example can expand to other practical applications for battery management systems such as state-of-health monitoring. [ABSTRACT FROM AUTHOR]

This paper presents battery aging models based on high-current incremental capacity features in the presence of battery cycling profiles characterized by fast charging conditions. In particular, the main peak area under the incremental capacity graph is proposed as a capacity indicator. A dataset from the Toyota Research Institute is analyzed. Batteries’ cycling data are characterized by various single- or double-step fast charges in constant current to reach 80% of the battery state of charge; the remaining charge process is performed by a 1C charge. Depending on the battery, a linear or logarithmic model was identified as the best suitable for representing the capacity–peak area relationship. The generalization capabilities of the proposed models are evaluated by performing an inference analysis of the fitting results over groups of batteries. Finally, we evaluated the prediction performance of the models by adopting a cross-validation approach.

An incremental capacity parametric model for batteries is proposed. The model is based on Verhulst’s logistic equations and distributions in order to describe incremental capacity peaks. The model performance is compared with polynomial models and is demonstrated on a commercial lithium-ion cell. Experimental data features low-current discharges performed at temperatures ranging from −20 °C to 55 °C. The results demonstrate several advantages of the model compared to empirical models. The proposed model enables a clear description of the geometric features of incremental capacity peaks. It also doubles as an open circuit voltage model as the voltage curve can be fully recovered from parameterization on incremental capacity curves. The study of temperature sensitivity show that peak geometric parameters can be modelled as a function of temperature. An example of practical application is then displayed by using the model to estimate battery state-of-charge from voltage and temperature measurements. This example can expand to other practical applications for battery management systems such as state-of-health monitoring.