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In this work we propose a method for extracting, modelling, and predicting the resistance of Lithium-ion batteries directly from the battery dynamic mission profile. While the extraction of the mainly relied on data manipulation and bookkeeping, the modelling and subsequent prediction of the resistance used a log-linear model. It is shown that the estimated log-linear model can be used to create a posterior probability distribution of the age of the battery, given an internal resistance measurement and the SOC value at which it was measured. This distribution was used calculate the expected age of the battery, and the expected age was compared to weekly check-ups. At an SOC of 80% a mean absolute error (MAE), between the weekly check-ups and the expected age,of 5.83 weeks (706 FEC) was achieved. Furthermore, it is shown that by introducing a decision threshold the MAE could be reduced as far as 2.65 weeks (321 FEC). Lastly, a method is introduced for handling cases where the SOC was not known exactly.

The resistance is one of the parameters that de scribes the performance of Lithium-ion (Li-ion) batteries, as it offers information about the battery efficiency and its power capability. However, similar to other performance parameters of Li-ion batteries, the resistance is dependent on the operating conditions and increases while the battery is aging. Traditionally, to capture these dependencies, Li-ion cells are aged at different conditions using synthetic mission profi les and periodically the aging tests are stopped in order to measure the resistance at standard conditions. Most of the times, even though accurate information about the resistance behavior is obtained, they do not reflect the behavior from real-life applications. Thus, in this work we propose a method for extracting, modelling, and predicting the resistance directly from the battery dynamic mission profile. While the extraction mainly relied on data manipulation and bookkeeping, the modelling and subsequent prediction of the resistance used a log-linear model.

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