The PV-HESS and PV-PRL SOC profiles explained in , were applied to three LFP cells each, and their capacity was measured. These profiles, used for dynamic conditions validation, exhibit the following characteristics: The PV-HESS profile lasts 15.50 hours with a 1-minute resolution, has a charge and discharge Ah-throughput of 1.562𝐶 𝑁 , and features an average SOC of 51.4%. The PV-PRL profile, on the other hand, spans 22.85 hours with a 1-minute resolution, a charge Ah-throughput of 0.732𝐶 𝑁 , and an average SOC of 51.2%. Both profiles have varying charge-and discharge-rates, with the PV-HESS profile exhibiting higher rates than the PV-PRL profile. The minimum and maximum SOCs are also provided, showing that PV-HESS has a wider SOC range (from 51.4% to 80.0%) compared to PV-PRL (from 36.3% to 61.4%). For PV-HESS, the initial SOC is 28%, and it is repeated 1368 times at a temperature of 40.8°C. For PV-PRL, the initial SOC is 60%, with 928 repetitions at a temperature of 40.6°C. The measured battery capacity for these tests is 3 Ah. Figure presents a comparative analysis between our proposed approach and the Naumann model as described in . As illustrated in Figure (a), all the true measured capacity losses for the PV-HESS profile fall within the predicted upper and lower bounds of our model for 95% of the confidence interval, while a few points for the PV-PRL profile lie outside these bounds. This discrepancy is attributed to the lack of cyclic data for shallow cycles in our training dataset. For the PV-HESS profile, the average charge and discharge rates are approximately 0.2C, which aligns with one of the test points in our dataset, resulting in more accurate predictions. In contrast, the PV-PRL profile has average charge and discharge rates of 0.073 C and 0.057 C, respectively, which were not represented in our training data, leading to less accurate predictions for these shallow cycles. Furthermore, Figure (b) illustrates that the mean prediction error percentage for the proposed approach remains tightly controlled within ±0.2 % for PV-HESS. In contrast, the Naumann method starts with a higher error margin that significantly worsens after the 600th day. For the PV-PRL scenario, the prediction errors from both methods are comparably similar. Our model, which is chemistry agnostic and free from expert knowledge in the field of batteries physio-chemical modelling, matches or surpasses the Naumann model, particularly in high-cyclic applications where the Naumann model struggles to accurately capture the underlying behavior. This suggests that our model provides a more precise depiction of capacity loss mechanisms, especially in scenarios that involve frequent cycling. Although more interpretation could be added, this highlights the potential for future improvements. With further tweaks and enhancements, our model could potentially demonstrate even better performance than the Naumann model.