Accurate prediction of Remaining Useful Life (RUL) is critical for ensuring the reliability and safety of Lithium-ion Batteries (LiBs) in applications ranging from portable electronics to electric vehicles. However, capacity fading and complex aging mechanisms make RUL estimation challenging. This work presents a hybrid model based on Particle Swarm Optimization (PSO) and Variational Mode Decomposition (VMD) to evaluate the RUL of LiBs. The optimal hyperparameters of the VMD are determined with PSO, before the decomposition of the capacity data into Intrinsic Mode Functions (IMFs). Then, each IMF is input to a Transformer model for time-series forecasting and RUL estimation. The performance of the methodology is assessed with the NASA B0018 and CALCE C37 datasets with a 50%-50% training–testing split. The results show that this proposed hybrid model achieves superior accuracy and robustness compared to other state-of-the-art models.

It has been shown that an analytical model including amplitude, phase shift, and the mean value of the phase currents in seven-phase electrical machines can be used as relevant information for fault detection, isolation, and estimation. However, this model fails to estimate the fault severity accurately under noisy conditions, especially for faults affecting the mean value. Therefore, the model is extended with the noise as the fourth parameter. Two approaches are considered before the estimation performance is evaluated: the noise level is estimated, or the currents are first denoised. The simulation results with different combinations of noise level and fault severity show that both approaches are efficient and enhance the fault severity estimation, even under high-noise conditions (SNR as low as 5dB). Besides, the analytical model, including the noise, agrees well with the numerical model.

This paper presents a fast and efficient fault isolation method in 7-phase electrical machines based on the phase currents projections in the stationary reference frames. The study considers both non incipient faults with 15% to 30% fault severities, and incipient ones whose severities vary 1% to 6%. The noise level effect on the fault isolation is also considered. The fault features are extracted from the transformed currents in the frequency domain. The features are processed with a multi-step classification methodology based on usual techniques (principal component analysis, linear discriminant analysis and support vector machine). The simulation results show that the fault classification under low noise level conditions is effective with an accuracy higher than 98%. However, when the noise level increases, the proposal fails to classify incipient faults.

International audience