Machine learning‐based model for lithium‐ion batteries in BMS of electric/hybrid electric aircraft

Abstract

Summary Reliable operation and control of battery packs can lead to increasing applications of batteries as energy sources for mobile power systems such as electric/hybrid electric aircraft. If the operation of a battery pack is controlled and monitored thoroughly, the safety in the battery system of an electrified aircraft can be guaranteed. The battery model has many applications in battery management systems such as battery performance analysis and fault detection. To achieve an accurate fault diagnosis for electric aircraft, an intelligent fault detection scheme within an accurate battery cell model is required. In this study, an adaptive lithium‐ion battery model is proposed in which models' parameters are estimated by a supervised machine learning paradigm. This adaptive battery model is developed based on a second order equivalent circuit model, which has a good representation of lithium‐ion batteries dynamics. Comparative verification experiments show good accuracy and robustness of the machine learning‐based parameter estimator lead to an accurate battery model with an average error less than 0.4%. Moreover, to see the effectiveness of this machine learning‐based model in fault detection applications, a model‐based fault diagnosis scheme is developed. Finally, the analysis of fault diagnosis tests under different test conditions proves that the proposed adaptive battery model can significantly improve the fault diagnosis accuracy of batteries.

Machine Learning-Based Data-Driven Fault Detection/Diagnosis of Lithium-Ion Battery: A Critical Review

“…Advancement is also required to make the model suitable for real-time prediction with an improved level of diagnosis efficiency and reliability. In another study conducted by Hashemi et al [60], while SVM was not directly employed for fault diagnosis, a fusion of SVM and GPR termed machine learning parameter estimator (MLPE) was used for ECM parameter estimation of LIB. Thereafter, the fault diagnostic strategy is very similar to other ECM-based fault diagnostic methods such as residual generation and comparison with normal operating states.…”

Applying Machine Learning to Rechargeable Batteries: From the Microscale to the Macroscale

“…Similar to theoretical microscale methods, ML has also demonstrated its superiority and potential in combination with experimental studies [63] . Its incorporation with experiments can be classified into three categories: assisting experimental result analysis (e.g.…”

State‐of‐Health Prediction for Lithium‐Ion Batteries Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise‐Gate Recurrent Unit Fusion Model

“…Besides the emerging technology on advanced electrode materials that can hold more energy and long-lasting life, a battery management system (BMS) can help the electrical energy storage system maintain excellent performance without changing new batteries frequently. Core functions of advanced BMS mainly include state of health estimation (SOH), [4][5][6] state of charge (SOC) estimation, [7] electrode aging estimation, [8] temperature control, [9] current/voltage monitoring, [10] fault diagnosis, [11] and cell inconsistency evaluation. [12] Among these functions, SOH estimation is one of the most important ones.…”