Fault Prognosis and Isolation of Lithium-Ion Batteries in Electric Vehicles Considering Real-Scenario Thermal Runaway Risks

Abstract

Advanced safe battery storage systems with health prognostic performance are vital for electric vehicles. Various faults of lithium-ion batteries are usually undetectable in their early stage due to their concealment and graduality. This article presents a real-time fault diagnosis and isolation scheme for real-scenario batteries using the normalized discrete wavelet decomposition. The early frequency-domain features of the fault signals are extracted utilizing the high-frequency detail wavelet components, and a multilevel fault prognosis strategy is developed considering complex charging/driving characteristics under real-vehicle operating conditions. The verification results, implemented on loose wire connection batteries and real-scenario thermal runaway batteries, demonstrate that the proposed method can accurately extract and locate the hidden fault signals even under small magnitudes and effectively detecting and isolating battery faults before thermal runaway. Furthermore, significant reliability and stability of the proposed method are verified on more real-vehicle operation data, enabling online monitorable and traceable of battery faults before triggering thermal runaway, safeguarding drivers and passengers in real-world vehicular operation.