Omariba, Zachary BosireZhang, LijunSun, Dongbai2024-03-012024-03-012018-05-28Z. B. Omariba, L. Zhang and D. Sun, "Remaining useful life prediction of electric vehicle lithium-ion battery based on particle filter method," 2018 IEEE 3rd International Conference on Big Data Analysis (ICBDA), Shanghai, China, 2018, pp. 412-416, doi: 10.1109/ICBDA.2018.8367718.https://www.researchgate.net/publication/324909176_Remaining_Useful_Life_Prediction_of_Electric_Vehicle_Lithium-Ion_Battery_Based_on_Particle_Filter_Methodhttps://repository.nrf.go.ke/handle/123456789/625Lithium-ion batteries are popular today as their applications spans from portable electronics, electric vehicles, military, and aerospace applications. These batteries form a core component of these systems making them critical to the systems functional capability. Remaining useful life prediction is essential therefore as failure to which can lead to reduced performance, and or even catastrophic failure. The remaining useful life estimates are obtained by evaluating successive probability distributions of degrading states. If battery capacity is less than the failure threshold it poses a major danger to electric vehicles. This is because the battery capacity is an important indicator to monitor state of health (SOH), and its value can be less than the failure threshold due to degradation. This paper makes use of NASA's battery dataset to form the observed data sequence for prediction of remaining useful life. Afterwards a particle filter (PF) algorithm is used to perform the prediction of remaining useful life.enEgerton UniversityArticle