Battery Modeling and Lifetime Prediction

Abstract

Battery Management Systems (BMS) are gaining greater interest by researchers due to the excessive increase of battery dependent electrical/electronic systems. Batteries are becoming more abundantly used worldwide, mainly in wireless mobile electrical devices, as well as Hybrid Electric Vehicles (HEVs) and Electric Vehicles (EVs). Moreover, batteries emerged as the only device capable of storing transformed energy, henceforth they formed the power banks of all renewable energy systems extending from solar panels, wind turbines, etc. This paper targets modeling various types of batteries, which implemented into the BMS can give an insight on their performance. Parameters of three common models for various types of batteries were identified. Moreover, a common method that gives an insight on the lifespan of any battery under examination was found. This technique was based on several measurements taken at the laboratory and relies on using the Bayesian classifier for finding the state of health of a tested battery. Fast methods are introduced starting from modeling batteries to knowing their lifespan.

A prediction method for voltage and lifetime of lead–acid battery by using machine learning

“…However, the interesting thing is that most of the vehicle applications are still dominated by lead–acid batteries. Therefore, we reviewed the related prediction techniques for lithium-ion batteries (Hinchi and Tkiouat, 2018; Khumprom and Yodo, 2019; Laayouj and Jamouli, 2015; Li et al., 2016; Liu et al., 2010; Ren et al., 2018; Zhang et al., 2018) and the literature on machine learning for battery life prediction (Álvarez Antón et al., 2013; Berecibar, 2019; Chaoui et al., 2017; Rezvani et al., 2011; Salameh et al., 2017; Zhang et al., 2017).…”