A Mapping Study of Machine Learning Methods for Remaining Useful Life
  Estimation of Lead-Acid Batteries
Sérgio F Chevtchenko Elisson da Silva Rocha Bruna Cruz Ermeson Carneiro de Andrade Danilo Ricardo Barbosa de Araújo
Energy storage solutions play an increasingly important role in modern
infrastructure and lead-acid batteries are among the most commonly used in the
rechargeable category. Due to normal degradation over time, correctly
determining the battery's State of Health (SoH) and Remaining Useful Life (RUL)
contributes to enhancing predictive maintenance, reliability, and longevity of
battery systems. Besides improving the cost savings, correct estimation of the
SoH can lead to reduced pollution though reuse of retired batteries. This paper
presents a mapping study of the state-of-the-art in machine learning methods
for estimating the SoH and RUL of lead-acid batteries. These two indicators are
critical in the battery management systems of electric vehicles, renewable
energy systems, and other applications that rely heavily on this battery
technology. In this study, we analyzed the types of machine learning algorithms
employed for estimating SoH and RUL, and evaluated their performance in terms
of accuracy and inference time. Additionally, this mapping identifies and
analyzes the most commonly used combinations of sensors in specific
applications, such as vehicular batteries. The mapping concludes by
highlighting potential gaps and opportunities for future research, which lays
the foundation for further advancements in the field.