State of Health Estimation of Lithium-Ion Batteries based on the CC-CV Charging Curve and Neural Network

ABSTRACT

Nowadays, the lithium-ion batteries are used as a major component of power in many applications. This battery faces the aging phenomenon that results in its degradation and reduced efficiency in supplying and storing the required energy during its life cycle. Therefore, one of the important aspects of a battery management system is the State-of-Health (SOH) to ensure battery safety and reliability. SOH estimation, which indicates the aging level of a battery, is a challenging issue due to the complex aging mechanisms and factors that cause battery degradation. On the other hand, the application of each of the SOH estimation methods is limited under a number of compulsory constraints such as battery operation under full discharge cycles. The batteries are rarely fully discharged under actual operating conditions; therefore, so in this paper, according to the CC-CV charging protocol, two health indices (HIs) that are less dependent on the starting the charging cycle are introduced. Here, these two indices prove to be highly correlated with capacity changes over the life of the battery. Due to the proper performance of the neural network in fitting the nonlinear curves, it is used to establish a correlation between these indices and the battery SOH. The analysis and evaluation of the performance confirms the consistency and efficiency of the proposed method so that the maximum estimation error is less than 2%.

KEYWORDS:

• Health indicator (HI)
• Li-ion battery
• Neural network
• State of health (SOH)

Additional information

Notes on contributors

Ali Ghasemi Siani

Ali Ghasemi Siani received his BS degree in electrical engineering in 2016 from Mohajer University and MS degree in power electronic and electrical machines engineering in 2019 from Maleke Ashtar University of Technology, Iran. His research areas and interests are renewable energies, power electronic converters, and battery management systems. Email: [email protected]

Mehdi Mousavi Badjani

Seyed Mehdi Mousavi Badjani received his BS degree in electronic engineering from Isfahan University of Technology, Isfahan, in 1998, and MS degree and PhD in electrical engineering from Iran University of science and Technology Tehran, Iran, in 2000 and 2008, respectively. His research areas and interests are renewable energies, battery management systems, satellite power systems and pulse power.

Hadi Rismani

Mohammad Hadi Rismani received his BS degree in electronic engineering from University of Technology, Shiraz, in 2011, and MS degree and PhD in electrical engineering from Maleke Ashtar University of Technology, Iran, in 2011 and 2017, respectively. His research areas and interests are renewable energies, battery management systems, power system control and stability, EMI, and pulse power. Email: [email protected]

Mojtaba Saeedimoghadam

Mojtaba Saeedimoghadam received his BS degree in electrical engineering from Islamic Azad University, Kazeroon Branch, Kazeroon, Iran, in 2008, and MS degree in electrical engineering from Islamic Azad University, Najaf Abad Branch, Esfahan, Iran, in 2010. He is PhD student in electrical engineering at Birjand University now. His research areas of interest are renewable energies, micro-grids and battery management systems. Email: [email protected]