Abstract
Lithium-ion (Li-ion) cells degrade after repeated cycling and the cell capacity fades while its resistance increases. Degradation of Li-ion cells is caused by a variety of physical and chemical mechanisms and it is strongly influenced by factors including the electrode materials used, the working conditions and the battery temperature. At present, charging voltage curve analysis methods are widely used in studies of battery characteristics and the constant current charging voltage curves can be used to analyze battery aging mechanisms and estimate a battery’s state of health (SOH) via methods such as incremental capacity (IC) analysis. In this paper, a method to fit and analyze the charging voltage curve based on a neural network is proposed and is compared to the existing point counting method and the polynomial curve fitting method. The neuron parameters of the trained neural network model are used to analyze the battery capacity relative to the phase change reactions that occur inside the batteries. This method is suitable for different types of batteries and could be used in battery management systems for online battery modeling, analysis and diagnosis.
Highlights
The energy crisis and environmental concerns have led to significant developments in electric vehicle technology and energy storage stations over the last few decades [1]
(2) An incremental capacity (IC) curve calculation method is proposed based on the proposed neural network model, and the results show improved performance when compared with the traditional voltage differentiation method
Several constant current charging voltage curve analysis methods, including the point counting method, the polynomial curve fitting method, and a proposed new method based on a neural network model, are compared and analyzed for the purposes of Li-ion cell aging mechanism analysis and battery state of health (SOH) estimation in a battery management system (BMS) in an EV
Summary
The energy crisis and environmental concerns have led to significant developments in electric vehicle technology and energy storage stations over the last few decades [1]. The Liion battery is one of the most critical components for energy storage because of its high energy and power density, long lifetime, and lack of a memory effect [2, 3]. The cell capacity fades and its resistance increases as a result, and this cell degradation affects the cell’s energy storage ability and its output power capability. To improve the performance and reliability of the battery system, it is necessary to develop an appropriate battery management system (BMS). The BMS is required to estimate the battery state [5] and the battery’s state of health (SOH) in particular [6]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
