A novel aging parameter method for online estimation of Lithium-ion battery states of charge and health

Real time monitoring and health managements of the battery require reliable estimation algorithms that are adaptive to the surrounding environment. However, modeling the performance degradation of batteries can be extremely challenging, as there are numerous factors that influence battery's both internal and external conditions. This paper proposes a data driven approach to calculate a new aging parameter for Lithium ion batteries by combining the ideas of incremental capacity analysis and voltage drops in discrete time. With this new parameter, we employ regression analysis and curve fitting methods to build two forecasting algorithms to track the state of charge (SOC) and state of health (SOH) of Lithium-ion battery under constant current discharge conditions. Particularly the proposed SOC and SOH models are decoupled from each other, and assume no prior relationships between open circuit voltage (OCV) and SOC since their input values are terminal measured voltages only. We also suggest how the proposed scheme can be extended to other operating conditions such as constant voltage/power. Overall, this paper introduces a novel aging parameter of Lithium-ion batteries, that provides a new paradigm of the battery state estimations.