Abstract
This paper focuses on predicting the End of Life and End of Discharge of Lithium ion batteries using a battery capacity fade model and a battery discharge model. The proposed framework will be able to estimate the Remaining Useful Life (RUL) and the Remaining charge through capacity fade and discharge models. A particle filter is implemented that estimates the battery’s State of Charge (SOC) and State of Life (SOL) by utilizing the battery’s physical data such as voltage, temperature, and current measurements. The accuracy of the prognostic framework has been improved by enhancing the particle filter state transition model to incorporate different environmental and loading conditions without retuning the model parameters. The effect of capacity fade in the reduction of the EOD (End of Discharge) time with cycling has also been included, integrating both EOL (End of Life) and EOD prediction models in order to get more accuracy in the estimations.
Highlights
Lithium ion batteries are widely used in spacecraft, aircraft, and electric vehicles
This research involves the development of an improved prognostic model to determine End of Life (EOL) and End of Discharge (EOD), increasing the accuracy of the remaining useful life (RUL) prediction of the lithium ion batteries
Qin et al [19] signified the importance of state of health (SOH) prognostics for safe and reliable use of lithium ion batteries; and proposed a rest time-based prognostic framework (RTPF) in which the beginning time interval of two adjacent cycles is adopted to reflect the rest time, in order the accurately predict the regeneration phenomena and improve long-term prediction performance of battery SOH
Summary
Lithium ion batteries are widely used in spacecraft, aircraft, and electric vehicles. The model has been used in a particle filter framework to predict the RUL for individual discharge cycles as well as for cycle life An extension to He et al.’s [7] work was proposed by Xing et al [6]. This research involves the development of an improved prognostic model to determine End of Life (EOL) and End of Discharge (EOD), increasing the accuracy of the RUL prediction of the lithium ion batteries. Qin et al [19] signified the importance of state of health (SOH) prognostics for safe and reliable use of lithium ion batteries; and proposed a rest time-based prognostic framework (RTPF) in which the beginning time interval of two adjacent cycles is adopted to reflect the rest time, in order the accurately predict the regeneration phenomena and improve long-term prediction performance of battery SOH. The effect of capacity fade in the reduction of the EOD time with cycling has been included, integrating both EOL and EOD prediction models in order to get more accuracy in the estimations
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