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Appl. Sci. 2017, 7(11), 1172; https://doi.org/10.3390/app7111172

Enhanced Prognostic Model for Lithium Ion Batteries Based on Particle Filter State Transition Model Modification

1
Centre for Structures, Assembly and Intelligence Automation, Cranfield University, Bedford MK430AL, UK
2
School of Engineering and IT, Charles Darwin University, Casuarina 0815, Australia
3
School of Aerospace, Transport and Manufacturing, Cranfield University, Bedford MK430AL, UK
*
Author to whom correspondence should be addressed.
Received: 12 September 2017 / Accepted: 7 November 2017 / Published: 15 November 2017
(This article belongs to the Special Issue Battery Management and State Estimation)
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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. View Full-Text
Keywords: IVHM (Integrated Vehicle Health Monitoring); probability density function; capacity fade; Remaining Useful Life (RUL) estimation; State of Charge IVHM (Integrated Vehicle Health Monitoring); probability density function; capacity fade; Remaining Useful Life (RUL) estimation; State of Charge
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Arachchige, B.; Perinpanayagam, S.; Jaras, R. Enhanced Prognostic Model for Lithium Ion Batteries Based on Particle Filter State Transition Model Modification. Appl. Sci. 2017, 7, 1172.

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