TY - GEN
T1 - Battery prognostics using a self-cognizant dynamic system approach
AU - Bai, Guangxing
AU - Wang, Pingfeng
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/9/8
Y1 - 2015/9/8
N2 - This paper proposes a new self-cognizant dynamic system approach for Battery PHM, that incorporates an artificial neural network model into a dual extended Kalman filter (DEKF) algorithm. A feed-forward neural network (FFNN) structure is employed to approximate a complex battery system response that is mostly treated as impossible modeling work due to inaccessible system physics. After training by historical data, the FFNN model is embedded into a DEKF algorithm to track down system dynamics. The required recursive computation, which is used to update the FFNN model during collecting the online measurement, is also derived in this paper. To validate the proposed SCDS approach, a battery dynamic system is introduced as an experimental application. After modeling the battery system by an FFNN model and a state-space model, the state-of-charge (SoC) and state-of-health (SoH) are estimated with updating the FFNN model by the proposed approach. Experimental results illustrate that the proposed approach improves the efficiency and accuracy for battery PHM.
AB - This paper proposes a new self-cognizant dynamic system approach for Battery PHM, that incorporates an artificial neural network model into a dual extended Kalman filter (DEKF) algorithm. A feed-forward neural network (FFNN) structure is employed to approximate a complex battery system response that is mostly treated as impossible modeling work due to inaccessible system physics. After training by historical data, the FFNN model is embedded into a DEKF algorithm to track down system dynamics. The required recursive computation, which is used to update the FFNN model during collecting the online measurement, is also derived in this paper. To validate the proposed SCDS approach, a battery dynamic system is introduced as an experimental application. After modeling the battery system by an FFNN model and a state-space model, the state-of-charge (SoC) and state-of-health (SoH) are estimated with updating the FFNN model by the proposed approach. Experimental results illustrate that the proposed approach improves the efficiency and accuracy for battery PHM.
KW - Dual extended Kalman filter
KW - Feed-Forward Neural Networks
KW - Lithium-ion battery
KW - Prognostics and Health Management
KW - Self-Cognizant Dynamic System
KW - State-of-Charge
KW - State-of-Health
UR - http://www.scopus.com/inward/record.url?scp=84957916447&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84957916447&partnerID=8YFLogxK
U2 - 10.1109/ICPHM.2015.7245023
DO - 10.1109/ICPHM.2015.7245023
M3 - Conference contribution
AN - SCOPUS:84957916447
T3 - 2015 IEEE Conference on Prognostics and Health Management: Enhancing Safety, Efficiency, Availability, and Effectiveness of Systems Through PHAf Technology and Application, PHM 2015
BT - 2015 IEEE Conference on Prognostics and Health Management
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - IEEE Conference on Prognostics and Health Management, PHM 2015
Y2 - 22 June 2015 through 25 June 2015
ER -