Fuel cells remaining useful life estimation using an extended Kalman Filter

Author(s):  
Mathieu Bressel ◽  
Mickael Hilairet ◽  
Daniel Hissel ◽  
Belkacem Ould Bouamama
Author(s):  
Matteo Rubagotti ◽  
Simona Onori ◽  
Giorgio Rizzoni

This paper proposes a strategy for estimating the remaining useful life of automotive batteries based on dual Extended Kalman Filter. A nonlinear model of the battery is exploited for the on-line estimation of the State of Charge, and this information is used to evaluate the actual capacity and predict its future evolution, from which an estimate of the remaining useful life is obtained with suitable margins of uncertainty. Simulation results using experimental data from lead-acid batteries show the effectiveness of the approach.


Author(s):  
Darielson A. Souza ◽  
João L. O. Torres ◽  
João Paulo P. Gomes ◽  
Leonardo R. Rodrigues ◽  
Rômulo N. de C. Almeida ◽  
...  

2020 ◽  
Vol 14 ◽  
Author(s):  
Dangbo Du ◽  
Jianxun Zhang ◽  
Xiaosheng Si ◽  
Changhua Hu

Background: Remaining useful life (RUL) estimation is the central mission to the complex systems’ prognostics and health management. During last decades, numbers of developments and applications of the RUL estimation have proliferated. Objective: As one of the most popular approaches, stochastic process-based approach has been widely used for characterizing the degradation trajectories and estimating RULs. This paper aimed at reviewing the latest methods and patents on this topic. Methods: The review is concentrated on four common stochastic processes for degradation modelling and RUL estimation, i.e., Gamma process, Wiener process, inverse Gaussian process and Markov chain. Results: After a briefly review of these four models, we pointed out the pros and cons of them, as well as the improvement direction of each method. Conclusion: For better implementation, the applications of these four approaches on maintenance and decision-making are systematically introduced. Finally, the possible future trends are concluded tentatively.


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