Pugalenthi, K., Park, H., Hussain, S., & Raghavan, N. (2021). Hybrid Particle Filter Trained Neural Network for Prognosis of Lithium-Ion Batteries. IEEE Access, 9, 135132–135143. https://doi.org/10.1109/access.2021.3116264
Abstract:
Prognostics and Health Management (PHM) plays a key role in Industry 4.0 revolution
by providing smart predictive maintenance solutions. Early failure detection and prediction of remaining
useful life (RUL) of critical industrial machines/components are the main challenges addressed by PHM
methodologies. In literature, model-based and data-driven methods are widely used for RUL estimation.
Model-based methods rely on empirical/phenomenological degradation models for RUL prediction using
Bayesian formulations. In many cases, the lack of accurate physics-based models emphasizes the need to
resort to machine learning based prognostic algorithms. However, data-driven methods require extensive
machine failure data incorporating all possible operating conditions along with all possible failure modes
pertaining to that particular machine / component, which are seldom available in their entirety. In this work,
we propose a three-stage hybrid prognostic algorithm (HyA) combining model-based (Particle Filters-PF)
and data-driven (Neural Networks-NN) methods in a unique way. The proposed method aims to overcome
the need for accurate degradation modeling or extensive failure data sets. In the rst stage, a feedforward
neural network is used to formulate lithium-ion battery's degradation trends and the correspondingNNmodel
parameters are used to de ne the initial prior distribution of PF algorithm. In the second stage, the PF
algorithm optimizes the model parameters and the posterior model parameter distributions are utilized to
`warm-start' the neural network used for prognosis and the third/ nal stages focuses on prognosis and RUL
estimation using the trained NN model leveraging on the posterior distributions of the PF ne-tuned weights
and biases. The proposed method is demonstrated on CALCE and NASA lithium-ion battery capacity
degradation datasets. The ef cacy of the proposed hybrid algorithm is evaluated using root mean square
error (RMSE) values and alpha-lambda prognostic metrics. Also, the impact of the NN architecture on the
prediction accuracy and computational load are analyzed.
License type:
Attribution 4.0 International (CC BY 4.0)
Funding Info:
This research / project is supported by the Ministry of Education - Research Student Scholarship (RSS)
Grant Reference no. : IGIPAMD180
This research / project is supported by the Agency for Science, Technology and Research - MTC Programmatic
Grant Reference no. : A20H5b0142