A Review of Life Prediction Methods for PEMFCs in Electric Vehicles
Abstract
:1. Introduction
2. Data-Driven Approaches
2.1. Echo State Network
2.2. Long- and Short-Term Memory Network
2.3. Adaptive Neural Fuzzy Inference System
2.4. Nonlinear Autoregressive Exogenous Neural Network
2.5. Relevance Vector Machine
2.6. Gaussian Process Regression
2.7. Extreme Learning Machine
2.8. Digital Twin
3. Model-Based Approaches
3.1. Particle Filter
3.2. Kalman Filtering
3.3. Degradation Mechanism Model
3.4. Empirical Model
4. Hybrid Approaches
5. Prospects and Challenges of PEMFC
6. Conclusions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
ANFIS | Adaptive neuro-fuzzy inference system | ARMA | Autoregressive and moving average |
ARIMA | Autoregressive integrated moving average | ACF | Autocorrelation function |
BP | Backpropagation | DSPK | Double sigma point Kalman filter |
DBN | Deep belief network | DE | Differential evolution |
DGP | Deep Gaussian process | EKF | Extended Kalman filter |
ESN | Echo state network | ELM | Extreme learning machine |
FDKF | Frequency-domain Kalman filter | FDT | Future degradation trend |
GP | Gaussian process | GA | Genetic algorithm |
GPR | Gaussian process regression | KF | Kalman filter |
LSSVM | Least squares support vector machine | LSTM | Long- and short-term memory network |
MRVR | Multi-kernel relevance vector regression | MIMO | Multiple-input multiple-output |
MEA | Mind evolutionary algorithm | NOE | Nonlinear output error |
NARX | Nonlinear autoregressive exogenous | NSD | Navigation sequence-driven |
PSO | Particle swarm optimization | PAM | Physical aging model |
PF | Particle filter | RUL | Remaining useful life |
PEMFC | Proton exchange membrane fuel cell | RPF | Regularized particle filter |
RVM | Relevance vector machine | SPGP | Sparse pseudo-input Gaussian process |
SSA | Singular spectrum analysis | SOC | State of charge |
UKF | Unscented Kalman filter | VAE | Variational auto-encoded |
WOA | Whale optimization algorithm |
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Tang, A.; Yang, Y.; Yu, Q.; Zhang, Z.; Yang, L. A Review of Life Prediction Methods for PEMFCs in Electric Vehicles. Sustainability 2022, 14, 9842. https://doi.org/10.3390/su14169842
Tang A, Yang Y, Yu Q, Zhang Z, Yang L. A Review of Life Prediction Methods for PEMFCs in Electric Vehicles. Sustainability. 2022; 14(16):9842. https://doi.org/10.3390/su14169842
Chicago/Turabian StyleTang, Aihua, Yuanhang Yang, Quanqing Yu, Zhigang Zhang, and Lin Yang. 2022. "A Review of Life Prediction Methods for PEMFCs in Electric Vehicles" Sustainability 14, no. 16: 9842. https://doi.org/10.3390/su14169842