Remaining useful life prediction for lithium-ion batteries based on a hybrid model combining the long short-term memory and Elman neural networks
By Xiaoyu Li, Lei Zhang, Zhenpo Wang, Pengxiang Dong. Journal of Energy Storage, 2019
388 citations recorded by the source corpus.
Abstract This paper presents a novel hybrid Elman-LSTM method for battery remaining useful life prediction by combining the empirical model decomposition algorithm and long short-term memory and Elman neural networks. The empirical model decomposition algorithm is employed to decompose the recorded battery capacity verse cycle number data into several sub-layers. The recurrent long short-term memory and Elman neural networks are then established to predict high- and low-frequency sub-layers, respectively. Comprehensive battery test datasets have been collected and used for model parameterization and performance evaluation. The comparison results indicate that the proposed hybrid Elman-LSTM model yields superior performance relative to the other counterparts and can predict the battery remaining useful life with high accuracy. The relative prediction errors are 3.3% and 3.21% based on two unseen datasets, respectively.
Subfields: Automotive Engineering, Electrical and Electronic Engineering, Safety, Risk, Reliability and Quality
View Paper: https://www.semanticscholar.org/paper/f64fd53e3e360b57c4841f737ae6aaed2e564ee8
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Citations in the corpus
- Lithium Battery State-of-Health Estimation via Differential Thermal Voltammetry With Gaussian Process Regression (2021, methodology): “[8], [24] proposed a dual-GPR framework”
- LSTM-Based Battery Remaining Useful Life Prediction With Multi-Channel Charging Profiles (2020, neutral): “In using LSTM for RUL prediction, the state-of-theart technique uses only the historic capacity data {Ck} as input [1], [15], and the input and output sizes are same [19], [20].”
- A review on the long short-term memory model (2020, methodology): “Li et al. (2019) presented a lithium-ion battery RUL prediction based on the empirical mode decomposition algorithm in combination with a novel Elman-LSTM hybrid network.”
- Deep Learning for Time Series Forecasting: A Survey (2020, neutral): “156,157,195 Deep-learning techniques are also widely applied to architecture, as can be seen in the in-depth study conducted by the authors in Ref.”