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<p>The accurate prediction of the remaining useful life (RUL) for lithium-ion batteries (LIBs) is crucial for the effective management of electric vehicle energy systems. However, the prediction accuracy and adaptability are frequently compromised by the phenomenon of capacity regeneration. To surmount this challenge, a novel RUL prediction methodology is introduced for LIBs that integrates variational mode decomposition (VMD) with deep learning techniques. Initially, the LIBs capacity data are decomposed at multiple scales using VMD to extract the intrinsic mode functions (IMFs) that represent the stochastic fluctuations in battery capacity and residual (RES) components that characterize the global degradation trend. The RES trend and each IMFs are modelled using a deep belief network (DBN) and a Bayesian optimized gated recurrent unit (GRU) network, respectively. The resultant predictions are subsequently combined to estimate the final RUL. Finally, the observed data is used to test the predictive performance of the proposed model. The results demonstrate that the proposed model achieves high accuracy and strong robustness for RUL prediction compared with other alternative prediction models, with the average relative error strictly controlled within 0.5%.</p>

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Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Variational Mode Decomposition and Deep Learning Integrated Approach

  • 期刊论文

  • 发表日期：

    2026年4月22日

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  • Xiaokang Ma,
  • Hui Liu,
  • Lijin Han,
  • Saihan Chen,
  • Mingyi Li

摘要

The accurate prediction of the remaining useful life (RUL) for lithium-ion batteries (LIBs) is crucial for the effective management of electric vehicle energy systems. However, the prediction accuracy and adaptability are frequently compromised by the phenomenon of capacity regeneration. To surmount this challenge, a novel RUL prediction methodology is introduced for LIBs that integrates variational mode decomposition (VMD) with deep learning techniques. Initially, the LIBs capacity data are decomposed at multiple scales using VMD to extract the intrinsic mode functions (IMFs) that represent the stochastic fluctuations in battery capacity and residual (RES) components that characterize the global degradation trend. The RES trend and each IMFs are modelled using a deep belief network (DBN) and a Bayesian optimized gated recurrent unit (GRU) network, respectively. The resultant predictions are subsequently combined to estimate the final RUL. Finally, the observed data is used to test the predictive performance of the proposed model. The results demonstrate that the proposed model achieves high accuracy and strong robustness for RUL prediction compared with other alternative prediction models, with the average relative error strictly controlled within 0.5%.