Lithium-Ion Battery Lifetime Prediction Model Based on a Fusion Expert Network

Journal article

Accurate prediction of the State of Health (SOH) of lithium-ion batteries is essential for improving the safety and longevity of energy storage systems. This paper introduces ExpertMixer, a novel model based on a fused expert network for SOH estimation. By combining the strengths of state space models and recurrent neural networks, the model effectively handles the joint optimization of long-sequence dependency modeling and complex dynamic feature extraction. To improve temporal representation, ExpertMixer utilizes sampling time-based rotary position encoding (RoPE). It consists of two expert modules: a Mamba module designed to capture global degradation trends and an LSTM module focused on modeling local dynamic fluctuations. These are adaptively fused through a learnable gating mechanism that supports multi-scale feature integration. Experiments performed on the NASA PCoE dataset show that ExpertMixer achieves optimal performance on the NASA L subset, with an average MAE of 1.047 and RMSE of 1.603. It surpasses the traditional CNN BiGRU model, which had an MAE of 2.286, by 54.2%, and improves upon the advanced SambaMixer model, which had an MAE of 1.072, by 2.3%. Under low-temperature conditions using Battery 47, the model reduces the prediction error for nonlinear degradation to an MAE of 0.539, significantly exceeding all compared methods. Ablation studies verify the effectiveness of the dual-expert structure and fusion mechanism; removing the gating module results in an 18.7% decrease in performance. This research offers a new framework for lithium battery life prediction that demonstrates improved accuracy and generalization capability, suggesting potential practical value for intelligent energy storage management.

Authors: Meng, Y; Sun, Q; Wang, Z; Yang, Q; Song, Y

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: MDPI
• Journal: Batteries
• Volume: 11
• Issue: 12
• Pages: 440
• Article number: 440
• Publication date: 2025-11-27
• Acceptance date: 2025-11-21
• DOI: 10.3390/batteries11120440
• EISSN: 2313-0105
• ISSN: 2313-0105
• Language: English
• Keywords: remaining useful life; lithium-ion battery health prognostics; long short-term memory networks; expert fusion; state space models