The remaining useful lifetime (RUL) and state of charge (SoC) of rechargeable lithium-ion batteries (LIBs) are two integral parts to ensure LIBs working reliably and safely for transportation electrification systems. The two together reflect the state of a battery in use. However, existing capacity estimation approaches focus on separately modeling one of them, and no one has proposed a unified estimation model that is applicable to both RUL and SoC estimation yet. In this article, we propose a unified deep learning method that can be implemented for both RUL and SoC estimation. The proposed method leverages long short-term memory recurrent neural networks to achieve state-of-the-art accurate capacity estimation for LIBs under complex operating conditions. Notably, the unified method can perform not only one-step-ahead prediction but also multistep-ahead estimation with high accuracy, achieving RUL estimation error within ten cycles and SoC estimation error within 0.13%. Experimental data collected from battery testing systems with simulated complex operating conditions are used to train the method. A series of comparative experiments are conducted to compare our method with other existing methods. The experimental results show that our method can increase estimation accuracy and robustness for LIBs estimation problems via capturing the long-term dependencies among battery degradation data.
Data-Driven Estimation of Remaining Useful Lifetime and State of Charge for Lithium-Ion Battery
IEEE Transactions on Transportation Electrification ; 8 , 1 ; 356-367
01.03.2022
3214600 byte
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Remaining battery charge estimation system and remaining battery charge estimation method
Europäisches Patentamt | 2020
|Remaining Useful Life Prediction of Lithium-ion Battery Based on Data-Driven and Multi-Model Fusion
SAE Technical Papers | 2022
|Remaining Useful Life Prediction of Lithium-ion Battery Based on Data-Driven and Multi-Model Fusion
British Library Conference Proceedings | 2022
|Remaining Useful Life Prediction of Lithium-ion Battery Based on Data-Driven and Multi-Model Fusion
British Library Conference Proceedings | 2022
|