Data-Driven Estimation of Remaining Useful Lifetime and State of Charge for Lithium-Ion Battery

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.

Automated summary

The article proposes a unified deep learning method for the estimation of RUL and SoC in rechargeable lithium-ion batteries. The method leverages LSTM recurrent neural networks to achieve accurate capacity estimation under complex operating conditions. Experimental results show that the method can increase estimation accuracy for LIBs by capturing long-term dependencies in battery degradation data.