Data-driven Estimation of Remaining Useful Lifetime and State of Charge for Lithium-ion Battery

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 applicable to both RUL and SoC estimation yet. In this paper, 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 multi-step ahead estimation with high accuracy, achieving RUL estimation error within 10 cycles and SoC estimation error within 0.13%. Experimental data collected from battery testing system with simulated complex operating conditions is 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 problem via capturing the long-term dependencies among battery degradation data.