Adaptive Online Gated Recurrent Unit for Lithium-Ion Battery SOC Estimation

The Li-ion batteries are commonly used for Electric Vehicles (EVs) and aerospace applications. One of the essential parameters in Li-ion batteries is state of charge (SOC) that shows the available energy in a battery. Various methods were proposed for SOC estimation. Since the battery has a nonlinear equations, it is important to use a method that does not require the system model. In the present study, a new Adaptive Online Gated Recurrent Unit (GRU) method is proposed for the State of Charge (SOC) estimation. It is a kind of deep Recurrent Neural Network(RNN) which solved the vanishing gradient problem in RNNs with GRU units. For Optimization a robust adaptive Online gradient learning method is used. This method is able to tune online the learning rate in the process. Adaptive GRU is a nondependent method from the nonlinear batteries model and simplifies the mathematical computation. The proposed technique is implemented on the real dataset of LifePO4 Li-ion batteries for finding SOC estimation. The exprimental result indicate that the Adaptive GRU method is more accurate than simple RNN.

[1]  Yoshua Bengio,et al.  Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling , 2014, ArXiv.

[2]  Mohamed Becherif,et al.  Experimental validation for Li-ion battery modeling using Extended Kalman Filters , 2017 .

[3]  Michael Pecht,et al.  State of charge estimation for Li-ion batteries using neural network modeling and unscented Kalman filter-based error cancellation , 2014 .

[4]  John B Goodenough,et al.  The Li-ion rechargeable battery: a perspective. , 2013, Journal of the American Chemical Society.

[5]  Wei Sun,et al.  State of charge estimation of lithium-ion batteries using optimized Levenberg-Marquardt wavelet neural network , 2018, Energy.

[6]  Luigi Martirano,et al.  A Nearly Zero-Energy Microgrid Testbed Laboratory: Centralized Control Strategy Based on SCADA System , 2020, Energies.

[7]  Zhe Li,et al.  A review on the key issues of the lithium ion battery degradation among the whole life cycle , 2019, eTransportation.

[8]  Zhisong Pan,et al.  Robust and Adaptive Online Time Series Prediction with Long Short-Term Memory , 2017, Comput. Intell. Neurosci..

[9]  Azah Mohamed,et al.  A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: Challenges and recommendations , 2017 .

[10]  Wen-Yeau Chang,et al.  The State of Charge Estimating Methods for Battery: A Review , 2013 .

[11]  Dirk Uwe Sauer,et al.  Advanced mathematical methods of SOC and SOH estimation for lithium-ion batteries , 2013 .

[12]  C. Moo,et al.  Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries , 2009 .