Estimation of SOH of Lithium-Ion Batteries Based on PSO-Bi GRU-Attention Network
Zhanying Hou, Weiqing Xu, Guanwei Jia, Jia Wang, Maolin Cai
Abstract
The State of Health (SOH) of lithium-ion batteries is a critical parameter that characterizes their actual lifespan, and its accurate assessment ensures the safe and reliable operation of batteries. However, in practical applications, SOH cannot be directly measured. To further improve the accuracy of SOH estimation for lithium-ion batteries, this study employs the Particle Swarm Optimization (PSO) algorithm to search for the optimal hyperparameters of the Bidirectional Gated Recurrent Unit (Bi GRU) neural network, enabling the prediction of time series information. Additionally, Attention Mechanism (AM) is integrated to allocate weights to the prediction results, resulting in the SOH prediction for lithium-ion batteries. The propose model is validated using the B0005 battery from the NASA lithium battery dataset. Experimental results demonstrate that, compared to the Bi GRU-Attention and Bi GRU models, the propose model reduces the Root Mean Square Error (RMSE) by 52.34% and 66.88%, respectively.