Aging prediction and state of charge estimation of a LiFePO4 battery using input time-delayed neural networks

作者：Chaoui Hicham^*; Ibe Ekeocha Chinemerem C; Gualous Hamid

来源：Electric Power Systems Research, 2017, 146: 189-197.

DOI：10.1016/j.epsr.2017.01.032

• Artificial neural network (ANN)
• Input time-delayed neural network (ITDNN)
• Lithium iron phosphate (LiFePO4)
• Open circuit voltage (OCV)
• Root mean squared error (RMSE)
• State of charge (SOC)
• State of health (SOH)

This paper presents an intelligent state of charge (SOC) and state of health (SOH) estimation method for lithium-ion batteries using an input time-delayed neural network. Unlike other estimation strategies, this technique requires no prior knowledge of the battery's model or parameters. Instead, it uses ambient temperature variations and previous battery's voltage and current data to accurately predict its SOC and SOH. The presented method compensates for the nonlinear patterns in battery characteristics such as hysteresis, variance due to electrochemical properties, and battery degradation due to aging. This technique is evaluated using a LiFePO4 battery and experimental results highlight its high accuracy, simplicity, and robustness.

• 出版日期2017-5