Journal
ELECTRIC POWER SYSTEMS RESEARCH
Volume 146, Issue -, Pages 189-197Publisher
ELSEVIER SCIENCE SA
DOI: 10.1016/j.epsr.2017.01.032
Keywords
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)
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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. (C) 2017 Elsevier B.V. All rights reserved.
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