The accurate estimation of the State of Health (SOH) and Remaining Useful Life (RUL) has been a subject of keen interest due to its impact on safety and condition-based maintenance services. A number of approaches have been proposed to tackle this problem based either on a model-driven or on a data-driven framework. Due to the electro-chemical complexity involved in battery aging, they are yet to achieve the accuracy required, especially, for real-world applications. This is because of the difficulty either in identifying the time-varying nature of model parameters and in collecting the real-world training dataset from widely varying modes of battery usage.
In this paper, we propose a method of estimating SOH and RUL simultaneously in such a way as to contribute to its real-world applicability. First, noticing that battery aging causes the time sequence of charging and discharging voltage and current in a cycle to be shortened and dispersed, we define an aging index, referred to here as the time compensated entropy, for SOH and RUL. Second, for LSTM-based RUL prediction, we optimize the number of SOH input and the RUL prediction sequences for the minimum prediction error associated with a sequence of cycles. Third, we adopt a progressive framework of LSTMs such that whatever learned from the prior predictions are transferred to the subsequent prediction, starting with learned SOH. For experimental verification, we train the proposed progressive LSTM network based on CALCE datasets and apply to various cases of charging and discharging cycles. With SOH estimated online, we achieve less than 10 cycles of accuracy in RUL prediction, moving closer to real-world applicability.
How to Cite
SOH estimation, RUL prediction, State of Health, lithium-ion battery, battery management system, deep learning, neural networks
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