Abstract
An electrochemical lithium-ion battery model is well known to be suited for effectively describing the microstructure evolution in charging and discharging processes of a lithium-ion battery with practically realizable complexity. This paper presents a neural network-based parameter estimation scheme to identify the parameters of an electrochemical lithium-ion battery model in a near-optimal and real-time manner in order to consistently observe the electrochemical states of batteries. The network is first trained to learn the dynamics of the electrochemical lithium-ion battery model, and then, it is applied to estimate the parameters with available finite-time measurements of voltage, current, temperature, and state of charge. In order to efficiently learn the dynamic characteristics of a lithium-ion battery, a well-known recurrent neural network, called a long short-term memory model, is employed with other techniques such as batch normalization, dropout, and stochastic gradient descent with warm restarts for learning speed enhancement and regularization. Using synthetic and experimental data, we show that the proposed estimation scheme works well, finding parameters and recovering the voltage profiles within the root-mean-square error of 0.43% and 26 mV, respectively, even with measurements obtained within a sufficiently short interval of time.
Highlights
As one of the most promising energy storage devices, lithiumion batteries have been actively used in various fields
Lithium-ion batteries have been expanding their applications to the fields of energy storage systems [1] and electric vehicles [2], [3]
This paper proposes a parameter estimation method based on deep learning to overcome the disadvantages of the existing parameter estimation schemes
Summary
As one of the most promising energy storage devices, lithiumion batteries have been actively used in various fields. In this paper, a network is designed to estimate the capacity of the battery, and the additional parameters representing its electrochemical states For this purpose, a recurrent neural network (RNN) is used to consider the correlation between the time-series data comprising the voltage, current, temperature, and state of charge (SOC) of the battery used in network learning and grasp its dynamic characteristics. Actual battery aging mechanisms are considered to generate data for training the RNN, which can be reasonably described by properly setting the parameter values of the electrochemical lithium-ion battery model. Even though the electrochemical lithium-ion battery model parameter estimation is of growing importance for the safe and efficient operation, many battery management system (BMS) could not achieve it in real-time manner In this perspective, the overall contributions of this study are as follows:. The overall governing equations and the corresponding parameters for the employed models are represented in Table 1 and 2, respectively
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