Abstract
Lithium-ion battery is one of the core components of electric vehicles, and the state of charge-state of health estimation results of it is the key to restrict the safe and efficient use of it, which then affects the comprehensive performance of electric vehicles. However, SOC and SOH of lithium-ion batteries have a coupling relationship, and have fast and slow time-varying characteristics respectively, with inconsistent time scales. Hence, it is necessary to carry out SOC-SOH collaborative estimation and select a suitable time scale, which can ensure the accuracy and robustness of SOC-SOH collaborative estimation without consuming too much calculation cost. This article proposed an innovative hybrid optimization network to improve the ability of the analysis and feature extraction capability of the input sequences for precise SOC estimation. This hybrid network fully combines the advantages of convolutional neural network, bidirectional long short-term memory, attention mechanism. Additionally, kepler optimization algorithm is applied for hyperparameter optimization of the hybrid network for the first time according to our knowledge, and SOH is also estimated accurately for more ideal SOC estimation results. The experimental results of lithium-ion batteries indicate that the innovative hybrid optimization network can reach ideal SOC estimation results under different working conditions and ambient temperatures. The mean absolute error and root mean square error are 0.55% and 0.72% respectively, only about a third of the SOC estimation results without considering SOH, which means that SOC-SOH collaborative estimation are very essential. Hence, this article is of great significance for the development of smarter battery management system.
Published Version
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