Abstract
A novel redox flow battery–single flow Zinc-Nickle battery is introduced in this paper. Based on the experimental data of battery under pulsed discharge, An improved Extreme Learning Machine(ELM) model of single-flow zinc-nickel battery which combined with equivalent circuit and Extreme Learning Machine was established. Compared with simple ELM model, the proposed model can be used to simulate the variation of single-flow zinc-nickel battery terminal voltage with higher accuracy in discharge process. Based on the model of single-flow zinc-nickel battery, the variation of battery State-of-Charge (SoC) in discharge process is calculated by using adaptive unscented kalman filter (AUKF). The experimental results show that SoC estimation value could converge to actual value nearby with fast speed by using AUKF based on proposed model, even if initial SoC error is large. Compared with unscented Kalman filter based on simple ELM model, this method has better estimation precision.
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The project supported by the National Natural Science Foundation of China No. 61364007.
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Lin, X., Guo, Y., Cheng, J., Guo, Z., Yan, X. (2018). An Improved Extreme Learning Machine Model and State-of-Charge Estimation of Single Flow Zinc-Nickle Battery. In: Deng, Z. (eds) Proceedings of 2017 Chinese Intelligent Automation Conference. CIAC 2017. Lecture Notes in Electrical Engineering, vol 458. Springer, Singapore. https://doi.org/10.1007/978-981-10-6445-6_67
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DOI: https://doi.org/10.1007/978-981-10-6445-6_67
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