Estimation of Battery State of Charge Based on Improved Noise Adaptation Method
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-16-2090-4_56
Reference18 articles.
1. Liu X, Deng X, He Y et al (2019) A dynamic state-of-charge estimation method for electric vehicle lithium-ion batteries. Int J Energy Res 13(1):257–267
2. Jiao M, Wang D, Qiu J et al (2019) A GRU-RNN based momentum optimized algorithm for SOC estimation. J Power Electron 31(12):459–467
3. Xu Y, Hu M, Zhou A et al (2020) State of charge estimation for lithium-ion batteries based on adaptive dual Kalman filter. Int J Perform Eng 77(2):1255–1272
4. Li B, Bei S, Yang F et al (2019) Estimation algorithm research for lithium battery SOC in electric vehicles based on adaptive unscented Kalman filter. Electr Power Syst Res 31(12):8171–8183
5. Ding N, Prasad K, Tek T et al (2020) State of charge estimation of a composite lithium-based battery model based on an improved extended Kalman filter algorithm. J Clean Prod 4(4):156–163
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