Comparative Study of EKF and UKF for SOC Estimation of Lithium-ion Batteries
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Publisher
IEEE
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http://xplorestaging.ieee.org/ielx7/8864995/8880798/08880915.pdf?arnumber=8880915
Cited by 9 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Utilizing the Unscented Kalman Filter for Estimating State of Charge in Lithium-Ion Batteries of Electric Vehicles;2023 IEEE 3rd International Conference on Applied Electromagnetics, Signal Processing, & Communication (AESPC);2023-11-24
2. State of charge estimation and error analysis of lithium-ion batteries for electric vehicles using Kalman filter and deep neural network;Journal of Energy Storage;2023-11
3. Improved forgetting factor recursive least square and adaptive square root unscented Kalman filtering methods for online model parameter identification and joint estimation of state of charge and state of energy of lithium-ion batteries;Ionics;2023-09-20
4. High-Precision and Robust SOC Estimation of LiFePO4 Blade Batteries Based on the BPNN-EKF Algorithm;Batteries;2023-06-20
5. Improved Fixed Range Forgetting Factor-Adaptive Extended Kalman Filtering (FRFF-AEKF) Algorithm for the State of Charge Estimation of High-Power Lithium-Ion Batteries;International Journal of Electrochemical Science;2022-11
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