Search Results - "Fan, Yongcun"

A critical review of improved deep learning methods for the remaining useful life prediction of lithium-ion batteries by Wang, Shunli, Jin, Siyu, Bai, Dekui, Fan, Yongcun, Shi, Haotian, Fernandez, Carlos

“…As widely used for secondary energy storage, lithium-ion batteries have become the core component of the power supply system and accurate remaining useful life…”
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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 by Zhu, Tao, Wang, Shunli, Fan, Yongcun, Zhou, Heng, Zhou, Yifei, Fernandez, Carlos

“…The estimation of the state of charge (SOC) and state of energy (SOE) of lithium-ion batteries is very important for the battery management system (BMS) and…”
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An optimized quantum particle swarm optimization–extended Kalman filter algorithm for the online state of charge estimation of high-capacity lithium-ion batteries under varying temperature conditions by Wu, Wenjie, Wang, Shunli, Liu, Donglei, Fan, Yongcun, Mo, Daijiang, Fernandez, Carlos

“…The core focus of the battery management system (BMS) is accurate state of charge (SOC) estimation of the lithium-ion batteries. To solve the problem of…”
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A novel variable activation function-long short-term memory neural network for high-precision lithium-ion battery capacity estimation by Wang, Yangtao, Wang, Shunli, Fan, Yongcun, Zhang, Hansheng, Xie, Yanxin, Fernandez, Carlos

“…Capacity estimation of lithium-ion batteries is significant to achieving the effective establishment of the prognostics and health management (PHM) system of…”
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A novel safety assurance method based on the compound equivalent modeling and iterate reduce particle‐adaptive Kalman filtering for the unmanned aerial vehicle lithium ion batteries by Wang, Shunli, Fernandez, Carlos, Fan, Yongcun, Feng, Juqiang, Yu, Chunmei, Huang, Kaifeng, Xie, Wei

“…The safety assurance is very important for the unmanned aerial vehicle lithium ion batteries, in which the state of charge estimation is the basis of its…”
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A hybrid kernel extreme learning machine modeling method based on improved dung beetle algorithm optimization for lithium-ion battery state of health estimation by Mo, Daijiang, Wang, Shunli, Zhang, Mengyun, Fan, Yongcun, Wang, Yangtao, Zeng, Jiawei

“…The state of health (SOH) of lithium-ion batteries is an important indicator for evaluating the degradation of battery performance, which is crucial in battery…”
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A Novel Adaptive Function—Dual Kalman Filtering Strategy for Online Battery Model Parameters and State of Charge Co-Estimation by Fan, Yongcun, Shi, Haotian, Wang, Shunli, Fernandez, Carlos, Cao, Wen, Huang, Junhan

“…This paper aims to improve the stability and robustness of the state-of-charge estimation algorithm for lithium-ion batteries. A new internal…”
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A Novel Adaptive Back Propagation Neural Network-Unscented Kalman Filtering Algorithm for Accurate Lithium-Ion Battery State of Charge Estimation by Wang, Yangtao, Wang, Shunli, Fan, Yongcun, Xie, Yanxin, Fernandez, Carlos

“…Accurate State of Charge (SOC) estimation for lithium-ion batteries has great significance with respect to the correct decision-making and safety control. In…”
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Co-Estimation of State-of-Charge and State-of-Health for High-Capacity Lithium-Ion Batteries by Xiong, Ran, Wang, Shunli, Feng, Fei, Yu, Chunmei, Fan, Yongcun, Cao, Wen, Fernandez, Carlos

“…To address the challenges of efficient state monitoring of lithium-ion batteries in electric vehicles, a co-estimation algorithm of state-of-charge (SOC) and…”
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Incremental Capacity Curve Health-Indicator Extraction Based on Gaussian Filter and Improved Relevance Vector Machine for Lithium–Ion Battery Remaining Useful Life Estimation by Fan, Yongcun, Qiu, Jingsong, Wang, Shunli, Yang, Xiao, Liu, Donglei, Fernandez, Carlos

“…Accurate prediction of the remaining useful life (RUL) of lithium–ion batteries is the focus of lithium–ion battery health management. To achieve…”
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Improved anti-noise adaptive long short-term memory neural network modeling for the robust remaining useful life prediction of lithium-ion batteries by Wang, Shunli, Fan, Yongcun, Jin, Siyu, Takyi-Aninakwa, Paul, Fernandez, Carlos

“…•An improved ANA-LSTM model is built for RUL prediction of lithium-ion batteries.•Multiple feature collaboration is conducted for internal parameter…”
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A novel charged state prediction method of the lithium ion battery packs based on the composite equivalent modeling and improved splice Kalman filtering algorithm by Wang, Shunli, Fernandez, Carlos, Yu, Chunmei, Fan, Yongcun, Cao, Wen, Stroe, Daniel-Ioan

“…As the unscented Kalman filtering algorithm is sensitive to the battery model and susceptible to the uncertain noise interference, an improved iterate…”
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An optimized multi-segment long short-term memory network strategy for power lithium-ion battery state of charge estimation adaptive wide temperatures by Liu, Donglei, Wang, Shunli, Fan, Yongcun, Fernandez, Carlos, Blaabjerg, Frede

“…With the development of intelligentization and network connectivity of new energy vehicles, the estimation of power lithium-ion battery state of charge (SOC)…”
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Optimized multi-hidden layer long short-term memory modeling and suboptimal fading extended Kalman filtering strategies for the synthetic state of charge estimation of lithium-ion batteries by Xie, Yanxin, Wang, Shunli, Zhang, Gexiang, Fan, Yongcun, Fernandez, Carlos, Blaabjerg, Frede

“…With the demand for high-endurance lithium-ion batteries in new energy vehicles, communication and portable devices, high energy density lithium-ion batteries…”
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A novel adaptive dual extended Kalman filtering algorithm for the Li‐ion battery state of charge and state of health co‐estimation by Xu, Wenhua, Wang, Shunli, Jiang, Cong, Fernandez, Carlos, Yu, Chunmei, Fan, Yongcun, Cao, Wen

“…Summary Accurate prediction of the state of health (SOH) of Li‐ion battery has an important role in the estimation of battery state of charge (SOC), which can…”
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A novel feedback correction-adaptive Kalman filtering method for the whole-life-cycle state of charge and closed-circuit voltage prediction of lithium-ion batteries based on the second-order electrical equivalent circuit model by Wang, Shunli, Takyi-Aninakwa, Paul, Fan, Yongcun, Yu, Chunmei, Jin, Siyu, Fernandez, Carlos, Stroe, Daniel-Ioan

“…•A novel feedback correction-adaptive Kalman filtering (FC-AKF) method is proposed for the online SOC and CCV co-prediction.•An improved second-order…”
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A novel fuzzy‐extended Kalman filter‐ampere‐hour (F‐EKF‐Ah) algorithm based on improved second‐order PNGV model to estimate state of charge of lithium‐ion batteries by Liu, Donglei, Wang, Shunli, Fan, Yongcun, Xia, Lili, Qiu, Jingsong

“…Aiming at the problem that it is difficult to accurately estimate the state of charge (SOC) of lithium‐ion batteries in the strongly nonlinear interval, a…”
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A novel Drosophila‐back propagation method for the lithium‐ion battery state of charge estimation adaptive to complex working conditions by Xu, Hong, Wang, Shunli, Fan, Yongcun, Qiao, Jialu, Xu, Wenhua

“…Summary Accurate state of charge (SOC) for the lithium‐ion battery is not only related to user experience but also the top target to avoid overcharge and…”
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An improved forgetting factor recursive least square and unscented particle filtering algorithm for accurate lithium-ion battery state of charge estimation by Hao, Xueyi, Wang, Shunli, Fan, Yongcun, Xie, Yanxin, Fernandez, Carlos

“…As an indispensable part of the battery management system, accurately predicting the estimation of the state of charge (SOC) has attracted more attention,…”
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Enhanced multi-constraint dung beetle optimization-kernel extreme learning machine for lithium-ion battery state of health estimation with adaptive enhancement ability by Mo, Daijiang, Wang, Shunli, Fan, Yongcun, Takyi-Aninakwa, Paul, Zhang, Mengyun, Wang, Yangtao, Fernandez, Carlos

“…Accurately estimating the state of health (SOH) of lithium batteries is a critical and challenging task in battery management systems. Data-driven models are…”
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