An improved limited memory-Sage Husa-cubature Kalman filtering algorithm for the state of charge and state of energy co-estimation of lithium-ion batteries based on hysteresis effect-dual polarization model.

Zhu, Chenyu; Wang, Shunli; Yu, Chunmei; Hai, Nan; Fernandez, Carlos; Guerrero, Josep M.

doi:10.1016/j.energy.2024.132465

An innovative square root - untraced Kalman filtering strategy with full-parameter online identification for state of power evaluation of lithium-ion batteries. (2024)
Journal Article
WANG, S., DANG, Q., GAO, Z., LI, B., FERNANDEZ, C. and BLAABJERG, F. 2024. An innovative square root - untraced Kalman filtering strategy with full-parameter online identification for state of power evaluation of lithium-ion batteries. Journal of energy storage [online], 104(part B), article number 114555. Available from: https://doi.org/10.1016/j.est.2024.114555

In the context of the thriving development of new energy vehicles, lithium-ion batteries, as a crucial component of the power storage system, will increasingly contribute to the strategic advancement of the industry, while this paper addresses three... Read More about An innovative square root - untraced Kalman filtering strategy with full-parameter online identification for state of power evaluation of lithium-ion batteries..

An improved progressive window-strong tracking multiple fading algorithm for the state of charge and state of energy co-estimation of lithium-ion batteries under variable temperatures. (2024)
Journal Article
ZHU, C., WANG, S., YU, C., HAI, N., FERNANDEZ, C., GUERRERO, J.M. and HUANG, Q. 2024. An improved progressive window-strong tracking multiple fading algorithm for the state of charge and state of energy co-estimation of lithium-ion batteries under variable temperatures. Journal of energy storage [online], 104(part A), article number 114444. Available from: https://doi.org/10.1016/j.est.2024.114444

To ensure the safety of batteries and plan their use reasonably, the accuracy of the state of charge (SOC) of batteries is a key evaluation indicator. State of energy (SOE) is a supplement to SOC to prevent system misjudgment. This paper proposes an... Read More about An improved progressive window-strong tracking multiple fading algorithm for the state of charge and state of energy co-estimation of lithium-ion batteries under variable temperatures..

Multi-temperature capable enhanced bidirectional long short term memory-multilayer perceptron hybrid model for lithium-ion battery SOC estimation. (2024)
Journal Article
ZHOU, Y., WANG, S., FENG, R., XIE, Y. and FERNANDEZ, C. 2024. Multi-temperature capable enhanced bidirectional long short term memory-multilayer perceptron hybrid model for lithium-ion battery SOC estimation. Energy [online], 312, article number 133596. Available from: https://doi.org/10.1016/j.energy.2024.133596

In this study, we propose a novel hybrid modeling framework for State of Charge (SOC) estimation across a broad temperature spectrum. First, we build a hybrid model to optimize stacked layers of stacked bidirectional long short term memory networks b... Read More about Multi-temperature capable enhanced bidirectional long short term memory-multilayer perceptron hybrid model for lithium-ion battery SOC estimation..

Strong robust state of health estimation of lithium-ion batteries based on aging feature mechanism analysis and improved mixed kernel least squares support vector regression model. (2024)
Journal Article
FENG, R., WANG, S., YU, C., HAI, N. and FERNANDEZ, C. 2024. Strong robust state of health estimation of lithium-ion batteries based on aging feature mechanism analysis and improved mixed kernel least squares support vector regression model. Ionics [online], 30(12), pages 8033-8052. Available from: https://doi.org/10.1007/s11581-024-05893-8

The state of health (SOH) of lithium-ion batteries is a decisive factor in ensuring the stability of electric vehicle systems. To solve the problem of low accuracy and robustness of lithium-ion battery SOH prediction models, this article proposes a d... Read More about Strong robust state of health estimation of lithium-ion batteries based on aging feature mechanism analysis and improved mixed kernel least squares support vector regression model..

Innovative multiscale fusion - antinoise extended long short-term memory neural network modeling for high precision state of health estimation of lithium-ion batteries. (2024)
Journal Article
TAO, J., WANG, S., CAO, W., CUI, Y., FERNANDEZ, C. and GUERRERO, J.M. 2024. Innovative multiscale fusion - antinoise extended long short-term memory neural network modeling for high precision state of health estimation of lithium-ion batteries. Energy [online], 312, article number 133541. Available from: https://doi.org/10.1016/j.energy.2024.133541

An accurate assessment of lithium-ion (Li-ion) batteries' state of health (SOH) is essential for the safe operation of new energy systems and extended battery life. Health factors were extracted by studying the aging test data of Li-ion batteries to... Read More about Innovative multiscale fusion - antinoise extended long short-term memory neural network modeling for high precision state of health estimation of lithium-ion batteries..

An accurate state-of-charge estimation of lithium-ion batteries based on improved particle swarm optimization-adaptive square root cubature kalman filter. (2024)
Journal Article
WANG, S., ZHANG, S., WEN, S. and FERNANDEZ, C. 2024. An accurate state-of-charge estimation of lithium-ion batteries based on improved particle swarm optimization-adaptive square root cubature kalman filter. Journal of power sources [online], 624, article number 235594. Available from: https://doi.org/10.1016/j.jpowsour.2024.235594

The state of charge (SOC) of lithium-ion batteries (LIBs) is regarded as the fundamental parameter of the battery management system (BMS). In this paper, a parameter optimization method for mobile estimation windows based on particle swarm optimizati... Read More about An accurate state-of-charge estimation of lithium-ion batteries based on improved particle swarm optimization-adaptive square root cubature kalman filter..

Microalloy Mg-based degradation implant for intra-osteal fixation. (2024)
Journal Article
NIU, T., ZHANG, Y., LIU, S., et al. 2024. Microalloy Mg-based degradation implant for intra-osteal fixation. Materialia [online], 38, article number 102258. Available from: https://doi.org/10.1016/j.mtla.2024.102258

The bottleneck for Mg-based degradable implants lies in the mismatching relationship between mechanical properties and degradable rate, resulting in the rapid failure during the in-vivo degradable process and potential toxic role. Herein microalloy-c... Read More about Microalloy Mg-based degradation implant for intra-osteal fixation..

An improved multi-innovation error compensation-long-short-term memory network modeling method for high-precision state of charge estimation of lithium-ion batteries. (2024)
Journal Article
QIQIAO, W., SHUNLI, W., WEN, C., HAIYING, G., FERNANDEZ, C. and GUERRERO, J.M. 2024. An improved multi-innovation error compensation-long-short-term memory network modeling method for high-precision state of charge estimation of lithium-ion batteries. Ionics [online], 30(11), pages 6969-6984. Available from: https://doi.org/10.1007/s11581-024-05831-8

Accurately estimating lithium-ion batteries' state of charge (SOC) is a vital decision-making technique in battery management systems (BMS), essential to ensuring operational safety and prolonging battery lifespan. The multi-innovation error compensa... Read More about An improved multi-innovation error compensation-long-short-term memory network modeling method for high-precision state of charge estimation of lithium-ion batteries..

An improved particle swarm optimization-cubature Kalman particle filtering method for state-of-charge estimation of large-scale energy storage lithium-ion batteries. (2024)
Journal Article
WANG, C., WANG, S., ZHANG, G., TAKYI-ANINAKWA, P., FERNANDEZ, C. and TAO, J. 2024. An improved particle swarm optimization-cubature Kalman particle filtering method for state-of-charge estimation of large-scale energy storage lithium-ion batteries. Journal of energy storage [online], 100(B), article number 113619. Available from: https://doi.org/10.1016/j.est.2024.113619

With the global demand for large-scale energy storage strategies, lithium-ion batteries with high energy densities have emerged as the primary energy storage systems. State-of-charge (SOC) is a critical state parameter for energy storage systems that... Read More about An improved particle swarm optimization-cubature Kalman particle filtering method for state-of-charge estimation of large-scale energy storage lithium-ion batteries..

Improved chaotic particle butterfly optimization-cubature Kalman filtering for accurate state of charge estimation of lithium-ion batteries adaptive to different temperature conditions. (2024)
Journal Article
YANG, J., WANG, S., GAO, H., FERNANDEZ, C. and GUERRERO, J.M. 2024. Improved chaotic particle butterfly optimization-cubature Kalman filtering for accurate state of charge estimation of lithium-ion batteries adaptive to different temperature conditions. Ionics [online], 30(11), pages 6933-6949. Available from: https://doi.org/10.1007/s11581-024-05777-x

Accurate state of charge (SOC) estimation of lithium-ion batteries can effectively help battery management system better manage the charging and discharging process of batteries, providing important reference basis for the use planning of power vehic... Read More about Improved chaotic particle butterfly optimization-cubature Kalman filtering for accurate state of charge estimation of lithium-ion batteries adaptive to different temperature conditions..

Improved K-means clustering-genetic backpropagation modeling for online state-of-charge estimation of lithium-ion batteries adaptive to low-temperature conditions. (2024)
Journal Article
HAI, N., WANG, S., HUANG, Q., XIE, Y. and FERNANDEZ, C. 2024. Improved K-means clustering-genetic backpropagation modeling for online state-of-charge estimation of lithium-ion batteries adaptive to low-temperature conditions. Journal of energy storage [online], 99(B), article number 113399. Available from: https://doi.org/10.1016/j.est.2024.113399

Accurate state-of-charge (SOC) estimation of lithium-ion batteries (LIBs) in low temperatures is significant to maximize their performance and application. An improved K-means clustering-genetic backpropagation (KMC-GBP) algorithm consisting of five... Read More about Improved K-means clustering-genetic backpropagation modeling for online state-of-charge estimation of lithium-ion batteries adaptive to low-temperature conditions..

Acoustic emission wave propagation in pipeline sections and analysis of the effect of coating and sensor location. (2024)
Journal Article
RAJENDRAN, V., PRATHURU, A., FERNANDEZ, C. and FAISAL, N. [2024]. Acoustic emission wave propagation in pipeline sections and analysis of the effect of coating and sensor location. Nondestructive testing and evaluation [online], Latest Articles. Available from: https://doi.org/10.1080/10589759.2024.2390996

This paper presents an experimental investigation in which acoustic emission (AE) wave was generated through a pencil lead break (PLB) as a point source on two pipeline sections made of mild steel and titanium. The pipelines (bare, epoxy phenolic coa... Read More about Acoustic emission wave propagation in pipeline sections and analysis of the effect of coating and sensor location..

Battery asynchronous fractional-order thermoelectric coupling modeling and state of charge estimation based on frequency characteristic separation at low temperatures. (2024)
Journal Article
ZENG, J., WANG, S., CAO, W., ZHOU, Y., FERNANDEZ, C. and GUERRERO, J.M. 2024. Battery asynchronous fractional-order thermoelectric coupling modeling and state of charge estimation based on frequency characteristic separation at low temperatures. Energy [online], 307, article number 132730. Available from: https://doi.org/10.1016/j.energy.2024.132730

Due to the widespread application of lithium-ion batteries in new energy vehicles and energy storage fields, fractional-order theory and coupling modeling methods have developed rapidly in battery modeling. This paper proposes an asynchronous fractio... Read More about Battery asynchronous fractional-order thermoelectric coupling modeling and state of charge estimation based on frequency characteristic separation at low temperatures..

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. (2024)
Journal Article
WU, W., WANG, S., LIU, D., FAN, Y., MO, D. and FERNANDEZ, C. 2024. 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. Ionics [online], 30(10), pages 6163-6177. Available from: https://doi.org/10.1007/s11581-024-05749-1

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 improper selection of the noise covariance matrix in the extended Kalman filter (EKF) algorithm,... Read More about 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..

Enhanced multi-constraint dung beetle optimization-kernel extreme learning machine for lithium-ion battery state of health estimation with adaptive enhancement ability. (2024)
Journal Article
MO, D., WANG, S., FAN, Y., TAKYI-ANINAKWA, P., ZHANG, M., WANG, Y. and FERNANDEZ, C. 2024. Enhanced multi-constraint dung beetle optimization-kernel extreme learning machine for lithium-ion battery state of health estimation with adaptive enhancement ability. Energy [online], 307, article number 132723. Available from: https://doi.org/10.1016/j.energy.2024.132723

Accurately estimating the state of health (SOH) of lithium batteries is a critical and challenging task in battery management systems. Data-driven models are widely used for SOH estimation but still suffer from the difficulty of balancing speed, accu... Read More about Enhanced multi-constraint dung beetle optimization-kernel extreme learning machine for lithium-ion battery state of health estimation with adaptive enhancement ability..

High precision estimation of remaining useful life of lithium-ion batteries based on strongly correlated aging feature factors and AdaBoost framework. (2024)
Journal Article
FENG, R., WANG, S., YU, C. and FERNANDEZ, C. 2024. High precision estimation of remaining useful life of lithium-ion batteries based on strongly correlated aging feature factors and AdaBoost framework. Ionics [online], 30(10), pages 6215-6237. Available from: https://doi.org/10.1007/s11581-024-05740-w

In response to the current issue of low accuracy and robustness in the remaining useful life (RUL) model of lithium-ion batteries. In the framework of AdaBoost, a lithium-ion battery life prediction model based on an improved whale optimization algor... Read More about High precision estimation of remaining useful life of lithium-ion batteries based on strongly correlated aging feature factors and AdaBoost framework..

A hybrid algorithm for the state of energy estimation of lithium-ion batteries based on improved adaptive-forgotten-factor recursive least squares and particle swarm optimized unscented particle filtering. (2024)
Journal Article
SHEN, X., WANG, S., YU, C., LI, Z. and FERNANDEZ, C. 2024. A hybrid algorithm for the state of energy estimation of lithium-ion batteries based on improved adaptive-forgotten-factor recursive least squares and particle swarm optimized unscented particle filtering. Ionics [online], 30(10), pages 6197-6213. Available from: https://doi.org/10.1007/s11581-024-05716-w

State of energy (SOE) estimation of lithium-ion batteries is the basis of electric vehicle driving range prediction. To improve the estimation accuracy of SOE under complex dynamic working conditions, this paper takes the ternary lithium-ion battery... Read More about A hybrid algorithm for the state of energy estimation of lithium-ion batteries based on improved adaptive-forgotten-factor recursive least squares and particle swarm optimized unscented particle filtering..

An improved forgetting factor recursive least square and extended particle filtering algorithm for accurate lithium-ion battery state of energy estimation. (2024)
Journal Article
SHEN, X., WANG, S., YU, C., LI, Z. and FERNANDEZ, C. 2024. An improved forgetting factor recursive least square and extended particle filtering algorithm for accurate lithium-ion battery state of energy estimation. Ionics [online], 30(10), pages 6179-6195. Available from: https://doi.org/10.1007/s11581-024-05698-9

State of energy (SOE) estimation of lithium-ion batteries is the basis for electric vehicle range prediction. To improve the estimation accuracy of SOE under complex dynamic operating conditions. In this paper, ternary lithium-ion batteries are used... Read More about An improved forgetting factor recursive least square and extended particle filtering algorithm for accurate lithium-ion battery state of energy estimation..

An improved dung beetle optimizer- hybrid kernel least square support vector regression algorithm for state of health estimation of lithium-ion batteries based on variational model decomposition. (2024)
Journal Article
ZHU, T., WANG, S., FAN, Y., HAI, N., HUANG, Q. and FERNANDEZ, C. 2024. An improved dung beetle optimizer- hybrid kernel least square support vector regression algorithm for state of health estimation of lithium-ion batteries based on variational model decomposition. Energy, [online], 306, article 132464. Available from: https://doi.org/10.1016/j.energy.2024.132464

Accurate prediction of the state of health (SOH) of lithium-ion batteries is important for real-time monitoring and safety control of lithium-ion batteries. In this paper, a hybrid kernel least square support vector regression (HKLSSVR) prediction mo... Read More about An improved dung beetle optimizer- hybrid kernel least square support vector regression algorithm for state of health estimation of lithium-ion batteries based on variational model decomposition..

An improved limited memory-Sage Husa-cubature Kalman filtering algorithm for the state of charge and state of energy co-estimation of lithium-ion batteries based on hysteresis effect-dual polarization model. (2024)
Journal Article
ZHU, C., WANG, S., YU, C., HAI, N., FERNANDEZ, C. and GUERRERO, J.M. 2024. An improved limited memory-Sage Husa-cubature Kalman filtering algorithm for the state of charge and state of energy co-estimation of lithium-ion batteries based on hysteresis effect-dual polarization model. Energy [online], 306, article number 132465. Available from: https://doi.org/10.1016/j.energy.2024.132465

The accurate estimation of battery state of charge (SOC) and state of energy (SOE) are the key technologies in researching battery management systems. Due to the uncertainty of prior noise in practical engineering, traditional cubature Kalman filters... Read More about An improved limited memory-Sage Husa-cubature Kalman filtering algorithm for the state of charge and state of energy co-estimation of lithium-ion batteries based on hysteresis effect-dual polarization model..

All Outputs (290)