State of charge estimation of Li-ion battery for underwater vehicles based on EKF–RELM under temperature-varying conditions

Zhang, Feng; Zhi, Hui; Zhou, Puzhe; Hong, Yuandong; Wu, Shijun; Zhao, Xiaoyan; Yang, Canjun

doi:10.1016/j.apor.2021.102802

Cited by 8 publications

(5 citation statements)

References 26 publications

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“…In Equation (23), Qk-1 is the noise variance at time k-1. If the process noise Qk-1 is not fixed and changes with the filter, the DE algorithm is used to obtain its variance according to the process noise at different times, and select the optimal solution from Q0 to Qk.…”

Section: Differential Evolution Extended Kalman Filter Algorithmmentioning

confidence: 99%

“…Kalman filter is the most commonly used method for SOC estimation, such as extended Kalman filter [22,23], untraced Kalman filter [24,25], cubature Kalman filter [26,27], central difference Kalman filter [28], linear Kalman filter [29], etc. The Kalman filter is an optimal linear state estimation method that uses a recursive approach to solve linear filtering problems [30].…”

Section: Introductionmentioning

confidence: 99%

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Online Parameter Identification and State of Charge Estimation of Lithium-Ion Batteries Based on Improved Artificial Fish Swarms Forgetting Factor Least Squares and Differential Evolution Extended Kalman Filter

Xiao

Wang

et al. 2022

J. Electrochem. Soc.

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State of Charge (SOC) estimation is the focus of battery management systems, and it is critical to accurately estimate battery SOC in complex operating environments. To weaken the impact of unreasonable forgetting factor values on parameter estimation accuracy, an artificial fish swarm (AFS) strategy is introduced to optimize the forgetting factor of forgetting factor least squares (FFRLS) and to model the lithium-ion battery using a first-order RC model. A new method AFS-FFRLS is proposed for online parameter identification of the first-order RC model. In SOC estimation, it is not reasonable to fix the process noise covariance, and the differential evolution (DE) algorithm is combined with the extended Kalman filter (EKF) algorithm to achieve dynamic adjustment of the process noise covariance. A joint algorithm named AFS-FFRLS-DEEKF is proposed to estimate the SOC. to verify the reasonableness of the proposed algorithm, experiments are conducted under HPPC, BBDST and DST conditions, and the average errors of the joint algorithm under the three conditions are 1.9%, 2.7% and 2.4%, respectively. The validation results show that the joint algorithm improves the accuracy of SOC estimation.

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Section: Differential Evolution Extended Kalman Filter Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Online Parameter Identification and State of Charge Estimation of Lithium-Ion Batteries Based on Improved Artificial Fish Swarms Forgetting Factor Least Squares and Differential Evolution Extended Kalman Filter

Xiao

Wang

et al. 2022

J. Electrochem. Soc.

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show abstract

“…[22,23] Compared with online methods, offline methods are widely used for parameter identification due to their simple and mature processes. [24][25][26] The hybrid power pulse characteristic (HPPC) experiment plays an important role in offline parameter identification. In this experiment, the battery is tested under specific conditions to obtain the date for model parameter identification.…”

Section: Introductionmentioning

confidence: 99%

State of Charge Estimation for Lithium‑Ion Batteries Based on Extended Kalman Particle Filter and Orthogonal Optimized Battery Model

Shi,

Zhang,

et al. 2024

Advcd Theory and Sims

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State of charge (SOC) is a key state variable in lithium‑ion battery management system. The battery model and estimation algorithm are important factors that affect the accuracy of SOC estimation. In this paper, the optimization battery model is created by optimizing the hybrid power pulse characteristic (HPPC) parameter combination through orthogonal analysis. The simulation results demonstrate that optimizing the HPPC parameter combinations can improve battery modeling accuracy. Then, an extended Kalman particle filter (EKPF) algorithm is proposed by using the extended Kalman filter (EKF) algorithm as the density function of the particle filter (PF) algorithm. The EKPF algorithm is verified under the dynamic stress test and Beijing bus dynamic stress test conditions, the root mean absolute errors and root mean square errors in all cases are less than 1.5%. The experimental results show that the EKPF algorithm can combine the advantages of EKF and PF to estimate lithium‐ion battery SOC accurately.

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“…50 Studying the influence of temperatures in the operation of lithium-ion batteries, Wang et al 51 developed a parameter identification method with the particle swarm optimization based on the constant current discharge test and estimated the SOC using the EKF at temperatures of 5 and 25 C. The verification of their proposed method is carried out only at different discharge rates but not with different test conditions and low temperatures (0 C and À10 C), which highly influences the performance of lithium-ion batteries to show the robustness of the proposed method. Zhang et al 52 estimated the SOC based on EKF and a regularized extreme learning machine (EKF-RELM) method that models the relationship between the lithium-ion battery's parameters and temperature. However, the study is conducted based on a time-varying temperature range, and their proposed methods were not verified under different working conditions.…”

Section: Introductionmentioning

confidence: 99%

“…The verification of their proposed method is carried out only at different discharge rates but not with different test conditions and low temperatures (0°C and −10°C), which highly influences the performance of lithium‐ion batteries to show the robustness of the proposed method. Zhang et al 52 estimated the SOC based on EKF and a regularized extreme learning machine (EKF‐RELM) method that models the relationship between the lithium‐ion battery’s parameters and temperature. However, the study is conducted based on a time‐varying temperature range, and their proposed methods were not verified under different working conditions.…”

Section: Introductionmentioning

confidence: 99%

A strong tracking adaptive fading‐extended Kalman filter for the state of charge estimation of lithium‐ion batteries

Takyi‐Aninakwa

Wang

Zhang

et al. 2022

Intl J of Energy Research

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State of charge estimation of Li-ion battery for underwater vehicles based on EKF–RELM under temperature-varying conditions

Cited by 8 publications

References 26 publications

Online Parameter Identification and State of Charge Estimation of Lithium-Ion Batteries Based on Improved Artificial Fish Swarms Forgetting Factor Least Squares and Differential Evolution Extended Kalman Filter

Online Parameter Identification and State of Charge Estimation of Lithium-Ion Batteries Based on Improved Artificial Fish Swarms Forgetting Factor Least Squares and Differential Evolution Extended Kalman Filter

State of Charge Estimation for Lithium‑Ion Batteries Based on Extended Kalman Particle Filter and Orthogonal Optimized Battery Model

A strong tracking adaptive fading‐extended Kalman filter for the state of charge estimation of lithium‐ion batteries

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