An improved variable forgetting factor recursive least square-double extend Kalman filtering based on global mean particle swarm optimization algorithm for collaborative state of energy and state of health estimation of lithium-ion batteries

Long, Tao; Wang, Shunli; Wen, Chenglu; Zhou, Heng; Fernandez, Carlos

doi:10.1016/j.electacta.2023.142270

Cited by 25 publications

(2 citation statements)

References 43 publications

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“…Additionally, the accuracy of parameter estimation error decreased and the RLS method was unable to track the changes in system parameters online constantly. To overcome this shortcoming, FFRLS was carried out ( Long et al, 2023 ).…”

Section: Methodsmentioning

confidence: 99%

Research on adaptive impedance control technology of upper limb rehabilitation robot based on impedance parameter prediction

Zhang,

Li,

Tao

et al. 2024

Front. Bioeng. Biotechnol.

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Introduction: With the aggravation of aging and the growing number of stroke patients suffering from hemiplegia in China, rehabilitation robots have become an integral part of rehabilitation training. However, traditional rehabilitation robots cannot modify the training parameters adaptively to match the upper limbs’ rehabilitation status automatically and apply them in rehabilitation training effectively, which will improve the efficacy of rehabilitation training.Methods: In this study, a two-degree-of-freedom flexible drive joint rehabilitation robot platform was built. The forgetting factor recursive least squares method (FFRLS) was utilized to estimate the impedance parameters of human upper limb end. A reward function was established to select the optimal stiffness parameters of the rehabilitation robot.Results: The results confirmed the effectiveness of the adaptive impedance control strategy. The findings of the adaptive impedance control studies showed that the adaptive impedance control had a significantly greater reward than the constant impedance control, which was in line with the simulation results of the variable impedance control. Moreover, it was observed that the levels of robot assistance could be suitably modified based on the subject’s different participation.Discussion: The results facilitated stroke patients’ upper limb rehabilitation by enabling the rehabilitation robot to adaptively change the impedance parameters according to the functional status of the affected limb. In clinic therapy, the proposed control strategy may help to adjust the reward function for different patients to improve the rehabilitation efficacy eventually.

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Section: Methodsmentioning

confidence: 99%

Research on adaptive impedance control technology of upper limb rehabilitation robot based on impedance parameter prediction

Zhang,

Li,

Tao

et al. 2024

Front. Bioeng. Biotechnol.

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“…Additionally, capacity estimation has already been combined with SoE estimation in the literature. Long et al develop in [44] a framework for simultaneous SoH and SoE estimation with a joint battery model. The SoE and the SoH are estimated with the EKF, demonstrating the potential of coupling existing model-based state estimation techniques coupled with SoE estimation.…”

Section: Opportunities and Challenges Of Soe Estimationmentioning

confidence: 99%

Understanding the Energy Potential of Lithium‐Ion Batteries: Definition and Estimation of the State of Energy

Quade

Jöst

Sauer

et al. 2023

Batteries & Supercaps

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An accurate estimation of the residual energy, i. e., State of Energy (SoE), for lithium‐ion batteries is crucial for battery diagnostics since it relates to the remaining driving range of battery electric vehicles. Unlike the State of Charge, which solely reflects the charge, the SoE can feasibly estimate residual energy. The existing literature predominantly focuses on showcasing diverse methods with a gap in conducting in‐depth analysis and comparison of the SoE. The scope of this work is to provide a comprehensive understanding of the SoE by discussing the feasibility and applicability of various definitions and estimation approaches from the literature. For the first time, we classify existing SoE definitions, considering the differences between the inherent stored and usable energy. In the absence of a unified definition in the literature, we propose two physically feasible definitions. Based on the proposed definitions, we critically analyze the practical challenges of SoE estimation. Additionally, to examine the accuracy of the most suitable SoE definition, we conduct an experimental evaluation using a commercial NCA/C+Si cell. Compared to the State of stored Energy, we demonstrate that the State of Charge underestimates the residual energy by more than 3 %, highlighting the importance of adequate SoE estimation.

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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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An improved variable forgetting factor recursive least square-double extend Kalman filtering based on global mean particle swarm optimization algorithm for collaborative state of energy and state of health estimation of lithium-ion batteries

Cited by 25 publications

References 43 publications

Research on adaptive impedance control technology of upper limb rehabilitation robot based on impedance parameter prediction

Research on adaptive impedance control technology of upper limb rehabilitation robot based on impedance parameter prediction

Understanding the Energy Potential of Lithium‐Ion Batteries: Definition and Estimation of the State of Energy

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

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