A chaotic firefly - Particle filtering method of dynamic migration modeling for the state-of-charge and state-of-health co-estimation of a lithium-ion battery performance

Qiao, Jialu; Wang, Shunli; Yu, Chunmei; Yang, Xiao Guang; Fernandez, Carlos

doi:10.1016/j.energy.2022.126164

Cited by 24 publications

(7 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The SOC was put forward by the United States Advanced Battery Consortium (USABC), which gives the remaining capacity of the battery relative to the maximum available capacity that can be released. 131 It is presented as a percentage and cannot be directly measured.…”

Section: Digital Management For Batterymentioning

confidence: 99%

Towards interactional management for power batteries of electric vehicles

Xie

et al. 2023

RSC Adv.

View full text Add to dashboard Cite

show abstract

Section: Digital Management For Batterymentioning

confidence: 99%

Towards interactional management for power batteries of electric vehicles

Xie

et al. 2023

RSC Adv.

View full text Add to dashboard Cite

show abstract

“…[11,12] The Ampere hour DOI: 10.1002/adts.202301022 (AH) method estimates SOC by integrating the charge/discharge current, but it relies on accurate initial SOC information and is susceptible to error accumulation. [13] The open circuit voltage (OCV) method estimates the SOC through the relationship between the equilibrium terminal voltage and SOC. It usually takes several hours for the battery to reach a stable state, which is unsuitable for online estimation.…”

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

View full text Add to dashboard Cite

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.

show abstract

“…Qiao et al took the Thevenin equivalent circuit model as the initial model and improved it to form a dynamic migration model. In addition, a chaotic firefly particle filter method was proposed to realize the collaborative estimation of SOC and SOH for lithium-ion batteries [17]. However, the traditional method of modeling the ECM of lithium-ion batteries is usually based on integer-order modeling.…”

Section: Introductionmentioning

confidence: 99%

Lithium-Ion Battery Modeling and State of Charge Prediction Based on Fractional-Order Calculus

Zhang

Yang

et al. 2023

Mathematics

View full text Add to dashboard Cite

Predicting lithium-ion batteries’ state of charge (SOC) is essential to electric vehicle battery management systems. Traditional lithium-ion battery models mainly include equivalent circuit models (ECMs) and electrochemical models (EMs). ECMs are based on integer-order component modeling, which cannot characterize the internal electrochemical reaction mechanism of the battery, resulting in lower SOC prediction accuracy. In contrast, due to their complex structure, EMs are limited in their application. This study takes lithium batteries as the research object and proposes a fractional-order impedance model (FOIM) that characterizes the dynamic properties of the internal behavior of lithium-ion batteries using fractional-order elements. Considering the highly nonlinear characteristics of lithium-ion batteries, this study introduces the theory of fractional-order calculus into the extended Kalman filter (EKF) algorithm, and proposes the fractional-order extended Kalman filter (FEKF) algorithm applied to the prediction of battery charge state. Comparative analysis of simulation and experimental results shows that the accuracy of the FOIM, compared to ECMs, is significantly improved. The FEKF algorithm has good robustness in estimating the SOC, and the SOC prediction accuracy achieved with the algorithm is also improved compared with that obtained using the EKF algorithm of the integer-order model.

show abstract

A chaotic firefly - Particle filtering method of dynamic migration modeling for the state-of-charge and state-of-health co-estimation of a lithium-ion battery performance

Cited by 24 publications

References 43 publications

Towards interactional management for power batteries of electric vehicles

Towards interactional management for power batteries of electric vehicles

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

Lithium-Ion Battery Modeling and State of Charge Prediction Based on Fractional-Order Calculus

Contact Info

Product

Resources

About