Parameter identification of a lithium‐ion battery based on the improved recursive least square algorithm

Ren, Biying; Xie, Chenxue; Sun, Xiaodong; Zhang, Qi; Yan, Dan Lei

doi:10.1049/iet-pel.2019.1589

Cited by 43 publications

(18 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where the RC parameters are obtained by parameter identification for lithium-ion battery. The RLS method, a procedure to find the optimal match for the data by minimizing the sum of the square of errors, is efficient in identifying the match parameters of the battery [34][35][36]. The RC parameters of the DP model are generally preset for a specific temperature (e.g., 25 °C).…”

Section: Electricalmentioning

confidence: 99%

Sensorless Coestimation of Temperature and State-of-Charge for Lithium-Ion Batteries Based on a Coupled Electrothermal Model

Bai

Zhang

Gao

et al. 2023

International Journal of Energy Research

View full text Add to dashboard Cite

Accurate estimations of the temperature and the state-of-charge (SOC) are of extreme importance for the safety of lithium-ion battery operation. Traditional battery temperature and SOC estimation methods often omit the relation between battery temperature and SOC, which may lead to significant errors in the estimations. This study presents a coupled electrothermal battery model and a coestimation method for simultaneously estimating the temperature and SOC of lithium-ion batteries. The coestimation method is performed by a coupled model-based dual extended Kalman filter (DEKF). The coupled estimators utilizing electrochemical impedance spectroscopy (EIS) measurements, rather than utilizing direct battery surface measurements, are adopted to estimate the battery temperature and SOC, respectively. The information being exchanged between the temperature estimator and the SOC estimator effectively improves the estimation accuracy. Extensive experiments show that, in contrast with the EKF-based separate estimation method, the DEKF-based coestimation method is more favorable in reducing errors for estimating both the temperature and SOC even if the battery core temperature has increased by 17°C or more during the process of test.

show abstract

Section: Electricalmentioning

confidence: 99%

Sensorless Coestimation of Temperature and State-of-Charge for Lithium-Ion Batteries Based on a Coupled Electrothermal Model

Bai

Zhang

Gao

et al. 2023

International Journal of Energy Research

View full text Add to dashboard Cite

show abstract

“…The most direct way to simulate the hysteresis effect is to use the average open circuit voltage (OCV) curves of the battery charge and discharge as the OCV curves of the model, but this method can cause large hysteresis errors in the model in the case of complex battery conditions. (13) Plett (14) proposed an improved model that fitted the hysteresis effect as a function of state of charge (SOC) and time. The method improves the accuracy of the model but increases its complexity, making the operation substantially slower.…”

Section: Literature Reviewmentioning

confidence: 99%

Equivalent Circuit Modeling and Parameter Identification for Lithium-ion Batteries Based on Improved Barnacle Mating Optimizer

Li¹,

Lin²,

Li³

et al. 2022

Sensors and Materials

View full text Add to dashboard Cite

To address the current problems of accuracy and reliability of the equivalent circuit models for lithium-ion batteries and sensors, in this study we propose an improved barnacle mating optimizer (IBMO) to accurately identify parameters. First, an improved second-order RC model (ISO RC) is derived by segmenting the battery hysteresis voltage and introducing it into the second-order RC model (SO RC model) as a controlled source, which better reflects the dynamic effects of the battery than the SO RC model. Second, the tent chaos mapping, cosine control factor, and Levy flight strategy are introduced to enhance the convergence performance of the IBMO. Its convergence performance is verified by comparison with other existing intelligent algorithms using five single-peak and four multipeak test functions. Finally, the IBMO-ISO RC model is established by using the IBMO as the identification strategy of the ISO RC model in the simulation of two real operating conditions of lithium-ion batteries. The root mean square error of the model under the two operating conditions is only 0.0431 and 0.0483, and the mean absolute percentage error is only 0.38 and 0.56%, outperforming other existing models. The IBMO proposed in this study enables higher model recognition accuracy, making it highly convenient for lithium-ion battery and sensor-related research.

show abstract

“…The characteristics of the Li-ion battery usually change with external factors such as temperature, current rate, and aging, etc. It is easily realized that the RC parameters in the battery ECM vary with those external factors in real applications [11]. Thus, the suitability and accuracy of ECMs for a specific battery are closely related to the parameter identification method.…”

Section: Introductionmentioning

confidence: 99%

Online Identification of Lithium-ion Battery Model Parameters with Initial Value Uncertainty and Measurement Noise

Meng

Liu

et al. 2023

Chin. J. Mech. Eng.

View full text Add to dashboard Cite

Online parameter identification is essential for the accuracy of the battery equivalent circuit model (ECM). The traditional recursive least squares (RLS) method is easily biased with the noise disturbances from sensors, which degrades the modeling accuracy in practice. Meanwhile, the recursive total least squares (RTLS) method can deal with the noise interferences, but the parameter slowly converges to the reference with initial value uncertainty. To alleviate the above issues, this paper proposes a co-estimation framework utilizing the advantages of RLS and RTLS for a higher parameter identification performance of the battery ECM. RLS converges quickly by updating the parameters along the gradient of the cost function. RTLS is applied to attenuate the noise effect once the parameters have converged. Both simulation and experimental results prove that the proposed method has good accuracy, a fast convergence rate, and also robustness against noise corruption.

show abstract

Parameter identification of a lithium‐ion battery based on the improved recursive least square algorithm

Cited by 43 publications

References 33 publications

Sensorless Coestimation of Temperature and State-of-Charge for Lithium-Ion Batteries Based on a Coupled Electrothermal Model

Sensorless Coestimation of Temperature and State-of-Charge for Lithium-Ion Batteries Based on a Coupled Electrothermal Model

Equivalent Circuit Modeling and Parameter Identification for Lithium-ion Batteries Based on Improved Barnacle Mating Optimizer

Online Identification of Lithium-ion Battery Model Parameters with Initial Value Uncertainty and Measurement Noise

Contact Info

Product

Resources

About