Co-estimation of model parameters and state-of-charge for lithium-ion batteries with recursive restricted total least squares and unscented Kalman filter

Zhu, Rui; Duan, Bin; Zhang, Junming; Zhang, Qi; Zhang, Chenghui

doi:10.1016/j.apenergy.2020.115494

Cited by 87 publications

(32 citation statements)

References 47 publications

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“…Combined with the impedance fitting results in Figure 7, the sum of R sei and R ct is close to R c in value and change trend. That means the quantitative values of LLI calculated by Equations (2) and (4) are still comparable in subsequent analysis. Besides, the results of R ohm and R w fitted by the two models are very similar, so the combination of methods will also not affect the comparability of the quantitative results for CL and LAM.…”

Section: (R Ohm )mentioning

confidence: 78%

“…To cope with the energy crisis and increasingly prominent environmental issues, research and development of electric vehicles has become an important trend [1]. Compared with other types of batteries, lithium-ion batteries have been widely used in electric vehicles because of their higher energy density and longer cycle life [2]. Some advanced post-lithium batteries will have a wider perspective application after the improvement of safety issues and cycle stability [3].…”

Section: Introductionmentioning

confidence: 99%

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Quantitative Analysis of Degradation Modes of Lithium-Ion Battery under Different Operating Conditions

et al. 2021

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The degradation mode is of great significance for reducing the complexity of research on the aging mechanisms of lithium-ion batteries. Previous studies have grouped the aging mechanisms into three degradation modes: conductivity loss (CL), loss of lithium inventory (LLI) and loss of active material (LAM). Combined with electrochemical impedance spectroscopy (EIS), degradation modes can be identified and quantified non-destructively. This paper aims to extend the application of this method to more operating conditions and explore the impact of external factors on the quantitative results. Here, we design a quantification method using two equivalent circuit models to cope with the different trends of impedance spectra during the aging process. Under four conditions, the changing trends of the quantitative values of the three degradation modes are explored and the effects of the state of charge (SoC) and excitation current during EIS measurement are statistically analyzed. It is verified by experiments that LLI and LAM are the most critical aging mechanisms under various conditions. The selection of SoC has a significant effect on the quantitative results, but the influence of the excitation current is not obvious.

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Section: (R Ohm )mentioning

confidence: 78%

Section: Introductionmentioning

confidence: 99%

Quantitative Analysis of Degradation Modes of Lithium-Ion Battery under Different Operating Conditions

et al. 2021

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“…The derivation processes are as follows. 45 Applying the Laplace transformation to Equation ( 1), the frequency-domain function of the Thevenin model can be formulated as:…”

Section: Model Parameter Identification Methodsmentioning

confidence: 99%

A comparative study of the influence of different open circuit voltage tests on model‐based state of charge estimation for lithium‐ion batteries

Ren

et al. 2021

Int J Energy Res

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A predetermined relationship between open circuit voltage (OCV) and state of charge (SoC) is the premise of model-based SoC estimation algorithms. Commonly used OCV tests to obtain the offline OCV-SoC curves include the low current OCV test and the incremental OCV test. In this paper, in addition to comparing the low current OCV test with the incremental OCV test which applies 0.5 C current-rate, the incremental OCV tests which apply 0.2, 0.3, and

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“…Compared with other models, the RC model has the advantages of simple structure and high precision. 17 SOC estimation accuracy depends on the accuracy of the model, 18 and the model parameter identification method directly determines the reliability and accuracy of the model. ECMs can be used for parameter identification using online and offline data.…”

Section: Introductionmentioning

confidence: 99%

A novel online model parameters identification method with anti‐interference characteristics for lithium‐ion batteries

Miao

Chen²,

Mao

et al. 2021

Int J Energy Res

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Model-based state of charge (SOC) estimation method depends on the accuracy of the online identified battery model. However, when battery model parameters are identified by conventional recursive least squares (RLS), voltage and current noise will lead to the deviation of parameters and further affect the accuracy of SOC estimation. To analyze the difference influence of voltage and current noise, a simulation is carried out under the same noise variance of both voltage and current. Results indicate that voltage noise is the main cause of parameter deviation. Based on these results, a novel method, the auxiliary model recursive least square (AM-RLS) which enjoys low computational complexity is proposed to compensate the parameter deviation caused by voltage noise. The AM-RLS method is further combined with an extendedKalman filter (EKF) to estimate SOC in real time. Both simulation and experimental results show that AM-RLS can effectively attenuate the parameter deviation and SOC estimation error under noise interference. Compared with RLS, the maximum SOC errors of the proposed method are reduced by 2.6% and 5.86% respectively under two current profiles.

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Co-estimation of model parameters and state-of-charge for lithium-ion batteries with recursive restricted total least squares and unscented Kalman filter

Cited by 87 publications

References 47 publications

Quantitative Analysis of Degradation Modes of Lithium-Ion Battery under Different Operating Conditions

Quantitative Analysis of Degradation Modes of Lithium-Ion Battery under Different Operating Conditions

A comparative study of the influence of different open circuit voltage tests on model‐based state of charge estimation for lithium‐ion batteries

A novel online model parameters identification method with anti‐interference characteristics for lithium‐ion batteries

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