Joint estimation of lithium-ion battery state of charge and capacity within an adaptive variable multi-timescale framework considering current measurement offset

Jiang, Bo; Dai, Haifeng; Wei, Xuezhe; Xu, Tianjiao

doi:10.1016/j.apenergy.2019.113619

Cited by 103 publications

(29 citation statements)

References 40 publications

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“…In addition, it is reported that the current sensor bias [22] has a serious impact on the accuracy of SOC estimation. To deal with this problem, Dai et al [23] selected an adaptive variable multi-timescale framework to realize the co-estimation of battery SOC and SOH for eliminating the current measurement offset. Hosny et al [24] applied a non-linear Kalman filter to an augmented model for reducing the influence of the biases in measurements on battery SOC estimation.…”

Section: Introductionmentioning

confidence: 99%

Online SOC Estimation Based on Simplified Electrochemical Model for Lithium-Ion Batteries Considering Current Bias

Pang

et al. 2021

Energies

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State of Charge (SOC) is essential for a smart Battery Management System (BMS). Traditional SOC estimation methods of lithium-ion batteries are usually conducted using battery equivalent circuit models (ECMs) and the impact of current sensor bias on SOC estimation is rarely considered. For this reason, this paper proposes an online SOC estimation based on a simplified electrochemical model (EM) for lithium-ion batteries considering sensor bias. In EM-based SOC estimation structure, the errors from the current sensor bias are addressed by proportional–integral observer. Then, the accuracy of the proposed EM-based SOC estimation is validated under different operating conditions. The results indicate that the proposed method has good performance and high accuracy in SOC estimation for lithium-ion batteries, which facilitates the on-board application in advanced BMS.

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Section: Introductionmentioning

confidence: 99%

Online SOC Estimation Based on Simplified Electrochemical Model for Lithium-Ion Batteries Considering Current Bias

Pang

et al. 2021

Energies

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“…The existing estimation methods can be divided into two categories: model-based methods and data-driven methods [16,17]. Battery models, including ECMs [18,19] and electrochemical models [20], can be used to estimate the characteristic parameters that are highly related to SOH. Kim et al [21] established an ECM and used a dual extended Kalman filter to jointly estimate SOC and SOH.…”

Section: Introductionmentioning

confidence: 99%

Joint Estimation of Inconsistency and State of Health for Series Battery Packs

et al. 2021

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Battery packs are applied in various areas (e.g., electric vehicles, energy storage, space, mining, etc.), which requires the state of health (SOH) to be accurately estimated. Inconsistency, also known as cell variation, is considered a significant evaluation index that greatly affects the degradation of battery pack. This paper proposes a novel joint inconsistency and SOH estimation method under cycling, which fills the gap of joint estimation based on the fast-charging process for electric vehicles. First, fifteen features are extracted from current change points during the partial charging process. Then, a joint estimation system is designed, where fusion weights are obtained by the analytic hierarchy process and multi-scale sample entropy to evaluate inconsistency. A wrapper is used to select the optimal feature subset, and Gaussian process regression is implemented to estimate the SOH. Finally, the estimation performance is assessed by the test data. The results show that the inconsistency evaluation can reflect the aging conditions, and the inconsistency does affect the aging process. The wrapper selection method improves the accuracy of SOH estimation by about 75.8% compared to the traditional filter method when only 10% of data is used for model training. The maximum absolute error and root mean square error are 2.58% and 0.93%, respectively.

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“…The capacity and internal resistance of the battery are widely used SOH indicators (Jiang et al, 2019). Many data-driven SOH estimation methods such as support-vector machine (Deng et al, 2016), relevance vector machine (Zheng and Fang, 2015), Gaussian process regression (Liu et al, 2020a;Liu et al, 2020b), and extreme learning machine (Pan et al, 2018) are proposed to estimate SOH based on the capacity.…”

Section: Introductionmentioning

confidence: 99%

Determination of Optimal Indicators Based on Statistical Analysis for the State of Health Estimation of a Lithium-Ion Battery

et al. 2021

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Online state of health (SOH) estimation is essential for lithium-ion batteries in a battery management system. As the conventional SOH indicator, the capacity is challenging to be estimated online. Apart from the capacity, various indicators related to the internal resistance are proposed as indicators for the SOH estimation. However, research gaps still exist in terms of optimal resistance-related indicators, online acquisition of indicators, temperature disturbance elimination, and state of charge (SOC) disturbance elimination. In this study, the equivalent circuit model parameters are identified based on recursive least square method in dynamic working conditions in the life span. Statistical analysis methods including multiple stepwise regression analysis and path analysis are introduced to characterize the sensitivity of the parameters to SOH estimation. Based on the above approach, the coupling relationship between the parameters is comprehensively analyzed. Results indicate that the ohmic resistance R0 and the diffusion capacitance Cd are the most suitable parameters for the SOH indication. Furthermore, R0 and Cd are proved to be exponentially correlated to the ambient temperature, while SOC demonstrates a quadratic trend on them. To eliminate the disturbance caused by the ambient temperature and SOC, a compensating method is further proposed. Finally, a mapping relationship between SOH and the indicators under normal operations is established. SOH can be estimated with the maximum error of 2.301%, which proves the reliability and feasibility of the proposed indicators and estimation method.

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Joint estimation of lithium-ion battery state of charge and capacity within an adaptive variable multi-timescale framework considering current measurement offset

Cited by 103 publications

References 40 publications

Online SOC Estimation Based on Simplified Electrochemical Model for Lithium-Ion Batteries Considering Current Bias

Online SOC Estimation Based on Simplified Electrochemical Model for Lithium-Ion Batteries Considering Current Bias

Joint Estimation of Inconsistency and State of Health for Series Battery Packs

Determination of Optimal Indicators Based on Statistical Analysis for the State of Health Estimation of a Lithium-Ion Battery

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