Estimation for state-of-charge of lithium-ion battery based on an adaptive high-degree cubature Kalman filter

Linghu, Jinqing; Kang, Longyun; Liu, Ming; Luo, Xin; Feng, Yuanbin; Lu, Chusheng

doi:10.1016/j.energy.2019.116204

Cited by 82 publications

(44 citation statements)

References 38 publications

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“…Consequently, the regulation parameters k P , k I , and k D of PID unit in the measurement correction step can be solved by equations (26) and (27).…”

Section: Complexitymentioning

confidence: 99%

“…From equations (26) and (28), we can get the PID feedback unit by setting innovation vector, and the PID coefficients k P , k I , and k D can be solved by the expressions of gains k k , k k− 1 , and k k− 2 in three sampling points. ree innovation data at times k, k-1, and k-2 are used when updating the state at time k. e main reason for increasing the robustness of the improved algorithm is to reuse innovative data to update the state at adjacent times.…”

Section: Complexitymentioning

confidence: 99%

“…e major disadvantage of EKF is that only one-order Taylor series expansion is used to approximate the nonlinear state of LIB, and the higher-order expansion terms are ignored, which inevitably brings about linearization error [25]. e UKF is developed for SOC estimation of LIB by using the unscented transformation (UT) to replace the multiorder Taylor series expansion [26]. Because the UT is used for approximating the SOC distribution characteristic without the Jacobian matrix, the SOC estimation result shows that the UKF has better performance than the EKF in robustness and precision.…”

Section: Introductionmentioning

confidence: 99%

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A State of Charge Estimation Method for Lithium‐Ion Battery Using PID Compensator‐Based Adaptive Extended Kalman Filter

et al. 2021

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With the widespread application of electric vehicles, the study of the power lithium-ion battery (LIB) has broad prospects and great academic significance. The state of charge (SOC) is one of the key parts in battery management system (BMS), which is used to provide guarantee for the safe and efficient operation of LIB. To obtain the reliable SOC estimation result under the influence of simple model and measurement noise, a novel estimation method with adaptive feedback compensator is presented in this paper. The simplified dynamic external electrical characteristic of LIB is represented by the one-order Thevenin equivalent circuit model (ECM) and then the ECM parameters are identified by the forgetting factor recursive least squares method (FFRLS). Fully taking into account the feedback effect of terminal voltage innovation, the combination of adaptive extended Kalman filter (AEKF) and innovation vector-based proportional-integral-derivative (PID) feedback is proposed to estimate the LIB SOC. The common single proportional feedback of Kalman filter (KF) is replaced by the innovation vector-based PID feedback, which means that the multiple prior terminal voltage innovation is used in the measurement correction step of KF. The results reveal that the AEKF with PID feedback compensation strategy can improve the SOC estimation performance compared with the common AEKF, and it reveals good robust capability and rapid convergence speed for initial SOC errors. The maximum absolute error and average absolute error for SOC estimation are close to 4% and 2.6%, respectively.

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“…Consequently, the regulation parameters k P , k I , and k D of PID unit in the measurement correction step can be solved by equations (26) and (27).…”

Section: Complexitymentioning

confidence: 99%

Section: Complexitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A State of Charge Estimation Method for Lithium‐Ion Battery Using PID Compensator‐Based Adaptive Extended Kalman Filter

et al. 2021

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“…Reference 13 concluded that the best choice for LiNiMnCoO 2 (NMC) LiBs is the Thevenin model while Reference 14 concluded that the DP model performs the best in the Dynamic Stress Test (DST). In addition, different filter algorithms, such as Gaussian process-based filter methods including extended Kalman filter (EKF), 16 adaptive extended Kalman filter (AEKF), 17 unscented Kalman filter (UKF), 18 central difference Kalman filter (CDKF), 18 cubature Kalman filter (CKF) 19 and probability-based filter algorithms including particle filter (PF), 20 unscented particle filter (UPF), 21 and cubature particle filter (CPF), 22,23 have been widely used for online SoC estimation. And the performance of different filter algorithms is usually compared in terms of tracking accuracy, convergence behavior, and computation time.…”

Section: Introductionmentioning

confidence: 99%

“…25 For EECMs, the parameters needed to be identified consist of two parts: (a) the relationship between OCV and SoC and (b) the Ohmic resistance and the resistancecapacitance (RC) parallel network. The commonly used PIM methods to identify the Ohmic resistance and RC parallel network include least squares (LS) method, 19,24 recursive LS method with a forgetting factor (FFRLS), 26,27 KF family algorithms 28,29 and H-∞ algorithm. 30 For the relationship between OCV and SoC, the most commonly used method is to conduct a specific OCV test and analyze the experimental data to obtain an offline OCV-SoC curve.…”

Section: Introductionmentioning

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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State‐of‐charge estimation for LiNi₀_.₆Co₀_.₂Mn₀_.₂O₂/graphite batteries using the compound method with improved extended Kalman filter and long short‐term memory network

Zhou

et al. 2020

Intl J of Energy Research

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Summary Because it is necessary for electric vehicle (EV) driver to know the remaining power of the EV battery during driving, thus it is very valuable to research the precise state‐of‐charge estimation, which can update the residual capacity of battery and prevent the occurrence of overcharge and over discharge phenomenon for battery. As compared with most model‐based methods, neural network‐based methods can directly use battery surface temperature as the input parameters of the model. In here, the state‐of‐charge of LiNi0.6Co0.2Mn0.2O2/graphite batteries is estimated using the compound method with improved extended Kalman filter and long short‐term memory network. The improved extended Kalman filter algorithm is composed of recursive least square method with forgetting factor algorithm, generalized regression neural network and extended Kalman filter algorithm. For the compound state‐of‐charge estimation method, the state‐of‐charge values are first estimated using the improved extended Kalman filter, and then optimized using long short‐term memory network. The datasets of dynamic stress test at 25°C are applied to train the long short‐term memory network, while the datasets under different conditions are used as validation datasets. The results reveal that the mean absolute error, maximum absolute error, and root mean square error for their combination method are less than 1%, 3%, 1.1%, respectively. Among three kinds of state‐of‐charge estimation methods, the compound method has the highest estimation accuracy under different working conditions.

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Estimation for state-of-charge of lithium-ion battery based on an adaptive high-degree cubature Kalman filter

Cited by 82 publications

References 38 publications

A State of Charge Estimation Method for Lithium‐Ion Battery Using PID Compensator‐Based Adaptive Extended Kalman Filter

A State of Charge Estimation Method for Lithium‐Ion Battery Using PID Compensator‐Based Adaptive Extended Kalman Filter

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

State‐of‐charge estimation for LiNi₀_.₆Co₀_.₂Mn₀_.₂O₂/graphite batteries using the compound method with improved extended Kalman filter and long short‐term memory network

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Estimation for state-of-charge of lithium-ion battery based on an adaptive high-degree cubature Kalman filter

Cited by 82 publications

References 38 publications

A State of Charge Estimation Method for Lithium‐Ion Battery Using PID Compensator‐Based Adaptive Extended Kalman Filter

A State of Charge Estimation Method for Lithium‐Ion Battery Using PID Compensator‐Based Adaptive Extended Kalman Filter

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

State‐of‐charge estimation for LiNi0.6Co0.2Mn0.2O2/graphite batteries using the compound method with improved extended Kalman filter and long short‐term memory network

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State‐of‐charge estimation for LiNi₀_.₆Co₀_.₂Mn₀_.₂O₂/graphite batteries using the compound method with improved extended Kalman filter and long short‐term memory network