Fusion estimation strategy based on dual adaptive Kalman filtering algorithm for the state of charge and state of health of hybrid electric vehicle Li‐ion batteries

Ren, Pu; Wang, Shunli; Chen, Xianpei; Huang, Junhan; He, Mingfang

doi:10.1002/er.7643

Cited by 17 publications

(4 citation statements)

References 44 publications

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“…The EKF is a local linearization method based on recursive estimation of the minimum mean square error with first-order Taylor expansion. 23,24 Herein, introducing EKF into PF to form EKPF approach can alleviate particle degradation and improve the stability and robustness. The general expression of its state equation is:…”

Section: Battery Soc Estimationmentioning

confidence: 99%

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A Composite State of Charge Estimation for Electric Vehicle Lithium-Ion Batteries Using Back-Propagation Neural Network and Extended Kalman Particle Filter

Pang

Geng

Liu

et al. 2022

J. Electrochem. Soc.

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Accurate estimation of battery state of charge (SOC) plays a crucial role for facilitating intelligent battery management system development. Due to the high nonlinear relationship between the battery open-circuit voltage (OCV) and SOC, and the shortcomings of traditional polynomial fitting approach, it is an even more challenging task for predicting battery SOC. To address these challenges, we present a composite SOC estimation approach for lithium-ion batteries using back-propagation neural network (BPNN) and extended Kalman particle filter (EKPF). First, a second-order resistance capacitance model is established to make parameters identification of a lithium-ion battery cell using recursive least squares algorithm with forgetting factors (FFRLS) approach. Then, BPNN is used to fit the desired OCV-SOC relationship with relatively high precision. Next, by incorporating the extended Kalman filter (EKF) into the particle filter (PF), an expected EKPF approach is presented to realize the SOC estimation. Finally, the performances of SOC estimation using different methods, namely the PF, EKF and the EKPF are compared and analyzed under constant current discharge and urban dynamometer driving schedule working conditions. The experimental results show that the proposed method has higher accuracy and robustness compared to the other two SOC estimation methods.

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Section: Battery Soc Estimationmentioning

confidence: 99%

“…Based on the ECM of LIBs, proportional integral observer, 21 sliding mode observer, 22 Kalman filter (KF), [23][24][25] and particle filter (PF) 26,27 are extensively utilized for realizing the accurate estimation of SOC. In term of traditional KF method, it is only applicable to linear systems.…”

mentioning

confidence: 99%

A Composite State of Charge Estimation for Electric Vehicle Lithium-Ion Batteries Using Back-Propagation Neural Network and Extended Kalman Particle Filter

Pang

Geng

Liu

et al. 2022

J. Electrochem. Soc.

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“…Commonly used equivalent circuit models are classified as the RC model, the Thevenin model, the Rint model, the PNGV model and the GNL model. 19,20 Based on the ECM, the Kalman filter (KF) family method has become a main research technique for battery SOC estimation, 21,22 including: the extended KF algorithm, 23,24 the multiinnovation extended KF algorithm, 25,26 the unscented KF algorithm, 27,28 the dual KF algorithm, 29,30 and the cubature KF algorithm. 31,32 However, the KF family methods require high precision for the battery model, which is directly related to the model complexity.…”

Section: The Model-based Estimation Schemesmentioning

confidence: 99%

A beetle antennae search optimized recurrent extreme learning machine for battery state of charge estimation

Wang

2022

Intl J of Energy Research

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Summary The extreme learning machine (ELM) is a single‐hidden layer feedforword neural network (FNN) without training hidden layer weights/biases. By constructing a recurrent extreme learning machine (Recurrent‐ELM) with time delay lines to model battery dynamic characteristics, a beetle antennae search based recursive least squares (BAS‐RLS) method is explored to online realize the state of charge (SOC) estimation. The contents include: (1) To decrease the computational burden, the ELM model with fixed hidden layer weights is adopted to model battery SOC, and a RLS algorithm is studied to online estimate SOC by using the sampled terminal voltages and currents; (2) To solve the modeling accuracy problem, a Recurrent‐ELM model with past/present voltages and currents, and past SOC as inputs is constructed by adding time delay lines to capture battery dynamic characteristics, so as to promote the battery modeling accuracy; (3) To determine suitable neuron numbers in the hidden layer, the BAS method is introduced to find the optimal neuron number in the hidden layer to promote intelligence of the Recurrent‐ELM based RLS algorithm. Simulation results indicate that the proposed model and method has high precision in SOC estimation compared with traditional method.

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“…Equivalent circuit model not only relies on realtime measurement data, but also requires intelligent algorithms such as Kalman filtering or particle swarm filtering to identify model parameters. [18][19][20] In literature, 21 an improved dual adaptive Kalman filter-based algorithm was proposed to improve the accuracy of parameter identification. In literature, 22 an improved unscented particle filter algorithm based on linear optimization combined resampling was proposed.…”

Section: Introductionmentioning

confidence: 99%

A state‐of‐health estimation method considering capacity recovery of lithium batteries

Guo

Zhu³

et al. 2022

Intl J of Energy Research

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At present, the rapid development of new energy sources makes lithium-ion batteries (LIBs) widely used, but LIBs will inevitably age during using. State of health (SOH) is a direct reflection to the aging of LIBs, so it is necessary to estimate the SOH. During the aging process of the LIBs, the phenomenon of capacity recovery will occur if the battery is standing for too long. Existing SOH estimation methods based on neural network do not propose countermeasures for the phenomenon, but in fact, capacity recovery is inevitable and it has a great impact on SOH estimation. According to this vacancy, this paper proposes a SOH estimation method based on double bi-directional long shortterm memory (DBiLSTM) model, which can accurately estimate recovered capacity and improve accuracy of SOH estimation. First, the capacity of LIB is decomposed at multiple scales using wavelet analysis, and the smooth and fluctuating components are obtained. Then six features are proposed based on the changes in the battery after aging. The proposed features are decomposed into new features suitable for the two components. Finally, the smooth component and the fluctuation component are estimated synchronously, and the estimated results are reconstructed to obtain the final estimated SOH. The method proposed in this paper is verified in the NASA dataset and compared with the bi-directional long short-term memory (BiLSTM) model. Comparing with the direct estimation by BiLSTM, the root mean square error (RMSE) is reduced by at least 0.0084 and the mean absolute percentage error (MAPE) is reduced by at least 0.52% when the battery capacity fluctuates greatly. The experimental results show that the proposed method can significantly improve the accuracy of SOH estimation when the capacity fluctuates greatly.Abbreviations: BiLSTM, bi-directional long short-term memory; CC, constant current; CNN, convolution neural network; CV, constant voltage; DBiLSTM, double bi-directional long short-term memory; FN, Nth feature; FN HF , the high frequency components of the Nth feature; FN LF , the low frequency component of the Nth feature; LIB, lithium-ion battery; LSTM, long short-term memory; MAPE, mean absolute percentage error; MLP, multi-layer perceptron; PCoE, prognostics center of excellence; RMSE, root mean square error; SOH, state of health; SVR, support vector regression.

show abstract

Fusion estimation strategy based on dual adaptive Kalman filtering algorithm for the state of charge and state of health of hybrid electric vehicle Li‐ion batteries

Cited by 17 publications

References 44 publications

A Composite State of Charge Estimation for Electric Vehicle Lithium-Ion Batteries Using Back-Propagation Neural Network and Extended Kalman Particle Filter

A Composite State of Charge Estimation for Electric Vehicle Lithium-Ion Batteries Using Back-Propagation Neural Network and Extended Kalman Particle Filter

A beetle antennae search optimized recurrent extreme learning machine for battery state of charge estimation

A state‐of‐health estimation method considering capacity recovery of lithium batteries

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