Model improvement and SOC estimation based on aluminium ion batteries

State of charge (SOC) of the lithium-ion batteries is one of the key parameters of the battery management system, which the performance of SOC estimation guarantees energy management efficiency and endurance mileage of electric vehicles. However, accurate SOC estimation is a difficult problem owing to complex chemical reactions and nonlinear battery characteristics. In this paper, the method of the dynamic neural network is used to estimate the SOC of the lithium-ion batteries, which is improved based on the classic close-loop nonlinear auto-regressive models with exogenous input neural network (NARXNN) model, and the open-loop NARXNN model considering expected output is proposed. Since the input delay, feedback delay, and hidden layer of the dynamic neural network are usually selected by empirically, which affects the estimation performance of the dynamic neural network. To cover this weakness, sine cosine algorithm (SCA) is used for global optimal dynamic neural network parameters. Then, the experimental results are verified to obtain the effectiveness and robustness of the proposed method under different conditions. Finally, the dynamic neural network based on SCA is compared with unscented Kalman filter (UKF), back propagation neural network based on particle swarm optimization (BPNN-PSO), least-squares support vector machine (LS-SVM), and Gaussian process regression (GPR), the results show that the proposed dynamic neural network based on SCA is superior to other methods.

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A nonlinear fractional-order H∞ observer for SOC estimation of battery pack of electric vehicles

Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering ◽

10.1177/0954407021994349 ◽

2021 ◽

pp. 095440702199434

Author(s):

Wei Yue ◽

Cong-zhi Liu ◽

Liang Li ◽

Xiang Chen ◽

Fahad Muhammad

Keyword(s):

Fractional Order ◽

Estimation Method ◽

Equivalent Circuit Model ◽

Lithium Ion ◽

Open Circuit ◽

Design Criterion ◽

Observer Design ◽

Nonlinear Observer ◽

Battery Pack ◽

Soc Estimation

This work is focused on designing a fractional-order [Formula: see text] observer and applying it into the state of charge (SOC) estimation for lithium-ion battery pack system. Firstly, a fractional order equivalent circuit model based on the fractional capacitor is established and identified. Secondly, the SOC estimation method based on the fractional-order [Formula: see text] observer is proposed. The nonlinear intrinsic relationship between the open-circuit voltage and SOC is described as a polynomial function, and its Lipschitz proposition has been discussed. Then, the nonlinear observer design criterion is established based on the Lyapunov method. Finally, the effectiveness of the proposed method is verified with high accuracy and robustness by the experiment results.

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Lithium-ion Battery SOC Estimation Based on Weighted Adaptive Recursive Extended Kalman Filter Joint Algorithm

2020 IEEE 8th International Conference on Computer Science and Network Technology (ICCSNT) ◽

10.1109/iccsnt50940.2020.9304993 ◽

2020 ◽

Author(s):

Jianfeng Wang ◽

Zhaozhen Zhang

Keyword(s):

Kalman Filter ◽

Lithium Ion Battery ◽

Extended Kalman Filter ◽

Lithium Ion ◽

Soc Estimation

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A Study of Prediction Model Improvement for Air-Oxidation Breakaway in a Postulated Spent Nuclear Fuel Pool Complete Loss of Coolant Accident

Sustainability ◽

10.3390/su13031442 ◽

2021 ◽

Vol 13 (3) ◽

pp. 1442

Author(s):

Sanggil Park ◽

Jaeyoung Lee ◽

Min Bum Park

Keyword(s):

Kinetic Model ◽

Nuclear Fuel ◽

Spent Nuclear Fuel ◽

Complete Loss ◽

Air Oxidation ◽

Loss Of Coolant Accident ◽

Spent Nuclear Fuel Pool ◽

Loss Of Coolant ◽

Model Derivation ◽

Model Improvement

The temperature of zirconium alloy cladding on the postulated spent nuclear fuel pool complete loss of coolant accident is abruptly increased at a certain time and the cladding is almost fully oxidized to weak ZrO2 in the air. This abrupt temperature escalation phenomenon induced by the air-oxidation breakaway is called a zirconium fire. Although an air-oxidation breakaway kinetic model correlated between time and temperature has been implemented in the MELCOR code, it is likely to bring about unexpected large errors because of many limitations of model derivation. This study suggests an improved time–temperature correlated kinetic model using the Johnson–Mehl equation. It is based on that the air-oxidation breakaway is initiated by the phase transformation from the tetragonal to monoclinic ZrO2 at the oxide–metal interface in the cladding. This new model equation is also evaluated with the Zry-4 air-oxidation literature data. This equation resulted in the almost similar air-oxidation breakaway timing to the actual experimental data at 800 °C. However, at 1000 °C, it showed an error of about 8 min. This could be inferred from the influence of the ZrN phase change due to the nitrogen existing in air.

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Parameter Identification and State-of-Charge Estimation for Lithium-Ion Batteries Using Separated Time Scales and Extended Kalman Filter

Energies ◽

10.3390/en14041054 ◽

2021 ◽

Vol 14 (4) ◽

pp. 1054

Author(s):

Kuo Yang ◽

Yugui Tang ◽

Zhen Zhang

Keyword(s):

Kalman Filter ◽

Parameter Identification ◽

Open Circuit Voltage ◽

Lithium Ion ◽

Open Circuit ◽

State Of Charge ◽

Recursive Least Squares ◽

Model Parameters ◽

Soc Estimation ◽

Battery Model

With the development of new energy vehicle technology, battery management systems used to monitor the state of the battery have been widely researched. The accuracy of the battery status assessment to a great extent depends on the accuracy of the battery model parameters. This paper proposes an improved method for parameter identification and state-of-charge (SOC) estimation for lithium-ion batteries. Using a two-order equivalent circuit model, the battery model is divided into two parts based on fast dynamics and slow dynamics. The recursive least squares method is used to identify parameters of the battery, and then the SOC and the open-circuit voltage of the model is estimated with the extended Kalman filter. The two-module voltages are calculated using estimated open circuit voltage and initial parameters, and model parameters are constantly updated during iteration. The proposed method can be used to estimate the parameters and the SOC in real time, which does not need to know the state of SOC and the value of open circuit voltage in advance. The method is tested using data from dynamic stress tests, the root means squared error of the accuracy of the prediction model is about 0.01 V, and the average SOC estimation error is 0.0139. Results indicate that the method has higher accuracy in offline parameter identification and online state estimation than traditional recursive least squares methods.

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