A Simplified Model-Based State-of-Charge Estimation Approach for Lithium-Ion Battery With Dynamic Linear Model

Meng, Jinhao; Stroe, Daniel‐Ioan; Ricco, Mattia; Luo, Guangzhao; Teodorescu, Remus

doi:10.1109/tie.2018.2880668

Cited by 148 publications

(50 citation statements)

References 34 publications

(36 reference statements)

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“…The SMC shown the potential to be insensitive to the parameter variations and external disturbances. A nonisolated zeta converter is used to SMC in the closed loop control system shown in fig (8). The SMC surface value is zero this controller is satisfied.…”

Section: Sliding Mode Controllermentioning

confidence: 99%

Analysis of Isolated and Non-Isolated Zeta Converter based on Battery Charger

K¹,

Sunila²

2020

IJERT

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Conventional battery chargers are due to the nonlinear nature of a diode-bridge rectifier used for the AC-DC conversion. A single phase non-isolated zeta converter topology is designed and implemented for charging a 100 Ah,48 V lead acid battery[1].The challenges of non-isolated zeta converter topology is reduced heat loss and smooth charge of battery. The simulation results shows that THD of the input current is low as compared to isolated zeta converter.The proposed converter is controlled using the sliding mode controller(SMC). By proper tuning the controller changes the charging mode from constant current to constant voltage. The analysis between the isolated and non-isolated battery shows that the non-isolated battery charger can achieve the increasing the state of charge and smooth charging. percent italics mode

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Section: Sliding Mode Controllermentioning

confidence: 99%

Analysis of Isolated and Non-Isolated Zeta Converter based on Battery Charger

K¹,

Sunila²

2020

IJERT

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“…Lithium-ion batteries are now widely used for many applications such as home appliances, smartphones, power tools, energy storage systems and electric vehicles because of high energy density, high electromotive force, high output voltage, low self-discharge, low voltage drop and easy management [1], [2]. However, battery degradation begins immediately after batteries are manufactured, and when 70% or 80% of initial capacity remains, batteries need be replaced for safe operation [3].…”

Section: Introductionmentioning

confidence: 99%

LSTM-Based Battery Remaining Useful Life Prediction With Multi-Channel Charging Profiles

et al. 2020

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Remaining useful life (RUL) prediction of lithium-ion batteries can reduce the risk of battery failure by predicting the end of life. In this paper, we propose novel RUL prediction techniques based on long short-term memory (LSTM). To estimate RUL even in the presence of capacity regeneration phenomenon, we consider multiple measurable data from battery management system such as voltage, current and temperature charging profiles whose patterns vary as aging. Unlike the traditional LSTM prediction that matches input layer with output layer as one-to-one structure, we leverage many-to-one structure to be flexible for various input types and to substantially reduce the number of parameters for better generalization. Using the NASA lithium-ion battery datasets, we verify the accuracy of the proposed LSTM-based RUL prediction. The experimental results show that the proposed single-channel LSTM model improves the mean absolute percentage error (MAPE) by 39.2% compared to the baseline LSTM model. Furthermore, the proposed multi-channel LSTM model significantly improves the MAPE, e.g., by 63.7% compared to the baseline; the proposed model achieves 0.47-1.88% of MAPE while the state-of-the-art baseline LSTM shows 0.6-6.45% of MAPE. INDEX TERMS Lithium-ion battery, long short-term memory, remaining useful life, capacity estimation.

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“…Dong, et al [18] used EKF for parameter identification and PF (Particle filter) for SOC estimation. Meng, et al [19] and Dong, et al [20] used RLS (Recursive least squares) for parameter identification and EKF for SOC estimation. The above methods achieve high SOC estimation accuracy under lab testing conditions.…”

Section: Introductionmentioning

confidence: 99%

A State of Charge Estimation Method of Lithium-Ion Battery Based on Fused Open Circuit Voltage Curve

et al. 2020

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The open circuit voltage (OCV) and model parameters are critical reference variables for a lithium-ion battery management system estimating the state of charge (SOC) accurately. However, the polarization effect reduces the accuracy of the OCV test, and the model parameters coupled to the polarization voltage increase the non-linearity of the cell model, all challenging SOC estimation. This paper presents an OCV curve fusion method based on the incremental and low-current test. Fusing the incremental test results without polarization effect and the low current test results with non-linear characteristics of electrodes, the fusion method improves the OCV curve’s accuracy. In addition, we design a state observer with model parameters and SOC, and the unscented Kalman filter (UKF) method is employed for co-estimation of SOC and model parameters to eliminate the drift noise effects. The SOC estimation root mean square error (RMSE) of the proposed method achieves 0.99% and 1.67% in the pulse constant current test and dynamic discharge test, respectively. Experimental results and comparisons with other methods highlight the SOC estimation accuracy and robustness of the proposed method.

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A Simplified Model-Based State-of-Charge Estimation Approach for Lithium-Ion Battery With Dynamic Linear Model

Cited by 148 publications

References 34 publications

Analysis of Isolated and Non-Isolated Zeta Converter based on Battery Charger

Analysis of Isolated and Non-Isolated Zeta Converter based on Battery Charger

LSTM-Based Battery Remaining Useful Life Prediction With Multi-Channel Charging Profiles

A State of Charge Estimation Method of Lithium-Ion Battery Based on Fused Open Circuit Voltage Curve

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