State-of-charge Estimation of Batteries Based on Open-circuit Voltage and Time Series Neural Network

2019 ◽  
Author(s):  
Jianhua Li ◽  
Mingsheng Liu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Open Circuit Voltage ◽  
Open Circuit ◽  
State Of Charge ◽  
State Of Charge Estimation

scholarly journals A Nonlinear-Model-Based Observer for a State-of-Charge Estimation of a Lithium-Ion Battery in Electric Vehicles

Energies ◽  
2019 ◽  
Vol 12 (17) ◽  
pp. 3383 ◽  
Author(s):  
Woo-Yong Kim ◽  
Pyeong-Yeon Lee ◽  
Jonghoon Kim ◽  
Kyung-Soo Kim
Keyword(s):  
Lithium Ion Battery ◽  
Nonlinear Model ◽  
Open Circuit Voltage ◽  
Lithium Ion ◽  
Open Circuit ◽  
The State ◽  
State Of Charge ◽  
Battery Cell ◽  
Model Based ◽  
State Of Charge Estimation

This paper presents a nonlinear-model-based observer for the state of charge estimation of a lithium-ion battery cell that always exhibits a nonlinear relationship between the state of charge and the open-circuit voltage. The proposed nonlinear model for the battery cell and its observer can estimate the state of charge without the linearization technique commonly adopted by previous studies. The proposed method has the following advantages: (1) The observability condition of the proposed nonlinear-model-based observer is derived regardless of the shape of the open circuit voltage curve, and (2) because the terminal voltage is contained in the state vector, the proposed model and its observer are insensitive to sensor noise. A series of experiments using an INR 18650 25R battery cell are performed, and it is shown that the proposed method produces convincing results for the state of charge estimation compared to conventional SOC estimation methods.

Switching Adaptive Observer for Lithium-Ion Battery State of Charge Estimation

2014 ◽  
Author(s):  
Yonghua Li ◽  
R. Dyche Anderson
Keyword(s):  
Open Circuit Voltage ◽  
Lithium Ion ◽  
Open Circuit ◽  
State Of Charge ◽  
Adaptive Observer ◽  
Parameter Estimator ◽  
Propulsion Systems ◽  
Battery State Of Charge ◽  
State Of Charge Estimation ◽  
Recursive Parameter Estimator

A switching adaptive observer is proposed for estimation of state of charge (SOC) for lithium ion batteries used in electrified automotive propulsion systems. The base observer includes (i) a parameter estimation subsystem including a recursive parameter estimator for identifying battery parameters and (ii) an open circuit voltage (OCV) estimation subsystem including a nonlinear adaptive observer for estimating battery OCV. A timer as well as excitation level determination decides when the ampere-hour integration based SOC or estimated OCV based SOC is used as output. Using this approach, transient response of the adaptive SOC estimator is greatly improved. Examples are used to show the effectiveness of the proposed approach.

scholarly journals ESTIMASI STATE OF CHARGE BATERAI LITHIUM POLYMER MENGGUNAKAN BACK PROPAGATION NEURAL NETWORK

2020 ◽  
Vol 12 (2) ◽  
pp. 140-149
Author(s):  
Mohammad Imron Dwi Prasetyo ◽  
Hasnira Hasnira ◽  
Novie Ayub Windarko ◽  
Anang Tjahjono
Keyword(s):  
Neural Network ◽  
Renewable Energy ◽  
Open Circuit Voltage ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Open Circuit ◽  
State Of Charge ◽  
Input Layer

Baterai merupakan salah satu komponen yang penting dalam konteks implementasi renewable energy. Jenis Baterai yang memiliki kepadatan dalam penyimpanan energy adalah lithium polymer. Parameter dalam baterai yang harus diperhatikan adalah estimasi State Of Charge (SOC). Pada umumnya estimasi SOC baterai menggunakan metode coloumb counting karena tingkat kesulitanya rendah. Namun terdapat kelemahan dari sisi ketergantungan terhadap utilitas sensor arus yang digunakan sebagai akumulasi dari integral arus yang masuk maupun arus yang keluar terhadap waktu. Dalam penelitian ini menyajikan Back Propagation Neural Network (BPNN) sebagai algoritma untuk estimasi SOC berdasarkan kurva karakteristik OCV – SOC. Kurva karakteristik OCV – SOC baterai didapatkan dari pengujian pulsa baterai. Tegangan, arus, dan waktu discharging baterai digunakan sebagai input layer BPNN pertama untuk estimasi Open Circuit Voltage (OCV). OCV akan dilearning sebagai input layer BPNN kedua untuk estimasi SOC baterai. Hasil dari simulasi estimasi SOC didapatkan galat rata-rata sebesar 0.479% terhadap SOC riil berdasarkan kurva karakteristik OCV – SOC.

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