FEED FORWARD NEURAL NETWORK SEBAGAI ALGORITMA ESTIMASI STATE OF CHARGE BATERAI LITHIUM POLYMER

Estimasi State Of Charge (SOC) baterai merupakan parameter terpenting dalam Battery Management System (BMS), terlebih sebagai aplikasi dari mobil listrik dan smart grid. SOC tidak dapat dilakukan pengukuran secara langsung, sehingga diperlukan metode estimasi untuk mendapatkan nilai tersebut. Beberapa metode yang pernah diusulkan adalah coloumb counting dan open circuit voltage. Akan tetapi coloumb counting memiliki kelemahan dalam hal inisialisasi SOC awal dan memiliki ketergantungan terhadap sensor arus. Sedangkan metode open circuit voltage hanya dapat digunakan pada baterai dalam kondisi idel. Pada penelitian ini diusulkan metode algoritma Feed Forward Neural Network (FFNN) untuk estimasi SOC baterai lithium polymer. Algoritma ini dapat menyelesaikan sistem nonlinier seperti yang dimiliki oleh baterai lithium polymer. Arsitektur FFNN dibangun dua kali (dual neural) untuk estimasi OCV dan SOC. FFNN pertama dengan input tegangan, arus, dan waktu charging maupun discharging untuk estimasi OCV. OCV hasil training neural pertama digunakan sebagai input FFNN kedua untuk estimasi SOC. Hasil dari estimasi ini didapatkan dengan nilai hidden neuron 11 pada neural pertama dan hidden neuron 4 pada neural kedua.Keywords: SOC, BMS, Coloumb Counting, OCV, FFNNEstimasi State Of Charge (SOC) baterai merupakan parameter terpenting dalam Battery Management System (BMS), terlebih sebagai aplikasi dari mobil listrik dan smart grid. SOC tidak dapat dilakukan pengukuran secara langsung, sehingga diperlukan metode estimasi untuk mendapatkan nilai tersebut. Beberapa metode yang pernah diusulkan adalah coloumb counting dan open circuit voltage. Akan tetapi coloumb counting memiliki kelemahan dalam hal inisialisasi SOC awal dan memiliki ketergantungan terhadap sensor arus. Sedangkan metode open circuit voltage hanya dapat digunakan pada baterai dalam kondisi idel. Pada penelitian ini diusulkan metode algoritma Feed Forward Neural Network (FFNN) untuk estimasi SOC baterai lithium polymer. Algoritma ini dapat menyelesaikan sistem nonlinier seperti yang dimiliki oleh baterai lithium polymer. Arsitektur FFNN dibangun dua kali (dual neural) untuk estimasi OCV dan SOC. FFNN pertama dengan input tegangan, arus, dan waktu charging maupun discharging untuk estimasi OCV. OCV hasil training neural pertama digunakan sebagai input FFNN kedua untuk estimasi SOC. Hasil dari estimasi ini didapatkan dengan nilai hidden neuron 11 pada neural pertama dan hidden neuron 4 pada neural kedua.Kata kunci: SOC, BMS, Coloumb Counting, OCV, FFNN

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Estimating the State of Health of Lead-Acid Battery Using Feed-Forward Neural Network

Journal of Circuits System and Computers ◽

10.1142/s0218126622500815 ◽

2021 ◽

Author(s):

D. Selvabharathi ◽

N. Muruganantham

Keyword(s):

Neural Network ◽

Heuristic Method ◽

Conventional Technique ◽

Open Circuit ◽

The State ◽

Battery Management System ◽

Battery Management ◽

State Of Health ◽

Feed Forward Neural Network ◽

Feed Forward

A Battery Management System (BMS) can prolong the life of the battery but it depends on the accuracy of the adopted scheme. Different techniques have been developed to enhance the BMS by monitoring the State of Health (SOH) of the battery. In this paper, the detection of battery voltage is analyzed by using the cycle counting method, which is a conventional technique and compared with Artificial Neural Network (ANN), a heuristic method. The advantage of the proposed ANN method is that SOH can be monitored without disconnecting the battery from the load. Also, the sampling data to the ANN are derived from various techniques including Open Circuit Voltage (OCV) method, Ambient temperature measurement, and valley point detection. A feed-forward backpropagation algorithm is used to achieve the purpose of real-time monitoring of the LAB. The results show that the precise estimation of SOH can be obtained by Feed-Forward Neural Network (FFNN) when trained with more sampling data.

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Estimation of State of Charge (SoC) Using Modified Coulomb Counting Method With Open Circuit Compensation For Battery Management System (BMS)

JAREE (Journal on Advanced Research in Electrical Engineering) ◽

10.12962/jaree.v5i1.150 ◽

2021 ◽

Vol 5 (1) ◽

Author(s):

Puspita Ningrum ◽

Novie Ayub Windarko ◽

Suhariningsih Suhariningsih

Keyword(s):

Management System ◽

Voltage Divider ◽

Open Circuit ◽

Voltage Sensor ◽

State Of Charge ◽

Battery Management System ◽

Battery Management ◽

Li Ion Battery ◽

Control Center ◽

Li Ion

Abstract— Battery is one of the important components in the development of renewable energy technology. This paper presents a method for estimating the State of Charge (SoC) for a 4Ah Li-ion battery. State of Charge (SoC) is the status of the capacity in the battery in the form of a percentage which makes it easier to monitor the battery during use. Coulomb calculations are widely used, but this method still contains errors during integration. In this paper, SoC measurement using Open Circuit Voltage Compensation is used for the determination of the initial SoC, so that the initial SoC reading is more precise, because if the initial SoC reading only uses a voltage sensor, the initial SoC reading is less precise which affects the next n second SoC reading. In this paper, we present a battery management system design or commonly known as BMS (Battery Management System) which focuses on the monitoring function. BMS uses a voltage sensor in the form of a voltage divider circuit and an ACS 712 current sensor to send information about the battery condition to the microcontroller as the control center. Besides, BMS is equipped with a protection relay to protect the battery. The estimation results of the 12volt 4Ah Li-ion battery SoC with the actual reading show an error of less than 1%.Keywords—Battery Management System, Modified Coulomb Counting, State of Charge.

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Development of Life-cycle Detection and Extending Function for Reusing Automotive Lithium-ion Battery – Verified in 52V LiMnNiCoO2 Platform

10.21203/rs.3.rs-20563/v1 ◽

2020 ◽

Author(s):

Wu-Yang Sean ◽

Ana Pacheco

Keyword(s):

Life Cycle ◽

Lithium Ion Battery ◽

Management System ◽

Open Circuit Voltage ◽

Lithium Ion ◽

Open Circuit ◽

Battery Pack ◽

Battery Management System ◽

Battery Management ◽

Cycle Detection

Abstract For reusing automotive lithium-ion battery, an in-house battery management system is developed. To overcome the issues of life cycle and capacity of reused battery, an online function of estimating battery’s internal resistance and open-circuit voltage based on adaptive control theory are applied for monitoring life cycle and remained capacity of battery pack simultaneously. Furthermore, ultracapacitor is integrated in management system for sharing peak current to prolong life span of reused battery pack. The discharging ratio of ultracapacitor is adjusted manually under Pulse-Width-Modulation signal in battery management system. In case study in 52V LiMnNiCoO2 platform, results of estimated open-circuit voltage and internal resistances converge into stable values within 600(s). These two parameters provide precise estimation for electrical capacity and life cycle. It also shows constrained voltage drop both in the cases of 25% to 75% of ultracapacitors discharging ratio compared with single battery. Consequently, the Life-cycle detection and extending functions integrated in battery management system as a total solution for reused battery are established and verified.

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Kalman filtering state of charge estimation for battery management system based on a stochastic fuzzy neural network battery model

Energy Conversion and Management ◽

10.1016/j.enconman.2011.06.003 ◽

2012 ◽

Vol 53 (1) ◽

pp. 33-39 ◽

Cited By ~ 102

Author(s):

Long Xu ◽

Junping Wang ◽

Quanshi Chen

Keyword(s):

Neural Network ◽

Kalman Filtering ◽

Management System ◽

Fuzzy Neural Network ◽

State Of Charge ◽

Battery Management System ◽

Battery Management ◽

Battery Model ◽

Fuzzy Neural ◽

State Of Charge Estimation

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An Open Circuit Voltage Model Fusion Method for State of Charge Estimation of Lithium-Ion Batteries

Energies ◽

10.3390/en14071797 ◽

2021 ◽

Vol 14 (7) ◽

pp. 1797

Author(s):

Quanqing Yu ◽

Changjiang Wan ◽

Junfu Li ◽

Lixin E ◽

Xin Zhang ◽

...

Keyword(s):

Open Circuit Voltage ◽

Stress Test ◽

Lithium Ion ◽

Open Circuit ◽

State Of Charge ◽

Battery Management System ◽

Fusion Method ◽

Battery Management ◽

Model Fusion ◽

Cubature Kalman Filter

The mapping between open circuit voltage (OCV) and state of charge (SOC) is critical to the lithium-ion battery management system (BMS) for electric vehicles. In order to solve the poor accuracy in the local SOC range of most OCV models, an OCV model fusion method for SOC estimation is proposed. According to the characteristics of the experimental OCV–SOC curve, the method divides SOC interval (0, 100%) into several sub-intervals, and respectively fits the OCV curve segments in each sub-interval to obtain a corresponding number of OCV sub-models with local high precision. After that, the OCV sub-models are fused through the continuous weight function to obtain fusional OCV model. Regarding the OCV curve obtained from low-current OCV test as the criterion, the fusional OCV models of LiNiMnCoO2 (NMC) and LiFePO4 (LFP) are compared separately with the conventional OCV models. The comparison shows great fitting accuracy of the fusional OCV model. Furthermore, the adaptive cubature Kalman filter (ACKF) is utilized to estimate SOC and capacity under a dynamic stress test (DST) at different temperatures. The experimental results show that the fusional OCV model can effectively track the performance of the OCV–SOC curve model.

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Open-Circuit Voltage-Based State of Charge Estimation of Lithium-ion Battery Using Dual Neural Network Fusion Battery Model

Electrochimica Acta ◽

10.1016/j.electacta.2015.12.001 ◽

2016 ◽

Vol 188 ◽

pp. 356-366 ◽

Cited By ~ 73

Author(s):

Xuanju Dang ◽

Li Yan ◽

Kai Xu ◽

Xiru Wu ◽

Hui Jiang ◽

...

Keyword(s):

Neural Network ◽

Lithium Ion Battery ◽

Open Circuit Voltage ◽

Lithium Ion ◽

Open Circuit ◽

State Of Charge ◽

Battery Model ◽

State Of Charge Estimation

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Battery Management for Small Hydroponic Systems and Cultivation Experiments

International Journal of Manufacturing Materials and Mechanical Engineering ◽

10.4018/ijmmme.2021070102 ◽

2021 ◽

Vol 11 (3) ◽

pp. 21-36

Author(s):

Satoru Yamaguchi ◽

Takuya Motosugi ◽

Yoshihiko Takahashi

Keyword(s):

Open Circuit Voltage ◽

Low Cost ◽

Open Circuit ◽

State Of Charge ◽

Battery Management ◽

Charge Capacity ◽

Hydroponic System ◽

Soc Estimation ◽

Hydroponic Systems ◽

Electronic Load

A small hydroponic system that can use sustainable energy such as solar power has been developed. However, the amount of power generated is not constant, and in the case of unstable weather, enough power cannot be obtained. Therefore, it is necessary to store the generated energy in a battery. In order to design low-cost charging equipment, it is necessary to use a smaller battery and to estimate the remaining charge capacity (state of charge: SOC) accurately. To provide an accurate SOC estimation for such systems, a fusion of CI (current integral) and OCV (open circuit voltage) methods is proposed. When using this method, it is necessary to frequently disconnect the electronic load. In these experiments, the optimum disconnection duration, the effects on plants of frequent battery disconnection, and cutting off of the lighting were investigated.

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