The state of charge estimation for rechargeable batteries based on artificial neural network techniques

Ismail, Mohamed M.; Hassan, Mohamed

doi:10.1109/codit.2013.6689633

Cited by 8 publications

(2 citation statements)

References 10 publications

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“…However, the required models may need to be adjusted or rebuilt when battery characteristics change, and the complexity of the models will significantly affect their SOC-estimating performance. On the other hand, some researchers have proposed intelligent algorithms for SOC estimation, such as artificial neural network (ANN) [30][31][32], fuzzy logic [33][34][35], support vector [36,37], and adaptive network-based fuzzy inference system (ANFIS) [38][39][40]. These methods do not require complicated equivalent circuits or models for obtaining the nonlinear characteristics of batteries, instead it is done through their learning and inference capabilities.…”

Section: Advantages Disadvantagesmentioning

confidence: 99%

Design and Implementation of a Hybrid Real-Time State of Charge Estimation Scheme for Battery Energy Storage Systems

2019

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In order to maximize the operating flexibility and optimize the system performance of a battery energy storage system (BESS), developing a reliable real-time estimation method for the state of charge (SOC) of a BESS is one of the crucial tasks. In practice, the accuracy of real-time SOC detection can be interfered with by various factors, such as battery's intrinsic nonlinearities, working current, temperature, and aging level, etc. Considering the feasibility in practical applications, this paper proposes a hybrid real-time SOC estimation scheme for BESSs based on an adaptive network-based fuzzy inference system (ANFIS) and Coulomb counting method, where a commercially available lead-acid battery-based BESS is used as the research target. The ANFIS allows effective learning of the nonlinear characteristics in charging and discharging processes of a battery. In addition, the'Coulomb counting method with an efficiency adjusting mechanism is simultaneously used in the proposed scheme to provide a reference SOC for checking the system reliability. The proposed estimating scheme was first simulated in a Matlab software environment and then implemented with an experimental hardware setup, where an industrial-grade digital control system using DS1104 as the control kernel and dSPACE Real-Time Interface (RTI) interface were used. Results from both simulation and experimental tests verify the feasibility and effectiveness of the proposed hybrid SOC estimation algorithm.Keywords: battery energy storage system (BESS); state of charge (SOC); adaptive network-based fuzzy inference system (ANFIS) Processes 2020, 8, 2 2 of 22 As a result, real-time SOC can be determined based on a given initial SOC value. This process can be expressed as (1):

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Section: Advantages Disadvantagesmentioning

confidence: 99%

Design and Implementation of a Hybrid Real-Time State of Charge Estimation Scheme for Battery Energy Storage Systems

2019

Processes

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“…Adaptive models are becoming mainstream and associated SOC estimation methods are becoming simpler. The third type includes artificial neural networks [28][29][30], support vector machine [31], and radical…”

Section: Introductionmentioning

confidence: 99%

A State of Charge Estimation Method Based on Adaptive Extended Kalman-Particle Filtering for Lithium-ion Batteries

Xia

Guo

Wang³

et al. 2018

Energies

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A state of charge (SOC) estimation method is proposed. An Adaptive Extended Kalman Particle filter (AEKPF) based on Particle Filter (PF) and Adaptive Kalman Filter (AKF) is used in order to decrease the error and reduce calculations. The second-order resistor-capacitor (RC) Equivalent Circuit Model (ECM) is used to identify dynamic parameters of the battery. After testing (include Dynamic Stress test (DST), New European Driving Cycle (NEDC), Federal Urban Dynamic Schedule (FUDS), Urban Dynamometer Driving Schedules (UDDS), etc.) at different temperatures and times, it was found that the AEKPF exhibits greater tolerance for high system noise (10% or higher) and provides more accurate estimations under common operating conditions.

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Battery Charge Management for Hybrid PV/Wind/Fuel Cell with Storage Battery

Bendary

Ismail

2019

Energy Procedia

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The state of charge estimation for rechargeable batteries based on artificial neural network techniques

Cited by 8 publications

References 10 publications

Design and Implementation of a Hybrid Real-Time State of Charge Estimation Scheme for Battery Energy Storage Systems

Design and Implementation of a Hybrid Real-Time State of Charge Estimation Scheme for Battery Energy Storage Systems

A State of Charge Estimation Method Based on Adaptive Extended Kalman-Particle Filtering for Lithium-ion Batteries

Battery Charge Management for Hybrid PV/Wind/Fuel Cell with Storage Battery

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