Online battery state-of-charge estimation methods in micro-grid systems

Boulmrharj, Sofia; Ouladsine, Radouane; NaitMalek, Y.; Bakhouya, Mohamed; Zine-Dine, Khalid; Khaidar, Mohammed; Siniti, M.

doi:10.1016/j.est.2020.101518

Cited by 47 publications

(21 citation statements)

References 42 publications

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“…A battery can only be expected to last several years if good battery material and charge regulator is employed [32]. It protects the batteries from overcharging and overdischarging, both of them are harmful to the batteries [33].…”

Section: Charge Controller Sizingmentioning

confidence: 99%

Optimization of Hybrid Wind and Solar Power Generator at Izazi, Tanzania

Omari

2021

JENRR

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Solar can be converted directly into electrical energy by using solar photovoltaic (PV) which convert solar radiation by the photoelectric effect, wind energy can be converted into electrical energy by using alternator coupled with a wind turbine. Solar power system consists of solar panels, solar PV cells and batteries for storing DC energy. Solar energy is available only during the day time whereas wind energy is available throughout the day; it is only depending upon the atmospheric conditions. Wind and solar are complimentary to each other and therefore makes the system more reliable throughout the year. The study at Izazi village, Iringa – Tanzania shows that the available solar energy and wind energy are potential and sufficient for solar-wind hybrid technology. Using the data obtained from NASA for local wind and solar resources for Izazi village Iringa, Tanzania. The simulation using homer analysis software, shows that to reach the minimum cost, the solar PV modules should contribute more energy than wind turbine. The optimization results obtained therefore shows the solar-wind hybrid system can provide a solution for supplying electricity at Izazi. This model result from Izazi village can be applied easily to other villages with similar environmental condition .

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Section: Charge Controller Sizingmentioning

confidence: 99%

Optimization of Hybrid Wind and Solar Power Generator at Izazi, Tanzania

Omari

2021

JENRR

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“…The existing SOC estimation methods are mainly divided into three categories: basic methods (such as the looking-up table and ampere-hour integral methods), data-driven methods and model-based methods [3]. In contrast to the other two methods, the modelbased methods display favourable accuracy and robustness for SOC estimation in BMS due to the closed-loop control and insensitivity to the external disturbances [4]. It is well-known that there are two types of widely used battery models, including equivalent circuit model (ECM) [5,6] and electrochemical model (EM) [7,8].…”

Section: Introductionmentioning

confidence: 99%

Online SOC Estimation Based on Simplified Electrochemical Model for Lithium-Ion Batteries Considering Current Bias

Pang

et al. 2021

Energies

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State of Charge (SOC) is essential for a smart Battery Management System (BMS). Traditional SOC estimation methods of lithium-ion batteries are usually conducted using battery equivalent circuit models (ECMs) and the impact of current sensor bias on SOC estimation is rarely considered. For this reason, this paper proposes an online SOC estimation based on a simplified electrochemical model (EM) for lithium-ion batteries considering sensor bias. In EM-based SOC estimation structure, the errors from the current sensor bias are addressed by proportional–integral observer. Then, the accuracy of the proposed EM-based SOC estimation is validated under different operating conditions. The results indicate that the proposed method has good performance and high accuracy in SOC estimation for lithium-ion batteries, which facilitates the on-board application in advanced BMS.

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“…The Ah integration method calculates SOC by integrating the current flowing through batteries with a known initial SOC value [7]. It is simple and widely implemented in practice [8].…”

Section: Introductionmentioning

confidence: 99%

State of charge prediction framework for lithium-ion batteries incorporating long short-term memory network and transfer learning

Liu

Shu

et al. 2021

Journal of Energy Storage

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This study investigates accurate state of charge estimation algorithms for lithium-ion batteries based on the long short-term memory recurrent neural network and transfer learning. The long short-term memory network with the five typical layer topology is firstly constructed to learn the dependency of state of charge on measured variables. The transfer learning algorithm with fine-tuning strategy is then exploited to regulate the parameters of fully connected layer and share the knowledge of other layers. By this manner, the information from the source data can be applied to predict state of charge of other batteries with less training data. Additionally, a rolling learning method is developed to update the model parameters when the battery capacity is degraded. The precision and robustness of the proposed framework are comprehensively validated through comparative analysis of multitudinous sets of hyperparameters and methods. The experimental results manifest that the developed framework highlights precise estimation capability of state of charge at different aging states and time-varying temperature conditions. In addition, the proposed algorithm is verified feasible when transferred to different batteries based on only 30% training data.

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Online battery state-of-charge estimation methods in micro-grid systems

Cited by 47 publications

References 42 publications

Optimization of Hybrid Wind and Solar Power Generator at Izazi, Tanzania

Optimization of Hybrid Wind and Solar Power Generator at Izazi, Tanzania

Online SOC Estimation Based on Simplified Electrochemical Model for Lithium-Ion Batteries Considering Current Bias

State of charge prediction framework for lithium-ion batteries incorporating long short-term memory network and transfer learning

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