State of Charge (SoC) Estimation of Battery Energy Storage System (BESS) Using Artificial Neural Network (ANN) Based on IoT- Enabled Embedded System

Utama, Putu Handre Kertha; Husniyyah, Hilda Hamdah; Haq, Irsyad Nashirul; Pradipta, Justin; Leksono, Edi

doi:10.1109/ica52848.2021.9625697

Cited by 3 publications

(2 citation statements)

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“…The Data Lake will also be responsible for creating a battery model to estimate the State of Charge (SoC). The battery model is a deep neural network (DNN) based on [23]. Data gathered in the Data Lake will be utilized for training the DNN model.…”

Section: Digital Twin Platformmentioning

confidence: 99%

Development of Digital Twin Platform for Electric Vehicle Battery System

Utama,

Haq,

Leksono

et al. 2023

IJSTT

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The battery system in electric vehicles needs proper monitoring and control to ensure reliable, efficient, and safe operation. Recent advancement in cyber-physical technology has brought the emerging digital twin concept. This concept opens a new possibility of real-time condition monitoring and fault diagnosis of the battery system. Although it sounds promising, the concept implementation still faces many challenges. One of the challenges is the availability of a platform to develop digital twins, which involves data pipelines and modeling tools. The data pipeline will include the acquisition, storing, and extract-transform-load (ETL) with high velocity, volume, value, variety, and veracity data, known as big data. The modeling tools must provide applications to build the high-fidelity model, one of the required elements of the digital twin. Based on those urgencies, this paper proposes a platform that facilitates a digital twinning of the battery system in an electric vehicle. The platform is built on the open-source framework CDAP, equipped with a data pipeline and modeling tools. It has run several performance tests with different computation resource configurations and workloads. Doubling the processing power can reduce 12% of computation time while increasing memory size by four times only reduces 10% of computation time. The result shows that the processing power affects the performance digital twin platform more than the memory size.

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Section: Digital Twin Platformmentioning

confidence: 99%

Development of Digital Twin Platform for Electric Vehicle Battery System

Utama,

Haq,

Leksono

et al. 2023

IJSTT

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“…Utama et al [15] recommended SOC approximation with an artificial neural network (ANN) to decrease the approximation error because of physical parameters and decrease computational cost by means of an IoTassisted embedded scheme. Asaad et al [16] suggest a Battery Monitoring System (BMS) with coulomb counting technique for SoC approximation and message based MQ Telemetry Transport (MQTT) as the transmission method.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Bacterial Foraging Optimization with Sparse Autoencoder for Energy Systems

L¹,

Vedaiyan²,

Xavier³

et al. 2023

Computer Systems Science and Engineering

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The Internet of Things (IoT) technologies has gained significant interest in the design of smart grids (SGs). The increasing amount of distributed generations, maturity of existing grid infrastructures, and demand network transformation have received maximum attention. An essential energy storing model mostly the electrical energy stored methods are developing as the diagnoses for its procedure was becoming further compelling. The dynamic electrical energy stored model using Electric Vehicles (EVs) is comparatively standard because of its excellent electrical property and flexibility however the chance of damage to its battery was there in event of overcharging or deep discharging and its mass penetration deeply influences the grids. This paper offers a new Hybridization of Bacterial foraging optimization with Sparse Autoencoder (HBFOA-SAE) model for IoT Enabled energy systems. The proposed HBFOA-SAE model majorly intends to effectually estimate the state of charge (SOC) values in the IoT based energy system. To accomplish this, the SAE technique was executed to proper determination of the SOC values in the energy systems. Next, for improving the performance of the SOC estimation process, the HBFOA is employed. In addition, the HBFOA technique is derived by the integration of the hill climbing (HC) concepts with the BFOA to improve the overall efficiency. For ensuring better outcomes for the HBFOA-SAE model, a comprehensive set of simulations were performed and the outcomes are inspected under several aspects. The experimental results reported the supremacy of the HBFOA-SAE model over the recent state of art approaches.

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