Design and Build Voltage and Current Monitoring Parameters Device of Rechargeable Batteries in Real-Time Using the INA219 GY-219 Sensor

Setiawan, Budhy

doi:10.23960/jemit.v4i2.137

Cited by 2 publications

(1 citation statement)

References 8 publications

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“…In the context of battery monitoring and management, IoT offers a promising solution for real-time data acquisition and analysis [14]. By integrating IoT devices, sensors, and connectivity, we can gather comprehensive data on various parameters such as voltage [8,15] and temperature [8]. This rich dataset enables the development of predictive models and provides insights into the health conditions and remaining lifespan of lead-acid batteries.…”

Section: Introductionmentioning

confidence: 99%

Online Voltage and Degradation Value Prediction of Lead Acid Battery Using Gaussian Process Regression

Winata,

Surantha

2023

Applied Sciences

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Monitoring battery voltage is important to ensure a steady supply of energy. A crucial aspect to avoid failure is estimating the voltage required by the battery load. Lead acid batteries play a vital role as engine starters when the generators are activated. The generator engine requires an adequate voltage to initiate the power generation process. This article discusses three prediction models for estimating the voltage and degradation values based on data-driven methods. The machine-learning models used were Gaussian process regression (GPR), Support Vector Regression (SVR), and Random Forest. The prediction results were compared using evaluation metrics, such as the Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (R2). The implementation of the Internet of Things (IoT) was demonstrated to collect real-time battery data using a voltage sensor and a temperature sensor as inputs for the prediction model. According to the experiment, the Random Forest algorithm provided highly accurate predictions, with an RMSE of 0.0401, MAE of 0.0241, and R-squared of 0.9651.

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Section: Introductionmentioning

confidence: 99%

Online Voltage and Degradation Value Prediction of Lead Acid Battery Using Gaussian Process Regression

Winata,

Surantha

2023

Applied Sciences

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In Operando Health Monitoring for Lithium-Ion Batteries in Electric Propulsion Using Deep Learning

Vajja,

Serov,

Sudarshan

et al. 2024

Batteries

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Battery management systems (BMSs) play a vital role in understanding battery performance under extreme conditions such as high C-rate testing, where rapid charge or discharge is applied to batteries. This study presents a novel BMS tailored for continuous monitoring, transmission, and storage of essential parameters such as voltage, current, and temperature in an NCA 18650 4S lithium-ion battery (LIB) pack during high C-rate testing. By incorporating deep learning, our BMS monitors external battery parameters and predicts LIB’s health in terms of discharge capacity. Two experiments were conducted: a static experiment to validate the functionality of BMS, and an in operando experiment on an electrically propelled vehicle to assess real-world performance under high C-rate abuse testing with vibration. It was found that the external surface temperatures peaked at 55 °C during in operando flight, which was higher than that during static testing. During testing, the deep learning capacity estimation algorithm detected a mean capacity deviation of 0.04 Ah, showing an accurate state of health (SOH) by predicting the capacity of the battery. Our BMS demonstrated effective data collection and predictive capabilities, mirroring real-world conditions during abuse testing.

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Design and Build Voltage and Current Monitoring Parameters Device of Rechargeable Batteries in Real-Time Using the INA219 GY-219 Sensor

Cited by 2 publications

References 8 publications

Online Voltage and Degradation Value Prediction of Lead Acid Battery Using Gaussian Process Regression

Online Voltage and Degradation Value Prediction of Lead Acid Battery Using Gaussian Process Regression

In Operando Health Monitoring for Lithium-Ion Batteries in Electric Propulsion Using Deep Learning

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