Comparative Study of Kalman Filter and H infinity Filter for Current Sensorless Battery Health Analysis

Putra, Wahyu Sukestyastama; Kuswanto, Jeki; Ashari, Wahid Miftahul; Koprawi, Muhammad

doi:10.1109/icoiact53268.2021.9563993

Cited by 3 publications

(1 citation statement)

References 23 publications

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“…Putra pada [18][19] menguraikan bahwa algoritma kalman filter memiliki tiga bagian utama yaitu bagian penentuan nilai awal, prediksi dan koreksi. Penentuan nilai awal perlu dilakukan karena Kalman filter merupakan sebuah sistem kausal.…”

Section: Metodeunclassified

Deteksi Anomali Konduktivitas Air Menggunakan Kalman Filter

Putra

Koprawi

Ashari

et al. 2022

Buletin Ilmiah Sarjana Teknik Elektro

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Water quality is an essential part of shrimp farming. Data integrity is one of the challenges in building a water conductivity monitoring system. Data read by the sensor should represent the physical conditions that occur. However, some factors can cause abnormal data changes. This abnormal data change can occur due to sensor damage or an attempt to sabotage the pool. In this study, a data anomaly detection algorithm was built using the Kalman filter and standard deviation to solve the problem of determining the normal range of data. The designed algorithm was then tested and evaluated using Arduino nano, Arduino mega, and Wemos D1 Microcontrollers to determine the algorithm's performance on limited computing devices. Based on the data analysis that has been carried out, it is found that the anomaly detection algorithm based on the Kalman filter has an accuracy of 92.5% and can detect anomaly data that occurs with TPF = 1 and FNR = 0 values. The implementation of the detection algorithm on the microcontroller shows that WEMOS D1 (ESP8266) has an excellent average computational speed of 27.99 us. As for the stability of the Arduino Nano (ATMEGA328) and Arduino Mega 2560 (ATMEGA 2560) microcontrollers, the computation time deviation is about 2.8 us. Kualitas air merupakan bagian penting pada budidaya udang. Salah satu tantangan dalam membangun sebuah sistem monitoring konduktivitas air adalah Keutuhan data. Suatu data yang terbaca oleh sensor seharusnya mewakili kondisi fisik yang terjadi. Akan tetapi ada faktor-faktor dapat menyebabkan perubahan data yang tidak wajar. Perubahan data yang tidak wajar ini dapat terjadi karena disebabkan kerusakan sensor maupun adanya upaya sabotase pada kolam. Pada penelitian ini dibangun sebuah algoritma deteksi anomali data menggunakan Kalman filter dan standar deviasi untuk mengatasi masalah penentuan rentang data normal. Algoritma yang dirancang kemudian diuji dan dievaluasi dengan menggunakan Mikrokontroller Arduino nano, Arduino mega dan Wemos D1 untuk mengetahui performa algoritma yang dirancang pada perangkat komputasi terbatas. Berdasarkan analisis data yang telah dilakukan didapatkan hasil bahwa algoritma deteksi anomali berbasis kalman filter memiliki akurasi 92,5% dan dapat mendeteksi data anomali yang terjadi dengan nilai TPF =1 dan FNR=0. Implementasi algoritma deteksi pada mikrokontroller menunjukkan bahwa WEMOS D1 (ESP8266) memiliki rata-rata kecepatan komputasi yang baik yaitu 27,99 us. Sedangkan untuk kestabilan mikrokontroller Arduino Nano (ATMEGA328) dan Arduino Mega 2560 (ATMEGA 2560) memiliki deviasi waktu komputasi sekitar 2,8 us.

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Section: Metodeunclassified

Deteksi Anomali Konduktivitas Air Menggunakan Kalman Filter

Putra

Koprawi

Ashari

et al. 2022

Buletin Ilmiah Sarjana Teknik Elektro

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Sensitivity Analysis of Battery Digital Twin Design Variables Using Genetic Programming

Vandana,

Bose,

Garg

2023

2023 IEEE 3rd International Conference on Sustainable Energy and Future Electric Transportation (SEFET)

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Recurrent Neural Networks for Estimating the State of Health of Lithium-Ion Batteries

Teixeira,

Calili,

Almeida

et al. 2024

Batteries

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Rapid technological changes and disruptive innovations have resulted in a significant shift in people’s behavior and requirements. Electronic gadgets, including smartphones, notebooks, and other devices, are indispensable to everyday routines. Consequently, the demand for high-capacity batteries has surged, which has enabled extended device autonomy. An alternative approach to address this demand is battery swapping, which can potentially extend the battery life of electronic devices. Although battery sharing in electric vehicles has been well studied, smartphone applications still need to be explored. Crucially, assessing the batteries’ state of health (SoH) presents a challenge, necessitating consensus on the best estimation methods to develop effective battery swap strategies. This paper proposes a model for estimating the SoH curve of lithium-ion batteries using the state of charge curve. The model was designed for smartphone battery swap applications utilizing Gated Recurrent Unit (GRU) neural networks. To validate the model, a system was developed to conduct destructive tests on batteries and study their behavior over their lifetimes. The results demonstrated the high precision of the model in estimating the SoH of batteries under various charge and discharge parameters. The proposed approach exhibits low computational complexity, low cost, and easily measurable input parameters, making it an attractive solution for smartphone battery swap applications.

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Comparative Study of Kalman Filter and H infinity Filter for Current Sensorless Battery Health Analysis

Cited by 3 publications

References 23 publications

Deteksi Anomali Konduktivitas Air Menggunakan Kalman Filter

Deteksi Anomali Konduktivitas Air Menggunakan Kalman Filter

Sensitivity Analysis of Battery Digital Twin Design Variables Using Genetic Programming

Recurrent Neural Networks for Estimating the State of Health of Lithium-Ion Batteries

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