Kittiratsatcha, Supat

Loading...
Thumbnail Image
Preferred name
Kittiratsatcha, Supat
Alternative Name
Kittiratsatcha, S.
Main Affiliation
Email
supat.ki@kmitl.ac.th

Settings

Now showing 1 - 2 of 2
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    A Standalone Real-Time State of Charge Estimator Using the Long Short-Term Memory Artificial Neural Network
    (2024-01-01)
    Praisan, Akkarawat
    ;
    Polmai, Sompob
    ;
    Kittiratsatcha, Supat
    Precise monitoring of the State-of-Charge (SoC) ensures optimal Lithium-ion (Li-ion) battery utilization, avoiding overcharging and deep discharging. Furthermore, it contributes to maximizing battery enhancement and longevity. However, a Li-ion battery's behavior is non-linear and varies under several conditions, making direct SoC measurement impossible. Currently, machine learning techniques are extensively used to estimate SoC; unfortunately, these models are typically executed in software only and cannot be realistically implemented in hardware. Consequently, this article proposes an approach for estimating SoC using a Recurrent Neural Network (RNN) with an improved Long Short-Term Memory (LSTM) cell. The proposed network was trained and tested on MATLAB using datasets of driving profiles for Electric Vehicles (EVs), which included battery voltage, current, and temperature while charging and discharging under various temperature conditions. Afterwards, the trained model was transferred to STM32-Cube-AI for validation and deployment of the proposed network for hardware implementation. Thanks to the STM32 microcontroller's performance, the network operates smoothly on a microcontroller and can estimate the SoC accurately and rapidly. Although Li-ion batteries have different characteristics at each temperature condition, the LSTM can predict the remaining SoC for each time step within a millisecond. Moreover, the experimental results show the proposed approach's quick convergence to the ground truth, substantiated by Root-Mean-Square-Errors (RMSEs) of around 4.5%, 2%, and 1.9% at 0 °C, 25 °C, and 45 °C, respectively. As a result, the proposed LSTM can be practically implemented for a real-time application with accuracy.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    ACCURATE STATE OF CHARGE ESTIMATION OF LITHIUM-ION BATTERY USING RECURRENT AND NON-RECURRENT NEURAL NETWORKS FOR WLTP DRIVING PROFILES
    (2024-12-01)
    Praisan, Akkarawat
    ;
    Polmai, Sompob
    ;
    Kittiratsatcha, Supat
    Estimating the state of charge (SoC) of a battery is essential to maximize its performance and ensure reliable operation and battery life. Nowadays, many countries are increasingly adopting electric vehicles (EVs) with lithium-ion batteries due to their high specific energy and long service life. This paper presents a method for estimating the state of charge of lithium-ion batteries using artificial neural networks, specifically the Feedforward Neural Network (FNN) and Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM), through a data-driven approach. The training and testing of the networks are conducted using recorded datasets of the battery, based on the WLTP driving profiles class 2 and class 3. These driving profiles are specifically designed for testing electric vehicles, thereby enhancing the realism of the state of charge estimation by the network. In terms of the analytical aspect, the FNN was able to train the network faster due to its simpler structure, requiring less computation. On the other hand, the LSTM demonstrated more accurate SoC estimation with fewer response oscillations, thanks to its ability to learn and adapt network parameters internally.
      2