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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
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Polmai, Sompob
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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.
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An overview of reinforcement learning-based approaches for smart home energy management systems with energy storages
(2024-09-01)
Pinthurat, Watcharakorn
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Surinkaew, Tossaporn
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Hredzak, Branislav
The paper's state-of-the-art review focuses on an in-depth evaluation of smart home energy management systems which employ reinforcement learning-based methods to integrate energy storages. In order to optimize energy consumption and improve overall sustainability while maintaining technical and economic constraints, the paper first investigates the multi-faceted aspects of integrating energy storages into smart homes. Second, an overview of a smart home system and a theoretical background of reinforcement learning-based algorithms are given and discussed. Consequently, this study delves into the challenges and benefits of integrating energy storage, specifically looking at ways to lessen the impact of renewable sources’ intermittency, improve grid stability, and streamline efficient energy storage management. Thirdly, the paper highlights the beneficial features of smart home energy storage integration, including reduced costs, increased system resilience, and improved energy efficiency. Therefore, cutting-edge reinforcement learning-based methods utilized in smart home energy management systems that incorporate energy storage are thoroughly examined by evaluating their effectiveness and adaptability, taking into account both multi-agent and single-agent reinforcement learning-based methods. Finally, the study identifies potential research directions, including the development of hybrid reinforcement learning algorithms, integration of demand-side management strategies, and addressing privacy and security concerns in reinforcement learning-based smart home energy management systems. While some research has made use of single-agent reinforcement learning, smart home energy storage systems that use energy storages seldom use multi-agent reinforcement learning techniques. Researchers, practitioners, and policymakers will be able to use this work as a foundation to build smart, sustainable home energy systems.
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A number of RC pairs consideration of electrical equivalent circuit model of li-ion battery
(2020-07-01)
Kamrueng, Chairat
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Kittiratsatcha, Supat
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Polmai, Sompob
In 20th century, handheld electronic appliances have changed our live style and make things as comfortable as never had before. Of course, the main important part of those devices is the battery. Li-ion battery is the most used not only in those small appliances such as smart phone, tablet, laptop, etc., but also in high power application like in electric vehicle or backup system for power grid. To fully utilize the battery for those applications, a suitable electrical equivalent circuit model must be selected for precise estimation of battery voltage and remaining capacity. Thevenin-based equivalent circuit model with various number of RC pairs has been used in many literatures which give different accuracy of voltages results. This paper proposed the consideration for number of RC pairs for equivalent circuit model using curve fitting method and the goodness of fit results base on the values of root mean square error at several state of charge of battery. The results show good indicator between simple circuit model with low accuracy or complex circuit model with higher accuracy.
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Battery monitoring system by detecting dead battery cells
(2015-08-17)
Hommalai, Chanikarn
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Khomfoi, Surin
The development of battery monitoring system by detecting dead battery cell is proposed in this paper. The experiment of charging and discharging process is tested in different values of the current in order to compare the experimental parameter at different condition. During the process, the parameters of battery, which are current, voltage, and temperature are send to display on the computer and analyzed in order to detect the dead battery cell. The monitoring device also shows the parameter of battery during testing. CAN (Controlled Area Network Communication) is used to transfer all data between the microcontroller and the proposed PLC together with a computer. The outputs showing on the displaying screen are voltage per cell, current of charging or discharging, temperature within battery, power density, and also energy density. The dead battery cells and efficiency of battery can also be indicated by this monitor device.
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Bee colony optimization of battery capacity and location for alleviation of voltage rise by photovoltaic generation in power distribution system
(2013-03-28)
Chaiyatham, Theerawut
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Ngamroo, Issarachai
Recently, an installation of photovoltaic (PV) power generation in power distribution systems has been paid attention to around the world. Nevertheless, the PV generation may cause a reverse power flow which results in the voltage rise problem. To tackle this problem, the battery energy storage can be applied. However, the optimal placement and capacity of battery are very important issue to be considered due to the high cost of battery. This paper applies the bee colony optimization to determining the optimal location and size of battery. Simulation results with many scenarios confirm the control effect of battery optimized by the proposed method. © 2013 ICIC International.
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Bee colony optimization of battery capacity and placement for mitigation of voltage rise by PV in radial distribution network
(2012-12-01)
Chaiyatham, Theerawut
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Ngamroo, Issarachai
At present, the penetration of photovoltaic (PV) power generation in the distribution network tends to increase considerably. However, when the PV sources are located at the ending bus of the radial network, they may cause a reverse power flow and a voltage rise problem. To reduce the voltage rise, the battery energy storage can be used. Nevertheless, the inevitable problem of battery is the high installation cost. The optimal placement and capacity of battery are the vital issue to be considered. In this paper, the bee colony optimization is applied to find the optimal placement and capacity of battery in the radial distribution network for alleviating the voltage rise due to PV sources. Simulation results under several scenarios indicate the superior control effect of the optimized battery over the non-optimal battery. © 2012 IEEE.
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Design of 5x step-up sc dc-dc converter for industrial wireless transmitter
(2011-08-01)
Pongswatd, Sawai
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Kunsri-U-Chane, Kachane
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Smerpituk, Krit
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Eguchi, Kei
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Sasaki, Hirofumi
In this paper, a 5x step-up switched-capacitor (SC) DC-DC converter utilizing solar energy for industrial wireless transmitters is presented. A wireless transmitter can be supplied by its internal battery in combination with the proposed converter for long battery runtime. A Rosemount wireless transmitter from Emerson Process Management Company is used as a target application example in process measurement. The proposed converter is designed for receiving 1.5 V solar cells and powering 7.5 V connected in parallel with wireless transmitter battery. The validity of circuit design is conrmed by theoretical analysis and SPICE simulation. The derived formulas will be helpful to estimate circuit characteristics. Moreover, SPICE simulation values agree well with theoretical results.

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