An Analysis of Battery Degradation in the Integrated Energy Storage System with Solar Photovoltaic Generation

Lee, Munsu; Park, Jinhyeong; Na, Sun-Ik; Choi, Hyung Sik; Bu, Byeong-Sik; Kim, Jong-Hoon

doi:10.3390/electronics9040701

Cited by 17 publications

(7 citation statements)

References 45 publications

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“…Similarly, Reihani et al [32] used linear regression and other optimization algorithms for efficient battery energy management systems. Lee et al [33] provided a data driven-based model that takes into account battery degradation during the discharge and charge phase.…”

Section: B Data Driven-based Approachesmentioning

confidence: 99%

Batteries State of Health Estimation via Efficient Neural Networks With Multiple Channel Charging Profiles

et al. 2021

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The prognostics and health management (PHM) plays the main role to handle the risk of failure before its occurrence. Next, it has a broad spectrum of applications including utility networks, energy storage systems (ESS), etc. However, an accurate capacity estimation of batteries in ESS is mandatory for their safe operations and decision making policy. ESS comprises of different storage mechanisms such as batteries, capacitors, etc. Consequently, the measurement of different charging profiles (CPs) has a strong relation to battery capacity. These profiles include temperature (T), voltage (V), and current (I) where the CPs patterns vary as the battery ages with cycles. Consequently, estimating a battery capacity, the conventional methods practice single channel charging profile (SCCP) and hop multiple channel CPs (MCCPs) that cause incorrect battery health estimation. To tackle these issues, this article proposes MCCPs based battery management system (BMS) to estimate batteries health/capacity through the deep learning (DL) concept where the patterns in these CPs are changed as the battery ages with time and cycles. Thus, we deeply investigate both machine learning (ML) and DL based methods to provide a concrete comparative analysis of our method. The adaptive boosting (AB) and support vector regression (SVR) are widely compared with long short-term memory (LSTM), multi-layer perceptron (MLP), bi-directional LSTM (BiLSTM), and convolutional neural network (CNN) to attain the appropriate approach for battery capacity and state of health (SOH) estimation. These approaches have a high learning capability of inter-relation between the battery capacity and variation in CPs patterns. To validate and verify the proposed technique, we use NASA battery dataset and experimentally prove that BiLSTM outperforms all the approaches and obtains the smallest error values for MAE, MSE, RMSE, and MAPE using MCCPs compared to SCCP.

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Section: B Data Driven-based Approachesmentioning

confidence: 99%

Batteries State of Health Estimation via Efficient Neural Networks With Multiple Channel Charging Profiles

et al. 2021

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“…[ 31 ] Few simpler techniques such as regression and linear optimization methods have been tried to estimate state‐of‐health of the battery with relatively good accuracy. [ 32 ]…”

Section: Introductionmentioning

confidence: 99%

A Graph‐Based Lithium‐Ion Battery Parameter Estimation Approach to Produce Diverse Synthetic Data

Channegowda

Lachappa

Jagwani

2022

Advcd Theory and Sims

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Energy dense lithium-ion batteries are extensively used in all portable electronic devices and in electric vehicles as well. State-of-charge estimation of these batteries has been of considerable commercial interest as this key metric can be construed as the available range in electric vehicles. State-of-charge is also important to ascertain the remaining usage time in battery powered devices. In this paper a graph neural network-based approach is employed to estimate key battery parameters such as, voltage, battery capacity, etc. To the best of the authors' knowledge, this is the first paper to employ a graph-based approach to improve battery estimates. The pairwise interdependencies within the battery dataset are exploited to provide better battery estimates. The graph-based approach is compared with related statistical methods to highlight the effectiveness of this approach.

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“…The temperature model of PV systems was also discussed in the works [18], [19]. The temperature has a major impact on the lifetime of the PV system, especially when batteries are incorporated into the PV module [20], [21]. PV degradation rate affected by different weather conditions was studied in the work [22].…”

Section: Introductionmentioning

confidence: 99%

Changes in the Efficiency of Photovoltaic Energy Conversion in Temperature Range With Extreme Limits

Libra

Petrík

Poulek

et al. 2021

IEEE J. Photovoltaics

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The efficiency of the photovoltaic energy conversion depends on the temperature significantly. We monitored the behavior of I-V characteristics of the PV cell based on monocrystalline silicon in temperature range with extreme limits from −170°C to +100°C. We have not yet found a similar measurement in this temperature interval. The temperature of PV modules without radiation concentration can reach values of −100°C to +100°C on the Earth's surface. The temperature range may be few wider in space. Changes of I-V characteristics and P-V characteristics are discussed in terms of the theory of solids. The open-circuit voltage dependence is approximately linear over a wide temperature range, but saturation occurs at temperatures around −150°C, which is also explained in accordance with the theory of semiconductors. The decrease in energy conversion efficiency with increasing temperature has a value of about 0.5%/°C throughout the whole temperature range possible on the Earth's surface. If there are large changes in the temperature of the PV modules during operation of the PV system, the electrical voltage of the PV modules will also change considerably. In space applications, these fluctuations may be greater. This must be taken into account when designing PV systems (especially for deep space missions). For example, electronic inverters are sensitive to overvoltage or undervoltage.

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An Analysis of Battery Degradation in the Integrated Energy Storage System with Solar Photovoltaic Generation

Cited by 17 publications

References 45 publications

Batteries State of Health Estimation via Efficient Neural Networks With Multiple Channel Charging Profiles

Batteries State of Health Estimation via Efficient Neural Networks With Multiple Channel Charging Profiles

A Graph‐Based Lithium‐Ion Battery Parameter Estimation Approach to Produce Diverse Synthetic Data

Changes in the Efficiency of Photovoltaic Energy Conversion in Temperature Range With Extreme Limits

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