State of charge estimation of lithium-ion batteries based on ultrasonic guided waves by chirped signal excitation
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State of charge
Accurate State Of Charge estimations are one of the critical functionalities of Battery Management Systems, whether it is for single-cell batteries or multiple-cells batteries. However, battery-SOCs are usually generalized without any consideration about cell disparities. In this presentation, we propose to define a more relevant battery-SOC by analyzing the already existing studies about SOC estimation for single cell (cell-SOC) and by developing a battery-SOC which takes cell disparities into account. A focus is also done to explore how to link uncertainties of cells estimations and accuracy of the final battery-SOC in order to give reliable information to the user about the charge amount stored in the battery. The algorithm is then implemented for numeric analysis and comparisons.
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This paper presents a new and reliable battery state estimation method for Ni-Cd and Ni-MH batteries used in portable applications. The proposed method performs the following two basic functions. The first one determines battery state, detecting deteriorated batteries. The second one guarantees a safe fast-charge without negative effects on battery life, analyzing the previous charge state of the battery. Due to this easily implemented method, effective and universal NiCd/NiMH battery fast-chargers can be obtained by using few and inexpensive components.
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Batteries have an important thing in development of energy needs. A good performance battery, will support the device it supports. The energy that can save a battery is limited, so the battery will increase its charge and discharge cycles. Incorrect charging and discharging processes can cause battery performance to decrease. Therefore battery management is needed so that the battery can reach the maximum. One aspect of battery management is setting the state which is the ratio of available energy capacitance to maximum energy capacity. One method for estimating load states is the fuzzy logic method, namely by assessing the input and output systems of prediction. Predictor of State of Charge use Mamdani Fuzzy Logic that have temperature and voltage as input variables and State of Charge as output variable. A result of prediction State of Charge battery is represented by the number of Root Mean Square Error. Battery in charge condition has 2.7 for RMSE and level of accuracy 81.5%. Whereas Battery in discharge condition has RMSE 1.5 and level of accuracy 84.7%.
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배터리를 사용하고 있는 시스템에서 배터리의 잔존 용량에 대한 정보는 매우 중요하며, 따라서 정확한 SOC(State of Charge)의 추정이 필요하다. 배터리는 노화됨에 따라 전체 사용 가능 용량이 줄어들고 성능이 떨어지는데 이러한 노화의 영향을 고려하지 않는 배터리의 SOC 추정 방법은 추정의 정확도가 떨어지는 단점이 있다. 따라서 본 논문에서는 배터리의 노화 상태를 고려하여 배터리의 SOC를 추정하는 새로운 방법을 제안한다. 제안한 방법에서는 배터리의 전압-SOC 특성 곡선을 Boltzmann 방정식을 사용하여 모델링하고 노화 지표를 정의하며, 노화 지표를 Boltzmann 방정식 모델과 결합하여 SOC를 추정한다. 따라서 제안한 방법은 배터리의 노화 상태를 SOC 추정에 반영하여 노화된 배터리에 대한 정확한 SOC 추정이 가능하다. 또한 새 배터리와 1년 사용한 배터리에 대한 실험과 시뮬레이션을 통하여 제안한 방법의 유효성을 확인한다. Proper operation of the battery powered systems depends on the accuracy of the battery SOC(State of Charge) estimation, therefore it is critical for those systems that SOC is accurately determined. The SOC of the battery is related to the battery aging and the SOC estimation methods without considering the aging of the battery are not accurate. In this paper, a new method that accurately estimate the SOC of the battery is proposed considering the aging of the battery. A mathematical model for the Battery SOC-OCV(Open Circuit Voltage) relationship is presented using Boltzmann equation and aging indicator is defined, and then the SOC is estimated combining the mathematical model and aging indicator. The proposed method takes the aging of the battery into consideration, which leads to an accurate estimation of the SOC. The simulations and experiments show the effectiveness of the proposed method for improving the accuracy of the SOC estimation.
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The present work aims to show the limits when managing the state of charge (SOC) of different types of batterie. Charge control appear the most important techniques that ensure safety and a good health of batteries. The majority of charge controllers try to optimise the charging process. However, they can't prevent perfectly the fall phenomenon like sulfation. These charge controllers assume that identical cells compose the battery. Or a slight difference between cells capacities leads to a difference between their SOC. Hence, the risk of having some cells very well charged while others aren't. This phenomenon decreases significantly the battery capacity. For this purpose, we realised a data acquisition system to supervise the cell SOC. The experimental results show that controlling the SOC of the whole battery by making external measurements isn't sufficient, and a control of each cell is necessary to estimate correctly the global performance of the battery.
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Battery pack
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This paper presents an assay of the dynamic state of charge characteristics of a Li-ion battery. It is proposed an approach of forecasting battery's state of charge that can be used by battery management system to predict the remained quantity of charge within the battery at a certain time, based on real time data acquisition and using a simplified electrical circuit model that simulates, with small errors, the battery signal, with experimentally validation of the results.
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In this paper is presented a Matlab/Simulink-based modelling tool for estimation of the State of Charge of Li-ion battery cells. The model is based on lookup tables, which makes it easy to implement in complex battery bank models, containing a large number of cells. The proposed model allows determining the state of the charge for any current based on interpolations. The developed model takes into account the variation of the battery usable capacity by discharging with different C-rates. The model is also applicable for cells with different capacities or different characteristics, according to experimental measurements or the producer datasheets. The model input variables are the discharge current of the cell and the initial State of charge and the outputs are the cell voltage and the cell State of charge. The developed model is validated by experimental results.
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USable
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