State of charge

About: State of charge is a research topic. Over the lifetime, 12013 publications have been published within this topic receiving 201419 citations. The topic is also known as: SoC & SOC.

Investigation on the thermal hazards of 18650 lithium ion batteries by fire calorimeter

Abstract: In applications of lithium ion batteries, it is a requisite to precisely appraise their fire and explosion hazards. In the current study, a fire calorimeter is utilized to test the combustion performance of two commercial 18650 lithium ion batteries (LiCoO2 and LiFePO4) at different state of charge (SOC). Characteristics on thermal hazards of lithium ion batteries including surface temperature, time to ejection, mass loss, and heat release rate (HRR) are measured and evaluated. In case of thermal runaway, all the lithium ion batteries will rupture the can and catch fire even explode automatically. The solid electrolyte interface layer decomposition and the polymer separator shrinking are direct causes of the lithium ion battery fire. The experimental results show that the HRR and total heat generally rise as the SOC increases, whereas the time to first ejection and the time gap between first and second ejection decrease. LiCoO2 18650 battery shows higher explosion risk than LiFePO4 18650, as the former has released much more oxygen. The experimental combustion heats calculated and modified in the oxygen consumption method reveal that the internally generated oxygen have significant effect on the estimate of the heat, where the largest modified rate is 29.9 for 100 % SOC LiCoO2 18650 battery. The results can provide scientific basis for fire protection during the storage and distribution of lithium ion batteries.

Online Estimation of Power Capacity With Noise Effect Attenuation for Lithium-Ion Battery

Abstract: Accurate estimation of power capacity is critical to ensure battery safety margins and optimize energy utilization. Power capacity estimators based on online identified equivalent circuit model have been widely investigated due to the high accuracy and affordable computing cost. However, the impact of noise corruption which is common in practice on such estimators has never been investigated. This paper scrutinizes the effect of noises on model identification, state of charge (SOC) and power capacity estimation. An online model identification method based on adaptive forgetting recursive total least squares (AF-RTLS) is proposed to compensate the noise effect and attenuate the identification bias of model parameters. A Luenberger observer is further used in combination with the AF-RTLS to estimate the SOC in real time. Leveraging the estimated model parameters and SOC, a multiconstraint analytical method is proposed to online estimate the power capacity. Simulation and experimental results verify that the proposed method is superior in terms of estimation accuracy and the robustness to noise corruption.

Apparatus and method for estimating state of charge of battery using neural network

Abstract: Disclosed are an apparatus and a method for estimating a state of charge of a battery representing a non-linear characteristic by using a neural network. The apparatus includes a sensing section for detecting current, voltage and a temperature from a battery cell, a neural network performing a neural network algorithm and a learning algorithm based on data of the current, voltage and temperature transmitted thereto from the sensing section and present time data, thereby outputting the SOC of the battery estimated through a final learning algorithm, and a comparator for comparing an output value of the neural network with a predetermined target value and making the neural network iteratively perform the learning algorithm if a difference between the output value of the neural network and the predetermined target value is out of a predetermined limit, thereby update the learning algorithm to generate the final learning algorithm. The state of charge of the battery is precisely estimated through the neural network algorithm.