Improved SOC estimation of lithium-ion batteries with novel SOC-OCV curve estimation method using equivalent circuit model

Song, Youngbin; Park, Minjun; Seo, Minhwan; Kim, Sang Woo

doi:10.23919/splitech.2019.8783149

Cited by 37 publications

(20 citation statements)

References 15 publications

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“…Therefore, a novel OCV-SOC curve, which is appropriate for the calculated OCVs, is necessary to obtain accurate SOCs. The novel OCV-SOC curve can be obtained with the pre-defined R 0 and normal data, which are terminal voltages and constant currents for fully charging a fresh battery [41]. An initial value of normal SOC is defined as 0, and normal SOCs are then calculated using the Coulomb counting method.…”

Section: B Soc Estimationmentioning

confidence: 99%

Online Detection of Soft Internal Short Circuit in Lithium-Ion Batteries at Various Standard Charging Ranges

et al. 2020

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Soft internal short circuit (ISCr) in lithium-ion batteries is a latent risk, and it is a primary reason for thermal runaway with blaze and explosion. Early detection of ISCr is necessary to ensure safe utilization of the batteries. Based on the applications of batteries, load currents are considerably diverse and occasionally do not satisfy the persistent excitation condition, resulting in inaccurate detection of soft ISCr with existing model-based methods. Using constant current for standard charging of the batteries, this study proposes a novel and accurate model-based algorithm to detect soft ISCr online irrespective of the specific type of load currents. An equivalent circuit model of the battery with ISCr is used to extract open circuit voltage of the battery. Enhanced relationship between open circuit voltage and state of charge is obtained to estimate ISCr resistance as a fault index. To improve estimation accuracy of the fault index, factors affecting the ISCr resistance are analyzed and considered. Experiments incorporating various charging ranges and soft ISCr conditions below 100 are configured, and the proposed method is verified with experimental data. The results of the study indicate that the relative error of the estimated fault index does not exceed 6.4%; thereby, the battery management system is enabled to accurately detect an ISCr early.

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Section: B Soc Estimationmentioning

confidence: 99%

Online Detection of Soft Internal Short Circuit in Lithium-Ion Batteries at Various Standard Charging Ranges

et al. 2020

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“…To address the model nonlinearity, the LLKF approach considers only the slope (λ) in the SOC-VOC relation changes online at each local point, while the intercept (b) is constant. Except for λ, which is multiplied by the SOC to map the VOC, the state space matrices are derived normally from the dynamics of the battery model in (3)- (5). Two initial SOCs (50% and 100%) are considered for both the LLKF and EKF, whereas the actual initial SOC of the battery was 90%.…”

Section: ) Soc Estimation With the Proposed Battery Modelmentioning

confidence: 99%

“…This approach works by assigning an SOC value for each value of the battery output voltage in the opencircuit state [2][3][4]. The most appropriate manner of applying the OCV method is by using a 2D lookup table of SOC and OCV values [5]. Generally, the OCV-based method requires a few simple components to be implemented.…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Lithium-Ion Battery Model for Estimating the State of Charge and Degraded Capacity Using an Optimized Extended Kalman Filter

Imran

Flaih³

2020

IEEE Access

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“…In the discrete method, V e is measured directly from long rested OCV after application of a current pulse. Thereupon, this experiment can take a very long time and has dependency on the current rate [12]. On the contrary, in the continuous method V e is found from the V b when a small constant current in the range of C/30 is applied [9,13].…”

Section: Introductionmentioning

confidence: 99%

“…Although process of change in between these two values can be explained by use of a complex electro-chemical model of the battery, a simple linear change according to the SoC status will be used here. Consequently, Equation ( 11) can be advanced into Equation (12).…”

mentioning

confidence: 99%

Open-Loop Dynamic Modeling of Low-Budget Batteries with Low-Power Loads

Masoudinejad

2020

Batteries

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Understanding dynamic behavior of a battery and the possibility of simulating it is necessary during design of IoT systems. This plays a critical role, especially for industrial devices with low-power demands planned for long lasting operation. While cost limitation mostly leads to use of the low-budget batteries with fast degradation, a model of these batteries supplying low-power loads is provided here. The overall model is in open-loop form because no access to the terminal measurements is available during the design phase. The identification process of the relation between the state of charge and related electromotive force as a key element of the model is discussed. Moreover, guidelines are suggested for identification of this relation. Furthermore, SoC estimation based on the Coulomb counting is modified to include a dynamic inter-cycle aging factor. This factor enables replication of the degradation within a single cycle. In spite of simplicity of this concept, it is able to reduce the model’s estimation error evaluated with two different types of loads. The overall model provides promising results with relative errors less than 0.2%.

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Improved SOC estimation of lithium-ion batteries with novel SOC-OCV curve estimation method using equivalent circuit model

Cited by 37 publications

References 15 publications

Online Detection of Soft Internal Short Circuit in Lithium-Ion Batteries at Various Standard Charging Ranges

Online Detection of Soft Internal Short Circuit in Lithium-Ion Batteries at Various Standard Charging Ranges

An Enhanced Lithium-Ion Battery Model for Estimating the State of Charge and Degraded Capacity Using an Optimized Extended Kalman Filter

Open-Loop Dynamic Modeling of Low-Budget Batteries with Low-Power Loads

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