State of charge (SoC) estimation on LiFePO&lt;inf&gt;4&lt;/inf&gt; battery module using Coulomb counting methods with modified Peukert

Leksono, Edi; Haq, Irsyad Nashirul; Iqbal, Muhammad; Soelami, F.X. Nugroho; Merthayasa, I Gde Nyoman

doi:10.1109/rict-icevt.2013.6741545

Cited by 23 publications

(14 citation statements)

References 5 publications

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“…This test is repeated at each SOC, and the average value of τ₂ is used to calculate diffusion factor ζ in Equ. (2). In these results, the extracted charge transfer overvoltage and diffusion overvoltage can be drawn as Fig.…”

Section:  mentioning

confidence: 91%

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State Estimation Technique for VRLA Batteries for Automotive Applications

Duong¹,

Tran²,

Choi³

et al. 2016

Journal of Power Electronics

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The state-of-charge (SOC) and state-of-health (SOH) estimation of batteries play important roles in managing batteries for automotive applications. However, an accurate state estimation of a battery is difficult to achieve because of certain factors, such as measurement noise, highly nonlinear characteristics, strong hysteresis phenomenon, and diffusion effect of batteries. In certain vehicular applications, such as idle stop-start systems (ISSs), significant errors in SOC/SOH estimation may lead to a failure in restarting a combustion engine after the shut-off period of the engine when the vehicle is at rest, such as at a traffic light. In this paper, a dual extended Kalman filter algorithm with a dynamic equivalent circuit model of a lead-acid battery is proposed to deal with this problem. The proposed algorithm adopts a battery model by taking into account the hysteresis phenomenon, diffusion effect, and parameter variations for accurate state estimations of the battery. The validity of the proposed algorithm is verified through experiments by using an absorbed glass mat valve-regulated lead-acid battery and a battery sensor cable for commercial ISS vehicles.

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Section:  mentioning

confidence: 91%

“…Several methods of estimating the SOC and SOH of a battery have been proposed by using coulomb counting, artificial neutron networks (ANNs), fuzzy logic (FL), and extended Kalman filters (EKFs) [2]- [10]. The coulomb counting method can be simply implemented by integrating the battery current over time [11].…”

Section: Introductionmentioning

confidence: 99%

State Estimation Technique for VRLA Batteries for Automotive Applications

Duong¹,

Tran²,

Choi³

et al. 2016

Journal of Power Electronics

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“…Monitoring aspect cannot be separated from the control aspect. To run proper control of battery charging and discharging processes, a fast, precise and accurate monitoring system is required [1]. An ideal BMS will be energy efficient with low power consumption in achieving the full capacity of battery.…”

Section: Introductionmentioning

confidence: 99%

Development of battery management system for cell monitoring and protection

Haq

Leksono

Iqbal

et al. 2014

2014 International Conference on Electrical Engineering and Computer Science (ICEECS)

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Battery has an important role as energy storage in electricity system utilization such as in electric vehicle and in smart microgrid system. Battery Management System (BMS) is needed to treat the dynamics of energy storage process in the battery in order to improve the performance and extend the life time of battery. In this paper, BMS cell monitoring and protection has been designed and tested for Lithium Ferro Phosphate (LFP) battery cells. The BMS cell monitoring function has been able to measure the battery parameters such as the voltage and current dynamics of each cell. The data taken from the BMS cell monitoring experiment is used to estimate the state of charge (SOC) of battery which is based on coulomb counting with coulomb efficiency ratios. The BMS cell monitoring function has successfully demonstrated the presence of unbalanced cell voltages during both processes of charging and discharging as well. From the analysis, the existence of capacity and energy fades was also investigated for every discharging and charging cycles. Based on the BMS cell protection experiment results, overcharged and over discharged protections have successfully been demonstrated for the battery cells. The charging process is disabled when the voltage of the corresponding battery cell exceeds its high limit (HLIM) at 3.65V, and the battery will be available for charging when all of the cell voltages are below their boundary limits (CAVL) at 3.3V. The discharging process will be disabled when the battery cell voltage is lower than the corresponding low limit (LLIM) at 2.5 V. The battery will be available again when all battery cell voltages are above their discharge available (DAVL) voltage at 2.8V. The proposed BMS cell monitoring and protection has shown its function as a data acquisition system, safety protection, ability to determine and predict the state of charge of the battery, and ability to control the battery charging and discharging.A BMS which flexible enough to protect different types of batteries and can provide all the safety features, has been a recent topic of development and research in electric vehicle and alternative energy systems [3]. As described in [2], a comprehensive BMS should include functions for data acquisition, safety protection, ability to determine and predict the state of the battery, ability to control battery charging and discharging, cell balancing, thermal management, delivery of battery status and the authentication to a user interface, communication with all BMS components and the most important thing is to prolong battery life.

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“…Many recent studies have focused on SOC estimation. Methods for SOC estimation generally include voltage-based correction [2], [3], fuzzy logic-based [4]- [8], neural network [9]- [11], and Kalman filter [12]- [21] methods. Wei et al [2] proposed a method based on current time window to estimate battery SOC of fuel cells for hybrid electric cars.…”

Section: Introductionmentioning

confidence: 99%

“…Wei et al [2] proposed a method based on current time window to estimate battery SOC of fuel cells for hybrid electric cars. Leksono et al [3] proposed a coulomb counting method with modified Peukert for SOC estimation on a LiFePO4 battery. The voltage-based correction method periodically revised SOC estimation by battery voltage.…”

Section: Introductionmentioning

confidence: 99%

State-of-charge Estimation for Lithium-ion Batteries Using a Multi-state Closed-loop Observer

Zhao¹,

Huang²,

Liu³

et al. 2014

Journal of Power Electronics

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Lithium-ion batteries are widely used in hybrid and pure electric vehicles. State-of-charge (SOC) estimation is a fundamental issue in vehicle power train control and battery management systems. This study proposes a novel model-based SOC estimation method that applies closed-loop state observer theory and a comprehensive battery model. The state-space model of lithium-ion battery is developed based on a three-order resistor-capacitor equivalent circuit model. The least square algorithm is used to identify model parameters. A multi-state closed-loop state observer is designed to predict the open-circuit voltage (OCV) of a battery based on the battery state-space model. Battery SOC can then be estimated based on the corresponding relationship between battery OCV and SOC. Finally, practical driving tests that use two types of typical driving cycle are performed to verify the proposed SOC estimation method. Test results prove that the proposed estimation method is reasonably accurate and exhibits accuracy in estimating SOC within 2% under different driving cycles.

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State of charge (SoC) estimation on LiFePO<inf>4</inf> battery module using Coulomb counting methods with modified Peukert

Cited by 23 publications

References 5 publications

State Estimation Technique for VRLA Batteries for Automotive Applications

State Estimation Technique for VRLA Batteries for Automotive Applications

Development of battery management system for cell monitoring and protection

State-of-charge Estimation for Lithium-ion Batteries Using a Multi-state Closed-loop Observer

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