H infinity observer based state of charge estimation for battery packs in electric vehicles

He, Fengxian; Shen, Weixiang; Kapoor, Ajay; Honnery, Damon; Dayawansa, Daya

doi:10.1109/iciea.2016.7603672

Cited by 8 publications

(7 citation statements)

References 17 publications

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“…In [23,25,26], these three kinds of methods decline the initial errors by complicated algorithms. The performance of these algorithms can be evaluated by the measurement errors and the integration complexity.…”

Section: Resultsmentioning

confidence: 99%

“…Considering the complexity of algorithm computation, the algorithm implemented in this paper has fewer multiplication and division, which also indicates less hardware resources occupation. In [23, 25, 26], these three kinds of methods decline the initial errors by complicated algorithms. The performance of these algorithms can be evaluated by the measurement errors and the integration complexity.…”

Section: Resultsmentioning

confidence: 99%

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Novel estimation solution on lithium‐ion battery state of charge with current‐free detection algorithm

Wang

Fan

Chen

et al. 2019

IET Circuits, Devices & Systems

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Lithium-ion battery as an efficient, sustainable, and clean energy for electric vehicles (EVs) and smart devices becomes more popular with the worldwide demand for reduction of greenhouse gas emission. In all kinds of applications, an accurate real-time estimation for state of charge (SOC) of battery is necessary. Some conventional methods usually need to sample both battery currents and voltages. This article presents a novel SOC estimation algorithm without current detection. This algorithm just acquires the port voltages of cell to calculate the open-circuit voltage (OCV) which is related to SOC. By extracting a large number of battery voltages based on a step response, some important parameters that can track battery working process are determined. In order to verify the algorithm feasibility and accuracy, it has been tested on a commercial common field-programmable gate array (FPGA) in different application conditions. The algorithm accuracy is mainly limited by model accuracy and sampling sensor accuracy. The maximum error between ideal SOC and calculated SOC by this algorithm is within 4%, and the mean error is about 0.99%. So, this high-feasibility, accredited accuracy, easy integration, and low-cost solution has bright potential in smarter future.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Novel estimation solution on lithium‐ion battery state of charge with current‐free detection algorithm

Wang

Fan

Chen

et al. 2019

IET Circuits, Devices & Systems

View full text Add to dashboard Cite

show abstract

“…The objective of their implementation is to find an optimal solution for a set of different plants that represent possible level of uncertainty or noise. Therefore, they have certain advantages over EKF, such as a higher robustness against uncertainties in the model and being able to deal with unknown noise statistics [124]. However, they require an important level of mathematical understanding and have a strong dependency on the plants used for their design.…”

Section: • H-infinity Observer (H ∞ )mentioning

confidence: 99%

Modelling and Estimation of Vanadium Redox Flow Batteries: A Review

et al. 2022

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Redox flow batteries are one of the most promising technologies for large-scale energy storage, especially in applications based on renewable energies. In this context, considerable efforts have been made in the last few years to overcome the limitations and optimise the performance of this technology, aiming to make it commercially competitive. From the monitoring point of view, one of the biggest challenges is the estimation of the system internal states, such as the state of charge and the state of health, given the complexity of obtaining such information directly from experimental measures. Therefore, many proposals have been recently developed to get rid of such inconvenient measurements and, instead, utilise an algorithm that makes use of a mathematical model in order to rely only on easily measurable variables such as the system’s voltage and current. This review provides a comprehensive study of the different types of dynamic models available in the literature, together with an analysis of the existing model-based estimation strategies. Finally, a discussion about the remaining challenges and possible future research lines on this field is presented.

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“…Furthermore, model-based methods are insensitive to the initial SOC and measurement noise. Kalman filter [16][17][18], H infinity filter [19,20], PI-observer [21], Particle filter [22], RTLS-based observer [23], FBCRLS-based observer [24] etc. are applied to calculate the correcting gain in the SOC estimation structure.…”

Section: Introductionmentioning

confidence: 99%

Influence of Battery Parametric Uncertainties on the State-of-Charge Estimation of Lithium Titanate Oxide-Based Batteries

et al. 2018

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State of charge (SOC) is one of the most important parameters in battery management systems, as it indicates the available battery capacity at every moment. There are numerous battery model-based methods used for SOC estimation, the accuracy of which depends on the accuracy of the model considered to describe the battery dynamics. The SOC estimation method proposed in this paper is based on an Extended Kalman Filter (EKF) and nonlinear battery model which was parameterized using extended laboratory tests performed on several 13 Ah lithium titanate oxide (LTO)-based lithium-ion batteries. The developed SOC estimation algorithm was successfully verified for a step discharge profile at five different temperatures and considering various initial SOC initialization values, showing a maximum SOC estimation error of 1.16% and a maximum voltage estimation error of 44 mV. Furthermore, by carrying out a sensitivity analysis it was showed that the SOC and voltage estimation error are only slightly dependent on the variation of the battery model parameters with the SOC.

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H infinity observer based state of charge estimation for battery packs in electric vehicles

Cited by 8 publications

References 17 publications

Novel estimation solution on lithium‐ion battery state of charge with current‐free detection algorithm

Novel estimation solution on lithium‐ion battery state of charge with current‐free detection algorithm

Modelling and Estimation of Vanadium Redox Flow Batteries: A Review

Influence of Battery Parametric Uncertainties on the State-of-Charge Estimation of Lithium Titanate Oxide-Based Batteries

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