Lithium-Ion Battery Parameters and State-of-Charge Joint Estimation Based on H-Infinity and Unscented Kalman Filters

Yu, Quanqing; Xiong, Rui; Lin, Cheng; Shen, Weixiang; Deng, Junjun

doi:10.1109/tvt.2017.2709326

Cited by 192 publications

(82 citation statements)

References 41 publications

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“…Therefore, controlling the temperature of the battery and establishing an appropriate thermal management system can make the lithium-ion battery safer and more stable, enhance its performance, and elongate its cycle life [1,2]. Many researchers have studied battery thermal management.…”

Section: Introductionmentioning

confidence: 99%

Determination of the Optimum Heat Transfer Coefficient and Temperature Rise Analysis for a Lithium-Ion Battery under the Conditions of Harbin City Bus Driving Cycles

Siyu

Chen

2017

Energies

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This study investigated the heat problems that occur during the operation of power batteries, especially thermal runaway, which usually take place in high temperature environments. The study was conducted on a ternary polymer lithium-ion battery. In addition, a lumped parameter thermal model was established to analyze the thermal behavior of the electric bus battery system under the operation conditions of the driving cycles of the Harbin city electric buses. Moreover, the quantitative relationship between the optimum heat transfer coefficient of the battery and the ambient temperature was investigated. The relationship between the temperature rise (T r ), the number of cycles (c), and the heat transfer coefficient (h) under three Harbin bus cycles have been investigated at 30 • C, because it can provide a basis for the design of the battery thermal management system. The results indicated that the heat transfer coefficient that meets the requirements of the battery thermal management system is the cubic power function of the ambient temperature. Therefore, if the ambient temperature is 30 • C, the heat transfer coefficient should be at least 12 W/m 2 K in the regular bus lines, 22 W/m 2 K in the bus rapid transit lines, and 32 W/m 2 K in the suburban lines.

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Section: Introductionmentioning

confidence: 99%

Determination of the Optimum Heat Transfer Coefficient and Temperature Rise Analysis for a Lithium-Ion Battery under the Conditions of Harbin City Bus Driving Cycles

Siyu

Chen

2017

Energies

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“…In order to solve nonlinear problem, Kalman filter is transformed into the EKF by linearizing about the covariance of the state and current mean. Because EKF can reduce the model error and improve the accuracy, it is suitable for battery SoC estimation [42][43][44]. It can be described as follows for the equation of state and the measurement:…”

Section: The Soc Correction Using Ekfmentioning

confidence: 99%

Using SoC Online Correction Method Based on Parameter Identification to Optimize the Operation Range of NI-MH Battery for Electric Boat

et al. 2018

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This paper discusses a design of a Battery Management System (BMS) solution for extending the life of Nickel-Metal Hydride (NI-MH) battery. Combined with application of electric boat, a State of Charge (SoC) optimal operation range control method based on high precision energy metering and online SoC correction is proposed. Firstly, a power metering scheme is introduced to reduce the original energy measurement error. Secondly, by establishing a model based parameter identification method and combining with Extended Kalman Filter (EKF) method, the estimation accuracy of SoC is guaranteed. Finally, SoC optimal operation range control method is presented to make battery running in the optimal range. After two years of operation, the battery managed by proposed method has much better status, compared to batteries that use AH integral method and fixed SoC operating range. Considering the SoC estimation of NI-MH battery is more difficult becausing special electrical characteristics, proposed method also would have a very good reference value for other types of battery management.

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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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Lithium-Ion Battery Parameters and State-of-Charge Joint Estimation Based on H-Infinity and Unscented Kalman Filters

Cited by 192 publications

References 41 publications

Determination of the Optimum Heat Transfer Coefficient and Temperature Rise Analysis for a Lithium-Ion Battery under the Conditions of Harbin City Bus Driving Cycles

Determination of the Optimum Heat Transfer Coefficient and Temperature Rise Analysis for a Lithium-Ion Battery under the Conditions of Harbin City Bus Driving Cycles

Using SoC Online Correction Method Based on Parameter Identification to Optimize the Operation Range of NI-MH Battery for Electric Boat

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

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