On-line adaptive battery impedance parameter and state estimation considering physical principles in reduced order equivalent circuit battery models part 2. Parameter and state estimation

Fleischer, Christian; Waag, Wladislaw; Heyn, Hans-Martin; Sauer, Dirk Uwe

doi:10.1016/j.jpowsour.2014.03.046

Cited by 126 publications

(51 citation statements)

References 14 publications

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“…The battery equal-number clustering method proposed in this paper is based on this method and the clustering rules are redesigned. The target of the method is partitioning the samples into N clusters and the amount of elements in each cluster is equal to M. The objective function of equal-number clustering is the same with the traditional K-means method, as shown in Equation (27). An adjusting rule is developed to equalize the numbers as below:…”

Section: The New Modified K-means Clustering Methodsmentioning

confidence: 99%

“…UOC is mainly decided by three parameters: the SoC's start points of positive and negative electrodes and the cycle range of SoC. RO is a pure resistance element, which mainly reflects the medium-high frequency (typically <1 Hz) impedance characteristic of EIS; the value of this parameter is approximate the sum of the battery bulk resistance Rbulk, SEI film resistance Rset and electric charge transfer resistance Rct [27], as is shown in Equation (2) …”

Section: Simplified Eis Modelmentioning

confidence: 99%

See 1 more Smart Citation

A Novel Grouping Method for Lithium Iron Phosphate Batteries Based on a Fractional Joint Kalman Filter and a New Modified K-Means Clustering Algorithm

Song

Wei

et al. 2015

Energies

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This paper presents a novel grouping method for lithium iron phosphate batteries. In this method, a simplified electrochemical impedance spectroscopy (EIS) model is utilized to describe the battery characteristics. Dynamic stress test (DST) and fractional joint Kalman filter (FJKF) are used to extract battery model parameters. In order to realize equal-number grouping of batteries, a new modified K-means clustering algorithm is proposed. Two rules are designed to equalize the numbers of elements in each group and exchange samples among groups. In this paper, the principles of battery model selection, physical meaning and identification method of model parameters, data preprocessing and equal-number clustering method for battery grouping are comprehensively described. Additionally, experiments for battery grouping and method validation are designed. This method is meaningful to application involving the grouping of fresh batteries for electric vehicles (EVs) and screening of aged batteries for recycling.

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Section: The New Modified K-means Clustering Methodsmentioning

confidence: 99%

Section: Simplified Eis Modelmentioning

confidence: 99%

A Novel Grouping Method for Lithium Iron Phosphate Batteries Based on a Fractional Joint Kalman Filter and a New Modified K-Means Clustering Algorithm

Song

Wei

et al. 2015

Energies

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show abstract

“…As for battery models, (semi-)empirical models based on equivalent circuit [1][2][3][4][5][6][7] and electrochemical models [8,9] are widely applied. Despite potential benefits from informative states and/or parameters of the electrochemical models, its implementation into the computationally light BMS is difficult.…”

Section: Introductionmentioning

confidence: 99%

“…Despite potential benefits from informative states and/or parameters of the electrochemical models, its implementation into the computationally light BMS is difficult. As for a state and/or parameter estimator, two different estimators are mainly used, such as the Kalman filter [1,2] and the least mean squares (LMS) filter [3][4][5][6][7]. With the respect of computational efficiency in BMS, the LMS filter is significantly effective to the Kalman filter due to the absence of complex matrix calculations such as inversions.…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of LIB Degradation using Estimating Cell Resistance for Hybrid Electric Vehicles

Park¹,

Sung²,

Lee³

2016

Journal of Electrical Engineering and Technology

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-To get computationally efficient algorithm which can diagnose the cell degradation by estimating the cell resistance in a battery management system (BMS), the ordinary least squares estimator (LMS filter) is employed to identify the parameter in the simple resistance model in the form of a first-order polynomial. As a result, the determined parameter estimate, the slope in the model, can be adapted to changes in the battery resistance. The devised algorithm is validated by using the cells with different state-of-health. These cells are aged at the elevated temperature over 24 weeks and tested at every four weeks to monitor changes in the cell resistance. Consequently, the devised algorithm can distinguish between the cell resistances which increase by 2 to 4% at every month.

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“…In order to further improve the accuracy of the LIB state estimation, some related technologies, such as observer theory [13][14][15], adaptive algorithms [16,17], bio-inspired algorithms [1,18] have been combined with the Kalman filter algorithm and obtained the expected effects. Furthermore, equivalent model parameter online identification algorithms [14,19] and LIB online state estimation algorithms, which aim to simplify the system structure and improve system operation efficiency, have been widely studied [20], and have also resulted in obvious progress on the basis of a number of studies.…”

Section: Introductionmentioning

confidence: 99%

State-of-Charge Estimation for Lithium-Ion Batteries Using a Kalman Filter Based on Local Linearization

2015

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State of charge (SOC) estimation is of great significance for the safe operation of lithium-ion battery (LIB) packs. Improving the accuracy of SOC estimation results and reducing the algorithm complexity are important for the state estimation. In this paper, a zeroaxial straight line, whose slope changes along with SOC, is used to map the predictive SOC to the predictive open circuit voltage (OCV), and thus only one parameter is used to linearize the SOC-OCV curve near the present working point. An equivalent circuit model is used to simulate the dynamic behavior of a LIB, updating the linearization parameter in the measurement equation according to the present value of the state variables, and then a standard Kalman filter is used to estimate the SOC based on the local linearization. This estimation method makes the output equation of the nonlinear battery model contain only one parameter related to its dynamic variables. This is beneficial to simplify the algorithm structure and to reduce the computation cost. The linearization method do not essentially lose the main information of the dynamic model, and its effectiveness is verified experimentally. Fully and a partially charged battery experiments indicate that the estimation error of SOC is better than 0.5%.

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On-line adaptive battery impedance parameter and state estimation considering physical principles in reduced order equivalent circuit battery models part 2. Parameter and state estimation

Cited by 126 publications

References 14 publications

A Novel Grouping Method for Lithium Iron Phosphate Batteries Based on a Fractional Joint Kalman Filter and a New Modified K-Means Clustering Algorithm

A Novel Grouping Method for Lithium Iron Phosphate Batteries Based on a Fractional Joint Kalman Filter and a New Modified K-Means Clustering Algorithm

Diagnosis of LIB Degradation using Estimating Cell Resistance for Hybrid Electric Vehicles

State-of-Charge Estimation for Lithium-Ion Batteries Using a Kalman Filter Based on Local Linearization

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