Adaptive Kalman filtering based internal temperature estimation with an equivalent electrical network thermal model for hard-cased batteries

Dai, Haifeng; Zhu, Letao; Zhu, Jiangong; Wei, Xuezhe; Sun, Zhonghui

doi:10.1016/j.jpowsour.2015.05.087

Cited by 97 publications

(48 citation statements)

References 45 publications

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“…In general, the concept behind Kalman filtering is that, at each time step, an initial prediction of the state vector is made, which is used to predict the corresponding model output (i.e., the terminal voltage) [61,62]. The true terminal voltage is then measured and compared to the battery model output.…”

Section: State Of Charge Estimationmentioning

confidence: 99%

Lithium Ion Batteries—Development of Advanced Electrical Equivalent Circuit Models for Nickel Manganese Cobalt Lithium-Ion

et al. 2016

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Abstract:In this paper, advanced equivalent circuit models (ECMs) were developed to model large format and high energy nickel manganese cobalt (NMC) lithium-ion 20 Ah battery cells. Different temperatures conditions, cell characterization test (Normal and Advanced Tests), ECM topologies (1st and 2nd Order Thévenin model), state of charge (SoC) estimation techniques (Coulomb counting and extended Kalman filtering) and validation profiles (dynamic discharge pulse test (DDPT) and world harmonized light vehicle profiles) have been incorporated in the analysis. A concise state-of-the-art of different lithium-ion battery models existing in the academia and industry is presented providing information about model classification and information about electrical models. Moreover, an overview of the different steps and information needed to be able to create an ECM model is provided. A comparison between begin of life (BoL) and aged (95%, 90% state of health) ECM parameters (internal resistance (R o ), polarization resistance (R p ), activation resistance (R p2 ) and time constants (τ) is presented. By comparing the BoL to the aged parameters an overview of the behavior of the parameters is introduced and provides the appropriate platform for future research in electrical modeling of battery cells covering the ageing aspect. Based on the BoL parameters 1st and 2nd order models were developed for a range of temperatures (15˝C, 25˝C, 35˝C, 45˝C). The highest impact to the accuracy of the model (validation results) is the temperature condition that the model was developed. The 1st and 2nd order Thévenin models and the change from normal to advanced characterization datasets, while they affect the accuracy of the model they mostly help in dealing with high and low SoC linearity problems. The 2nd order Thévenin model with advanced characterization parameters and extended Kalman filtering SoC estimation technique is the most efficient and dynamically correct ECM model developed.

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Section: State Of Charge Estimationmentioning

confidence: 99%

Lithium Ion Batteries—Development of Advanced Electrical Equivalent Circuit Models for Nickel Manganese Cobalt Lithium-Ion

et al. 2016

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“…Additionally, some improved algorithms based on these methods have been proposed, such as the adaptive Kalman filter [11], the unscented Kalman filter (UKF) [12][13][14][15], the strong tracking cubature Kalman filter (STCKF) [16], and the unscented PF [17]. The Coulomb counting method is widely used for SOC estimation of electric vehicles because it is a simple algorithm.…”

Section: Introductionmentioning

confidence: 99%

A Cubature Particle Filter Algorithm to Estimate the State of the Charge of Lithium-Ion Batteries Based on a Second-Order Equivalent Circuit Model

et al. 2017

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Abstract:The state of charge (SOC) is the residual capacity of a battery. The SOC value indicates the mileage endurance, and an accurate SOC value is required to ensure the safe use of the battery to prevent over-and over-discharging. However, unlike size and weight, battery power is not easily determined. As a consequence, we can only estimate the SOC value based on the external characteristics of the battery. In this paper, a cubature particle filter (CPF) based on the cubature Kalman filter (CKF) and the particle filter (PF) is presented for accurate and reliable SOC estimation. The CPF algorithm combines the CKF and PF algorithms to generate a suggested density function for the PF algorithm based on the CKF. The second-order resistor-capacitor (RC) equivalent circuit model was used to approximate the dynamic performance of the battery, and the model parameters were identified by fitting. A dynamic stress test (DST) was used to separately estimate the accuracy and robustness of the CKF and the CPF algorithms. The experimental results show that the CPF algorithm exhibited better accuracy and robustness than the CKF algorithm.

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“…), k 2 certainly depends on the heat dissipation condition, such as cooling wind speed and temperature. According to [23], k 2 also increases with this temperature gradient T s h T a m b . To take this effect into consideration, two cases are compared Fig 14 for time-varying k 2 , respectively.…”

Section: Ieee Trans Actions On Indus Trial Electronics B the Rma L Smentioning

confidence: 93%

“…Ba tte ry the rma l s ubmode l A battery thermal model consists of two parts: thermal generation and thermal transfer within and outside the battery. Althought the heat generation inside the battery is a complex electrochemical process, to build a simplified battery thermal model, three different heat generation calculation methods are widely adopted [6]- [9], [23], i.e.,…”

Section: Ieee Trans Actions On Indus Trial Electronicsmentioning

confidence: 99%

Improved Realtime State-of-Charge Estimation of LiFePO $_{\boldsymbol 4}$ Battery Based on a Novel Thermoelectric Model

Zhang

Deng

et al. 2017

IEEE Trans. Ind. Electron.

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Adaptive Kalman filtering based internal temperature estimation with an equivalent electrical network thermal model for hard-cased batteries

Cited by 97 publications

References 45 publications

Lithium Ion Batteries—Development of Advanced Electrical Equivalent Circuit Models for Nickel Manganese Cobalt Lithium-Ion

Lithium Ion Batteries—Development of Advanced Electrical Equivalent Circuit Models for Nickel Manganese Cobalt Lithium-Ion

A Cubature Particle Filter Algorithm to Estimate the State of the Charge of Lithium-Ion Batteries Based on a Second-Order Equivalent Circuit Model

Improved Realtime State-of-Charge Estimation of LiFePO $_{\boldsymbol 4}$ Battery Based on a Novel Thermoelectric Model

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