A novel framework for Lithium-ion battery modeling considering uncertainties of temperature and aging

Tang, Xiaopeng; Wang, Yujie; Zou, Changfu; Yao, Ke; Xia, Yongxiao; Gao, Furong

doi:10.1016/j.enconman.2018.10.082

Cited by 170 publications

(68 citation statements)

References 46 publications

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“…Due to the extremely complex discharging conditions, there exist many uncertainties that would affect the model accuracy. Considering the uncertainties of operating temperature, loading profiles, and battery aging, 30 At a given ambient temperature, both the battery and Thevenin model are assumed as a single-input single-output system, and different current excitations generate the corresponding terminal voltage responses. As illustrated in Figure 4, there is an excellent performance between the model response and the actual terminal voltage under different operating conditions.…”

Section: The Uncertainty Analysismentioning

confidence: 99%

Li‐ion battery state of health estimation through Gaussian process regression with Thevenin model

Lyu

Gao

2020

Int J Energy Res

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This paper proposes a model-based and data-driven joint method to estimate the state of health of Li-ion batteries. To accurately quantify battery degradation, a novel resistance-based aging feature is defined from the Thevenin model, and the defined aging feature is approximately linear with capacity degradation. An orthogonal experimental design and a two-way analysis of variance are used to validate the robustness of the defined aging feature. Considering the influence of temperature on battery performance, Box-Cox transformation is introduced to improve the aging feature linearity at low temperatures. Then, an estimator for state of health is established by using Gaussian process regression. Battery aging experiments are conducted to illustrate the estimation effect of the proposed method. The experimental results show that the proposed method has high estimation accuracy at different temperatures. Using the same aging feature, the backpropagation network and support vector regression are implemented to verify the generality of the estimation framework.

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Section: The Uncertainty Analysismentioning

confidence: 99%

Li‐ion battery state of health estimation through Gaussian process regression with Thevenin model

Lyu

Gao

2020

Int J Energy Res

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“…There are several studies that account for U oc (SoC) variation with SoH by implementing the offline identified response surface model of U oc with respect to SoC and remaining capacity [10][11][12]. Authors in [13] use the model migration method to adapt an offline trained model. An obvious disadvantage of this approach is related to the need of having a large data set from previously conducted aging experiments on the same cell type, as well as lack of temperature dependency in the response surface model.…”

Section: Introductionmentioning

confidence: 99%

Dual Nonlinear Kalman Filter-Based SoC and Remaining Capacity Estimation for an Electric Scooter Li-NMC Battery Pack

2020

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Accurate, real-time estimation of battery state-of-charge (SoC) and state-of-health represents a crucial task of modern battery management systems. Due to nonlinear and battery degradation-dependent behavior of output voltage, the design of these estimation algorithms should be based on nonlinear parameter-varying models. The paper first describes the experimental setup that consists of commercially available electric scooter equipped with telemetry measurement equipment. Next, dual extended Kalman filter-based (DEKF) estimator of battery SoC, internal resistances, and parameters of open-circuit voltage (OCV) vs. SoC characteristic is presented under the assumption of fixed polarization time constant vs. SoC characteristic. The DEKF is upgraded with an adaptation mechanism to capture the battery OCV hysteresis without explicitly modelling it. Parameterization of an explicit hysteresis model and its inclusion in the DEKF is also considered. Finally, a slow time scale, sigma-point Kalman filter-based capacity estimator is designed and inter-coupled with the DEKF. A convergence detection algorithm is proposed to ensure that the two estimators are coupled automatically only after the capacity estimate has converged. The overall estimator performance is experimentally validated for real electric scooter driving cycles.

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“…In conventional studies, many ECMs for Li-ion batteries and ECM parameter identification methods have been proposed for battery management system (BMS) applications: the high-order resistor-capacitor (RC)-ladder model, Randle model, Thevenin model, and partial differential equations model [15][16][17][18][19][20][21]. These models are sufficiently reliable for general state estimation algorithms, such as state-of-charge (SOC) and state-of-health (SOH), but not directly applicable to SOF, which is required to simulate the fast voltage response.…”

Section: Introductionmentioning

confidence: 99%

“…Figure 2a shows a conventional ECM based on the 1st RC-ladder, which consists of R s , R 1 , and C 1 . R s denotes the internal resistance of the battery and describes the ohmic voltage drop generated by an instantaneous change in the terminal current [15][16][17][18][19][20][21]. Moreover, the 1st RC-ladder simulates the polarization voltage as the curve of the exponential function, of which the time constant is determined by R 1 and C 1 .…”

Section: Introductionmentioning

confidence: 99%

Cranking Capability Estimation Algorithm Based on Modeling and Online Update of Model Parameters for Li-Ion SLI Batteries

2019

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The terminal voltage of a starting-lighting-ignition (SLI) battery can decrease to a value lower than the allowable voltage range because of the high discharge current required to crank the engine of a vehicle. To avoid the safety problems generated by this voltage drop, this paper proposes a cranking capability estimation algorithm. The proposed algorithm includes an equivalent circuit model for describing the instantaneous voltage response to the cranking current profile. This algorithm predicts the minimum value of the terminal voltage for the cranking transient period by analyzing the polarization voltage and dynamic characteristic of the equivalent circuit model. The estimation accuracy is adjusted by an online update for the parameters of the equivalent circuit model, which varies with temperature, aging, and other factors. The proposed algorithm was validated by experiments with a 60Ah LiFePO4-type SLI battery.

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A novel framework for Lithium-ion battery modeling considering uncertainties of temperature and aging

Cited by 170 publications

References 46 publications

Li‐ion battery state of health estimation through Gaussian process regression with Thevenin model

Li‐ion battery state of health estimation through Gaussian process regression with Thevenin model

Dual Nonlinear Kalman Filter-Based SoC and Remaining Capacity Estimation for an Electric Scooter Li-NMC Battery Pack

Cranking Capability Estimation Algorithm Based on Modeling and Online Update of Model Parameters for Li-Ion SLI Batteries

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