A Novel Temperature–Hysteresis Model for Power Battery of Electric Vehicles with an Adaptive Joint Estimator on State of Charge and Power

Lei, Xu; Zhao, Xi; Gui-ping, Wang; Liu, Weiyu

doi:10.3390/en12193621

Cited by 11 publications

(5 citation statements)

References 36 publications

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“…The improved Lipschitz consecutive model is suitable for nonlinear observer design. In particular, a battery empirical model, the Nernst model, is proposed in [23], which in order to represent the hysteresis effect adds s k •M term to the original Nernst equation, and in final this expression is as follows:…”

Section: Application Of Hysteresis Effect In Soc Estimationmentioning

confidence: 99%

Exploring the Hysteresis Effect of Li-Ion Batteries: Principle and Applications

Sun,

Zhang,

Shi

et al. 2023

Advances in Transdisciplinary Engineering

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In the process of research on lithium-ion batteries, it is found that the existence of hysteresis effect leads to errors in the research results. This paper focuses on the hysteresis effect of lithium-ion batteries. Firstly, the hysteresis phenomenon and polarization phenomenon of lithium-ion battery are explained, the principle of hysteresis effect is analyzed, and the main influencing factors of hysteresis effect are the thermodynamic entropy effect, mechanical stress and microscopic deformation inside lithium-ion particles. Then, the application of hysteresis effect in the estimation of various parameters (internal parameters, SOC, SOH) of lithium-ion batteries is introduced. Finally, the paper concludes and proposes an outlook for future research.

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Section: Application Of Hysteresis Effect In Soc Estimationmentioning

confidence: 99%

Exploring the Hysteresis Effect of Li-Ion Batteries: Principle and Applications

Sun,

Zhang,

Shi

et al. 2023

Advances in Transdisciplinary Engineering

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“…e AFNN rules are obtained from the expert knowledge during equalization. e expert knowledge improves the reliability of the algorithm [41][42][43].…”

Section: Equalization Control Algorithmmentioning

confidence: 99%

Active Equalization Strategy for Lithium-Ion Battery Packs Based on Multilayer Dual Interleaved Inductor Circuits in Electric Vehicles

Lei

Fan

et al. 2022

Journal of Advanced Transportation

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Lithium-ion batteries (LIBs) are widely used in electric vehicles (EVs) due to their superior power performance over other batteries. However, when connected in series, overcharged cells of LIBs face the risk of explosion, and undercharged cells decrease the life cycle of the battery. Eventually, the inconsistency phenomenon between cells resulting from manufacturing tolerance and usage process reduces the overall charging capacity of the battery and increases the risk of explosion after long-time use. Research has focused on synthesizing active material to achieve higher energy density and extended life cycle for LIBs while neglecting a comparative analysis of equalization technology on the performance of battery packs. In this paper, a nondissipative equalization structure is proposed to reconcile the inconsistency of series-connected LIB cells. In this structure, a circuit uses high-level equalization units to enable direct energy transfer between any two individual cells, and dual interleaved inductors in each equalization unit increase the equalization speed of a single cell in one equalization cycle by a factor of two. The circuit is compared with the classical inductor equalization circuit (CIEC), dual interleaved equalization circuit (DIEC), and parallel architecture equalization circuit (PAEC) in the states of standing, charging, and discharging, respectively, to validate the advantages of the proposed scheme. Considering the diversity of imbalance states, the state of charge (SOC) and terminal voltage are both chosen as the equalization criterion. The second-order RC model of the LIB and the adaptive unscented Kalman filter (AUKF) algorithm are employed for SOC estimation. For effective equalization, the adaptive fuzzy neural network (AFNN) is utilized to further reduce energy consumption and equalization time. The experiment results show that the AFNN algorithm reduces the total equalization time by approximately 37.4% and improves equalization efficiency by about 4.89% in contrast with the conventional mean-difference algorithm. Particularly, the experiment results of the equalization circuit verification certify that the proposed equalization structure can greatly accelerate the equalization progress and reduce the equalization loss compared to the other three equalization circuits.

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“…Taking 25 To obtain the ultracapacitor capacitances at different temperatures, a capacitance-T model is established based on the rated capacities. Furthermore, the mapping relationship between capacitance and temperature is acquired by polynomial fitting, as can be seen in Equation (6). Similarly, the parameters-T model can be described as Equations ( 7)-( 9), respectively.…”

Section: Evaluation Of Thevenin Modelmentioning

confidence: 99%

“…The State-of-Charge (SOC) indicates the current available capacitance, which is considered as a crucial state parameter of ultracapacitor. It is well known that the variable temperature environment has an unavoidable effect on the SOC estimation accuracy [6,7]. The conventional SOC estimation methods generally divided into coulomb counting method, characterization parameter method, data driven based method and model based filter method [8].…”

Section: Introductionmentioning

confidence: 99%

A Novel Ultracapacitor State-of-Charge Fusion Estimation Method for Electric Vehicles Considering Temperature Uncertainty

et al. 2022

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An ultracapacitor State-of-Charge (SOC) fusion estimation method for electric vehicles under variable temperature environment is proposed in this paper. Firstly, Thevenin model is selected as the ultracapacitor model. Then, genetic algorithm (GA) is adopted to identify the ultracapacitor model parameters at different temperatures (−10 °C, 10 °C, 25 °C and 40 °C). Secondly, a variable temperature model is established by using polynomial fitting the temperatures and parameters, which is applied to promote the ultracapacitor model applicability. Next, the off-line experimental data is iterated by adaptive extended Kalman filter (AEKF) to train the Nonlinear Auto-Regressive Model with Exogenous Inputs (NARX) neural network. Thirdly, the output of the NARX is employed to compensate the AEKF estimation and thereby realize the ultracapacitor SOC fusion estimation. Finally, the variable temperature model and robustness of the proposed SOC fusion estimation method are verified by experiments. The analysis results show that the root mean square error (RMSE) of the variable temperature model is reduced by 90.187% compared with the non-variable temperature model. In addition, the SOC estimation error of the proposed NARX-AEKF fusion estimation method based on the variable temperature model remains within 2.055%. Even when the SOC initial error is 0.150, the NARX-AEKF fusion estimation method can quickly converge to the reference value within 5.000 s.

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A Novel Temperature–Hysteresis Model for Power Battery of Electric Vehicles with an Adaptive Joint Estimator on State of Charge and Power

Cited by 11 publications

References 36 publications

Exploring the Hysteresis Effect of Li-Ion Batteries: Principle and Applications

Exploring the Hysteresis Effect of Li-Ion Batteries: Principle and Applications

Active Equalization Strategy for Lithium-Ion Battery Packs Based on Multilayer Dual Interleaved Inductor Circuits in Electric Vehicles

A Novel Ultracapacitor State-of-Charge Fusion Estimation Method for Electric Vehicles Considering Temperature Uncertainty

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