A data-driven multi-scale extended Kalman filtering based parameter and state estimation approach of lithium-ion polymer battery in electric vehicles

Xiong, Rui; Sun, Fuchun; Chen, Zheng; He, Haibo

doi:10.1016/j.apenergy.2013.07.061

Cited by 461 publications

(176 citation statements)

References 26 publications

Supporting

Mentioning

175

Contrasting

Order By: Relevance

“…The estimation procedure employed in the HF method is summarized in Table 3. The EKF method for battery SOC estimation is widely studied, and the process of this algorithm is introduced in Pérez et al, Hu et al, Xiong et al, Chiang et al, and Lee et al [16,[42][43][44][45]; thus, the details are not discussed in the current study. To achieve fairness, the parameters of EKF and STHF are assigned identical values, as listed in Table 5.…”

Section: Estimation Results With White Gaussian Noisesmentioning

confidence: 99%

Strong Tracking of a H-Infinity Filter in Lithium-Ion Battery State of Charge Estimation

et al. 2018

View full text Add to dashboard Cite

Abstract:The accuracy of state-of-charge (SOC) estimation, one of the most important functions of a battery management system (BMS), is the basis for the proper operation of an electric vehicle. This study proposes a method for accurate SOC estimation. To achieve a balance between accuracy and simplicity, a second-order resistor-capacitor equivalent circuit model is applied before the algorithm is deduced, and the parameters of the established model are determined using a fitting technique. Battery state space equations are then described. A strong tracking H-infinity filter (STHF) is proposed based on an H-infinity filter (HF) and a strong tracking filter. By introducing a suboptimal fading factor, the STHF approach can use the relevant information in the estimation residual sequence to update the estimation results. To verify the robustness of this approach, battery test experiments are performed at different temperatures on lithium-ion batteries. Finally, the SOC estimation results obtained using the STHF suggest that the STHF method exhibits high robustness against the measured noises and initial error. For comparison, the estimation results of the commonly used extended Kalman filter (EKF) and HF methods are also displayed. It is suggested that the proposed STHF approach obtains a more accurate SOC estimation.

show abstract

Section: Estimation Results With White Gaussian Noisesmentioning

confidence: 99%

Strong Tracking of a H-Infinity Filter in Lithium-Ion Battery State of Charge Estimation

et al. 2018

View full text Add to dashboard Cite

show abstract

“…Battery SOC, according to its definition, represents the ratio of available battery capacity compared with the current battery rated capacity [3]. The available battery capacity can influence and even determine how long a battery can be fully charged, and consequently how far a vehicle can drive.…”

Section: Introductionmentioning

confidence: 99%

A Novel State of Charge Estimation Algorithm for Lithium-Ion Battery Packs of Electric Vehicles

Chen

Shen

et al. 2016

Energies

Self Cite

View full text Add to dashboard Cite

This paper focuses on state of charge (SOC) estimation for the battery packs of electric vehicles (EVs). By modeling a battery based on the equivalent circuit model (ECM), the adaptive extended Kalman filter (AEKF) method can be applied to estimate the battery cell SOC. By adaptively setting different weighed coefficients, a battery pack SOC estimation algorithm is established based on the single cell estimation. The proposed method can not only precisely estimate the battery pack SOC, but also effectively prevent the battery pack from overcharge and over-discharge, thus providing safe operation. Experiment results verify the feasibility of the proposed algorithm.

show abstract

“…The second kind of models is the equivalent circuit model (ECM), where the battery is usually regarded as a mass point [8,9]. Therefore, they are suitable to be implanted in the battery management system (BMS) for the state of charge (SOC) or the 2 of 14 state of health (SOH) estimation [10][11][12][13]. Lin et al [14] and Forgez et al [15] added lumped-parameter thermal models to ECM to predict the thermal characteristics of the cell, which made the model more comprehensive.…”

Section: Introductionmentioning

confidence: 99%

Big-Data-Based Thermal Runaway Prognosis of Battery Systems for Electric Vehicles

Hong¹,

Wang²,

Liu³

2017

Preprint

View full text Add to dashboard Cite

This paper presents a thermal runaway prognosis scheme based on the big-data platform and entropy method for battery systems in electric vehicles. It can simultaneously realize the diagnosis and prognosis of thermal runaway caused by the temperature fault through monitoring battery temperature during vehicular operations. A vast quantity of real-time voltage monitoring data was collected in the National Service and Management Center for Electric Vehicles (NSMC-EV) in Beijing to verify the effectiveness of the presented method. The results show that the proposed method can accurately forecast both the time and location of the temperature fault within battery packs. Furthermore, a temperature security management strategy for thermal runaway is proposed on the basis of the Z-score approach and the abnormity coefficient is set to make real-time precaution of temperature abnormity.

show abstract

A data-driven multi-scale extended Kalman filtering based parameter and state estimation approach of lithium-ion polymer battery in electric vehicles

Cited by 461 publications

References 26 publications

Strong Tracking of a H-Infinity Filter in Lithium-Ion Battery State of Charge Estimation

Strong Tracking of a H-Infinity Filter in Lithium-Ion Battery State of Charge Estimation

A Novel State of Charge Estimation Algorithm for Lithium-Ion Battery Packs of Electric Vehicles

Big-Data-Based Thermal Runaway Prognosis of Battery Systems for Electric Vehicles

Contact Info

Product

Resources

About