An Adaptive Square Root Unscented Kalman Filter Approach for State of Charge Estimation of Lithium-Ion Batteries

Liu, Shulin; Cui, Naxin; Zhang, Chenghui

doi:10.3390/en10091345

Cited by 40 publications

(5 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For this, an IoT-based hardware setup was established for real-time monitoring of the charging status of a potential EV in a CS. In future work, we will demonstrate the real-time (Liu et al, 2017) Li-ion battery 50 RT UDDS RMSE = 3.1% CCT RMSE = 3.2% UKF (Chen et al, 2017) LiNMC 24 RT 1C CDT MAE = 2.56% HN Filter (Zhu et al, 2017) Li-ion cell 2.4 CT 1C CDT MAE = 3.96% BPNN (Hannan et al, 2018) LiMn 2 O 4 2 0 °C, 25°C and 45°C DST RMSE = 0.48%-1.47% FUDS RMSE = 0.57%-1.74% DNN (Chemali et al, 2018) Panasonic LiNiCoAlO 2 2.9 0°C, 10°C and 25°C US06 MAE = 1.85% HWFET MAE = 1.35% CNN (Huang et al, 2019 implementation of the proposed hybrid SOC estimation technique on a battery pack of an EV and then we will reserve a charging slot for an EV user in advance based on the estimated SOC level.…”

Section: Discussionmentioning

confidence: 99%

A new state of charge estimation technique of lithium-ion battery using adaptive extended Kalman filter and artificial neural network

Kazmi

Ulasyar

Khattak

et al. 2022

Transactions of the Institute of Measurement and Control

View full text Add to dashboard Cite

Due to rapid rise in climate change, world is seeing a major technology shift from conventional vehicles to electric vehicles (EVs). Battery’s state of charge (SOC) is a critical parameter in EVs whose accuracy affects not only the battery management system but can also influence the driving range estimation for an EV. In this paper, a simulation-based hybrid technique is presented, that combines both adaptive extended Kalman filter (AEKF) and artificial neural network (ANN) for SOC estimation of lithium-ion battery. The proposed hybrid technique is validated using five different EV driving cycles, that is, LA92, US06, urban dynamometer driving schedule (UDDS), highway fuel economy test (HWFET) and a mixed driving cycle at four different temperature values, that is, 0°C, 10°C, 25°C and 40°C. To facilitate the potential EV users, a real-time SOC estimator is also implemented. The real-time SOC estimated through open circuit voltage method and Coulomb counting method is further used for reservation of a charging slot in a nearby charging station. Moreover, to monitor the charging status of a potential EV from a remote location, a cloud-based Internet of things (IoT) platform, that is, ThingSpeak, is used. This results into the reduction of waiting time for an EV user at a charging station. In addition to this, a comparative analysis is carried out between proposed hybrid method and existing methods. It is demonstrated that our hybrid technique achieves lower root mean square error and higher accuracy as compared to existing methods at different operating conditions.

show abstract

Section: Discussionmentioning

confidence: 99%

A new state of charge estimation technique of lithium-ion battery using adaptive extended Kalman filter and artificial neural network

Kazmi

Ulasyar

Khattak

et al. 2022

Transactions of the Institute of Measurement and Control

View full text Add to dashboard Cite

show abstract

“…Yun et al [65] used something similar: a variable bayesian unscented Kalman filter coupled with a variable bayesian square-root cubature Kalman filter to estimate battery SoC. Liu et al [66] proposed an adaptive square root unscented Kalman filter that overcame the EKF and UKF and proved to be more accurate and stable, and they presented a better self-adaptive response to the system. Lv et al [67] addressed the adaptive UKF for finding noise that affected the system when using only UKF in SoC estimate tasks.…”

Section: Methods Based On Filtering Algorithmsmentioning

confidence: 99%

An Overview of Methods and Technologies for Estimating Battery State of Charge in Electric Vehicles

et al. 2023

View full text Add to dashboard Cite

Recently, electric vehicles have gained enormous popularity due to their performance and efficiency. The investment in developing this new technology is justified by the increased awareness of the environmental impacts caused by combustion vehicles, such as greenhouse gas emissions, which have contributed to global warming and the depletion of oil reserves that are not renewable energy sources. Lithium-ion batteries are the most promising for electric vehicle (EV) applications. They have been widely used for their advantages, such as high energy density, many cycles, and low self-discharge. This work extensively investigates the main methods of estimating the state of charge (SoC) obtained through a literature review. A total of 109 relevant articles were found using the prism method. Some basic concepts of the state of health (SoH); a battery management system (BMS); and some models that can perform SoC estimation are presented. Challenges encountered in this task are discussed, such as the nonlinear characteristics of lithium-ion batteries that must be considered in the algorithms applied to the BMS. Thus, the set of concepts examined in this review supports the need to evolve the devices and develop new methods for estimating the SoC, which is increasingly more accurate and faster. This review shows that these tools tend to be continuously more dependent on artificial intelligence methods, especially hybrid algorithms, which require less training time and low computational cost, delivering real-time information to embedded systems.

show abstract

“…Recently, researchers have provided many improvements to the KF method. Liu [ 23 ] proposed an adaptive square root unscented Kalman filter (ASRUKF) method to estimate the SOC of lithium-ion batteries, and the effectiveness of the ASRUKF method has been verified through experiments under different operating conditions with better accuracy, robustness and convergence. To overcome the regression least squares algorithm-based extended Kalman filter (RLS-EKF), an adaptive forgetting factor-based RLS-EKF (AFFRLS-EKF) SOC estimation strategy was used to improve the accuracy of SOC estimation under changes in battery charge and discharge conditions [ 24 ].…”

Section: Introductionmentioning

confidence: 99%

Stable and Accurate Estimation of SOC Using eXogenous Kalman Filter for Lithium-Ion Batteries

Lin

Tu³

et al. 2023

Sensors

View full text Add to dashboard Cite

The state of charge (SOC) for a lithium-ion battery is a key index closely related to battery performance and safety with respect to the power supply system of electric vehicles. The Kalman filter (KF) or extended KF (EKF) is normally employed to estimate SOC in association with the relatively simple and fast second-order resistor-capacitor (RC) equivalent circuit model for SOC estimations. To improve the stability of SOC estimation, a two-stage method is developed by combining the second-order RC equivalent circuit model and the eXogenous Kalman filter (XKF) to estimate the SOC of a lithium-ion battery. First, approximate SOC estimation values are observed with relatively poor accuracy by a stable observer without considering parameter uncertainty. Second, the poor accuracy SOC results are further fed into XKF to obtain relative stable and accurate SOC estimation values. Experiments demonstrate that the SOC estimation results of the present method are superior to those of the commonly used EKF method. It is expected that the present two-stage XKF method will be useful for the stable and accurate estimation of SOC in the power supply system of electric vehicles.

show abstract

An Adaptive Square Root Unscented Kalman Filter Approach for State of Charge Estimation of Lithium-Ion Batteries

Cited by 40 publications

References 32 publications

A new state of charge estimation technique of lithium-ion battery using adaptive extended Kalman filter and artificial neural network

A new state of charge estimation technique of lithium-ion battery using adaptive extended Kalman filter and artificial neural network

An Overview of Methods and Technologies for Estimating Battery State of Charge in Electric Vehicles

Stable and Accurate Estimation of SOC Using eXogenous Kalman Filter for Lithium-Ion Batteries

Contact Info

Product

Resources

About