A combination Kalman filter approach for State of Charge estimation of lithium-ion battery considering model uncertainty

Li, Yanwen; Chao, Wang; Gong, Jinfeng

doi:10.1016/j.energy.2016.05.047

Cited by 109 publications

(42 citation statements)

References 51 publications

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“…It is particularly challenging to design a robust SOC estimation algorithm under different working conditions, such as different current rate requirements, operating temperatures, uniformity of battery cells, and state of health levels. And the characteristics of dynamic battery are highly nonlinear in the application of electric vehicle (EV)/hybrid electric vehicles (HEVs) . As a close‐loop SOC estimation approach, model‐based filtering attracts more and more attentions in the study of SOC estimation in recent years .…”

Section: Introductionmentioning

confidence: 99%

“…And the characteristics of dynamic battery are highly nonlinear in the application of electric vehicle (EV)/hybrid electric vehicles (HEVs). 11 As a close-loop SOC estimation approach, model-based filtering attracts more and more attentions in the study of SOC estimation in recent years. 6,12,13 At first, The ECMs are used to describe the battery dynamic behaviors, with charge and discharge current input and terminal voltage output.…”

Section: Introductionmentioning

confidence: 99%

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Model-based unscented Kalman filter observer design for lithium-ion battery state of charge estimation

et al. 2017

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Summary Accurate battery state‐of‐charge is essential for both driver notification and battery management units reliability in electric vehicle/hybrid electric vehicle. It is necessary to develop a robust state of charge (SOC) estimation approach to cope with nonlinear dynamic battery systems. This paper proposed an estimation method to identify the SOC online based on equivalent circuit battery model and unscented Kalman filter technique. Firstly, the parameters of dynamic battery model are identified offline and validated through typical electric vehicle road operation to guarantee its precision. Then the performance with respect to converge time, observer accuracy, robustness against system modeling errors, and mismatched initial SOC guess values is investigated. The accuracy of proposed estimation algorithm is validated under improved hybrid power pulse characterization test and New European Driving Cycle. Experiment and numerical simulation results clearly demonstrate that the proposed method is highly reliable with good robustness to different operating conditions and battery aging.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Model-based unscented Kalman filter observer design for lithium-ion battery state of charge estimation

et al. 2017

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“…The decline in estimation accuracy attributes to the change of parameters of battery model. One possible solution is adopting an online parameter identification method to obtain the battery model parameters [42,43]. However, this is beyond the scope of this paper.…”

Section: Robustness Against Parameter Disturbancementioning

confidence: 99%

“…In practical application, the values of these parameters change dynamically due to various factors such as depth of discharge, ambient temperature, age effect, etc. Also, there are a number of studies about online parameter identification methods [42][43][44][45], which can be used to identify model parameters in real time. However, this is beyond the scope of this paper.…”

Section: Parameter Identificationmentioning

confidence: 99%

A Novel Observer for Lithium-Ion Battery State of Charge Estimation in Electric Vehicles Based on a Second-Order Equivalent Circuit Model

et al. 2017

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Accurate state of charge (SOC) estimation can prolong lithium-ion battery life and improve its performance in practice. This paper proposes a new method for SOC estimation. The second-order resistor-capacitor (2RC) equivalent circuit model (ECM) is applied to describe the dynamic behavior of lithium-ion battery on deriving state space equations. A novel method for SOC estimation is then presented. This method does not require any matrix calculation, so the computation cost can be very low, making it more suitable for hardware implementation. The Federal Urban Driving Schedule (FUDS), The New European Driving Cycle (NEDC), and the West Virginia Suburban Driving Schedule (WVUSUB) experiments are carried to evaluate the performance of the proposed method. Experimental results show that the SOC estimation error can converge to 3% error boundary within 30 s when the initial SOC estimation error is 20%, and the proposed method can maintain an estimation error less than 3% with 1% voltage noise and 5% current noise. Further, the proposed method has excellent robustness against parameter disturbance. Also, it has higher estimation accuracy than the extended Kalman filter (EKF), but with decreased hardware requirements and faster convergence rate.

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“…Despite the fact that this approach does not directly use the FL technique for the SoC estimation process, the model presented can be adapted to this task. Similarly, [125] uses FL by fuzzy self-tuning algorithms to update the model parameters of a second-order ECM that is used with an adaptive UKF to obtain SoC values. Hametner and Jakubek in [126] present a SoC estimation technique based on a purely data-driven model and a nonlinear fuzzy observer that uses KF theory for each local linear state space model.…”

Section: Adaptive Artificial-intelligence-based Techniques Estimationmentioning

confidence: 99%

SoC Estimation for Lithium-ion Batteries: Review and Future Challenges

2017

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Abstract:Energy storage emerged as a top concern for the modern cities, and the choice of the lithium-ion chemistry battery technology as an effective solution for storage applications proved to be a highly efficient option. State of charge (SoC) represents the available battery capacity and is one of the most important states that need to be monitored to optimize the performance and extend the lifetime of batteries. This review summarizes the methods for SoC estimation for lithium-ion batteries (LiBs). The SoC estimation methods are presented focusing on the description of the techniques and the elaboration of their weaknesses for the use in on-line battery management systems (BMS) applications. SoC estimation is a challenging task hindered by considerable changes in battery characteristics over its lifetime due to aging and to the distinct nonlinear behavior. This has led scholars to propose different methods that clearly raised the challenge of establishing a relationship between the accuracy and robustness of the methods, and their low complexity to be implemented. This paper publishes an exhaustive review of the works presented during the last five years, where the tendency of the estimation techniques has been oriented toward a mixture of probabilistic techniques and some artificial intelligence.

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A combination Kalman filter approach for State of Charge estimation of lithium-ion battery considering model uncertainty

Cited by 109 publications

References 51 publications

Model-based unscented Kalman filter observer design for lithium-ion battery state of charge estimation

Model-based unscented Kalman filter observer design for lithium-ion battery state of charge estimation

A Novel Observer for Lithium-Ion Battery State of Charge Estimation in Electric Vehicles Based on a Second-Order Equivalent Circuit Model

SoC Estimation for Lithium-ion Batteries: Review and Future Challenges

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