Online state and parameter estimation of Battery-Double Layer Capacitor Hybrid Energy Storage System

Dey, Satadru; Mohon, Sara; Pisu, Pierluigi; Ayalew, Beshah; Onori, Simona

doi:10.1109/cdc.2015.7402307

Cited by 16 publications

(6 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the convergence is poor when the load current is zero, and the model is only locally identifiable that calls for accurate initial values. They [25] later proposed a sliding mode observer, including temperature effects for online state estimation. They supposed some parameters are constant and did not study the influence of temperature on the parameters.…”

Section: Introductionmentioning

confidence: 99%

State of Charge Estimation for Lithium-Ion Battery with a Temperature-Compensated Model

et al. 2017

View full text Add to dashboard Cite

Accurate estimation of the state of charge (SOC) of batteries is crucial in a battery management system. Many studies on battery SOC estimation have been investigated recently. Temperature is an important factor that affects the SOC estimation accuracy while it is still not adequately addressed at present. This paper proposes a SOC estimator based on a new temperature-compensated model with extended Kalman Filter (EKF). The open circuit voltage (OCV), capacity, and resistance and capacitance (RC) parameters in the estimator are temperature dependent so that the estimator can maintain high accuracy at various temperatures. The estimation accuracy decreases when applied in high current continuous discharge, because the equivalent polarization resistance decreases as the discharge current increases. Therefore, a polarization resistance correction coefficient is proposed to tackle this problem. The estimator also demonstrates a good performance in dynamic operating conditions. However, the equivalent circuit model shows huge uncertainty in the low SOC region, so measurement noise variation is proposed to improve the estimation accuracy there.

show abstract

Section: Introductionmentioning

confidence: 99%

State of Charge Estimation for Lithium-Ion Battery with a Temperature-Compensated Model

et al. 2017

View full text Add to dashboard Cite

show abstract

“…The radial basis function (RBF) neural network is used to adjust the switch gain of the sliding mode observer, and the target factor recursive least square algorithm is used to identify the parameters of the equivalent model in real time. According to the electrochemical and thermal characteristics of the battery/supercapacitor, a realtime estimation scheme of the state parameters of the energy storage element composed of multiple sliding mode observers is designed in reference Dey et al (2015). This scheme simulates the internal characteristics of the energy storage element and verifies the convergence of the overall estimation scheme.…”

Section: Introductionmentioning

confidence: 99%

Research on Dynamic Equivalent SOC Estimation of Hybrid Energy Storage System Based on Sliding Mode Observer

et al. 2021

View full text Add to dashboard Cite

This article proposes a sliding mode observer based dynamic equivalent state of charge (ESOC) estimation method for hybrid energy storage system (HESS). Since different types of energy storage components and power electronics circuit are coupled in the HESS, the traditional SOC estimation method cannot reflect the real-time operation characteristics of the HESS. To tackle this problem, a sliding mode observer based on the model of the HESS is built in this article. By collecting the corresponding voltage and current signals, the internal parameters of energy storage elements can be observed accurately in real time. The dynamic ESOC is further defined with the idea of real-time charge balance to reflect the accurate available capacity of the HESS. Finally, the simulation results based on MATLAB/Simulink model are given to verify the feasibility of the proposed dynamic ESOC.

show abstract

“…The effectiveness of detection, isolation, and estimation of parametric faults were tested via simulation studies. Some works applied parameter estimation-based diagnosis approaches into battery-related applications such as hybrid electric vehicles or Hybrid Energy Storage Systems (HESS) [20,21], which strongly proved the applicability of parameter estimation in Li-ion battery systems. However, all the works mentioned above are limited with solving constant or slow-varying parameters.…”

Section: Introductionmentioning

confidence: 99%

Diagnosis Method for Li-Ion Battery Fault Based on an Adaptive Unscented Kalman Filter

Zheng

Chen

et al. 2017

Energies

View full text Add to dashboard Cite

Abstract:The reliability of battery fault diagnosis depends on an accurate estimation of the state of charge and battery characterizing parameters. This paper presents a fault diagnosis method based on an adaptive unscented Kalman filter to diagnose the parameter bias faults for a Li-ion battery in real time. The first-order equivalent circuit model and relationship between the open circuit voltage and state of charge are established to describe the characteristics of the Li-ion battery. The parameters in the equivalent circuit model are treated as system state variables to set up a joint state and parameter space equation. The algorithm for fault diagnosis is designed according to the estimated parameters. Two types of fault of the Li-ion battery, including internal ohmic resistance fault and diffusion resistance faults, are studied as a case to validate the effectiveness of the algorithm. The experimental results show that the proposed approach in this paper has effective tracking ability, better estimation accuracy, and reliable diagnosis for Li-ion batteries.

show abstract

Online state and parameter estimation of Battery-Double Layer Capacitor Hybrid Energy Storage System

Cited by 16 publications

References 12 publications

State of Charge Estimation for Lithium-Ion Battery with a Temperature-Compensated Model

State of Charge Estimation for Lithium-Ion Battery with a Temperature-Compensated Model

Research on Dynamic Equivalent SOC Estimation of Hybrid Energy Storage System Based on Sliding Mode Observer

Diagnosis Method for Li-Ion Battery Fault Based on an Adaptive Unscented Kalman Filter

Contact Info

Product

Resources

About