Estimation of the SOC and the SOH of li-ion batteries, by combining impedance measurements with the fuzzy logic inference

Zenati, Ali; Desprez, Ph.; Razik, Hubert

doi:10.1109/iecon.2010.5675408

Cited by 111 publications

(62 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In each step of iteration, besides all the state variables, several covariance values should also be updated. Artificial intelligence methods such as artificial neural networks and fuzzy-logic-based estimations could generate more accurate results, but they are too complex to be realized in a microcontroller such as a DSP [1,6] .…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An SOC estimation method based on sliding mode observer and the Nernst Equation

Chang

Zheng

2015

2015 IEEE Energy Conversion Congress and Exposition (ECCE)

View full text Add to dashboard Cite

show abstract

Section: Introductionmentioning

confidence: 99%

“…Accurate SOC estimation is a significant technology of battery energy storage systems because all battery management systems (BMS) rely on accurate SOC estimation [1,2] . If the estimation error of SOC is too large, it will lead to SOC imbalance between cells or even over-charging or overdischarging [3] .…”

Section: Introductionmentioning

confidence: 99%

An SOC estimation method based on sliding mode observer and the Nernst Equation

Chang

Zheng

2015

2015 IEEE Energy Conversion Congress and Exposition (ECCE)

View full text Add to dashboard Cite

show abstract

“…[13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31]. The Coulomb counting or current integration method, which measures the amount of charge taken out of or put into a battery in terms of ampere hours, is simple and has a low computational burden.…”

Section: Introductionmentioning

confidence: 99%

“…As a result, an additional recalibration may be required when the ampere hours counting is performed over a long period of time. The ANNs and FL approaches can estimate the SOC of a battery with an arbitrary initial SOC value [22][23][24][25][26]. The robustness of these models relies a lot on the quantity and the quality of the training data set.…”

Section: Introductionmentioning

confidence: 99%

State of Charge and State of Health Estimation of AGM VRLA Batteries by Employing a Dual Extended Kalman Filter and an ARX Model for Online Parameter Estimation

2017

View full text Add to dashboard Cite

State of charge (SOC) and state of health (SOH) are key issues for the application of batteries, especially the absorbent glass mat valve regulated lead-acid (AGM VRLA) type batteries used in the idle stop start systems (ISSs) that are popularly integrated into conventional engine-based vehicles. This is due to the fact that SOC and SOH estimation accuracy is crucial for optimizing battery energy utilization, ensuring safety and extending battery life cycles. The dual extended Kalman filter (DEKF), which provides an elegant and powerful solution, is widely applied in SOC and SOH estimation based on a battery parameter model. However, the battery parameters are strongly dependent on operation conditions such as the SOC, current rate and temperature. In addition, battery parameters change significantly over the life cycle of a battery. As a result, many experimental pretests investigating the effects of the internal and external conditions of a battery on its parameters are required, since the accuracy of state estimation depends on the quality of the information regarding battery parameter changes. In this paper, a novel method for SOC and SOH estimation that combines a DEKF algorithm, which considers hysteresis and diffusion effects, and an auto regressive exogenous (ARX) model for online parameters estimation is proposed. The DEKF provides precise information concerning the battery open circuit voltage (OCV) to the ARX model. Meanwhile, the ARX model continues monitoring parameter variations and supplies information on them to the DEKF. In this way, the estimation accuracy can be maintained despite the changing parameters of a battery. Moreover, online parameter estimation from the ARX model can save the time and effort used for parameter pretests. The validation of the proposed algorithm is given by simulation and experimental results.

show abstract

“…The age and history of a battery also have impacts on the capacity of a battery. According to [44], SoC and SoH could be estimated from temperature and impedance measurements.…”

Section: Factors Affecting Battery Modelsmentioning

confidence: 99%

Optimization of Experimental Model Parameter Identification for Energy Storage Systems

et al. 2013

View full text Add to dashboard Cite

Abstract:The smart grid approach is envisioned to take advantage of all available modern technologies in transforming the current power system to provide benefits to all stakeholders in the fields of efficient energy utilisation and of wide integration of renewable sources. Energy storage systems could help to solve some issues that stem from renewable energy usage in terms of stabilizing the intermittent energy production, power quality and power peak mitigation. With the integration of energy storage systems into the smart grids, their accurate modeling becomes a necessity, in order to gain robust real-time control on the network, in terms of stability and energy supply forecasting. In this framework, this paper proposes a procedure to identify the values of the battery model parameters in order to best fit experimental data and integrate it, along with models of energy sources and electrical loads, in a complete framework which represents a real time smart grid management system. The proposed method is based on a hybrid optimisation technique, which makes combined use of a stochastic and a deterministic algorithm, with low computational burden and can therefore be repeated over time in order to account for parameter variations due to the battery's age and usage.

show abstract

Estimation of the SOC and the SOH of li-ion batteries, by combining impedance measurements with the fuzzy logic inference

Cited by 111 publications

References 15 publications

An SOC estimation method based on sliding mode observer and the Nernst Equation

An SOC estimation method based on sliding mode observer and the Nernst Equation

State of Charge and State of Health Estimation of AGM VRLA Batteries by Employing a Dual Extended Kalman Filter and an ARX Model for Online Parameter Estimation

Optimization of Experimental Model Parameter Identification for Energy Storage Systems

Contact Info

Product

Resources

About