Online State-of-Health Assessment for Battery Management Systems

Micea, Mihai V.; Ungurean, Lucian; Cârstoiu, Gabriel; Groza, Voicu

doi:10.1109/tim.2011.2115630

Cited by 130 publications

(63 citation statements)

References 19 publications

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“…Most of the literature has adopted the fully charge/discharge cycle number N as the model parameter for battery's SOH and RUL estimation. Micea et al [36] employed N into a second-degree polynomial regression equation for SOH model and RUL estimation in which a least square criterion was set. He et al [37] used N to derive an exponential growth model in terms of the sum of two exponential functions to fit the degradation curves.…”

Section: Aging Parameters Of the Batterymentioning

confidence: 99%

“…He et al [37] used N to derive an exponential growth model in terms of the sum of two exponential functions to fit the degradation curves. Xing et al [38] compared two degradation models: one based on [37] and the other based on [36]. The comparison showed that the exponential model was superior to the polynomial one.…”

Section: Aging Parameters Of the Batterymentioning

confidence: 99%

“…The above equation will be used as well in the present study to reconstruct SOH degeneration model in [36]. The coefficients, via the least square fitting method of MATLAB ® , are obtained as −0.000001, 0.0003 and 0.8969 for γ1, γ2 and γ3, respectively.…”

Section: (A) Cycle Number and Soh Degradationmentioning

confidence: 99%

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Regression Models Using Fully Discharged Voltage and Internal Resistance for State of Health Estimation of Lithium-Ion Batteries

et al. 2015

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Accurate estimation of lithium-ion battery life is essential to assure the reliable operation of the energy supply system. This study develops regression models for battery prognostics using statistical methods. The resultant regression models can not only monitor a battery's degradation trend but also accurately predict its remaining useful life (RUL) at an early stage. Three sets of test data are employed in the training stage for regression models. Another set of data is then applied to the regression models for validation. The fully discharged voltage (Vdis) and internal resistance (R) are adopted as aging parameters in two different mathematical models, with polynomial and exponential functions. A particle swarm optimization (PSO) process is applied to search for optimal coefficients of the regression models. Simulations indicate that the regression models using Vdis and R as aging parameters can build a real state of health profile more accurately than those using cycle number, N. The Monte Carlo method is further employed to make the models adaptive. The subsequent results, however, show that this results in an insignificant improvement of the battery life prediction. A reasonable speculation is that the PSO process already yields the major model coefficients.

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Section: Aging Parameters Of the Batterymentioning

confidence: 99%

Section: Aging Parameters Of the Batterymentioning

confidence: 99%

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Regression Models Using Fully Discharged Voltage and Internal Resistance for State of Health Estimation of Lithium-Ion Batteries

et al. 2015

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“…An example of an alternative solution aimed at embedded systems with low computation power was proposed by Mircea et al [95], which estimates the battery SoH based on stored information about the maximum battery capacity as a function of the charge/ discharge cycles and making use of curve modelling and polynomial regression.…”

Section: Kalman Filtersmentioning

confidence: 99%

Energy-Aware Mobile Learning:Opportunities and Challenges

Moldovan

Weibelzahl

Muntean

2014

IEEE Commun. Surv. Tutorials

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Abstract-As mobile devices are becoming more powerful and affordable they are increasingly used for mobile learning activities. By enabling learners' access to educational content anywhere and anytime, mobile learning has both the potential to provide online learners with new opportunities, and to reach less privileged categories of learners that lack access to traditional e-learning services. Among the many challenges with mobile learning, the battery-powered nature of mobile devices and in particular their limited battery life, stands out as one issue that can significantly limit learners' access to educational content while on the move. Adaptation and personalisation solutions have widely been considered for overcoming the differences between learners and between the characteristics of their mobile devices. However, while various energy saving solutions have been proposed in order to provide mobile users with extended device usage time, the areas of adaptive mobile learning and energy conservation in wireless communications failed to meet under the same umbrella. This paper bridges the two areas by presenting an overview of adaptive mobile learning systems as well as how these can be extended to make them energy-aware. Furthermore, the paper surveys various approaches for energy measurement, modelling and adaptation, three major aspects that have to be considered in order to deploy energy-aware mobile learning systems. Discussions on the applicability and limitations of these approaches for mobile learning are also provided.

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“…In this case, it is difficult to use them for online estimations. Another kind of non-physics-based model estimation uses various curve equations between the practicable capacity and aging cycles [40][41][42][43][44], in which the parameters of the equations can be obtained by data fitting algorithms, and further they can be adjusted online by using a particle filtering (PF) approach [43] or by combining sets of training data based on Dempster-Shafer theory (DST) and the Bayesian Monte Carlo (BMC) method [44]. This method also needs a lot of accelerated aging test data to determine the curve equations, but it does not need complicated mathematic computations.…”

Section: Introductionmentioning

confidence: 99%

Prediction of State-of-Health for Nickel-Metal Hydride Batteries by a Curve Model Based on Charge-Discharge Tests

2015

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Based on charge-discharge cycle tests for commercial nickel-metal hydride (Ni-MH) batteries, a nonlinear relationship is found between the discharging capacity (C discharge , Ah) and the voltage changes in 1 s occurring at the start of the charging process (∆V charge , mV). This nonlinear relationship between C discharge and ∆V charge is described with a curve equation, which can be determined using a nonlinear least-squares method. Based on the curve equation, a curve model for the state-of-health (SOH) prediction is constructed without battery models and cycle numbers. The validity of the curve model is verified using (C discharge , ∆V charge ) data groups obtained from the charge-discharge cycle tests at different rates. The results indicate that the curve model can be effectively applied to predict the SOH of the Ni-MH batteries and the best prediction root-mean-square error (RMSE) can reach upto 1.2%. Further research is needed to confirm the application of this empirical curve model in practical fields.

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Online State-of-Health Assessment for Battery Management Systems

Cited by 130 publications

References 19 publications

Regression Models Using Fully Discharged Voltage and Internal Resistance for State of Health Estimation of Lithium-Ion Batteries

Regression Models Using Fully Discharged Voltage and Internal Resistance for State of Health Estimation of Lithium-Ion Batteries

Energy-Aware Mobile Learning:Opportunities and Challenges

Prediction of State-of-Health for Nickel-Metal Hydride Batteries by a Curve Model Based on Charge-Discharge Tests

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