Generalized Recursive Algorithm for Adaptive Multiparameter Regression

Koch, Brian J.

doi:10.1149/1.2128096

Cited by 82 publications

(52 citation statements)

References 31 publications

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“…Although there are various methods for capacity estimation, most of them can be grouped into one of the two broad categories: openloop [3][4][5][6] and closed-loop [7][8][9][10][11][12][13][14][15] methods. Systems in which the output has no effect upon the input are used for open-loop methods.…”

mentioning

confidence: 99%

Robust and Efficient Capacity Estimation Using Data-Driven Metamodel Applicable to Battery Management System of Electric Vehicles

Sung

Hwang

Nam

et al. 2016

J. Electrochem. Soc.

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For safe and reliable use of the battery for electric vehicles, diagnosis of its state-of-health (SOH) is essential. This is achieved by battery management systems (BMSs) that can monitor changes in the present capacity of the battery. Considering their limited computational resources, an efficient scheme is necessary. The data-driven metamodel is therefore used instead of complex battery models, which can simply capture changes in the shape of the charge curve as a battery ages. In consequence of the model reformulation, the charge curve refers to the time elapsed for charging against voltage. Under constant current charging, using time instead of capacity is favorable for computationally inexpensive BMSs. The aging-relevant parameter in the metamodel is estimated in the least-squares sense. In practice, this is often difficult as the shape of the charge curve, mostly its early part, is distorted by varying battery conditions before charging. For tolerating this distortion, a robust scheme is also required. The weighted least-squares is thus used such that the early part is given less weights whereas the later part is given more weights. The BMS-integrated metamodel and its parameter estimator are validated by using batteries with different SOH, which concludes an estimation error less than 3%. The California zero-emission vehicle (ZEV) regulation 1 was first adopted as part of the 1990 low-emission vehicle (LEV) program in the U.S., which has the aim of lowering greenhouse gas emissions and reducing petroleum consumption. The regulation is based on a credit scheme that provides automakers with credits for each ZEV they sell in California. The latest revision 1 was made to the regulation in 2012 and it will come into effect in 2018 and be in effect through 2025. According to the revised regulation, the credits earned per ZEV type will change. The credits for hybrid electric vehicles (HEVs) will become less and less and finally disappear by 2018. In contrast, the credits for battery electric vehicles (BEVs) will grow gradually. In addition, the longer range the BEVs can offer, the more credits they will receive. The revised regulation will apply to all automakers who annually sell more than 20,000 vehicles in California. If they fail to comply with the revised regulation, a $5,000 penalty per credit will be imposed. In this background, automakers that come under the revised regulation are expected to roll out ZEVs that can compete with internal combustion engine vehicles in the market. Here, ZEVs specifically involve BEVs and fuel cell electric vehicles (FCEVs). However, only the long-range BEVs are currently considered realistic and achievable in light of technology readiness and marketability.The long-range BEVs require batteries with high energy density. To use light-duty electric vehicles as an example, a range of more than 100 miles on a single charge could be realized with the battery pack energy density exceeding about 100 Wh/kg, which entails the battery cell energy density over 250 Wh/kg. As of now, a l...

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mentioning

confidence: 99%

Robust and Efficient Capacity Estimation Using Data-Driven Metamodel Applicable to Battery Management System of Electric Vehicles

Sung

Hwang

Nam

et al. 2016

J. Electrochem. Soc.

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“…Incorporation of a physics-based model along with the filter-based estimation technique improves the predictive capability of the model, in addition to providing a physical insight to the SOC estimates. For example, the SOC in the present approach is the ratio between the average concentration to the maximum concentration, of the active material within the electrode that limits the performance of the cell, unlike the empirical values predicted by the models in the literature [14][15][16][19][20][21]. For example, Plett [20] illustrated that addition of an arbitrary number of poles and zeros to the transfer function used to represent the cell in the model improves the degree of fit.…”

Section: Advantages and Limitations Of The Current Approachmentioning

confidence: 94%

State of charge estimation using an unscented filter for high power lithium ion cells

Santhanagopalan¹,

White

2009

Int. J. Energy Res.

151

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SUMMARYHigh power lithium ion batteries are increasingly used in power tools, hybrid electric vehicles and military applications, as a transient power source capable of delivering instant energy, around a relatively fixed state of charge (SOC). Maintaining the battery within pre-specified limits for SOC is important, since lithium ion batteries are prone to safety and/or performance issues during overcharge or rapid discharge below the cut-off voltages. With an increase in the number of cells used in the battery, SOC has a crucial role in cell balancing and optimization of the pack performance. Several techniques have been proposed for the SOC estimation. Most of the existing literature supports an empirical model based on either an electric circuit, arbitrary pole placement or an analytical expression with an arbitrary set of parameters. In spite of their simplicity, the empirical battery models do not provide information on the physical cell limitations. Alternatively, a rigorous electrochemical cell model, aimed at incorporating transport, kinetic and thermodynamic limitations, can be used to estimate parameters that hold a physical significance and hence provide an accurate measure of the cell performance. However, the demand for onboard estimation devices requires estimation techniques that are computationally efficient. In this work, estimation of the SOC of a lithium ion cell using an unscented filtering algorithm is illustrated. The relative advantages and disadvantages of the proposed methodology are briefly discussed.

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“…19, is common in dynamic estimation of parameters such as state of charge, which vary significantly within a single charge or discharge. 35 In these cases, the data collected in the vicinity of a particular time step must be weighted more compared to the data collected in the previous time steps. The current approach is an extension of this methodology.…”

Section: A200mentioning

confidence: 99%

Parameter Estimation and Model Discrimination for a Lithium-Ion Cell

Santhanagopalan

Guo

White

2007

J. Electrochem. Soc.

135

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Two different models were used to obtain transport and kinetic parameters using nonlinear regression from experimental charge/discharge curves of a lithium-ion cell measured at 35°C under four rates, C/5, C/2, 1C, and 2C, where the C rate is 1.656A . The Levenberg-Marquardt method was used to estimate parameters in the models such as the diffusion of lithium ions in the positive electrode. A confidence interval for each parameter was also presented. The parameter values lie within their confidence intervals. The use of statistical weights to correct for the scatter in experimental data as well as to treat one set of data in preference to other is illustrated. An F-test was performed to discriminate between the goodness of fit obtained from the two models.

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Generalized Recursive Algorithm for Adaptive Multiparameter Regression

Cited by 82 publications

References 31 publications

Robust and Efficient Capacity Estimation Using Data-Driven Metamodel Applicable to Battery Management System of Electric Vehicles

Robust and Efficient Capacity Estimation Using Data-Driven Metamodel Applicable to Battery Management System of Electric Vehicles

State of charge estimation using an unscented filter for high power lithium ion cells

Parameter Estimation and Model Discrimination for a Lithium-Ion Cell

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