Maximizing Parameter Identifiability of an Equivalent-Circuit Battery Model Using Optimal Periodic Input Shaping

Rothenberger, Michael J.; Anstrom, Joel; Brennan, Sean; Fathy, Hosam K.

doi:10.1115/dscc2014-6272

Cited by 24 publications

(21 citation statements)

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“…Remark 2: The ability to identify model parameters depends on the quantity and quality (uncertainties) of the available data. Although there is a rich literature on the experiment design [41]- [43], it remains unclear how to generate informative data for parameter estimation of FO systems. In [44], we applied the persistent excitation concept to the battery Randles circuit models, which are given by ordinary differential equations (ODEs).…”

Section: Bayesian Inference In Battery Systemsmentioning

confidence: 99%

Bayesian Inference in Non-Markovian State-Space Models With Applications to Battery Fractional-Order Systems

Jacob

Alavi

Mahdi

et al. 2018

IEEE Trans. Contr. Syst. Technol.

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Battery impedance spectroscopy models are given by fractional order (FO) differential equations. In the discrete-time domain, they give rise to state-space models where the latent process is not Markovian. Parameter estimation for these models is therefore challenging, especially for non-commensurate FO models. In this paper, we propose a Bayesian approach to identify the parameters of generic FO systems. The computational challenge is tackled with particle Markov chain Monte Carlo methods, with an implementation specifically designed for the non-Markovian setting. The approach is then applied to estimate the parameters of a battery non-commensurate FO equivalent circuit model. Extensive simulations are provided to study the practical identifiability of model parameters and their sensitivity to the choice of prior distributions, the number of observations, the magnitude of the input signal and the measurement noise.

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Section: Bayesian Inference In Battery Systemsmentioning

confidence: 99%

Bayesian Inference in Non-Markovian State-Space Models With Applications to Battery Fractional-Order Systems

Jacob

Alavi

Mahdi

et al. 2018

IEEE Trans. Contr. Syst. Technol.

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“…battery current and voltage, can dramatically influence the estimation accuracy. Therefore, it's necessary to optimize the battery current waveform in order to ensure signal richness and therefore identification accuracy [29], [30].…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Identification and Control Using Active Signal Injection for Series Hybrid Electric Vehicles Based on Dynamic Programming

Zhu

Song

Hou

et al. 2020

IEEE Trans. Transp. Electrific.

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Hybrid electric vehicles (HEVs) have an over-actuated system by including two power sources, a battery pack and an internal combustion engine. This feature of HEV is exploited in this paper to simultaneously achieve accurate identification of battery parameters/states. By actively injecting current signals, state of charge, state of health, and other battery parameters can be estimated in a specific sequence to improve the identification performance when compared to the case where all parameters and states are estimated concurrently using the baseline current signals. A dynamic programming strategy is developed to provide the benchmark results about how to balance the conflicting objectives corresponding to identification and system efficiency. The tradeoff between different objectives is presented to optimize the current profile so that the richness of signal can be ensured and the fuel economy can be optimized. In addition, simulation results show that the Root-Mean-Square error of the estimation can be decreased by up to 100% at a cost of less than 2% increase in fuel consumption.With the proposed simultaneous identification and control algorithm, the parameters/states of the battery can be monitored to ensure safe and efficient application of the battery for HEVs. NomenclatureLinearized OCV-SOC slope (V) Frontal area of the HEV (m 2 ) Constants of linearized OCV-SOC curve (V)

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“…47, the minimum eigenvalue of F info is compared to a threshold to determine identifiability, and in Ref. 52, the determinant of F info is used as the criterion. However, it is difficult to demonstrate the identifiability intuitively by using the Fisher information matrix directly, as neither its eigenvalue nor determinant has clear physical insight.…”

Section: Methodology: Fisher Information Matrix Andmentioning

confidence: 99%

“…In Ref. 52, the attempt is made to enhance the identifiability by choosing the current excitation that maximizes the determinant of the Fisher information matrix.…”

mentioning

confidence: 99%

Analytic Bound on Accuracy of Battery State and Parameter Estimation

Lin

Stefanopoulou

2015

J. Electrochem. Soc.

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Methods for battery state and parameter estimation have been widely investigated, while the achievable accuracy of the estimation remains a critical but somehow overlooked topic. In this paper, the analytic bounds on the accuracy of battery state and parameter estimation accounting for voltage measurement noises are derived based on the Fisher information matrix and Cramer-Rao bound analysis. The state and parameters under discussion include the state of charge, capacity and (ohmic) resistance. The estimation accuracy is influenced by the information contained in the data set used for estimation. It is found that the main contributing factors to the accuracy of SOC estimation are the slope of the OCV curve and number of data points, while the accuracy of capacity estimation is affected by both OCV slope and SOC variation, and that of resistance estimation depends heavily on the current magnitude. The analytic bounds are derived for both standalone estimation, where only one state/parameter is estimated, and combined estimation where they are estimated together. The loss of accuracy in combined estimation compared to standalone estimation is usually expected. However, when the current excitation satisfies certain patterns, such loss can be avoided. The conclusions can be used as guidelines for offline experiment design as well as online evaluation of the accuracy of adaptive state and parameter estimation. Battery state and parameter estimation has been studied extensively in literature. Critical battery states include state of charge (SOC), temperature, state of power (SOP) and state of health (SOH) among others. As for the parameters, some of them are commonly seen in almost all types of models, such as the (ohmic) internal resistance, capacity, and open circuit voltage (OCV). Others are model-specific, especially those relating to the transient battery dynamics, e.g. R-C circuits in the equivalent circuit model, or diffusion coefficient/electrical conductivity in the electrochemical model. In this paper, the discussion will be limited to SOC, resistance and capacity, but the methodology can be extended to other states and parameters. It is noted that state estimation and parameter estimation are greatly interconnected for two reasons. For one thing, some of the states are in fact defined based on certain parameters. For example, it is well known that as battery ages, its capacity decreases 1-3 while resistance grows. 4-6 Therefore, SOH is often defined as the ratio of the remaining capacity and the nominal capacity, or the ratio of the actual (degraded) resistance and the nominal resistance.7 For another, it has been shown that the accuracy of state estimation will be greatly affected by the precision of parameters. 8 Various methods have been proposed in literature for battery state and parameter estimation. For SOC estimation, the basic method is coulomb counting, 9-10 where the current is integrated over time to calculate the change in stored energy. Since this method is susceptible to inaccurate initial SO...

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Maximizing Parameter Identifiability of an Equivalent-Circuit Battery Model Using Optimal Periodic Input Shaping

Cited by 24 publications

References 0 publications

Bayesian Inference in Non-Markovian State-Space Models With Applications to Battery Fractional-Order Systems

Bayesian Inference in Non-Markovian State-Space Models With Applications to Battery Fractional-Order Systems

Simultaneous Identification and Control Using Active Signal Injection for Series Hybrid Electric Vehicles Based on Dynamic Programming

Analytic Bound on Accuracy of Battery State and Parameter Estimation

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