Parameter estimation of an electrochemistry-based lithium-ion battery model using a two-step procedure and a parameter sensitivity analysis

Jin, Ning; Danilov, Dmitri L.; Hof, Paul M.J. Van den; Donkers, M.C.F.

doi:10.1002/er.4022

Cited by 75 publications

(60 citation statements)

References 35 publications

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“…For parameter grouping, we first perform sensitivity analysis across the library of input profiles. After calculating the sensitivities [7]- [9] for 738 profiles through parallel computing, we apply the Gram-Schmidt process on S T y S y to reveal the orthogonalized sensitivity magnitudes and linear dependence. 29 Figure 4 visualizes the average sensitivity magnitudes via Graham-Schmidt orthogonalization over 738 profiles.…”

Section: Optimal Experimental Designmentioning

confidence: 99%

Optimal Experimental Design for Parameterization of an Electrochemical Lithium-Ion Battery Model

Park

Kato

Gima

et al. 2018

J. Electrochem. Soc.

100

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We consider the problem of optimally designing an excitation input for parameter identification of an electrochemical Li-ion battery model. The conventional approach to performing parameter identification uses standard test cycles. In contrast, we optimally design the input trajectory to maximize parameter identifiability in the sense of Fisher information. Specifically, we derive sensitivity equations for the electrochemical model. This approach enables parameter sensitivity analysis and optimal parameter fitting via gradient-based algorithms. This paper presents a general systematic approach to identify the electrochemical parameters in a non-invasive way. First, we group parameters into two sets: (i) equilibrium parameters, and (ii) dynamical parameters. We also divide the dynamical parameters into subsets by calculating orthogonalized sensitivity, which mitigates linear dependence between parameters. A large number of input profiles have been devised to constitute an input library. Then, the optimal inputs are selected from the input library to maximize the Fisher information, via convex programming. Using this framework a number of relevant experiments are obtained to parameterize. To validate our approach experimentally, we consider a 18650 Lithium nickel cobalt aluminum oxide battery. Compared to the conventional approach, our proposal achieves lower voltage RMSE across all experimental testing cycles.

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Section: Optimal Experimental Designmentioning

confidence: 99%

Optimal Experimental Design for Parameterization of an Electrochemical Lithium-Ion Battery Model

Park

Kato

Gima

et al. 2018

J. Electrochem. Soc.

100

View full text Add to dashboard Cite

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“…To better estimate battery states, various mathematical models have been established, including electrochemical models [26] and ECMs [27]. However, they differ greatly in accuracy, computation complexity and reliability.…”

Section: Battery Modeling and Analysismentioning

confidence: 99%

Model-Based Adaptive Joint Estimation of the State of Charge and Capacity for Lithium–Ion Batteries in Their Entire Lifespan

et al. 2020

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In this paper, a co-estimation scheme of the state of charge (SOC) and available capacity is proposed for lithium-ion batteries based on the adaptive model-based algorithm. A three-dimensional response surface (TDRS) in terms of the open circuit voltage, the SOC and the available capacity in the scope of whole lifespan, is constructed to describe the capacity attenuation, and the battery available capacity is identified by a genetic algorithm (GA), together with the parameters related to SOC. The square root cubature Kalman filter (SRCKF) is employed to estimate the SOC with the consideration of capacity degradation. The experimental results demonstrate the effectiveness and feasibility of the co-estimation scheme.Energies 2020, 13, 1410 2 of 15 initial values, and the latter is not suitable for online estimation, as it usually costs long shelving time to acquire the OCV value. With the development of computation technologies and machine learning, a variety of artificial intelligence-based, data-driven methods, such as neural networks [6] and support vector machines [7], are proposed for SOC estimation by establishing black-box models. Data-driven methods feature a strong nonlinear mapping capability with high accuracy; however, these approaches show high complexity, and require a considerable amount of training data. Alternatively, model-based methods have been widely investigated and applied for SOC estimation, thanks to the capabilities of online application, high precision and the independence of initial values. Conventional modeling manners mainly include electrochemical models and the equivalent circuit models (ECMs). Compared with complicated electrochemical models, ECM is commonly used to describe the electrical behavior of batteries, and subsequently to estimate the SOC due to its simplification and preferable precision. Yanwen Li et al. proposed a multi-model probability fusion algorithm to describe the battery's electrical characteristics, and subsequently estimate the SOC [8]. In model-based methods, the combination of the battery model and the intelligent filtering algorithm is a hotspot in SOC estimation research. The frequently used filtering algorithms include Kalman filtering (KF) [9], the H-infinity filter (HIF) [10], particle filter (PF) [11], and their various extensions. In particular, the extended KF (EKF) is widely employed to execute SOC estimation using a first-order Taylor expansion on the basis of the battery's nonlinear model [12]. Nonetheless, the second and higher order expansion is usually neglected, thus leading to slow a convergence rate, and even divergence. The unscented KF (UKF) is exploited to estimate battery SOC, based on the recursive unscented transformation to approximate the nonlinear observation without Taylor polynomial expansions [13]. The UKF shows better estimation precision and robustness than the EKF in strong, nonlinear systems [14]. On the basis of the radial-spherical cubature criterion, the cubature Kalman filter (CKF) leverages a set of volume points to appro...

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“…Several methods exist for assessing the practical identifiability of battery parameters. As sensitive parameters tend to have stronger identifiability, traditional sensitivity analysis is usually performed to investigate the practical identifiability of various parameters in an electrochemical model or an ECM . The parameter identifiability for series and parallel battery strings with lumped measurements is analyzed via linear and nonlinear observability tests.…”

Section: Introductionmentioning

confidence: 99%

A novel interval‐based approach for quantifying practical parameter identifiability of a lithium‐ion battery model

et al. 2020

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Summary Practical identifiability of battery model parameters, on which both modeling accuracy and robustness rely, is considered as a very important prerequisite for advanced onboard monitoring and control of Lithium‐ion batteries. In this paper, a novel confidence‐interval‐based approach is proposed for the quantification and assessment of the practical identifiability of a widely used second order battery equivalent circuit model (ECM). This method utilizes profile likelihood and likelihood ratio subset statistic to calculate each parameter's confidence interval, based on which a normalized index is further derived for facilitating quantification and fast comparison of the identifiability degree among different parameters. Using this approach, the practical identifiability of the second order ECM under lab‐collected experimental data is successfully evaluated, and the influences of several real‐world factors are systematically examined through extensive simulations. The results show that the open circuit voltage and ohmic internal resistance have a much larger degree of identifiability in all the investigated conditions. Some practically useful insights on performing battery parameter identification are also provided.

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Parameter estimation of an electrochemistry-based lithium-ion battery model using a two-step procedure and a parameter sensitivity analysis

Cited by 75 publications

References 35 publications

Optimal Experimental Design for Parameterization of an Electrochemical Lithium-Ion Battery Model

Optimal Experimental Design for Parameterization of an Electrochemical Lithium-Ion Battery Model

Model-Based Adaptive Joint Estimation of the State of Charge and Capacity for Lithium–Ion Batteries in Their Entire Lifespan

A novel interval‐based approach for quantifying practical parameter identifiability of a lithium‐ion battery model

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