Genetic identification and fisher identifiability analysis of the Doyle–Fuller–Newman model from experimental cycling of a LiFePO4 cell

Forman, Joel C.; Moura, Scott J.; Stein, Jeffrey L.; Fathy, Hosam K.

doi:10.1016/j.jpowsour.2012.03.009

Cited by 258 publications

(146 citation statements)

References 22 publications

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“…As the SoC increases, activation energy-which represents the energy barrier for the thermal activation During such conditions, intercalation becomes energetically less favourable causing the potential to drop to (or below) the lithium plating potential (0 V relative to Li/Li + ). Li plating then occurs at a rate proportional to the overpotential relative to the 0 V equilibrium [15].…”

Section: Expected Parameter Changes Resulting From Battery Degradationmentioning

confidence: 99%

“…Excluding the time required to compile and install the model for a new data set which takes in the order of 10 min, a single execution of the P2D model for a given parameter vector set took significantly less than one-second. This is considerably faster than 63 s for a single run reported in previous work [15], which can lead to hours of simulation time.…”

Section: Battery Ageing Diagnostics Using Parameter Identificationmentioning

confidence: 85%

“…Santhanagopalan et al [14] for example used the Levenberg-Marquardt optimization algorithm to identify a subset of five parameters for both the pseudo two-dimensional (P2D) porous electrode model and SP models using constant charge and discharge cycles. An important contribution to parameter identification was made by Forman et al [15] who identified the full set of parameters (88 scalars and function control points) of the P2D model using a genetic algorithm derived from the drive-cycle data from a plug-in hybrid electric vehicle (PHEV). In the context of battery degradation, perturbative terms have been introduced into the SP model and then identification used to obtain values of these perturbative parameters [16,17].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Characterising Lithium-Ion Battery Degradation through the Identification and Tracking of Electrochemical Battery Model Parameters

Perera²,

et al. 2016

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Lithium-ion (Li-ion) batteries undergo complex electrochemical and mechanical degradation. This complexity is pronounced in applications such as electric vehicles, where highly demanding cycles of operation and varying environmental conditions lead to non-trivial interactions of ageing stress factors. This work presents the framework for an ageing diagnostic tool based on identifying and then tracking the evolution of model parameters of a fundamental electrochemistry-based battery model from non-invasive voltage/current cycling tests. In addition to understanding the underlying mechanisms for degradation, the optimisation algorithm developed in this work allows for rapid parametrisation of the pseudo-two dimensional (P2D), Doyle-Fuller-Newman, battery model. This is achieved through exploiting the embedded symbolic manipulation capabilities and global optimisation methods within MapleSim. Results are presented that highlight the significant reductions in the computational resources required for solving systems of coupled non-linear partial differential equations.

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Section: Expected Parameter Changes Resulting From Battery Degradationmentioning

confidence: 99%

Section: Battery Ageing Diagnostics Using Parameter Identificationmentioning

confidence: 85%

Section: Introductionmentioning

confidence: 99%

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Characterising Lithium-Ion Battery Degradation through the Identification and Tracking of Electrochemical Battery Model Parameters

Perera²,

et al. 2016

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“…A total of 88 operational parameters and geometric characteristics were determined based on the charge/discharge curves. 15 Due to the fact that several Li-ion battery parameters are not identifiable with performance curves, this method can only provide a rough estimation. The predictive method could be improved with more accurate experimental data for the geometry and the material characteristics.…”

mentioning

confidence: 99%

An Inverse Method for Estimating the Electrochemical Parameters of Lithium-Ion Batteries

Jokar

Rajabloo

Désilets

et al. 2016

J. Electrochem. Soc.

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An electrochemical Parameter Estimation (PE) study of lithium-ion batteries for different materials is presented. The PE methodology is developed in Part I of the study and the challenges on the different materials for the positive electrode including LiCoO 2 , LiMn 2 O 4 and LiFePO 4 are examined in Part II. The most influential electrochemical parameters of the Li-ion battery are estimated by means of an inverse method. The inverse method rests on five elements: the input parameters, a direct model, the reference data, an objective function and an optimizer. Eight electrochemical variables are considered as the target of the PE study. A simplified version of Pseudo-two-Dimensional (P2D) model is developed for the direct model. The P2D model predictions coupled to a random noise function are employed to generate the reference data. The data include the cell potential values with respect to the battery capacity at low and high discharge rates. The least-squared function and Genetic Algorithm are employed as the objective function and its optimizer, respectively. The best time domain for the estimation of each parameter is calculated by using a sensitivity analysis performed for different discharge curves. Results show that the methodology remains accurate and stable at both low and high discharge rates. The simultaneous high power and energy density of Lithium-ion (Li-ion) batteries have made it the preferred device for storing electricity. As a result, Li-ion batteries are increasingly used in various applications including electronics and the automotive industry. Regardless of the shape and of the battery pack arrangement, the internal structure of the battery usually comprises four main components: Two electrodes, positive and negative, an electrolyte, and a separator. These components are made of materials that have been gradually modified over the years so as to improve the efficiency, the safety and the performance of the batteries and to reduce their cost. 1-3A Battery Management System (BMS) is crucial for monitoring the operation of the battery pack. The BMS must interact with all the elements of the system in order to control it and to protect the Li-ion cells. The intelligence of the BMS is based on a mathematical model that simulates and predicts the different operating conditions of the Li-ion battery pack. In high tech and automotive industries, the BMS usually relies on empirical-based models. These models are simple and provide fast response. They cannot however predict the performance of the battery as it ages. Moreover, they are only applicable to a specific cell, i.e., they cannot be transposed to other battery packs without recalibration. [4][5][6] Electrochemical-based models of Li-ion batteries, on the other hand, overcome these shortcomings. These models rest on chemical/electrochemical kinetics and transport equations. These Li-ion battery models are more complicated and CPU time-consuming than empirical based models. They are, on the other hand, more versatile and they provide reliable a...

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“…In Ref. 48, the Fisher-Information-based Cramer-Rao bounds, which quantifies the theoretical lower bound of the variance of estimates, are numerically calculated for parameters of an electrochemical battery model. In Ref.…”

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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Genetic identification and fisher identifiability analysis of the Doyle–Fuller–Newman model from experimental cycling of a LiFePO4 cell

Cited by 258 publications

References 22 publications

Characterising Lithium-Ion Battery Degradation through the Identification and Tracking of Electrochemical Battery Model Parameters

Characterising Lithium-Ion Battery Degradation through the Identification and Tracking of Electrochemical Battery Model Parameters

An Inverse Method for Estimating the Electrochemical Parameters of Lithium-Ion Batteries

Analytic Bound on Accuracy of Battery State and Parameter Estimation

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