Simultaneous model selection and parameter estimation for lithium‐ion batteries: A sequential MINLP solution approach

Shen, Jinhua; He, Yijun; Ma, Zi-Feng

doi:10.1002/aic.15030

Cited by 10 publications

(6 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…No method, known to the authors, exists to evaluate the voltage vs. current density relationship at a constant SOC in a battery. However, there are several methods [1][2][3][4][5][6] to evaluate SOC and predict performance. Notably these include Bernardi's pulse discharge method 3 and Wijewardana's method to estimate SOC, 5 as well as others.…”

mentioning

confidence: 99%

Pulse Polarization for Li-Ion Battery under Constant State of Charge: Part I. Pulse Discharge Experiments

DuBeshter

Jorné

2017

J. Electrochem. Soc.

View full text Add to dashboard Cite

Given the increased dependence on battery-powered devices, it is necessary to develop new and robust methods to evaluate and predict battery performance and state of charge. Batteries, including Li-Ion batteries, are unsteady state systems and a need exists to evaluate performance under constant state of charge (SOC). The purpose of this work was to develop pulse polarization curves (PPC) for a Li-Ion battery under constant SOC in order to identify individual overvoltages, such as charge transfer kinetics and mass transport, and their SOC dependence. Consequently, we elected to conduct pulse discharges at various duration and discharge current densities, under well maintained SOCs. Based on the results we were able to construct pulse polarization curves for constant SOCs of 10, 40 and 70% at various pulse durations of 2, 10 and 30 seconds. The experimentally obtained pulse polarizations suggest that the kinetic overpotential is captured in the 2 second pulse, while electrolyte Li+ concentration and Li solid state diffusion gradients are established in the 10 and 30 second pulse times, respectively. This allowed us to identify the individual overvoltages experimentally. An important consideration was that as the battery's SOC changes, the open circuit voltage (OCV) also changes. Therefore, the pulse discharge method we used needed to end at the same SOC in all cases to enable the construction of a PPC with the same OCV. To ensure that this method applied to various sizes and chemistries of batteries reliably, we ran pulse discharge experiments on a high Amp hour (15 Ah) large Li-Ion pouch battery (power) that used a NMC LMO cathode, as well as on a low Amp hour (0.04 Ah) small electrode coin battery (energy) with a CoO 2 cathode. In a subsequent paper we employed the Newman method to model the pulse discharges, allowing prediction of the performance of Li-Ion batteries and constructing their PPCs under constant SOC.

show abstract

mentioning

confidence: 99%

Pulse Polarization for Li-Ion Battery under Constant State of Charge: Part I. Pulse Discharge Experiments

DuBeshter

Jorné

2017

J. Electrochem. Soc.

View full text Add to dashboard Cite

show abstract

“…Identified parameters of the P2D electro chemical model can indicate battery internal characteristics accurately, but the charging scenario, ageing mechanism and COM are not analysed. A multi-objective optimisation approach is also applied for COM parameter identification treating charge/discharge data at different currents [92][93][94][95][96].…”

Section: Battery Parameters Extraction Techniques Using Grey Box Modelling Data-driven Approachmentioning

confidence: 99%

A Review on Battery Modelling Techniques

Tamilselvi¹,

Gunasundari²,

Karuppiah³

et al. 2021

Sustainability

133

View full text Add to dashboard Cite

The growing demand for electrical energy and the impact of global warming leads to a paradigm shift in the power sector. This has led to the increased usage of renewable energy sources. Due to the intermittent nature of the renewable sources of energy, devices capable of storing electrical energy are required to increase its reliability. The most common means of storing electrical energy is battery systems. Battery usage is increasing in the modern days, since all mobile systems such as electric vehicles, smart phones, laptops, etc., rely on the energy stored within the device to operate. The increased penetration rate of the battery system requires accurate modelling of charging profiles to optimise performance. This paper presents an extensive study of various battery models such as electrochemical models, mathematical models, circuit-oriented models and combined models for different types of batteries. It also discusses the advantages and drawbacks of these types of modelling. With AI emerging and accelerating all over the world, there is a scope for researchers to explore its application in multiple fields. Hence, this work discusses the application of several machine learning and meta heuristic algorithms for battery management systems. This work details the charging and discharging characteristics using the black box and grey box techniques for modelling the lithium-ion battery. The approaches, advantages and disadvantages of black box and grey box type battery modelling are analysed. In addition, analysis has been carried out for extracting parameters of a lithium-ion battery model using evolutionary algorithms.

show abstract

“…[73] Moreover, when the function form which describes the relationship between parameters and battery states is variable, the simultaneous optimization problem of the function structure 𝑦 and parameters 𝑥 would arise, which can be formulated as a mixed-integer nonlinear programming (MINLP) problem. [106] In practical application, the parameter identification procedure is usually different according to the different battery model type. For the ECM model, the OCV-SOC relationship is usually modeled by a predefined analytical function and the OCV model parameters could be independently estimated by OCV-SOC experimental data.…”

Section: Parameters Identificationmentioning

confidence: 99%

Design and management of lithium-ion batteries: A perspective from modeling, simulation, and optimization*

Wang¹,

Shen²,

He³

et al. 2020

Chinese Phys. B

View full text Add to dashboard Cite

Although the lithium-ion batteries (LIBs) have been increasingly applied in consumer electronics, electric vehicles, and smart grid, they still face great challenges from the continuously improving requirements of energy density, power density, service life, and safety. To solve these issues, various studies have been conducted surrounding the battery design and management methods in recent decades. In the hope of providing some inspirations to the research in this field, the state of the art of design and management methods for LIBs are reviewed here from the perspective of process systems engineering. First, different types of battery models are summarized extensively, including electrical model and multi-physics coupled model, and the parameter identification methods are introduced correspondingly. Next, the model based battery design methods are reviewed briefly on three different scales, namely, electrode scale, cell scale, and pack scale. Then, the battery model based battery management methods, especially the state estimation methods with different model types are thoroughly compared. The key science and technology challenges for the development of battery systems engineering are clarified finally.

show abstract

Simultaneous model selection and parameter estimation for lithium‐ion batteries: A sequential MINLP solution approach

Cited by 10 publications

References 30 publications

Pulse Polarization for Li-Ion Battery under Constant State of Charge: Part I. Pulse Discharge Experiments

Pulse Polarization for Li-Ion Battery under Constant State of Charge: Part I. Pulse Discharge Experiments

A Review on Battery Modelling Techniques

Design and management of lithium-ion batteries: A perspective from modeling, simulation, and optimization*

Contact Info

Product

Resources

About