Electrochemical modeling and parameter identification based on bacterial foraging optimization algorithm for lithium-ion batteries

Ma, Yan; Ru, Jingpei; Yin, Mingyue; Chen, Hong; Zheng, Weitao

doi:10.1007/s10800-016-0998-1

Cited by 19 publications

(4 citation statements)

References 31 publications

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“…The BFA has been successfully used to solve many engineering and mathematical problems because of its simplicity and high efficiency [173,174]. The BFA was also used to estimate the unknown parameters of the single particle EChM of a Li-ion battery [175].…”

Section: Bacterial Foraging Algorithm (Bfa)mentioning

confidence: 99%

Towards a Smarter Battery Management System for Electric Vehicle Applications: A Critical Review of Lithium-Ion Battery State of Charge Estimation

et al. 2019

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Energy storage system (ESS) technology is still the logjam for the electric vehicle (EV) industry. Lithium-ion (Li-ion) batteries have attracted considerable attention in the EV industry owing to their high energy density, lifespan, nominal voltage, power density, and cost. In EVs, a smart battery management system (BMS) is one of the essential components; it not only measures the states of battery accurately, but also ensures safe operation and prolongs the battery life. The accurate estimation of the state of charge (SOC) of a Li-ion battery is a very challenging task because the Li-ion battery is a highly time variant, non-linear, and complex electrochemical system. This paper explains the workings of a Li-ion battery, provides the main features of a smart BMS, and comprehensively reviews its SOC estimation methods. These SOC estimation methods have been classified into four main categories depending on their nature. A critical explanation, including their merits, limitations, and their estimation errors from other studies, is provided. Some recommendations depending on the development of technology are suggested to improve the online estimation.

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Section: Bacterial Foraging Algorithm (Bfa)mentioning

confidence: 99%

Towards a Smarter Battery Management System for Electric Vehicle Applications: A Critical Review of Lithium-Ion Battery State of Charge Estimation

et al. 2019

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“…Secondly, the polynomial approximation (PA) method [34,35], which is ubiquitous in the literature [22,27,[36][37][38][39][40][41]. In this case, polynomials of even degree (second and fourth) are assumed as solution profiles for Eq.…”

Section: Introductionmentioning

confidence: 99%

Residue grouping order reduction method in solid-phase lithium-ion battery models

2021

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Obtaining precise reducer order battery models is a key component in designing lithium-ion battery management systems. In general, with conventional order reduction techniques, low order is followed by low accuracy. To overcome such limitation, this paper presents a frequency-based constructive method to obtain broad-band low-order approximations of the solid-phase lithium-ion diffusion submodel, the core of the reference pseudo-two-dimensional electrochemical model of lithium-ion cells. The proposed method is a combination of an improvement on the residue grouping technique and of standard balancing and truncation/residualization methods borrowed from control systems theory. As main result, dramatic order reductions from 3000th to 5th-order with minimum accuracy losses are obtained. Taking a well-known case-study of a lithium-ion battery model, it is shown that 5th-order models offer approximations with maximum absolute errors of 0.5%, for characteristic times up to 9.54 × 10 4 s . The proposed method will help to obtain accurate real-time estimations of surface concentration and state of charge of lithium-ion batteries.

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“…main numerical problems [6]: (1) there are more states because of the existence of two dimensions; (2) The amount of calculation is large due to the existence of a large number of differential algebraic equations in the model. Common simplified models are electrochemical average model built by Domenico Di Domenico [7] and single particle model built by Santhanagopalan [8]. However, the electrochemical average model does not have a mature solution to the numerical problem (1), and the single particle model is not suitable for high charge-discharge ratio.…”

Section: Introductionmentioning

confidence: 99%

SOC Oriented Electrochemical-Thermal Coupled Modeling for Lithium-Ion Battery

et al. 2019

IEEE Access

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This paper proposes an electrochemical-thermal coupling model for state of charge (SOC) estimation of lithium-ion batteries (LIBs). In this paper, to solve two numerical problems in the quasi twodimensional model of LIBs, a simplified one-dimensional model is built. Considering that the parameters in the model are affected by temperature, the internal parameters of the battery which obey the Arrhenius equation are introduced. Based on the simplified one-dimensional model, the electrochemical-thermal coupling model is built. Finally, an adaptive SOC observer based on electrochemical-thermal coupling model is designed. The simulation results show that the electrochemical-thermal coupling model can be effectively applied to SOC estimation.INDEX TERMS Lithium-ion battery, pseudo two-dimensional, one-dimensional model, electrochemicalthermal coupling model, SOC estimation.

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Electrochemical modeling and parameter identification based on bacterial foraging optimization algorithm for lithium-ion batteries

Cited by 19 publications

References 31 publications

Towards a Smarter Battery Management System for Electric Vehicle Applications: A Critical Review of Lithium-Ion Battery State of Charge Estimation

Towards a Smarter Battery Management System for Electric Vehicle Applications: A Critical Review of Lithium-Ion Battery State of Charge Estimation

Residue grouping order reduction method in solid-phase lithium-ion battery models

SOC Oriented Electrochemical-Thermal Coupled Modeling for Lithium-Ion Battery

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