Polynomial approximation pseudo-two-dimensional battery model for online application in embedded battery management system

Deng, Zhongwei; Yang, Lin; Deng, Hao; Cai, Yunze; Li, Dongdong

doi:10.1016/j.energy.2017.10.097

Cited by 70 publications

(32 citation statements)

References 28 publications

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“…We first summarize the governing equations of the quadratic approximation model from literature [29], [33]. Our proposed modeling improvements discussed in section III relies upon this mathematical background.…”

Section: A Mathematical Backgroundmentioning

confidence: 99%

“…]︁ is to be determined. By applying the pertinent electrolytespecific boundary conditions of the DFN model and through a series of subsequent mathematical transformations (see Deng et al [33]), we obtain the set of Differential Algebraic Equations (DAEs) ( 13)-( 23) describing the quadratic approximation spatio-temporal model of ionic concentration.…”

Section: A Mathematical Backgroundmentioning

confidence: 99%

“…The electrode overpotentials are given by p = s,p − e p − p(33) n = s,n − e n − n(34) wherein contributions of the electrolyte potential terms are now included. Subtracting (34) from (33) and rearranging, cell = p − n + e p − e n ⏞ˉˉˉˉ⏟⏟ˉˉˉˉ⏞ Δ…”

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confidence: 99%

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A Composite Single Particle Lithium-Ion Battery Model Through System Identification

Gopalakrishnan

Offer

2022

IEEE Trans. Contr. Syst. Technol.

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In the realm of Lithium-ion (Li-ion) battery modeling, owing to its simplicity, the Single Particle Model (SPM) has long been considered to be a promising Reduced Order Model (ROM) candidate to usher in the era of Physics-Inspired Models (PIMs) in embedded applications. However, at high load currents, the standard SPM exhibits poor accuracies in computing the cell's terminal voltage, thereby rendering it unsuitable as the plant model in state-estimation tasks. A comprehensive evaluation of the salient electrolyte-enhanced SPMs from literature reveals that current solutions are either mathematically intractable or overly simplistic. For the ionic concentration in the electrolyte, the well-known quadratic approximation model, which straddles the boundary of computational complexity and mathematical tractability, reveals a poor temporal performance, particularly at the current collector interfaces. In this work, we retain the spatial dynamics of the quadratic approximation model, whilst proposing a novel approach using system identification techniques for its temporal dynamics. By employing linear approximations for the relevant subsystems, we identify discrete-time transfer functions of the de-biased number of moles per unit area of lithium ions in the electrolyte within each electrode region, yielding improved spatiotemporal accuracies for the electrolyte concentration profile. We then augment the standard SPM with the new system identificationbased electrolyte dynamics to arrive at an Electrolyte-Enhanced Composite Single Particle Model (EECSPM). Finally, we demonstrate superior performance of the EECSPM when compared with the incumbent state-of-the-art, thereby representing a concrete advancement towards the goal of using PIMs in real-time applications.

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Section: A Mathematical Backgroundmentioning

confidence: 99%

Section: A Mathematical Backgroundmentioning

confidence: 99%

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A Composite Single Particle Lithium-Ion Battery Model Through System Identification

Gopalakrishnan

Offer

2022

IEEE Trans. Contr. Syst. Technol.

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“…The pseudo-two-dimensional model (P2D) are widely used to describe the electrochemical behavior of lithium-ion batteries [14,19,20]. However, P2D is hard to use in onboard cases because of its complexity.…”

Section: Single-particle Modelmentioning

confidence: 99%

A State of Charge Estimator Based Extended Kalman Filter Using an Electrochemistry-Based Equivalent Circuit Model for Lithium-Ion Batteries

et al. 2018

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In this paper, an improved equivalent circuit model (ECM) considering partial electrochemical properties is developed for accurate state-of-charge (SOC). In the proposed model, the solid-phase diffusion process is calculated by a simple equation about particle surface SOC, and the double layer is simulated by two resistance-capacitance (RC) networks. To improve the global accuracy of the model, a subarea parameter-identification method based on particle swarm optimization is proposed, in order to determine the optimal model parameters in the entire SOC area. Then, an SOC estimator is developed based on extended kalman filter. The comparative study shows that a model considering solid-phase diffusion with two RC networks is the best choice. Finally, experimental results show that the accuracy of the proposed model is one times higher than that of the traditional ECM in the low SOC area, and is able to estimate SOC with errors less than 1% in the entire SOC area. Furthermore, estimation results of two types of batteries under two working conditions indicate that the developed model and SOC estimator have satisfactory global accuracy and guaranteed robustness with low computational complexity, which can be applied in real-time situations.

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“…With the development of EVs, battery management systems (BMSs) have gained extensive attention. Researchers have made significant efforts in the study of BMS, and fruitful results have been achieved [4][5][6][7][8][9][10]. BMS usually contains the following functions: state monitoring, such as state of charge (SOC) and state of health (SOH) estimation, cell balancing, and thermal management [11][12][13].…”

mentioning

confidence: 99%

Energy Management Optimization Based on Aging Adaptive Functional State Model of Battery For Intelligent Vehicles Driven by Internal Combustion Engine

Kong

Luo

Lian

et al. 2021

Preprint

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With the development of intelligent vehicles, increasing number of electronic and electrical devices are applied in vehicles, the necessary power is surging, battery protection is increasingly required, and higher demands are made for energy management. Most previous studies on battery characteristics have only focused on the battery itself, which is chemical characteristics. Further considering battery’s actual operating conditions on vehicles, battery’s functional characteristics are proposed and studied in this paper. Aging adaptive functional state model of battery for internal combustion engine vehicles is proposed, comprehensively revealing the operating characteristics covering the battery full life cycle. Thereafter, based on the model, a battery protection scheme is developed, including over-discharge and graded over-current protection to achieve comprehensive battery protection. Furthermore, a model-based energy management strategy is presented to achieve integrated optimization of fuel economy, battery protection, and vehicle power performance. Finally, tests are performed on the vehicle and test bench to verify the validity and feasibility of the proposed model and management scheme. Results reveal that the model can reflect battery’s functional features, over-current protection and over-discharge protection of the battery are achieved, and the vehicle start-up capability is secured. The proposed energy management strategy can effectively improve fuel economy.

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Polynomial approximation pseudo-two-dimensional battery model for online application in embedded battery management system

Cited by 70 publications

References 28 publications

A Composite Single Particle Lithium-Ion Battery Model Through System Identification

A Composite Single Particle Lithium-Ion Battery Model Through System Identification

A State of Charge Estimator Based Extended Kalman Filter Using an Electrochemistry-Based Equivalent Circuit Model for Lithium-Ion Batteries

Energy Management Optimization Based on Aging Adaptive Functional State Model of Battery For Intelligent Vehicles Driven by Internal Combustion Engine

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