Multi-Scale Parameter Identification of Lithium-Ion Battery Electric Models Using a PSO-LM Algorithm

Shen, Wenjing; Li, Han-Xiong

doi:10.3390/en10040432

Cited by 13 publications

(6 citation statements)

References 29 publications

(35 reference statements)

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“…The detailed definition of each term on the right-hand side of (3) is described in [43,44]. The first item on the right side of (3) is the electrochemical heat generated in the porous electrode sub-domain; the second and third items are the Joule heating rates in the current collectors.…”

Section: Thermal Model and Problem Formulationmentioning

confidence: 99%

A Karhunen-Loeve Galerkin Online Modeling Approach for the Thermal Dynamics of Li-Ion Batteries

Shen

Deng³

et al. 2020

IEEE Access

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The thermal dynamics of Li-ion batteries are very complicated, and the battery temperature is spatially distributed imbalanced from the battery interior to the surface. The thermal dynamics are commonly modeled by partial differential equations (PDEs); however, parameter identification for PDEs is difficult and time consuming. In this work, a Karhunen-Loeve Galerkin method is proposed to obtain a simple but effective low-order model for the distributed thermal dynamics of Li-ion batteries. The Karhunen-Loeve decomposition method is applied to capture the most representative spatial modes of the dynamics, while the Galerkin method is used to obtain the corresponding temporal modes. All the uncertain physical parameters in the temporal modes are identified by the Levenberg-Marquardt algorithm. The updated temporal modes synthesized with spatial modes can offer a fast estimation of the temperature distribution on the battery surface and thus has the potential to provide distributed temperature prediction for the battery management system. The proposed modeling scheme is tested on a 60Ah Li-ion battery cell, and the simulation result shows an excellent match in the temperature distribution and a faster computing speed than the rigorous physical model.

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Section: Thermal Model and Problem Formulationmentioning

confidence: 99%

A Karhunen-Loeve Galerkin Online Modeling Approach for the Thermal Dynamics of Li-Ion Batteries

Shen

Deng³

et al. 2020

IEEE Access

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“…These algorithms are widely applied to solve complex problems in various domains. For example, studies that are related to online learning [18], scheduling [19], multi-objective optimization [20], vehicle routing [21], medicine [22], data classification [23], energy system [24,25], etc. can find the footage of the usage.…”

Section: Introductionmentioning

confidence: 99%

Improved Metaheuristic Optimization Algorithm Applied to Hydrogen Fuel Cell and Photovoltaic Cell Parameter Extraction

2021

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As carriers of green energy, proton exchange membrane fuel cells (PEMFCs) and photovoltaic (PV) cells are complex and nonlinear multivariate systems. For simulation analysis, optimization control, efficacy prediction, and fault diagnosis, it is crucial to rapidly and accurately establish reliability modules and extract parameters from the system modules. This study employed three types of particle swarm optimization (PSO) algorithms to find the optimal parameters of two energy models by minimizing the sum squared errors (SSE) and roots mean squared errors (RMSE). The three algorithms are inertia weight PSO, constriction PSO, and momentum PSO. The obtained calculation results of these three algorithms were compared with those obtained using algorithms from other relevant studies. This study revealed that the use of momentum PSO enables rapid convergence (under 30 convergence times) and the most accurate modeling and yields the most stable parameter extraction (SSE of PEMFC is 2.0656, RMSE of PV cells is 8.839 · 10−4). In summary, momentum PSO is the algorithm that is most suitable for system parameter identification with multiple dimensions and complex modules.

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“…For example, researchers in [25,26] employed the differential evolution technique and the real-coded genetic algorithm to study the optimal ED problem for a CHP system, respectively. However, two main disadvantages of the heuristic algorithms cannot be simply neglected [27,28]. e first one is that the heuristic algorithms often suffer from local optimal issue.…”

Section: Introductionmentioning

confidence: 99%

“…e second one is that the computational time is unstable. Consequently, the mathematical optimization approaches have been widely researched [21][22][23][24][25][26][27][28][29][30][31][32] due to its advantages of computational efficiency and global optimal solution guarantee.…”

Section: Introductionmentioning

confidence: 99%

Robust Day-Ahead Dispatch for Integrated Power-Heat-Gas Microgrid considering Wind Power Uncertainty

Liu

Chen

et al. 2020

Mathematical Problems in Engineering

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Wind power generation has been widely deployed in the modern power system due to the issues of energy crisis and environment pollution. Meanwhile, the microgrid is gradually regarded as a feasible way to connect and accommodate the distributed wind power generations. Recently, more research studies also focus on incorporating various energy systems, for example, heat and gas into the microgrid in terms of satisfying different types of load demands. However, the uncertainty of wind power significantly impacts the economy of the integrated power-heat-gas microgrid. To deal with this issue, this paper presents a two-stage robust model to achieve the optimal day-ahead economic dispatch strategy considering the worst-case wind power scenarios. The first stage makes the initial day-ahead dispatch decision before the observation of uncertain wind power. The additional adjustment action is made in the second stage once the wind power uncertainty is observed. Based on the duality theory and Big-M approach, the original second-stage problem can be dualized and linearized. Therefore, the column-and-constraint generation algorithm can be further implemented to achieve the optimal day-ahead economic dispatch strategy for the integrated power-heat-gas microgrid. The experimental results indicate the effectiveness of the presented approach for achieving operation cost reduction and promoting wind power utilization. The robustness and the economy of the two-stage robust model can be balanced, of which the performances significantly outperform those of the single-stage robust model and the deterministic model.

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Multi-Scale Parameter Identification of Lithium-Ion Battery Electric Models Using a PSO-LM Algorithm

Cited by 13 publications

References 29 publications

A Karhunen-Loeve Galerkin Online Modeling Approach for the Thermal Dynamics of Li-Ion Batteries

A Karhunen-Loeve Galerkin Online Modeling Approach for the Thermal Dynamics of Li-Ion Batteries

Improved Metaheuristic Optimization Algorithm Applied to Hydrogen Fuel Cell and Photovoltaic Cell Parameter Extraction

Robust Day-Ahead Dispatch for Integrated Power-Heat-Gas Microgrid considering Wind Power Uncertainty

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