Prediction of the Heat Generation Rate of Lithium-Ion Batteries Based on Three Machine Learning Algorithms

Cao, Renfeng; Zhang, Xingjuan; Yang, Han

doi:10.3390/batteries9030165

Cited by 10 publications

(2 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…al. predicted the heat generation rate at 0.5C, 1C, 1.5C at 25 °C, and 1C at 20, 30, and 45 °C with three different machine learning algorithms: ANN, SVM (Support Vector Machine), and GPR (Gaussian Process Regression) [24]. ANN performed better than the other two algorithms in predicting heat generation rates [24].…”

Section: Introductionmentioning

confidence: 99%

Optimization and experimental validation of the air intake holes of the lithium-ion battery pack

Bakar,

Uysal,

Feyzioğlu

2024

J., eng., them. sci.

View full text Add to dashboard Cite

Energy storage systems enable the storage of energy and provide access to carbon-neutral, environmentally friendly energy whenever or wherever it is needed. Lithium-ion batteries are currently the most preferred type among various battery technologies and are widely used in energy storage systems. Some of the features that make lithium-ion batteries advantageous include high energy density, long life, low maintenance requirements, and high operating voltage. The growing demand for energy throughout the day increases the need for batteries with high storage capacity. However, the increased capacity also leads to heating issues in lithium-ion batteries. The heating problem in lithium-ion batteries can result in nonhomogeneous temperature distribution, shortened lifespan, thermal runaway, increased internal resistance, and performance loss. Therefore, an effective thermal management system is essential for cooling lithium-ion batteries. This study aims to provide insight into the forced air cooling of prismatic 280 Ah LiFePo4 batteries, which have limited information in the literature and are more prone to overheating compared to lower-capacity batteries. In this study, five different battery pack case designs, each with different sizes and numbers of air intake holes, were determined and modelled using the SolidWorks program. Within the battery pack cases, 16 280 Ah lithium-ion batteries are placed, and an axial fan is used to cool these batteries. Initially, computational fluid dynamics analyses of the five different designs were performed in the SolidWorks Flow Simulation program. An experiment was then conducted on the design that provided the most efficient thermal management to validate the numerical results. The selected design, fulfilling the purpose of homogeneous temperature distribution and having the minimum temperature difference between batteries, was designated as Design 5. It exhibited a 62 % improvement in cooling performance with a 0.25 °C temperature difference, indicating successful temperature homogeneity between batteries. During a two-hour experiment with a 140 A discharge current, temperature measurements were taken from the surfaces of the batteries using thermocouples. Finally, the maximum error rate between experimental and numerical studies was determined to be 1.47 %, indicating successful validation of the numerical study. The air intake hole optimization, a novel design approach, prevents temperature distribution inhomogeneity caused by the distance of the batteries to the fan and offers an effective way to cool down high-capacity 280 Ah batteries.

show abstract

Section: Introductionmentioning

confidence: 99%

Optimization and experimental validation of the air intake holes of the lithium-ion battery pack

Bakar,

Uysal,

Feyzioğlu

2024

J., eng., them. sci.

View full text Add to dashboard Cite

show abstract

“…The heat generation of LIBs is a topical issue. However, current research focus on machine learning algorithms [18,19], the spatial distribution of heat generation within the cell [20] or on the heat generation rate at the limits of the operating range of LIBs [21]. All these studies have in common the fact that on the modeling side they mostly rely on ECMs to predict the generated heat by using the (simplified) Bernardi equation.…”

Section: Introductionmentioning

confidence: 99%

Influence of Lithium-Ion-Battery Equivalent Circuit Model Parameter Dependencies and Architectures on the Predicted Heat Generation in Real-Life Drive Cycles

Auch,

Kuthada,

Giese

et al. 2023

Batteries

View full text Add to dashboard Cite

This study investigates the influence of the considered Electric Equivalent Circuit Model (ECM) parameter dependencies and architectures on the predicted heat generation rate by using the Bernardi equation. For this purpose, the whole workflow, from the cell characterization tests to the cell parameter identification and finally validation studies, is examined on a cylindrical 5 Ah LG217000 Lithium-Ion-Battery (LIB) with a nickel manganese cobalt chemistry. Additionally, different test procedures are compared with respect to their result quality. For the parameter identification, a Matlab tool is developed enabling the user to generate all necessary ECMs in one run. The accuracy of the developed ECMs is evaluated by comparing voltage prediction of the experimental and simulation results for the highly dynamic World harmonized Light vehicle Test Cycle (WLTC) at different states of charges (SOCs) and ambient temperatures. The results show that parameter dependencies such as hysteresis and current are neglectable, if only the voltage results are compared. Considering the heat generation prediction, however, the neglection can result in mispredictions of up to 9% (current) or 22% (hysteresis) and hence should not be neglected. Concluding the voltage and heat generation results, this study recommends using a Dual Polarization (DP) or Thevenin ECM considering all parameter dependencies except for the charge/discharge current dependency for thermal modeling of LIBs.

show abstract

Machine learning in electrochemical oxidation process: A mini-review

Li,

Zou,

Wang

et al. 2024

Chinese Chemical Letters

View full text Add to dashboard Cite

Prediction of the Heat Generation Rate of Lithium-Ion Batteries Based on Three Machine Learning Algorithms

Cited by 10 publications

References 37 publications

Optimization and experimental validation of the air intake holes of the lithium-ion battery pack

Optimization and experimental validation of the air intake holes of the lithium-ion battery pack

Influence of Lithium-Ion-Battery Equivalent Circuit Model Parameter Dependencies and Architectures on the Predicted Heat Generation in Real-Life Drive Cycles

Machine learning in electrochemical oxidation process: A mini-review

Contact Info

Product

Resources

About