Dynamical Analysis of Li-Ion Battery Module Applying Electro-Thermal System Model
Hoivala, Matti (2024)
2024
Konetekniikan DI-ohjelma - Master's Programme in Mechanical Engineering
Tekniikan ja luonnontieteiden tiedekunta - Faculty of Engineering and Natural Sciences
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Hyväksymispäivämäärä
2024-06-20
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tuni-202406107097
https://urn.fi/URN:NBN:fi:tuni-202406107097
Tiivistelmä
The electrochemistry of lithium-ion batteries is highly sensitive to temperature, leading to varying rates of charging and discharging among battery cells. These discrepancies can result in different operational states among the cells, preventing optimal usage of the battery system. Monitoring cell voltages during operation is crucial to prevent such issues. While some effects causing imbalances may self-correct, others persist and worsen over time if not addressed. Balancing, a process where cell states are equalized must be utilized. Balancing is not a straight forward process and does come with a drawbacks such as energy loss, reduced system efficiency and increased complexity.
This study aims to develop an electro-thermal system model using a module comprising 14 series connected cells to analyze the extent of imbalance following a drive cycle. The electrical characteristics of each cell are represented using an Equivalent Circuit Model (ECM), with parameters determined through empirical testing and parameter optimization. To incorporate temperature effects into the model, a state-space model representing the thermal behaviour is integrated with the electrical ECM model, forming a closed loop model. Thermal state-space model is identified through a number of Computational Fluid Dynamic (CFD) simulations. Coulombic efficiency (CE) of the cell chemistry is evaluated across various temperature and current levels and subsequently integrated into the ECM model.
To assess State of Charge (SOC) imbalance following a drive cycle, the Worldwide Harmonized Light Vehicles Test Cycle (WLTC) is utilized as an input for the system model. Exploring the system’s thermal sensitivity involves conducting multiple simulations with varying temperature levels and gradients. The thermal dynamics result from both cell heat generation and the design of cooling systems within the module.
The analysis revealed a clear correlation between SOC imbalance and temperature differentials among the cells. Moreover, the influence of temperature variance on SOC imbalance was quantified, indicating that significant temperature differences lead to notable SOC imbalances. Additionally, extreme high and low absolute temperature levels resulted in heightened SOC imbalances, with an optimal range identified between 25-30°C.
This study aims to develop an electro-thermal system model using a module comprising 14 series connected cells to analyze the extent of imbalance following a drive cycle. The electrical characteristics of each cell are represented using an Equivalent Circuit Model (ECM), with parameters determined through empirical testing and parameter optimization. To incorporate temperature effects into the model, a state-space model representing the thermal behaviour is integrated with the electrical ECM model, forming a closed loop model. Thermal state-space model is identified through a number of Computational Fluid Dynamic (CFD) simulations. Coulombic efficiency (CE) of the cell chemistry is evaluated across various temperature and current levels and subsequently integrated into the ECM model.
To assess State of Charge (SOC) imbalance following a drive cycle, the Worldwide Harmonized Light Vehicles Test Cycle (WLTC) is utilized as an input for the system model. Exploring the system’s thermal sensitivity involves conducting multiple simulations with varying temperature levels and gradients. The thermal dynamics result from both cell heat generation and the design of cooling systems within the module.
The analysis revealed a clear correlation between SOC imbalance and temperature differentials among the cells. Moreover, the influence of temperature variance on SOC imbalance was quantified, indicating that significant temperature differences lead to notable SOC imbalances. Additionally, extreme high and low absolute temperature levels resulted in heightened SOC imbalances, with an optimal range identified between 25-30°C.