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The temperature distribution is studied theoretically in a battery module stacked with 12 high-power Li-ion pouch cells. The module is cooled indirectly with ambient air through aluminum heat-sink plates or cooling plates sandwiched between each pair of cells in the module. Each of the cooling plates has an extended cooling fin exposed in the cooling air channel. The cell temperatures can be controlled by changing the air temperature and/or the heat transfer coefficient on the cooling fin surfaces by regulating the air flow rate. It is found that due to the high thermal conductivity and thermal diffusivity of the cooling plates, heat transfer of the cooling plate governs the cell temperature distribution by spreading the cell heat over the entire cell surface. Influence of thermal from the cooling fins is also simulated.

Ideal operation temperatures for Li-ion batteries fall in a narrow range from 20°C to 40°C. If the cell operation temperatures are too high, active materials in the cells may become thermally unstable. If the temperatures are too low, the resistance to lithium-ion transport in the cells may become very high, limiting the electrochemical reactions. Good battery thermal management is crucial to both the battery performance and life. Characteristics of various battery thermal management systems are reviewed. Analyses show that the advantages of direct and indirect air cooling systems are their simplicity and capability of cooling the cells in a battery pack at ambient temperatures up to 40°C. However, the disadvantages are their poor control of the cell-to-cell differential temperatures in the pack and their capability to dissipate high cell generations.

The OBD II and EOBD legislation have significantly increased the number of system components that have to be monitored in order to avoid emissions degradation. Consequently, the algorithm design and the related calibration effort is becoming more and more challenging. Because of decreasing OBD thresholds, the monitoring strategy accuracy, which is tightly related with the components tolerances and the calibration quality, has to be improved. A model-based offline simulation of the monitoring strategies allows consideration of component and sensor tolerances as well as a first calibration optimization in the early development phase. AVL applied and improved a methodology that takes into account this information, which would require a big effort using testbed or vehicle measurements. In many cases a component influence analysis is possible before hardware is available for testbed measurements.