Thermal management and control of battery cells is instrumental for achieving peak battery performance and extending the lifespan of the cells. Typically, battery thermal management refers to maintaining the temperature across the battery cells below a given threshold temperature, say 45°C, in all charge and discharge conditions. Furthermore, the cell temperatures should be as uniform as possible: It is not ideal to have hot and cool cells within a battery even if all cell temperatures remain below the threshold level.

In electric vehicle applications, battery thermal management is usually of the active type: Heat is drawn away from the cells by using a cooling medium (fluid). Typically, the cooling fluid is gaseous (air) or liquid (e.g. water), but some experimentation on so-called phase-change-materials has also been reported in the literature. Among these alternatives, air cooling is desirable due to its simple structure, low cost, and light weight. However, air cooling can be inefficient – resulting in large cell temperatures and temperature differences in the cells – because of the low specific heat capacity of air. Hence, an air-cooling battery thermal management system requires careful optimization of the flow arrangement. This is best achieved by using Computational Fluid Dynamics (CFD).

Systematic CFD-based cooling air flow arrangement optimization can be achieved by the following process:

  1. Development of a reasonably good 3D CFD model for a baseline design (to be improved by optimization);
  2. Simulation and experimental validation of the base case;
  3. Improving the base case design
    • By first principles (laws fluid mechanics),
    • By Mathematical methods;
  4. Validation of the optimal design by experimentation.

Here, a reasonably good CFD model is one which is an efficient compromise between computational complexity and model accuracy. Creating such a model requires expert knowledge of flow modeling, and the model should be validated by physical measurements, as suggested.

To illustrate the computational aspects of the above procedure, CFD-based optimization for an e-bus charging station battery system was carried out. This battery system consists of 16 GWL Power LiFePO4 cells (3.2 V / 20 Ah / <2 mOhm), and two Arctic cooling fans, each rated at 126 m3 per hour, in the enclosure shown above; note that the top is only open for illustration. In steady-state conditions, the highest specified discharge current of 60 A results in net heat generation of 115.2 W within the battery.

Creation of a simplified CFD model involves removing very small geometry details not relevant (or controllable) for air flow motion, and replacing those small details that are relevant for air flow motion with computationally cheaper approximations.

 

Simplified 3D CFD simulation model

 

Fan model calibration: Matching fan RPM to the specified volume flow rate

 

Front panel minor pressure loss coefficient estimation in a virtual wind tunnel

Using these sub-models, a surrogate model optimization yields insights into placement of flow control equipment (baffles) to improve air distribution within the battery compartment. Some results from this optimization study are show below. Computational optimization of the cooling air flow arrangement clearly yields lower average cell temperatures and more uniform temperature distribution across the cells.

 

Midsection temperature (C) – base case

 

Midsection temperature (C) – with flow blocking baffles at optimal locations

 

 

Text by Dr. Eero Immonen, Turku University of Applied Sciences