Electric vehicles are becoming more widely seen on roadways across the world, and they will only become more numerous in coming years. However, while electric vehicles have already come a long way, they still need further development to increase range and to charge quickly before they can become truly widespread. The battery, which is the most vital part of an electric vehicle, is also the most complex and subject to challenges addressing the roadblocks to wider distribution.

Temperature plays an important role in how well a battery will perform. If a battery is too hot, its lifespan is reduced due to chemical processes that alter its cells and accelerate its aging. If it is too cold, its performance is limited. Therefore, engineers must be able to predict the thermal performance of an electric vehicle’s battery pack as well as its electrical performance.

Doing so with the real hardware is not only challenging but expensive. Many issues exist regarding the physical testing of batteries. First of all, a battery’s electrical and thermal behavior is transient, and it may take several hours for the battery to reach a stabilized state, resulting in slow, drawn-out and costly testing. Batteries also change their behavior depending on temperature, age and state of charge. Temperature can be directly measured but age and state of charge must be estimated. Batteries display different thermal and electrical behavior at the beginning and end of life.

In addition, due to the transient nature of a battery’s thermal performance, multiple drive test cycles are needed to predict the impact of a thermal system on battery life. All of these factors make physical testing expensive and time-consuming.

Simulation, however, does not suffer from these issues. As an example of how simulation makes battery testing faster, easier and less expensive, a well-known automotive manufacturer developed a methodology to generate fast-running virtual battery models. Two models were generated: an electrical model and a thermal model.

The electrical model was generated to work with PowerTHERM and designed to predict both the dynamic changes in battery voltage and the heat generation on arbitrary drive cycles. The thermal model was implemented in both PowerTHERM and PowerFLOW. PowerTHERM was used for the conduction and radiation simulation, while PowerFLOW was used to simulate cooling airflow and coolant depending on the details of the cooling system. A reduced order model of the components in the battery pack was also generated for the purpose of quickly exporting the thermal and electrical models to system tools.

The researchers’ methodology was able to work with full-sized packs consisting of dozens of batteries. The models generated were both fast and accurate, allowing the automotive manufacturer’s thermal department to complete analysis of long-duration test cycles, including lifetime thermal predictions. The methodology was part of the company’s focus on developing better virtual engineering processes, and successfully replaced physical testing while saving time, money, and labor and bringing a high-quality electric vehicle to the market in time.