Power Hardware in the Loop (PHIL) Simulation of Battery Packs
Authors: Uppaluri Maitri , Pathak Chintan , Kolluri Suryanarayana , Jang Taejin , Dufek Eric J. , Subramanian Venkat R. , Schwartz Daniel T 
Published: February 2020

Testing battery pack configurations for their performance under different dynamic load profiles for grid and electric vehicle applications is expensive. Lowering the cost to understand the response of packs and their control strategies is critical. Power hardware in the loop (PHIL) simulation is widely used in power engineering for system and component testing. A simulator computes the model output in real-time, and then controls a power source to generate a dynamic power output with the expected current and voltage response of the emulated battery pack. This emulated battery pack power output can be coupled to a physical load to perform testing.1 Small time steps are useful to observe the transient response of the emulated battery when there is a sudden change in the load. PHIL simulations can also be useful to study the transient response of controllers and inverters present in the battery management system by connecting these devices to the simulator. PHIL simulations can be used to test the model’s accuracy2, to conduct repeatability experiments and to test battery performance under extreme conditions without compromising on safety.3 It provides an efficient approach to designing and testing new configurations of batteries from data of a single cell without building their real prototypes.

The computational speed required to solve the battery models chosen for PHIL simulations must be fast in order to achieve real-time implementation. Therefore, models chosen for these simulations tend to have less complexity. However, to provide a better understanding of a battery’s response under a specific load profile, it is necessary to use a model that that accurately represents the behavior of a real battery. Real-time simulations for physics-based battery models have been explored in the past.4
,
5
,
6 It is also essential to comprehend how different battery architectures and how aging affects the overall performance.

In literature, equivalent circuit models that describe charge transfer and diffusion dynamics have been used as the main way to capture battery dynamics.7
,
8 We first show results generated using SIMULINK equivalent circuit models that are compiled to run fast on our Opal-RT real-time simulator. The Opal-RT simulator drives an AMETEK 40 kW power supply with feedback to the model, enabling an emulated battery pack response capable of 10ms dynamic control. We evaluate pack configurations with physical parameters that correspond to either LCO or NMC 18650 cells. Overall, this represents a 400V, 10kW pack. A programmable load is used to modulate the emulated battery pack and monitor its dynamic response. We further explore implementing single particle models with our PHIL simulations to help understand the internal dynamics of the cell captured by these models. Physics-based models have rarely been used for PHIL.

Our results show that the PHIL emulation of the battery pack meets the expectations for the battery dynamics. However, we observe high frequency noise in the output signal of the emulated battery pack. Because this level of noise is not a trait present in real battery packs, we are exploring signal processing methods to filter and remove it, while retaining the essential dynamics of the emulated battery pack.

References

A. Viehweider, G. Lauss, and L. Felix, Simul. Model. Pract. Theory, 19, 1699–1708 (2011).

Y. He, W. Liu, and B. J. Koch, J. Power Sources, 195, 2969–2974 (2010).

H. Dai, X. Zhang, X. Wei, Z. Sun, J. Wang, and F. Hu, Electr. Power Energy Syst., 52, 174–184 (2013).

P. W. C. Northrop, V. Ramadesigan, S. De, and V. R. Subramanian, J. Electrochem. Soc., 158, A1461 (2011).

V. R. Subramanian, V. Boovaragavan, V. Ramadesigan, and M. Arabandi, J. Electrochem. Soc., 156, A260 (2009).

P. W. C. Northrop, V. Suthar, Bharatkumar Ramadesigan, S. Santhanagopalan, R. D. Braatz, and V. R. Subramanian, J. Electrochem. Soc., 161, E3149 (2014).

J. V. Barreras, C. Fleischer, A. E. Christensen, M. Swierczynski, E. Schaltz, S. J. Andreasen, and D. U. Sauer, IEEE Trans. Ind. Appl., 52, 5086–5099 (2016).

H. Song, T. Kim, J. Jeong, B. Kim, D. Shin, B. Lee, and H. Heo, IET Power Electron., 6, 417–424 (2013).