Abstract
In this paper, we propose a fast simulation method using a lightweight solver for EV batteries. In CPS, the simulation time should be reduced for real-time simulation by minimizing the overhead. In order to reduce the simulation time, the number of simulation steps needs to be decreased by a variable step size. To control the step size, a lightweight solver is introduced to predict the event as soon as possible before actual simulation. Through the prediction, a large step size can be used if there is no event, while a small step size can be used if there is an event. The simulation results show that our prediction-based method reduces the simulation time significantly, compared to the conventional non-prediction-based method.
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NO. 2019R1A2C1009894).
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Kyung, D., Joe, I. (2019). Prediction-Based Fast Simulation with a Lightweight Solver for EV Batteries. In: Silhavy, R., Silhavy, P., Prokopova, Z. (eds) Intelligent Systems Applications in Software Engineering. CoMeSySo 2019 2019. Advances in Intelligent Systems and Computing, vol 1046. Springer, Cham. https://doi.org/10.1007/978-3-030-30329-7_34
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DOI: https://doi.org/10.1007/978-3-030-30329-7_34
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