HILS System with Time Series Forecasting by Machine Learning Technique

EasyChair Preprint 13481

Abstract

HILS can be regarded as a kind of co-simulation technique that controls hardware components by real-time simulation based on measured data, and is widely used in automobile development. HILS needs to use the values from one step earlier when simulating with the data measured in the hardware section. In addition, delays occur during measurement due to communication between hardware and filtering of measurement signals. These factors reduce the accuracy and stability of HILS analysis. In this study, HILS based on time series forecasting using Long Short-Term Memory networks was investigated to reduce the effect of this delay. Experiments were conducted to evaluate the accuracy of the proposed method. In our conventional HILS, the analysis was performed using the measured value one step before. When the step time is extended up to 10 ms, the result differed from the result when the step size was set to 2 ms in conventional HILS. On the other hand, almost similar result was obtained by using of the time series forecasting even when the step time was set to 10 ms.

Keyphrases: hardware-in-the-loop simulation, machine learning, real-time simulation, vehicle dynamics

@booklet{EasyChair:13481,
  author    = {Taichi Shiiba and Takahiro Shimizu},
  title     = {HILS System with Time Series Forecasting by Machine Learning Technique},
  howpublished = {EasyChair Preprint 13481},
  year      = {EasyChair, 2024}}