Machine learning modeling for fuel cell-battery hybrid power system dynamics in a Toyota Mirai 2 vehicle under various drive cycles
| Autor: | Adithya Legala, Matthew Kubesh, Venkata Rajesh Chundru, Graham Conway, Xianguo Li |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2024 |
| Předmět: |
Artificial neural network (ANN)
Proton exchange membrane fuel cell (PEMFC) Fuel cell electric vehicle (FCEV) Fuel cell-battery electric-electric hybrid power system Data-based models Lithium-ion battery (LiB) Electrical engineering. Electronics. Nuclear engineering TK1-9971 Computer software QA76.75-76.765 |
| Zdroj: | Energy and AI, Vol 17, Iss , Pp 100415- (2024) |
| Druh dokumentu: | article |
| ISSN: | 2666-5468 |
| DOI: | 10.1016/j.egyai.2024.100415 |
| Popis: | Electrification is considered essential for the decarbonization of mobility sector, and understanding and modeling the complex behavior of modern fuel cell-battery electric-electric hybrid power systems is challenging, especially for product development and diagnostics requiring quick turnaround and fast computation. In this study, a novel modeling approach is developed, utilizing supervised machine learning algorithms, to replicate the dynamic characteristics of the fuel cell-battery hybrid power system in a 2021 Toyota Mirai 2nd generation (Mirai 2) vehicle under various drive cycles. The entire data for this study is collected by instrumenting the Mirai vehicle with in-house data acquisition devices and tapping into the Mirai controller area network bus during chassis dynamometer tests. A multi-input - multi-output, feed-forward artificial neural network architecture is designed to predict not only the fuel cell attributes, such as average minimum cell voltage, coolant and cathode air outlet temperatures, but also the battery hybrid system attributes, including lithium-ion battery pack voltage and temperature with the help of 15 system operating parameters. Over 21,0000 data points on various drive cycles having combinations of transient and near steady-state driving conditions are collected, out of which around 15,000 points are used for training the network and 6,000 for the evaluation of the model performance. Various data filtration techniques and neural network calibration processes are explored to condition the data and understand the impact on model performance. The calibrated neural network accurately predicts the hybrid power system dynamics with an R-squared value greater than 0.98, demonstrating the potential of machine learning algorithms for system development and diagnostics. |
| Databáze: | Directory of Open Access Journals |
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