Abstract

In order to evaluate a machine learning model that estimates the voltage of a lithium-ion battery, we built a new accuracy evaluation metric based on the Arrhenius law, and tested it to see if it could be used to judge the relative merits of machine learning models. Two types of machine learning model were selected. One was a type that performed discrete estimates in regions where data are not available, while the other was capable of performing continuous estimation. Then, discrete temperature data based on floating car data collected from electric vehicles were generated, and the models were trained and evaluated. The results confirmed that the commonly used evaluation metric for evaluating deviation from test data showed no clear differences in evaluation results between the two model types. On the other hand, the authors’ new evaluation metric based on the Arrhenius law showed clearer differences in the evaluations of the two models. Implementing this metric made it possible to evaluate a machine learning model even with limited collected data, thereby advancing the practical application of battery voltage estimation technology using machine learning models.

References

1. (1) Chian, T. Y., Wei, W. L. J., Ze, E. L. M., Ren, L. Z., Ping, Y. E., Abu Bakar, N. Z., Faizal, M., Sivakumar, S.: A Review on Recent Progress of Batteries for Electric Vehicles, International Journal of Applied Engineering Research, Vol. 14, No. 24, p. 4441-4461, (2019)
2. (2) Vidal, C., Gross, O., Gu, R., Kollmeyer, P., Emadi, A.: xEV Li-Ion Battery Low-Temperature Effects—Review, IEEE Transactions on Vehicular Technology, Vol. 68, No. 5, p. 4560-4572, (2019)
3. (3) Meyer, N., Whittal, I., Christenson, M., Loiselle-Lapointe, A.: The Impact of Driving Cycle and Climate on Electrical Consumption & Range of Fully Electric Passenger Vehicles, EVS26 – International Battery, Hybrid and Fuel Cell Electric Vehicle Symposium, (2012)
4. (4) Li, S., Li, J., He, H., Wang, H.: Lithium-ion battery modeling based on Big Data, Energy Procedia, Vol. 159, p. 168–173, (2019)
5. (5) Tran, M.-K., Panchal, S., Chauhan, V., Brahmbhatt, N., Mevawalla, A., Fraser, R., Fowler, M.: Python-Based Scikit-Learn Machine Learning Models for Thermal and Electrical Performance Prediction of High-Capacity Lithium-Ion Battery, Int. J. Energy Res., Vol. 46, Issue 2, p. 786-794, (2022)
6. (6) Heinrich, F., Klapper, P., Pruckner, M.: A comprehensive study on battery electric modeling approaches based on machine learning, Energy Informatics, Vol. 4, Article No. 17, (2021)
7. (7) National Institute of Advanced Industrial Science and Technology: Machine Learning Quality Management Guideline, 2nd Edition, (2022)
8. (8) https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeRegressor.html#sklearn.tree.DecisionTreeRegressor, 2021/1/15
9. (9) Rangarajan, S. P., Barsukov, Y., Mukherjee, P. P.: In Operando Impedance Based Diagnostics of Electrode Kinetics in Li-Ion Pouch Cells, Journal of The Electrochemical Society, Vol. 166, No. 10, A2131-A2141, (2019)

Author (organization or company)

• Takuma KAWAHARA（Innovative Research Excellence Power Unit & Energy）
• Yuki SATO（TechnoPro, Inc. TechnoPro Design Company）
• Koji SATO（TechnoPro, Inc. TechnoPro Design Company）

Publication Information

Article of Honda R&D Technical Review Vol.34 No.2 (35 - 41)

PDF

Citation Information