TY - GEN
T1 - Machine Learning Methods for Temperature Prediction of Autonomous Underwater Vehicles’ Battery Pack
AU - Li, Bo
AU - Wang, Mou
AU - Mao, Zhaoyong
AU - Song, Baowei
AU - Tian, Wenlong
AU - Sun, Qixuan
AU - Wang, Wenxin
N1 - Publisher Copyright:
© 2023, Beijing HIWING Sci. and Tech. Info Inst.
PY - 2023
Y1 - 2023
N2 - Battery pack layout is of great significance to enhance the thermal behavior of autonomous underwater vehicles(AUVs). Because battery pack layout is a high-dimensional and nonlinear problem, there is few research on this topic at present. In order to more accurately predict the maximum temperature (MT) and temperature difference (TD) for different battery pack layouts, two machine learning surrogate models were proposed in this paper, including support vector machine (SVM) and the feed-forward fully-connected neural network (FFN). Tens of thousands of battery pack layout scheme databases were obtained through the finite element method. Then, the machine learning based methods were used to predict the MT and TD of the battery pack. The simulation results of this paper showed that both FFN and SVM have low mean absolute percentage error (MAPE) and mean square error (MSE), which means FFN and SVM can accurately predict the temperature. Meanwhile, it can be found that SVM has more advantage in small-scale problem. The methods in this paper can provide guidance for temperature prediction of AUV’s battery pack layout.
AB - Battery pack layout is of great significance to enhance the thermal behavior of autonomous underwater vehicles(AUVs). Because battery pack layout is a high-dimensional and nonlinear problem, there is few research on this topic at present. In order to more accurately predict the maximum temperature (MT) and temperature difference (TD) for different battery pack layouts, two machine learning surrogate models were proposed in this paper, including support vector machine (SVM) and the feed-forward fully-connected neural network (FFN). Tens of thousands of battery pack layout scheme databases were obtained through the finite element method. Then, the machine learning based methods were used to predict the MT and TD of the battery pack. The simulation results of this paper showed that both FFN and SVM have low mean absolute percentage error (MAPE) and mean square error (MSE), which means FFN and SVM can accurately predict the temperature. Meanwhile, it can be found that SVM has more advantage in small-scale problem. The methods in this paper can provide guidance for temperature prediction of AUV’s battery pack layout.
KW - Autonomous underwater vehicle
KW - Battery pack layout
KW - Feed-forward fully-connected neural network
KW - Support vector machine
KW - Temperature prediction
UR - http://www.scopus.com/inward/record.url?scp=85151061612&partnerID=8YFLogxK
U2 - 10.1007/978-981-99-0479-2_295
DO - 10.1007/978-981-99-0479-2_295
M3 - 会议稿件
AN - SCOPUS:85151061612
SN - 9789819904785
T3 - Lecture Notes in Electrical Engineering
SP - 3204
EP - 3215
BT - Proceedings of 2022 International Conference on Autonomous Unmanned Systems, ICAUS 2022
A2 - Fu, Wenxing
A2 - Gu, Mancang
A2 - Niu, Yifeng
PB - Springer Science and Business Media Deutschland GmbH
T2 - International Conference on Autonomous Unmanned Systems, ICAUS 2022
Y2 - 23 September 2022 through 25 September 2022
ER -