| ELECTRIC VEHICLE BATTERY STATE OF CHARGE PREDICTION
BASED ON GRAPH CONVOLUTIONAL NETWORK |
| Geunsu Kim 1, Soohyeok Kang 1, Gyudo Park 1, Byung-Cheol Min 2 |
1Hyundai Kefico Corp, Hyundai Motor Group
2Purdue University |
|
|
|
|
|
|
|
| ABSTRACT |
|
The state of charge (SoC) of a vehicle battery can tend to vary depending on the driver’s driving patterns and
circumstances. To accurately predict the SoC level, it is necessary to consider various circumstances. That is why traditional
statistical models may not be sufficient. To address this issue, recurrent neural network (RNN) models have been proposed for
time series prediction tasks due to their superior performance. In this paper, we propose a new approach using a graph
convolutional network (GCN)-based model that shows better performance than RNN-based models. The GCN requires an
adjacency matrix as input, which represents the relationships between variables. We set this matrix to be learnable during model
training rather than predefined. We also use two different adjacency matrices: one with variables as nodes, and the other with
timestamps as nodes, to enhance the interpretability of the data by considering different elements as nodes. This allows the
model to interpret the data from different perspectives. The proposed GCN model was tested using real-world electric vehicle
(EV) data and demonstrated improved performance compared to RNN-based baselines. In addition, the GCN model has
advantage of being able to clearly express the relationships between variables in a graph, improving interpretabilty. |
| Key Words:
Electric vehicle (EV), State of charge (SoC), Time series prediction, Neural network, Graph convolutional
network |
|
|
|
Corresponding Author.
Byung-Cheol Min , Email. 
Preview
Full text via DOI
Download Citation 
Print 
