Problem Statement
1. Problem Overview
The objective of this project is to develop a highly accurate thermal estimation system for a Permanent Magnet Synchronous Motor (PMSM) deployed on an automotive test bench. In commercial electric vehicles, directly measuring the internal rotor temperature (`pm`) and various stator temperatures is economically prohibitive and structurally unreliable. However, precise real-time tracking of these thermal parameters enables automotive manufacturers to implement optimal control strategies, maximize the motor's power capability, and safely minimize manufacturing materials.
This challenge requires mapping electrical, mechanical, and ambient sensor readings to four distinct thermal target variables. Because the data represents sequential, real-world driving cycles over multiple hours, a key hurdle is ensuring robust generalization. To prevent future data leakage, models will be evaluated on entirely unseen driving cycles with later session timelines. Participants are tasked with solving a multi-output regression problem where they can choose to build unified multi-output models or design an ensemble of separate sub-models for each target. Additionally, handling cropped data segments containing missing values stands as a core, open challenge of this task.
2. Training Data
The training data is provided in a single tabular file, `emtpmsm train.csv`. All physical recordings are sequentially sampled at a constant rate of 2 Hz. The dataset includes the following columns:
Identifiers:
- `ID`: A unique identifier for each observation row.
- `profile_id`: A session identifier indicating distinct, continuous measurement cycles (ranging from 1 to 6 hours long). Each profile represents a unique driving cycle (e.g., hand-designed random walks in the speed-torque plane).
Input Features:
- `u_d`, `u_q`: Voltages in d/q-coordinates resulting from the motor control strategy.
- `i_d`, `i_q`: Currents in d/q-coordinates.
- `motor_speed`: The actual rotational speed achieved by the motor.
- `torque`: The resulting torque achieved by the motor system.
- `ambient`: The ambient temperature surrounding the test bench.
- `coolant`: The temperature of the cooling fluid.
Target Features (Outputs):
- `stator_winding`: Temperature of the stator winding.
- `stator_tooth`: Temperature of the stator tooth.
- `stator_yoke`: Temperature of the stator yoke.
- `pm`: Permanent magnet temperature (representing the internal rotor temperature).
Note on Data Quality: The dataset contains some cropped columns manifesting as `NaN` values. Developing an effective strategy to handle, impute, or bypass these missing values is left entirely open to the participants and constitutes a vital aspect of the feature engineering process.
3. Problem Requirements
- Input: Multi-variable sensor signals containing electrical, mechanical, and thermal ambient states across various continuous driving cycles.
- Output: Four continuous numerical predictions representing the estimated temperatures for `stator_winding`, `stator_tooth`, `stator_yoke`, and `pm`.
- Available Packages: Default Google Colab environment packages only. No external library installations are permitted.
- Internet Access & Dataset Constraints: Completely Offline — you must not upload external files or download data from the Internet during runtime. Participants are strictly prohibited from using alternative or auxiliary datasets alongside the provided files.
- Modeling Constraints: While the baseline solution processes rows as independent cross-sectional data, participants are free to implement time-series architectures (e.g., rolling windows, lagged features, or recurrent networks) if utilizing the 2 Hz sequential structure yields a reduction in validation loss.
- Evaluation Design: To reflect real-world deployment, the evaluation test set consists entirely of unseen `profile_id` sessions whose session IDs are strictly larger than those in the training set, enforcing a strict chronological split to prevent future data leakage.
4. Evaluation Metrics
The predictive performance of the regression models on the unseen test profiles will be evaluated using:
Root Mean Squared Error (RMSE): Measures the average magnitude of the residuals between the predicted temperatures and the true physical measurements. Minimizing the RMSE across all four distinct target categories ensures that the system yields reliable thermal boundaries for motor safety optimization.
(Kirchgässner, W., Wallscheid, O., & Böcker, J. (2021). Electric Motor Temperature. Kaggle. https://www.kaggle.com/dsv/2161054, DOI: 10.34740/KAGGLE/DSV/2161054)