A Latent Space Framework for Modeling Transient Engine Emissions Using Joint Embedding Predictive Architectures University of Kaiserslautern-Landau

Format:

Book

Conference/Event

Author/Creator:

Sundaram, Ganesh, author.

Gehra, Tobias, author.

Ulmen, Jonas, author.

Heubaum, Mirjan, author.

Görges, Daniel, author.

Günthner, Michael, author.

Conference Name:

WCX SAE World Congress Experience (2026-04-14 : Detroit, Michigan, United States)

Language:

English

Subjects (All):

Control systems.

Exhaust emissions.

Emissions control.

Emissions measurement.

Hybrid electric vehicles.

Machine learning.

Local Subjects:

Control systems.

Exhaust emissions.

Emissions control.

Emissions measurement.

Hybrid electric vehicles.

Machine learning.

Physical Description:

1 online resource

Place of Publication:

Warrendale, PA SAE International 2026

Summary:

Accurately modeling and controlling vehicle exhaust emissions, particularly during highly transient events such as rapid acceleration, is crucial for meeting stringent environmental regulations and optimizing modern powertrain systems. While conventional data-driven modeling methods, such as Multilayer Perceptrons (MLPs) and Long Short-Term Memory (LSTM) networks, have improved upon earlier phenomenological or physics-based models, they often struggle to capture the complex nonlinear dynamics of emission formation. These monolithic architectures attempt to learn from all available data, which increases their sensitivity to dataset variability. They often require increasingly deep and complex architectures to improve performance, thereby limiting their practical utility. This paper introduces a novel approach that overcomes these limitations by modeling emission dynamics in a structured latent space. Using a rich dataset combining real-world driving data from a Portable Emission Measurement System (PEMS) with high-frequency hardware-in-the-loop test bench measurements, a Joint Embedding Predictive Architecture (JEPA) is leveraged. This framework learns to abstract away irrelevant information and encode only the key factors governing emission behavior into a compact, robust latent representation. The resulting model demonstrates superior data efficiency and predictive accuracy across diverse transient regimes, exhibiting stronger generalization than the high-performing LSTM baseline. Structured pruning and post-training quantization are applied to the JEPA framework to enhance the model's suitability for real-world deployment. This combined strategy significantly reduces the model's computational footprint, minimizing inference time and memory demand, with only a marginal impact on accuracy. This yields a highly accurate model well suited to on-board implementation of advanced control strategies, such as model predictive control or model-based reinforcement learning, in both conventional and hybrid electric powertrains. The results indicate a clear pathway toward more efficient and robust emission control systems for next-generation vehicles

Notes:

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