Data-Based Modeling of Power-Split Hybrids Using Cascaded Neural Networks IFS, University of Stuttgart

Format:

Book

Conference/Event

Author/Creator:

Frey, Markus, author.

Contributor:

Grill, Michael

Itzen, Dirk

Kulzer, André Casal

Yang, Qirui

Conference Name:

2025 Sustainable Energy & Powertrains (2025-11-25 : Stuttgart, Germany)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2025

Summary:

Power-split hybrid powertrains represent one of the most advanced and complex types of powertrain systems. The combination of multiple energy sources and power paths offers great potential but results in complex interactions that require improved strategies for optimal efficiency and emission control. The development and optimization of such operating strategies typically involve algorithms that demand fast computational environments. Traditional high-accuracy numerical simulations of such a complex system are computationally expensive, limiting their applicability for extensive iterative optimizations and real-time applications. This paper introduces a data-based approach designed specifically to address this challenge by efficiently modeling the dynamic behavior of power-split hybrid powertrains using cascaded neural networks.Cascaded neural networks consist of interconnected subnetworks, each specifically trained to represent individual drivetrain components or subsystems. This modular structure allows the networks to cover a larger parameter space within each subnetwork effectively and enables the generation of larger and more targeted training datasets using simpler, separated numerical models. However, this approach is also challenging due to the need for step prediction, potential error accumulation and the requirement for feedback loops. Assumptions are necessary to overcome these limitations and improve the model predictions. The focus is on the possible connection to optimization algorithms with targeting key parameters such as battery state of charge, vehicle velocity, and emissions profiles.By significantly reducing computational demands compared to complex and detailed simulations, the cascaded data-driven models state a promising basis for real-time applications, enabling sophisticated control strategies tailored to the complexities inherent to hybrid electric vehicles. This method accelerates the optimization process, enhances adaptability and scalability of control strategies, and significantly contributes to the development of cleaner, more efficient vehicles

Notes:

Vendor supplied data

Publisher Number:

2025-01-0522

Access Restriction:

Restricted for use by site license