SAE Update - February 2025 - 47

PUBLICATIONS
In addition, the stator core
adopted annealing after the press
lamination to facilitate the growth
of crystal grains, enabling a
reduction in power losses
compared to the previous
generation motor. A fractionalpitch
winding was adopted, and
the amount of insulating resin
was reduced by using foaming
insulating paper that fixes the
windings with the stator core. As
a result, the end winding height
was reduced compared to the
previous model. Through these
actions, this motor achieves a
reduction of 26% in end winding
height and an increase of 23% in
power density, compared to the
previous model. "
Hydrogen Internal
Combustion Engine
Strategies for Heavy-Duty
Transportation: Engine and
System Level Perspective
Authors: Rafael Sari, Ashish
Shah, Praveen Kumar, David
Cleary, Sandeep Rairikar,
Shailesh Balkrishna Sonawane,
Sukrut S. Thipse
Excerpt: " Hydrogen internal
combustion engines (H2ICE) offer
a cost-effective solution to
decarbonize transport by
combining a lower carbon
UPDATE
intensity fuel with mature and established internal
combustion engine technology. While vehicles running
with hydrogen have been demonstrated over the years,
this fuel's physical and chemical properties require
modifications and upgrades on the vehicle from an
engine and system-level perspective. In addition,
market-specific regulatory and economic factors can
also constrain the realization of optimal hydrogen
powertrain architectures. Therefore, this paper reviews
the impact of hydrogen use on combustion, injection, air
management, and after-treatment systems, indicating
the different strategies used to enable effective H2ICE
strategies from an efficiency, cost, and safety
standpoint. "
STATE-OF-THE-ART
JOURNAL ARTICLES
How Drivers Lose
Control of the Car
Authors: Giampiero R. M.
Mastinu, Giorgio Previati,
Fabio Della Rossa, Massimiliano Gobbi, Marco Fainello
Excerpt: " After a severe lane change, a wind gust, or
another disturbance, the driver might be unable to
recover the intended motion. Even though this fact is
known by any driver, the scientific investigation and
testing on this phenomenon is just at its very
beginning, as a literature review, focusing on SAE
Mobilus® database, reveals. We have used different
mathematical models of car and driver for the basic
description of car motion after a disturbance.
Theoretical topics such as nonlinear dynamics,
February 2025
47
ARTICLE INFO
Article ID: 10-08-02-0016
© 2024 SAE International
doi:10.4271/10-08-02-0016
Multi-Output Physically Analyzed
Neural Network for the Prediction of
Tire-Road Interaction Forces
Raffaele Marotta,1 Salvatore Strano,1 Mario Terzo,1 and Ciro Tordela1
1UNINA: University of Naples Federico II, Industrial Engineering, Italy
Abstract
This article introduces an innovative method for predicting tire-road interaction forces by exclusively
utilizing longitudinal and lateral acceleration measurements. Given that sensors directly measuring
these forces are either expensive or challenging to implement in a vehicle, this approach fills a crucial
gap by leveraging readily available sensor data. Through the application of a multi-output neural
network architecture, the study focuses on simultaneously predicting the longitudinal, lateral, and
vertical interaction forces exerted by the rear wheels, specifically those involved in traction.
Experimental validation demonstrates the efficacy of the methodology in accurately forecasting
tire-road interaction forces. Additionally, a thorough analysis of the input-output relationships
elucidates the intricate dynamics characterizing tire-road interactions. This research underscores
the potential of neural network models to enhance predictive capabilities in vehicle dynamics,
offering insights that are valuable for various applications in automotive engineering and
control systems.
History
Received: 10 Jan 2024
Revised:
31 Mar 2024
Accepted: 24 Apr 2024
e-Available: 08 May 2024
Keywords
Tire-road interaction forces,
Artificial intelligence, Deep
learning, Neural network,
Virtual sensor
Citation
Marotta, R., Strano, S., Terzo,
M., and Tordela, C., " MultiOutput
Physically Analyzed
Neural Network for the
Prediction of Tire-Road
Interaction Forces, " SAE Int.
J. Veh. Dyn., Stab., and NVH
8(2):285-308, 2024,
doi:10.4271/10-08-02-0016.
ISSN: 2380-2162
e-ISSN: 2380-2170
285
SAE Update - February 2025

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