Parameterizing External Factors Influencing E-Motor Consumption Mercedes-Benz R&D India Pvt Limited

Format:

Book

Conference/Event

Author/Creator:

Kelkar, Kshitij, author.

Contributor:

Kanakannavar, Rohit

Conference Name:

Symposium on International Automotive Technology (2026) (2026-01-28 : Pune, India)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2026

Summary:

In recent times, a standard driving cycle is an excellent way to measure the electric range of EVs. This process is standardized and repeatable; however, it has some drawbacks, such as low active functions being tested in a controlled environment. This sometimes causes huge variations in the range between driving cycles and actual on-road tests. This problem of variation can be solved by on-road testing and testing a vehicle for customer-based velocity cycles. On-road measurement may be high on active functions while testing, which may give an exact idea of real-world consumption, but the repeatability of these test procedures is low due to excessive randomness. The repeatability of these cycles is low due to external factors acting on the vehicle during on-road testing, such as ambient temperature, driver behavior, traffic, terrain, altitude, and load conditions. No two measurements can have the same consumption, even if they are done on the same road with the same vehicle, due to the influence of the above-mentioned external factors. The current paper will portray a machine learning-based methodology to parameterize the external factors affecting e-motor consumption. By parameterizing these factors, on-road test results are normalized and further used for comparative studies. The paper also takes us through the process of data collection for this study, the parameterization process of external factors using ML models, for different driving scenarios and ambient temperature ranges. The ML models are developed in a MATLAB environment and can be reproduced in any other tool. Merits and demerits of each ML model are discussed along with ways and means to mitigate each external factor, which will make the testing procedure more robust and reliable. Thus, it helps in making automobiles more energy efficient

Notes:

Vendor supplied data

Publisher Number:

2026-26-0175

Access Restriction:

Restricted for use by site license