Fully Automated Global Powertrain Optimization for Electric Truck Using Active Learning Algorithm and Backwards Simulation Approach

2025-01-8536

04/01/2025

Features
Event
WCX SAE World Congress Experience
Authors Abstract
Content
Electric trucks, due to their weight and payload, need a different layout than passenger electric vehicles (EVs). They require multiple motors or multi-speed transmissions, unlike passenger EVs that often use one motor or a single-speed transmission. This involves determining motor size, number of motors, gears, and gear ratios, complicated by the powertrain system’s nonlinearity. The paper proposes using a stochastic active learning approach (Bayesian optimization) to configure the motors and transmissions for optimal efficiency and performance. Backwards simulation is applied to determine the energy consumption and performance of the vehicle for a rapid simulation of different powertrain configurations. Bayesian optimization, was used to select the electric drive unit (EDU) design candidates for two driving scenarios, combined with a local optimization (dynamic programming) for torque split. By optimizing the electric motor and transmission gears, it is possible to reduce energy consumption while also improving vehicle performance. Considering the cost implications, it is shown that a two-speed transmission would be sufficient for a 7.5 t medium-duty electric truck.
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DOI
https://doi.org/10.4271/2025-01-8536
Pages
9
Citation
Chen, B., Wellmann, C., Xia, F., Savelsberg, R. et al., "Fully Automated Global Powertrain Optimization for Electric Truck Using Active Learning Algorithm and Backwards Simulation Approach," SAE Technical Paper 2025-01-8536, 2025, https://doi.org/10.4271/2025-01-8536.
Additional Details
Publisher
Published
Apr 01
Product Code
2025-01-8536
Content Type
Technical Paper
Language
English