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Abstract The connectivity of modern vehicles allows for the monitoring and analysis of a large amount of sensor data from vehicles during their normal operations. In recent years, there has been a growing interest in utilizing this data for the purposes of predictive maintenance. In this paper, a multi-label transfer learning approach is proposed using 14 different pretrained convolutional neural networks retrained with engine simulation data to predict the failure conditions of a selected set of engine components. The retrained classifier networks are designed such that concurrent failure modes of an exhaust gas recirculation, compressor, intercooler, and fuel injectors of a four-cylinder diesel engine can be identified. Time-series simulation data of various failure conditions, which include performance degradation, are generated to retrain the classifier networks to predict which components are failing at any given time. The test results of the retrained classifier networks show that the overall classification performance is good, with the normalized value of mean average precision varying from 0.6 to 0.65 for most of the retrained networks. To the best of the authors’ knowledge, this work represents the first attempt to characterize such time-series data utilizing a multi-label deep learning approach. 

Hybrid electric vehicles can achieve better fuel economy than conventional vehicles by utilizing multiple power sources. While these power sources have been controlled by rule-based or optimization-based control algorithms, recent studies have shown that machine learning-based control algorithms such as online Deep Reinforcement Learning (DRL) can effectively control the power sources as well. However, the optimization and training processes for the online DRL-based powertrain control strategy can be very time and resource intensive. In this paper, a new offline–online hybrid DRL strategy is presented where offline vehicle data are exploited to build an initial model and an online learning algorithm explores a new control policy to further improve the fuel economy. In this manner, it is expected that the agent can learn an environment consisting of the vehicle dynamics in a given driving condition more quickly compared to the online algorithms, which learn the optimal control policy by interacting with the vehicle model from zero initial knowledge. By incorporating a priori offline knowledge, the simulation results show that the proposed approach not only accelerates the learning process and makes the learning process more stable, but also leads to a better fuel economy compared to online only learning algorithms. 

This paper proposes a 300 kW 24-slot/10-pole 6-phase stator-shifted fractional-slot concentrated winding spoke-type ferrite permanent magnet machine for electric truck applications. The proposed motor consists of a stator with dual three-phase windings positioned 75 degrees apart to reduce higher-order MMF harmonic order, and a rotor with an inexpensive and high-resistance ferrite permanent magnet in the spoke configuration. The simulated result of the stator-shifted machine is compared with a fabricated stator-shifted machine, and the results show good agreement with each other. To further reduce the torque ripple from 2.5 to 0.9% while maintaining a high maximum torque of 2980 Nm, circular voids with a diameter of 11 mm are embedded in the rotor. The proposed motor is evaluated for irreversible demagnetization, mechanical and thermal stability, and fault tolerant ability. To assess the proposed motor performance, the electric truck simulation model is constructed using MATLAB/Simulink and used to compare with the reported 12-slot/10-pole rare-earth permanent magnet-based machine. Compared to a previously reported six-phase rare-earth permanent magnet based flat-type machine, the proposed motor can save 4.3 kWh of energy with a USD 2512 lower cost while retaining a similar motor performance.