The energy management strategy has a great influence on the fuel economy of hybrid electric vehicles, and the equivalent consumption minimization strategy (ECMS) has proved to be a useful tool for the real-time optimal control of Hybrid Electric Vehicles (HEVs). However, the adaptation of the equivalent factor poses a major challenge in order to obtain optimal fuel consumption as well as robustness to varying driving cycles. In this paper, an adaptive-ECMS based on driving pattern recognition (DPR) is established for hybrid electric vehicles with continuously variable transmission. The learning vector quantization (LVQ) neural network model was adopted for the on-line DPR algorithm. The influence of the battery state of charge (SOC) on the optimal equivalent factor was studied under different driving patterns. On this basis, a method of adaptation of the equivalent factor was proposed by considering the type of driving pattern and the battery SOC. Besides that, in order to enhance drivability, penalty terms were introduced to constrain frequent engine on/off events and large variations of the continuously variable transmission (CVT) speed ratio. Simulation results showed that the proposed method efficiently improved the equivalent fuel consumption with charge-sustaining operations and also took into account driving comfort.

This empirical research creates and assesses a community interaction open-source learning framework. The framework established an efficient open-source learning environment for engineering courses to develop undergraduates’ sustainability competencies. The teaching practice of the framework was designed into three stages: course preparation, theory lecture, and project practice. In the teaching practice, community interaction elements were embedded, including community/student two-way selection, systemic teaching and difficulty discussion, expert/student negotiation on teaching forms, teacher/expert coordination on teaching contents, and expert/student two-way feedback on schedules. The interaction elements between students, teachers, and experts enhanced the effectiveness of open-source learning in engineering courses. The experimental results showed that the students exhibited a positive attitude and high participation in the learning procedure and reported a sense of achievement in the project practice. The open-source learning framework significantly improved systemic thinking, conceptual understanding, interdisciplinary collaboration and professional skills. It enhanced students’ key sustainability competencies and laid the foundation for them to become expertise-based innovators and open-source community contributors.

Hybrid electric vehicles equipped with continuously variable transmission show dramatic improvements in fuel economy and driving performance because they can continuously adjust the operating points of the power source. This article proposes an optimal control strategy for continuously variable transmission–based hybrid electric vehicles with a pre-transmission parallel configuration. To explore the fuel-saving potential of the given configuration, a ‘control-oriented’ quasi-static vehicle model is built, and dynamic programming is adopted to determine the optimal torque split factor and continuously variable transmission speed ratio. However, a single-criterion cost function will lead to undesirable drivability problems. To tackle this problem, the main factors affecting the driving performance of a continuously variable transmission–based hybrid electric vehicle are studied. On that basis, a multicriterion cost function is proposed by introducing drivability constraints. By varying the weighting factors, the trade-off between fuel economy and drivability can be evaluated under a predetermined driving cycle. To validate the effectiveness of the proposed method, simulation experiments are performed under four different driving cycles, and the results indicate that the proposed method greatly enhanced the drivability without significantly increasing fuel consumption. Compared to a single-criterion cost function, the use of multiple criteria is more representative of real-world driving behaviour and thus provides better reference solutions to evaluate suboptimal online controllers.

Stress imbalance significantly affects the performance of a press-pack insulated gate bipolar transistor (IGBT). Time-variant loads and conditions lead to the stress fluctuations, exacerbating the impacts. The conventional reliability optimization faces efficiency barriers due to the nested time-variant reliability analysis and design optimization. In this paper, a time-variant reliability optimization approach for press-pack IGBTs is proposed to address the efficiency issue of the IGBT reliability optimization. The performance functions of the maximum and typical stresses are formulated as the optimization objective and constraint. A time-variant reliability optimization model is formulated considering the stress balance reliability degradation within the service cycle. A decoupling algorithm is proposed to transform the nested optimization into a sequential iteration of static reliability optimization and time-variant reliability analysis. The reliability analysis utilizes the performance function continuity in the time domain to reduce the evaluations for the most likelihood points, thereby enhancing efficiency. Numerical and experimental results on an actual IGBT demonstrate the accuracy of the stress balance performance analysis. The time-variant reliability optimization based on the performance functions improves the stress balance performance by 16.3% and meets the reliability requirements within the service cycle. Compared with the conventional double-loop approach, the difference between the solution of the proposed approach with the reference solution is 0.4%, and the efficiency is 334 times that of the double-loop approach. The performance advantages in accuracy and efficiency exhibit the application potential of this approach.