An efficient, compact, and lightweight three-phase AC-DC power factor correction (PFC) converter becomes a necessity for the On-board chargers (OBCs) of the electric vehicle (EV) in conventional grid-to-vehicle (G2V) and vehicle-to-grid (V2G) charging scenarios. The commercially available OBCs have very limited power density despite the moderate efficiency under specific power levels.
In this dissertation, the integrated triangular current mode (iTCM) control is implemented to improve the power density (kW/L) and specific power (kW/kg) of the three-phase PFC converter (the front stage of OBCs) while maintaining high efficiency. Zero voltage switching (ZVS) turn-on is realized in the iTCM control with a higher switching frequency to reduce the LCL filter size without sacrificing the efficiency.
By adding an LC branch between the bridge leg and the mid-point of the DC link, the high-frequency and low-frequency current is split to minimize the inductor loss and to achieve a better inductor design. Analytical modeling and simulation in PLECS are firstly conducted to verify the idea of iTCM. Besides, the capacitor-current feedback achaotive damping method is implemented to prevent instability from the LC and LCL filters. Finally, the designed 11 kW three-phase AC-DC PFC converter, including the input LCL filter, achieves an efficiency of 98.61%, a power density of 6.5 kW/L and a specific power per weight of 0.72 kW/kg.
The proposed three-phase iTCM control is validated in a 3 kW SiC MOSFET-based AC-DC converter. The current and voltage waveform of iTCM, TCM and CCM is verified in the hardware platform with (DSP)-TMS320F28379D. The efficiency of the iTCM control achieves 96.38%, which improves by around 1% compared to CCM at the same power density level.

Highly automated vehicles (HAVs) have been introduced to the transportation system for the purpose of providing safer mobility. Considering the expected long co-existence period of HAVs and human-driven vehicles (HDVs), the safety operation of HAVs interacting with HDVs needs to be verified. To achieve this, HAVs' Operational Design Domain (ODD) needs to be identified under the scenario-based testing framework. In this study, a novel testing framework aiming at identifying the Safety performance boundary (SPB) is proposed, which assures the coverage of safety-critical scenarios and compatible with the black-box feature of HAV control algorithm. A surrogate model was utilized to approximate the safety performance of HAV, and a gradient descent searching algorithm was employed to accelerate the search for SPB. For empirical analyses, a three-vehicle following scenario was adopted and the Intelligent Driver Model (IDM) was tested as a case study. The results show that only 4% of the total scenarios are required to establish a reliable surrogate model. And the gradient descent algorithm was able to establish the SPB by identifying 97.42% of collision scenarios and only false alarming 0.29% of non-collision scenarios. Furthermore, the concept of safety tolerance was proposed to measure the possibilities of boundary scenarios dropping in safety performance. The applications of helping to construct ODD and compare different control algorithms were discussed. It shows that the IDM performs better than the Wiedemann 99 (W99) model with larger ODD.

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