The concept of wireless power is originated from Heinrich Hertz and well known by the work of Nikola Tesla. Magnetic resonance-based wireless power transfer has been widely applied in charging biomedical implants, consumer electronics, electric vehicles, and lightweight autonomous underwater vehicles due to its convenience and reliability. As a core part of a wireless charging system, coil design is of great importance. This dissertation will introduce four coil designs based on the applications of wirelessly charging electric vehicles and lightweight autonomous underwater vehicles.

Chapter 2 proposes an integrated coil design for bipolar coils in EV wireless chargers using LCC compensation topology, which simplifies the design, makes the system more compact, and increases the system power density. Finite element analysis by ANSYS MAXWELL is conducted to verify the proposed idea. In addition, a design method on improving system efficiency is given and experimental results demonstrate that wireless charging system with the proposed integrated coil design can transfer 3.0 kW at a DC-DC efficiency of 95.5%.

Chapter 3 extends the work of Chapter 2 and presents another integrated coil design which is compatible with unipolar coils in EV wireless chargers using LCC compensation topology. The aspect ratios of the compensated coils are study in ANSYS MAXWELL to minimize the extra cross-side coupling coefficient. A wireless charging with the proposed integrated coil design is built to achieve 3.0 kW power transfer at a DC-DC efficiency of 95.5%. Furthermore, a comparative study of the two wireless charging systems in Chapters 2 and 3 are conducted, and the results show the wireless charging system in Chapter 3 has competitive performance in fully aligned and door-to-door misaligned cases, and superior performance in vertical and front-to-rear misaligned cases.

Chapter 4 puts forward a three-phase coil design for lightweight autonomous underwater vehicles. Finite element analysis shows the proposed coil structure has concentrated magnetic fields, which have less adverse effects on the instrumentations within the AUV. A compensation method is presented and a three-phase wireless charging system is built to transfer 1.0 kW at a DC-DC efficiency of 92.41%.

Chapter 5 proposes a rotation-resilient coil design with a two-part reversely wound receiver for lightweight autonomous underwater vehicles in order to achieve a constant power transfer over rotational misalignment. Finite element analysis is performed to verify the proposed coil design and a mesh-current method is applied in analyzing the circuit. A wireless charging system is built to deliver 745 W at a DC-DC efficiency of 86.19% when the system is fully aligned, and efficient under worst-case rotational misalignment.

As electrified vehicles penetrate the market, consumers have been gradually experiencing the benefits of their high performance and contribution towards green living. However, the benefits of the new powertrain system bring with them many severe safety hazards, which hinder their development. The early detection of electric faults is an essential approach to identifying the occurrence of hazards and ensuring safe operation. This thesis studies state-of-the-art fault diagnosis methods for the battery system in electrified vehicles. Based on the uniqueness of the battery system, the self-redundant fault detection methods are proposed and studied in detail.

First, abundant experiments are conducted to capture the electrical and thermal behaviors of the lithium ion battery cells, serving as the basic energy storage elements in the commercial battery packs nowadays, and the simple threshold-based fault detection method is implemented to identify electric faults, including over charge, over discharge, external short circuit and internal short circuit.

Then, the model-based fault detection method is investigated to compensate the drawback of the threshold-based diagnosis method, in which the input information is ignored. The continuous-time system identification methods are introduced to estimate model parameters of the equivalent circuit model. The estimated model provides more accurate and robust fault detection performance compared with that of the traditional discrete-time system identification methods.

Next, the correlation-based fault diagnosis method is proposed for short circuit detections in the battery system. This method does not require the preliminary effort in battery modeling, identification and validation. More importantly, the correlation coefficient is not sensitive to variations in open circuit voltages and internal resistances, and thus is robust to cell inconsistencies in real applications.

After that, the theory of the interleaved voltage measurement method is developed to distinguish between sensor and cell failure without extra hardware components or battery models. The theory is then improved such that the constraint in sensor topology is removed by varying the sensor matrix. The feasibility of the measurement method is validated by simulation and experiment.

At last, the interleaved voltage measurement method is integrated with the correlation-based fault diagnosis method to achieve both the advantages. The viability of the integration is confirmed by experiment validation.

In summary, this thesis develops the self-redundant fault diagnosis approaches, including the correlation-based short circuit detection method and the interleaved voltage measurement method. These methods are specifically designed for the battery system, in which duplicative components and similar measurements are needed. The advantages of the proposed self-redundant fault diagnosis methods over the state-of-the-art redundancy-based methods are listed as follows,

1) The implementation of hardware redundancy is not necessary.

2) No cell testing, modeling and validation work are required.

3) The method is robust to cell inconsistencies in state of charge and state of health.

4) The ambiguity in cell and sensor failure is resolved upon fault occurrence.

The disadvantages of the proposed fault detection methods are,

1) More computation power is needed to calculate the correlation coefficient and solve the sensor matrices online.

2) The noise level of the voltage measurements is increased.