The objective of this paper is to implement a two-phase, interleaved, bidirectional DC/DC converter topology with an improved voltage conversion ratio for electric vehicle (EV) and DC-microgrid systems. In this study, a two-phase interleaved charge-pump topology is introduced to achieve a high voltage conversion ratio with very simple control circuits. In discharge mode, the circuit topology acts as a voltage-multiplier boost converter to achieve a high step-up conversion ratio (48 V to 240 V). In charge mode, the circuit topology acts as a voltage-divider buck converter to achieve a high voltage step-down conversion ratio (240 V to 48 V). The circuit configuration, operating principle, steady-state analysis and the closed-loop control of the proposed converter are presented. Experiments conducted on a laboratory prototype with 500 W power-rating are presented to verify the effectiveness. The maximum efficiency levels in discharge and charge modes are about 97.7% and 98.4% respectively.

In this study, mobile phones, wearable devices, temperature and human motion detectors are integrated as smart sensors for enabling smart air conditioning control. Smart sensors obtain feedback, especially occupants’ information, from mobile phones and wearable devices placed on human body. The information can be used to adjust air conditioners in advance according to humans’ intentions, in so-called intention causing control. Experimental results show that the indoor temperature can be controlled accurately with errors of less than ±0.1 °C. Rapid cool down can be achieved within 2 min to the optimized indoor capacity after occupants enter a room. It’s also noted that within two-hour operation the total compressor output of the smart air conditioner is 48.4% less than that of the one using On-Off control. The smart air conditioner with wearable devices could detect the human temperature and activity during sleep to determine the sleeping state and adjusting the sleeping function flexibly. The sleeping function optimized by the smart air conditioner with wearable devices could reduce the energy consumption up to 46.9% and keep the human health. The presented smart air conditioner could provide a comfortable environment and achieve the goals of energy conservation and environmental protection.

In this study, information pertaining to the development of artificial intelligence (AI) technology for improving the performance of heating, ventilation, and air conditioning (HVAC) systems was collected. Among the 18 AI tools developed for HVAC control during the past 20 years, only three functions, including weather forecasting, optimization, and predictive controls, have become mainstream. Based on the presented data, the energy savings of HVAC systems that have AI functionality is less than those equipped with traditional energy management system (EMS) controlling techniques. This is because the existing sensors cannot meet the required demand for AI functionality. The errors of most of the existing sensors are less than 5%. However, most of the prediction errors of AI tools are larger than 7%, except for the weather forecast. The normalized Harris index (NHI) is able to evaluate the energy saving percentages and the maximum saving rations of different kinds of HVAC controls. Based on the NHI, the estimated average energy savings percentage and the maximum saving rations of AI-assisted HVAC control are 14.4% and 44.04%, respectively. Data regarding the hypothesis of AI forecasting or prediction tools having less accuracy forms Part 1 of this series of research.

According to statistics, low-temperature waste heat below 300°C accounts for more than 89% of industrial waste heat. If the waste heat is not recycled, a large amount of low-temperature waste heat will be released into the atmosphere, thereby exacerbating global warming and posing a significant threat to human survival. Although the power generation efficiency of solid-state thermoelectric generation technology is lower than the organic Rankine cycle, it only requires a smaller construction area, which increases its market acceptance, applicability, and penetration. Especially in the pursuit of net-zero emissions by global companies, the importance of low-temperature waste heat recovery and power generation is even more prominent. The current thermoelectric conversion efficiency of commercial thermoelectric chips is about 5%. Power generation cost, thermoelectric conversion efficiency, and energy use efficiency are highly correlated with the commercialization of solid-state thermoelectric technology. This research shares five practical waste heat power generation cases commercialized by recycling three heat sources. It also points out the three significant challenges facing the commercialization of power generation from low-temperature waste heat recovery. This study analyzes 2,365 TEG patents submitted by 28 companies worldwide to determine the basic technology for realizing waste heat recovery through TEG and explore the potential commercialization of related waste heat recovery products. The future challenge for the large-scale commercialization of solid-state thermoelectric technology is not technological development but financial incentives related to changes in international energy prices and subsidies that promote zero carbon emissions.

An on-chip transformer with a ferrofluid magnetic core has been developed and tested. The transformer consists of solenoid-type coil and a magnetic core of ferrofluid, with the former fabricated by MEMS technology and the latter by a chemical co-precipitation method. The performance of the MEMS transformer with a ferrofluid magnetic core was measured and simulated with frequencies ranging from 100 kHz to 100 MHz. Experimental results reveal that the presence of the ferrofluid increases the inductance of coils and the coupling coefficient of transformer; however, it also increases the resistance owing to the lag between the external magnetic field and the magnetization of the material.