The pulse-based charging method for battery cells has been recognized as a fast and efficient way to overcome the shortcoming of a slow charging time in distributed battery cells, which is regarded as a connection of cells such as the Internet of Things (IoT). The pulse frequency for controlling the battery charge duration is dynamically controlled within a certain range in order to inject the maximum charge current into the battery cells. The optimal frequency is determined in order to minimize battery impedance. The adaptation of the proposed pulse duty and frequency decreases the concentration of the polarization by sensing the runtime characteristics of battery cells so that it guarantees a certain level of safety in charging the distributed battery cells within the operating temperature range of 5–45 °C. The sensed terminal voltage and temperature of battery cells are dynamically monitored while the battery is charging so as to adjust the frequency and duty of the proposed charging pulse method, thereby preventing battery degradation. The evaluation results show that a newly designed charging algorithm for the implemented charger system is about 18.6% faster than the conventional constant-current (CC) charging method with the temperature rise within a reasonable range. The implemented charger system, which is based on the proposed dynamic frequency and duty control by considering the cell polarization, charges to about 80% of its maximum capacity in less than 56 min and involves a 13 °C maximum temperature rise without damaging the battery.

A specially designed microcontroller with event-driven sensor data processing unit (EPU) is proposed to provide energy-efficient sensor data acquisition for Internet of Things (IoT) devices in rare-event human activity sensing applications. Rare-event sensing applications using a remotely installed IoT sensor device have a property of very long event-to-event distance, so that the inaccurate sensor data processing in a certain range of accuracy error is enough to extract appropriate events from the collected sensing data. The proposed signal-to-event converter (S2E) as a preprocessor of the conventional sensor interface extracts a set of atomic events with the specific features of interest and performs an early evaluation for the featured points of the incoming sensor signal. The conventional sensor data processing such as DSPs or software-driven algorithm to classify the meaningful event from the collected sensor data could be accomplished by the proposed event processing unit (EPU). The proposed microcontroller architecture enables an energy efficient signal processing for rare-event sensing applications. The implemented system-on-chip (SoC) including the proposed building blocks is fabricated with additional 7500 NAND gates and 1-KB SRAM tracer in 0.18 um CMOS process, consuming only 20% compared to the conventional sensor data processing method for human hand-gesture detection.

The rapid evolution of automotive software necessitates efficient and accurate development and verification processes. This study proposes a virtual electronic control unit (vECU) that allows for precise software testing without the need for hardware, thereby reducing developmental costs and enabling cloud-native development. The software was configured and built on a Hyundai Autoever AUTomotive Open System Architecture (AUTOSAR) classic platform, Mobilgene, and Renode was used for high-fidelity emulations. Custom peripherals in C# were implemented for the FlexTimer, system clock generator, and analog-to-digital converter to ensure the proper functionality of the vECU. Renode’s GNU debugger server function facilitates detailed software debugging in a cloud environment, further accelerating the developmental cycle. Additionally, automated testing was implemented using a vECU tester to enable the verification of the vECU. Performance evaluations demonstrated that the vECU’s execution order and timing of tasks and runnable entities closely matched those of the actual ECU. The vECU tester also enabled fast and accurate verification. These findings confirm the potential of the AUTOSAR-compatible Level-4 vECU to replace hardware in development processes. Future efforts will focus on extending capabilities to emulate a broader range of hardware components and complex system integration scenarios, supporting more diverse research and development efforts.