The Internet of Things has become an evolving area of wireless technologies. Energy storage plays a crucial role in developing, implementing, and deploying the Internet of Things (IoT) to help solve some of the problems facing our daily activities. It is known that in batteries, energy-stored systems are able to get spoiled due to undercharging or over-charging and might profoundly affect the operation of the whole system. Electric and hybrid vehicles need an exact state-of-charge approximation technique to improve battery safety and lifetime and prevent damage. An efficient battery managing system is vital to accurately indicate the battery operating temperature and state of charge and protect the Battery against cell disproportion. This paper presents a simulation-based Battery Management System (BMS) for e-bikes, it was implemented on Arduino Nano. The battery parameters such as Temperature, Voltage, Current, State-of-Charge (SoC), and Depth-of-Discharging (DoD) will be sent to a cloud server via the BOLT IoT module, and a data analysis tool will be used to approximate bad battery pack cells. To effectively estimate the state-of charge of the system battery, three different mathematical models for a single and packed battery management system techniques, namely, Coulomb Counting (CC), the Unscented Kalman filter (UKF), and the Extended Kalman Filter (EKF), were proposed and explained in this study. The battery Management System is expected to improve the overall performance of the IoT-enabled systems and will also contribute to mass acceptance of renewable energy systems since Battery Management systems would be one of its key components.

Orthogonal Frequency Division Multiplexing (OFDM) is a multicarrier modulation (MCM) scheme that plays a significant role in digital wireless communication. As a result of its high data rate capability and immunity to multipath fading effect, among others, OFDM remains an ideal technology for 5G communication systems and beyond. Unfortunately, a major drawback of this technology is the high peak-to-average power ratio (PAPR). Nevertheless, many techniques for reducing PAPR have been proposed in the past to address this problem. However, all the techniques previously used have limitations such as high computational complexity, data rate loss, high signal distortion, increase in bandwidth, increase in transmit power, and memory requirements. In this paper, a hybrid technique that combines Zadoff-Chu Transform (ZCT) precoding and Partial Transmit Sequence (PTS) for reducing high PAPR in OFDM signals is presented. To reduce the data rate loss and computational complexity in PTS when a large subblock is used, a Zadoff-Chu precoding is applied to OFDM symbols to precode the symbol before applying PTS with a fewer number of subblocks. An OFDM model was developed where ZCT and PTS techniques were implemented. The performance of the hybrid model was analyzed using Power Spectral Density (PSD), Complementary Cumulative Distribution Function (CCDF), and Bit Error Rate (BER). The simulation result indicates that the hybrid ZCT-PTS provides a better result than using either Zadoff-Chu precoding technique or Partial Transmit Sequence technique separately. A PAPR of 4.2 dB and BER of 3dB is achieved for hybrid ZCT-PTS. The hybrid technique provides a better system performance when compared with Conventional OFDM systems.