TY  - JOUR
AU  - Salleh, Nur Yasmin 
AU  - Yusof, Mohd Kamir 
AU  - Mansor, Nur Farraliza 
PY  - 2025
TI  - Optimization Consumption Power in Internet of Things Technology: A Systematic Review
JF  - Journal of Computer Science
VL  - 21
IS  - 3
DO  - 10.3844/jcssp.2025.685.703
UR  - https://thescipub.com/abstract/jcssp.2025.685.703
AB  - This study reviews algorithms for battery optimization, focusing on estimation methods and State of Charge (SOC) algorithms, which are crucial components of Battery Management Systems (BMS) designed to reduce power consumption. With the increasing global demand for electricity driven by rapid population growth, optimizing energy use has become critical. Accurate estimation of battery capacity is essential for extending battery lifespan and ensuring efficient power delivery. To monitor, control, and deliver the battery's power at its maximum efficiency, the BMS is introduced. This systematic review focuses on three key research questions: What is the purpose of optimization? What is the type of algorithm estimation method? What is the type of algorithm of SOC? Following systematic review guidelines, 21 articles were selected from an initial 1721 based on inclusion and exclusion criteria. The findings reveal that most algorithms aim to minimize battery power consumption. Data-driven methods and hybrid algorithms demonstrate superior performance compared to others, although further modifications are necessary to enhance their effectiveness. This review emphasizes the imperative of advancing those algorithms to improve BMS efficiency and satisfy growing demands for optimum energy consumption in Internet of Things technologies.