Anif Jamaluddin

Battery management systems are essential in electric vehicles and renewable energy applications, especially in terms of ensuring optimal battery health and performance and regarding the state of charge (SOC) in batteries consisting of many cells. The lifetime and efficiency of the battery depend on the accuracy of the SOC parameter estimation. Moreover, systems that apply active balancing technology are able to move cells with high SOC data to cells with low SOC. Many methods have been developed, but their long execution time makes them less optimal when applied. High-speed SOC estimation is required in active balancing technology, in addition to high accuracy. Therefore, this study proposes the estimation of SOC parameters using a statistical and metaheuristic approach from voltage and current input data in each battery cell. The experimental results showed that the metaheuristic-based method (ANFIS) had better RSME and R² values compared with the polynomial and linear regression or even the machine learning-based method (recurrent neural network) for training data.

Battery management systems are essential in electric vehicles and renewable energy applications, especially in ensuring optimal battery health and performance. Especially regarding the state of charge (SOC) in batteries consisting of many cells. The lifetime and efficiency of battery depend on the accuracy of the SOC parameter estimation. Moreover, the system that applies active balancing technology will move cells that have high SOC data to cells that have low SOC. Many methods have been developed, but the long execution time makes it less than optimal when applied. The speed of SOC estimation is also required in active balancing technology in addition to the accuracy factor. Therefore, this study proposes to estimate SOC parameters using a statistical and metaheuristic approach method from voltage and current input data in each battery cell. The experimental results showed that the metaheuristic-based method (ANFIS) had better RSME and R2 values compared to the polynomial and linear regression method.

Lithium-ion batteries have shown promising performance in high-energy storage systems for electric vehicles. The electrode material used in the battery affects the performance of the LIB. The material on the anode can be modified by adding activated carbon (AC) to the graphite. AC can be made from a variety of biomass wastes, including black betel leaf biomass. AC was prepared by hydrothermal carbonization method in an inert gas atmosphere and then activated with a KOH solution. AC material was then analyzed by SEM and FTIR. Li-ion batteries with 0%, 10%, and 20% activated carbon addition were tested with a battery analyzer. The resulting specific capacities of graphite-AC 0%, graphite-AC 10%, and graphite-AC 20% batteries were 115.57 mAh/g, 94.60 mAh/g, and 76.38 mAh/g, respectively. The battery was then cycle tested at a current of 0.5C, and the resulting battery with the addition of 20% activated carbon showed the best retention capacity of 88.34% after 50 cycles. The battery test results show that activated carbon from black betel leaves can be used as an anode material for lithium-ion batteries.