Abundantly available SiO₂ (silica) has great potential as an anode material for lithium-ion batteries because it is inexpensive and flexible. However, silicon oxide-based anode material preparation usually requires many complex steps. In this article, we report a facile method for preparing a SiO₂/C composite derived from coal combustion fly ash as an anode material for Li-ion batteries. SiO₂ was obtained by caustic extraction and HCl precipitation. Then, the SiO₂/C composite was successfully obtained by mechanical milling followed by heat treatment. The samples were characterized by X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), and scanning electron microscopy (SEM). Electrochemical properties were tested using an 18650 cylindrical cell utilizing LiNi_0.8Co_0.15Al_0.05O₂ (NCA) as the counter electrode. Based on the obtained results, the physiochemical characteristics and electrochemical performance, it was determined that SiO₂/C composites were greatly affected by the temperature of heat treatment. The best result was obtained with the SiO₂ content of 10% w/w, heating temperature of 500 °C, initial specific discharge capacity of 586 mAh g⁻¹ at 0.1 C (1 C = 378 mAh g⁻¹), and reversible capacity of 87% after 20 cycles. These results confirmed that the obtained materials had good initial discharge capacity, cyclability, high performance, and exhibited great potential as an anode material for LIBs.

This paper proposes a path planning algorithm using the hybridization of the rapidly-exploring random tree (RRT) and ant colony system (ACS) algorithms. The RRT algorithm can quickly generate paths. However, the resulting path is suboptimal. Meanwhile, the ACS algorithm can generate the optimal path from the suboptimal previous path information. Then, the proposed algorithm will combine the advantages of RRT with the ACS algorithm. Therefore, it can reach the optimal value with a good convergence speed. We call this proposed algorithm the RRT-ACS algorithm. This study developed a new method for hybridizing the RRT and ACS algorithms for path planning problems. This hybridization process is carried out using one of the ACS principles: the pseudorandom proportional rule. The performance of the proposed algorithm with the RRT*, informed RRT*, RRT*-connect, and informed RRT*-connect algorithms is tested with several benchmark cases. The test results from benchmark case tests with known optimal values indicate that the proposed algorithm has succeeded in achieving those optimal values. Furthermore, statistical tests have also been carried out to verify whether there is a significant difference in performance between the RRT-ACS algorithm and the existing algorithms. The test and statistical analysis results show that the RRT-ACS algorithm has good performance and convergence speed. We also discuss the stability, robustness, convergence, and rapidity of the RRT-ACS algorithm. The results indicates that the RRT-ACS algorithm may be used in applications that require fast and optimal path planning algorithms such as robots and autonomous vehicles.

This study proposes a path planning algorithm that combines an informed rapidly-exploring random tree (RRT*) and particle swarm optimization (PSO) algorithms. The informed RRT* algorithm can build optimal pathways quickly. However, in some cases, the informed RRT* strategy cannot help increase the convergence rate. Meanwhile, the PSO algorithm can be combined with other algorithms to improve algorithm performance. The proposed algorithm will combine the advantages of informed RRT* and PSO algorithms. The proposed algorithm is called the RRT-PSO algorithm. The RRT-PSO algorithm’s performance was compared to the informed RRT*, RRT*, RRT-ACS, and informed RRT*-connect algorithms using eight benchmark scenarios. The test results on benchmark scenarios with known optimal values demonstrate that the RRT-PSO algorithm achieves those optimal values. The statistical analysis shows that the RRT-PSO algorithm has a fast convergence rate compared to other comparison algorithms. The test results also demonstrate the RRT-PSO algorithm’s stability and robustness compared to other comparison algorithms. Our test results also show the RRT-PSO algorithm’s advantages over other PSO-based and traditional path planning algorithms. The results show that the RRT-PSO algorithm is suitable for applications requiring a fast and optimal path planning algorithm, such as self-driving cars and unmanned aerial vehicles (UAV).

Currently, the adoption of electric vehicles (EV) draws much attention, as the environmental issue of reducing carbon emission is increasing worldwide. However, different countries face different challenges during this transition, particularly developing countries. This research aims to create a framework for the transition to EV in Indonesia through Agent-Based Modeling (ABM). The framework is used as the conceptual design for ABM to investigate the effect of agents’ decision-making processes at the microlevel into the number of adopted EV at the macrolevel. The cluster analysis is equipped to determine the agents’ characteristics based on the categories of the innovation adopters. There are 11 significant variables and four respondents’ clusters: innovators, early majority, late majority, and the uncategorized one. Moreover, Twitter data analytics are utilized to investigate the information engagement coefficient based on the agents’ location. The agents’ characteristics which emerged from this analysis framework will be used as the fundamental for investigating the effect of agents’ specific characteristics and their interaction through ABM for further research. It is expected that this framework will enable the discovery of which incentive scheme or critical technical features effectively increase the uptake of EV according to the agents’ specific characteristics.

In 2021, the electric vehicles (EVs) market reached a record-breaking 6.5 million vehicles, and it will continuously grow to USD 31 million in 2030. However, the risk of battery damage should be reduced using a lightweight crashworthy protection system, which can be performed through design optimization to achieve maximum Specific Energy Absorption (SEA). Maximum SEA can be gained by selecting a material with a light weight and high energy absorption properties. An auxetic-shaped cell structure was used since its negative Poisson ratio yields better energy absorption. The research was performed by varying the auxetic cell shape (Re-entrant, Double Arrow, Star-shaped, Double-U), material selection (GFRP, CFRP, aluminum, carbon steel), and geometry variables until the maximum possible SEA was reached. The Finite Element Method (FEM) was used to simulate the impact and obtain the value of the SEA of the varied auxetic cellular structure design samples. The design variation amounted to 100 samples generated using Latin Hypercube Sampling (LHS) to distribute the variables. Finally, the Machine Learning method predicted the design that yielded maximum SEA. The optimization process through Machine Learning consisted of two processes: model approximation using an Artificial Neural Network (ANN) and variable optimization using a Nondominated Sorting Genetic Algorithm-II (NSGA-II). The optimization demonstrated that the maximum SEA resulted from Star-shaped auxetic cells and aluminum material with a thickness of 2.95 mm. This design yielded 1220% higher SEA compared to the baseline model. A numerical simulation was also carried out to validate the result. The prediction error amounted to 6.7%, meaning that the approximation model can successfully predict the most optimum design. After the complete battery system configuration simulation, the design could also prevent excessive battery deformation. Therefore, the optimized structure can protect the battery from failure.