Structural design optimization of 12/8 switched reluctance motor using single objectives genetic algorithms is explored. The objective of this optimization is to maximize the output torque using four parameters, namely rotor width of tooth root, rotor width of tooth tip, half of stator tooth width, and stator outer diameter. The result is the optimized motor has higher average torque of 25% compared to the initial design. The evaluation of motor model is finite element method. The 12/8 switched reluctance motor will be applied in a mini electric vehicle.

In transportation technology, the development of electric vehicle is growing rapidly. In the future, the availability of electrical power is crucial because every electric tool needs electrical power. Power plant must provide electrical power for all consumer include an electric vehicle. Sustainability of electrical power transmission and distribution must be considered as critical due to its high power consumption in the community. One of the problem to supply electrical power is how to keep the system’s voltage stability. Several variations on the load pattern and topological can lead to a substantial poor impact on the system. However, generation cost must be considered by utilities’ operator. This paper demonstrates a recently developed metaheuristic method called Chaotic Firefly Algorithm (CFA). Our simulation shows that this method may perform better than several well-known metaheuristic methods. Therefore, CFA may become a promising method to solve optimal power flow considering voltage stability cases.

The shifting trend of conventional to the electric drivetrain in automotive industries makes batteries become the most favorable energy storage. There are three types of battery cells that are commonly used for electric vehicles i.e., cylindrical cells, pouch cells, and prismatic cells. The use of active material such as lithium-ion in the battery of electric vehicles could bring some issues related to the safety field. For that reason, comprehensive research on battery failure analysis needs to be conducted. This paper reviews the recent progress of the use of battery cells in electric vehicles and some challenges which must be considered to assure their safety. There are a lot of studies on battery failure analysis, which mainly focuses on the appearance of a short circuit as the main cause of the thermal runaway event. Several proposals on predicting short circuits in the battery due to various loading are comprehensively discussed. Those research results can be considered to establish regulations in designing battery protectors.

An electric vehicle battery pack which is a gathering of battery modules which subsequently comprised of the battery cell is a primary source of control transmission for an Electric Vehicle (EV). The inappropriate design of the battery enclosure will cause many genuine issues, such as cracking, causing noise, or battery harm. At the same time, the weight of the battery enclosure is huge; in order to get better the driving range of the electric vehicle and diminish the influence of the battery on the vehicle dynamic performance and acceleration performance, it is essential to carry out the lightweight design of the battery enclosure. This paper reviews the multi-material battery enclosure design optimization, the multi- technologies, and a proficient Battery Management System (BMS) for compact battery pack design used to lightweight battery pack enclosure design; the multi-objective optimization approach for distinctive parameters of battery pack enclosure design optimization by diverse manufacturing techniques.

Currently, the transportation sectors are heading into the advancement of sustainable transportation technology to tackle the issue of unsustainable transportation system which leads to various drawbacks in the future. One of the innovation in sustainable transportation technology is the development of electric vehicle (EV). However, there are noticeable difference between sustainable transportation system and conventional transportation system that requires a massive and synchronous network of related stakeholders to keep innovating and catch up with the progression. On the other side, transportation sector is one of the sector with the massive development that has the consequences of high risk and uncertainty, which leads to the tendency of innovation avoidance. Therefore, the innovation network of EV needs to be nurtured to ensure its sustainability. From the previous study on various sectors, the innovation strategy focuses on two matters: innovation target and actor integration. The latter purpose is required to be conducted in an inclusive manner by engaging people from inside or outside the organization. In the case of innovation in sustainable transportation, the vertical approach plays a significant role in terms of fostering technological advancement. The main solution to reach this objective is through the well-established research ecosystem, which significantly supported by R&D investment by the government. The government takes the role of the innovation strategy implementer to establish a strong research network. Besides as the R&D funder and the hub between researcher and industry, the government also take a role to keep the dynamics of an ecosystem through interaction stimulation, while also engage the innovation results to be relevant with the social, technological, environmental, and economic problems. Moreover, the research center also takes an important role in the ecosystem, such as to engage reiteratively with the industry, become the open-innovation enabler, have the dedicated research agenda, become the multidisciplinary innovation enabler, collaborate with multi-stakeholders, has core research infrastructure, and become the innovation cultural hub. While the country’s industrialization demand keep rising, the research center could consistently become the intermediaries in conducting the research and build the relationship with other non-industry actor. This way, the high risk and uncertainty of innovation could be reduced.

Control strategy has an important role in electric vehicles. It determines the efficiency and performance of electric vehicles. This study analyzes the control strategies on electric vehicles by a bibliometric analysis using VOSviewer, Open Refine and Tableau Public software. In this study, the dataset was taken from Scopus. The number of articles used is 1403 documents. The keywords used in Scopus database based on TITLE-ABS-KEY (title, abstract, keyword) are "control strategy" AND "electric vehicle" OR "EV". Based on the result analysis, the number of studies on control strategies in electric vehicles continues to increase from 2013 to 2022. Result analysis of this study provides information that the latest research trend related to control strategies in electric vehicles is wireless power transfer, switched reluctance motor, energy consumption, robust control, disturbance observer, battery life, deep reinforcement learning, reinforcement learning, ECMS and fuzzy logic control. We find that the most influential and productive authors are from China.

The battery system in electric vehicles needs proper monitoring and control to ensure reliable, efficient, and safe operation. Recent advancement in cyber-physical technology has brought the emerging digital twin concept. This concept opens a new possibility of real-time condition monitoring and fault diagnosis of the battery system. Although it sounds promising, the concept implementation still faces many challenges. One of the challenges is the availability of a platform to develop digital twins, which involves data pipelines and modeling tools. The data pipeline will include the acquisition, storing, and extract-transform-load (ETL) with high velocity, volume, value, variety, and veracity data, known as big data. The modeling tools must provide applications to build the high-fidelity model, one of the required elements of the digital twin. Based on those urgencies, this paper proposes a platform that facilitates a digital twinning of the battery system in an electric vehicle. The platform is built on the open-source framework CDAP, equipped with a data pipeline and modeling tools. It has run several performance tests with different computation resource configurations and workloads. Doubling the processing power can reduce 12% of computation time while increasing memory size by four times only reduces 10% of computation time. The result shows that the processing power affects the performance digital twin platform more than the memory size.