Electric Motorcycle Research Articles

IoT-Based Control, Monitoring, and Protection System for 3-Phase Induction Motors in Electric Motorcycles

This study investigates the application of the Internet of Things (IoT) for wireless control and monitoring of a 3-phase electric motor using a smartphone. The system integrates PZEM-004T, DS18B20, and Hall Effect sensors to collect data on voltage, current, temperature, and rotational speed using the NodeMCU ESP8266 microcontroller. Measurements are displayed on an LCD and transmitted to the Blynk server for smartphone access. A comparative method evaluates the accuracy of sensor readings against standard measuring instruments. Results obtained an average percentage error of 0.5% for the R phase voltage, 0.2% for the S phase, and 0.1% for the T phase. Current measurements reveal errors of 5% for the R phase, 10.3% for the S phase, and 11.7% for the T phase. The control system’s performance varies with internet speed, with an average delay of 0.99 seconds on a 4G network and 2.51 seconds on 3G. Additionally, the study evaluates three protection mechanisms, demonstrating that the motor stops within 4.03 seconds in the event of a phase failure, while overcurrent and overheating protections activate within 8.47 seconds and 3.64 seconds, respectively. Overall, the findings affirm the viability of IoT in motor monitoring and control, emphasizing accuracy and response times under varying conditions.

An integrated policy approach for sustainable decarbonization pathways of energy system in a city under climate change scenarios

An integrated assessment model has been developed to estimate useful energy demand, followed by an optimization model for planning the energy supply. Four scenarios have been developed. The findings of this study highlight the impact of behavioral, climate change, and socio-economic factors on energy demand and supply. Specifically, these factors can lead to significant variations in demand, such as a more than 3.48-fold difference in cooling demand or a 1.75-fold difference in the transportation demand by cars across various scenarios.The use of a dual-fuel car and electric motorcycle is suggested in all scenarios. Waste processing has been done with a landfill in all scenarios except in the final years of the net-zero emission target scenario, where an incinerator is proposed. The supply model for electricity generation in the early years suggests the combined cycle power plant followed by solar energy at the end. However, in the low-cost electricity scenario, it is always suggested to import electricity. The sensitivity analysis showed that the emission penalty and electricity price are the most important parameters. The results of this study will help policymakers and decision-makers in energy system planning and provide researchers with appropriate information, tools, and methods for future research.

Good Practices for Designing a UI/UX Motorcycle Display: A Systematic Literature Review

Within the scope of the Mobilizing Agenda for the Development of Intelligent Green Mobility Products and Systems (A-MoVeR), specifically in the second PPS2 defined the presentation of a “new electric motorcycle, with high autonomy, aimed at promoting comfortable, efficient and green urban mobility"". In this context, we intend to develop user interfaces (UI) for an electric motorcycle that meet the end-user’s expectations by promoting optimal user experience and security. To achieve this goal, this paper provides a preliminary literature analysis, with a compilation of literature related to major aspects for developing an optimized User Interface (UI) and consequently increasing User eXperience (UX), specifying accessibility, adaptability, appeal, and conciseness of motorcycle interfaces in an attempt to determine its constructive qualities. Therefore, it was analyzed studies regarding filtering of displayed information; the controlling of a user’s focus and emotions through means of efficient visual representations; the differences in various types of input methods regarding user attention; and, the relevance of dynamic UI as a solution to a variety of problems related to UI/UX design. Therefore, a systematic literature review was performed, which resulted in the finding of various advantageous practices and ideas that are relevant to the design of a motorcycle’s UI/UX.

Structural optimization and battery temperature prediction of battery thermal management system based on machine learning

Lithium-ion batteries significantly extend the driving range for electric motorcycles. The battery thermal management system (BTMS) is critical for achieving optimal battery performance. Moreover, precise battery temperature prediction is essential for efficient thermal management. Therefore, a battery thermal management system integrating air and phase change material (PCM) cooling is proposed. Initially, the impact of PCM height, PCM thickness, and air velocity on battery temperature is analyzed. Subsequently, with cost minimization as the objective and ensuring that the maximum battery temperature remains below a threshold, the Black Kite Algorithm (BKA) is employed to optimize the BTMS structure. Finally, a BKA-Convolutional Neural Network (CNN)-Self Attention (SA) model is introduced for battery temperature prediction. The results indicate that increasing the thickness of the PCM and air velocity facilitates battery heat dissipation but with diminishing marginal effects. An increase in PCM height enhances battery cooling at low air velocities but becomes detrimental at high air velocities. The optimized PCM height is 35 mm, resulting in a cost of 0.073 USD for the BTMS per battery. Additionally, the BKA-CNN-SA model achieved a maximum error of 0.45 °C on the validation set and accurately predicted battery temperature changes before and after PCM melting.

The barriers, determinants, and willingness-to-pay in electric motorcycle conversion (EMC) adoption

The rise in motorcycle use in Southeast Asian countries like Indonesia has caused environmental issues and transitioning from fossil-fuel to electric motorcycles (EM) will reduce emissions and improve air quality. This study aims to investigate the barriers, determinants, and willingness-to-pay in electric motorcycle conversion (EMC). In a choice experiment, data from motorcycle users in Bali, Indonesia, was collected and analysed in this study using K-modes cluster analysis and the Mixed-Logit Model. The study identified different barriers to EMC among motorcyclist groups: mix-motor commuters and hardcore oldies concerned with financial challenges, all-day riders with mature motorcycles face a lack of information on EMC costs and procedures, and higher-power enthusiasts and newbies with light motorcycles concerned with daily travel disruptions during the conversion process. This study also found waiting and conversion time to play a role in EM adoption and travellers who use older motorcycles are the most likely to adopt EMC. Lower-income individuals tend to be more inclined towards EMC and younger demographics lean towards internal combustion engine (ICE) motorcycles. Moreover, the study indicates that EMC reduces the adoption of ICE motorcycles more than conventional EM. A 50% increase in conversion time lowers EMC adoption probability by 5.2%pts. and increases new ICE motorcycle adoption by 3%pts. and new EM adoption by 2.2%pts. Additionally, motorcyclists are more willing to invest in EMC if it means reducing conversion time/charging costs, particularly for older motorcycles. This study offers several policy recommendations for accelerating EMC adoption in Indonesia.

Analysis of factors ifluencing consumer purchase intention on electric motorcycle

Analysis of factors ifluencing consumer purchase intention on electric motorcycle

Estimating the market share of electric motorcycles: A system dynamics approach with the policy mix and sustainable life cycle costs

Introducing electric vehicles is critical to maintaining air quality and reducing carbon emissions. The Indonesian government has issued several regulations to stimulate the diffusion of electric vehicles. This research attempts to forecast the electric vehicle market share, especially electric motorcycles, by involving the policy mix and sustainable life cycle costs. We propose a system dynamics approach that takes into account a policy mix including 0% down payment without credit interest subsidies, tax abolition, expansion of charging station network, and sustainable life cycle costs, i.e., total cost of ownership, social, and environment. The system dynamics model has four modules: the electric motorcycle cost, the conventional motorcycle cost, the economy module, and the consumer market. The simulation results show that the electric motorcycle market share will increase positively in 2021–2030, reaching 5.7% in 2030. Based on the scenario simulation results, providing more charging stations and vehicle tax abolition can significantly boost the market share of electric motorcycles in Indonesia. The study provides valuable insights for policymakers in formulating more appropriate policy instruments to promote electric vehicle diffusion in Indonesia.

The fabrication of electric motorcycle trainers to eliminate automotive misconceptions

This study aims to develop and implement learning media in the form of electric motorbike trainers to improve students' understanding of electric vehicle technology. The research method used is research and development (RD) with the Successive Approximation Model (SAM) approach, which is applied in the electric vehicle technology course curriculum in the Automotive Engineering Education study programme. The participants in this study were undergraduate students involved in the learning process using an electric motorbike trainer. The research instruments included observation, interviews, questionnaires, and practical assessment sheets. The results showed that the designed electric motorbike trainer can be used properly and is feasible to operate in the learning process if used in accordance with procedures. Based on the assessment of material experts and media experts, this learning media is considered feasible to implement in learning. A survey of 47 students showed that 63% of respondents strongly agreed that this electric motorbike trainer is an effective innovation in supporting learning. In conclusion, the development of this electric motorbike trainer not only enriches the learning experience but also has the potential to improve students' understanding of electric vehicle technology. This research contributes to providing innovative learning media that is relevant to today's technological needs and can be adopted more widely in various technical education institutions.

Comparison and evaluation of YOLO models for vehicle detection on bicycle paths

<p>Non-permitted vehicles have taken over bicycle lanes in various Latin American cities as an alternative escape from traffic. Still, they do not foresee the risk to which they expose users of smaller vehicles, such as cyclists. Technological advancement has made researchers use deep learning (DL) to solve various problems in a city's traffic. However, no research has been found focusing on any issue of vehicles allowed or prohibited to travel on a bicycle lane. Therefore, in this article, the you only look once (YOLO) algorithm was used, taking the lightest models from the YOLOv4 to the most recent version, YOLOv8, to detect 05 classes of vehicles that transit or interfere in a bicycle lane, such as bicycles (Bi), motorcycles (Mo), electric motorcycles (ME), electric scooters (SE) and motorcycle cabs (Mt). When testing with the test images, the YOLOv8m model in 50 epochs, using a batch size of 32 and SGD optimizer, was the most optimal, obtaining F1 results with 88.00%, mAP@0:50 of 94.80% and mAP@0.50:0.95 of 76.60%, also had a training time of 1:28h using a Nvidia T4 GPU from Google Colab.</p>

Feasibility Study on MHEV Application for Motorbikes: Components Sizing, Strategy Optimization through Dynamic Programming and Analysis of Possible Benefits

Reducing CO2 emissions is becoming a particularly important goal for motorcycle manufacturers. A fully electric transition still seems far away, given the difficulties in creating an electric motorcycle with an acceptable range and mass. This opens up opportunities for the application of hybrid powertrains in motorcycles. Managing mass, cost, and volume is a challenging issue for motorcycles; therefore, an MHEV architecture represents an interesting opportunity, as it is a low-complexity and low-cost solution. Firstly, in this work, an adequate sizing of the powertrain components is studied for the maximum reduction in fuel consumption. This is performed by analyzing many different system configurations with different hybridization ratios. A 1D simulation of the motorcycle traveling along the homologation cycle (WMTC) is performed, and the powerunit use strategy is optimized for each configuration using the Dynamic Programming technique. The results are analyzed in order to highlight the impact of kinetic energy recovery and engine load-point shifting on fuel consumption reduction. The results show the applicability of MHEV technology to road motorcycles, thus providing a useful tool to analyze the cost/benefit ratio of this technology. The developed methodology is also suitable for different vehicles once a specific test cycle is known.

Assessment of the sustainability of online food delivery from the perspective of CO2 emissions and transport intensity: A case study in Jakarta

This study examines the impact of online food delivery on CO2 emissions and transport intensity in Jakarta, Indonesia, by integrating data from both delivery drivers and customers. A total of 235 delivery instances were analyzed to discern typical distances and patterns inherent in these services. Additionally, insights into the demand side were obtained through customer surveys, yielding 203 valid responses, that included waiting times, costs, and behaviors related to alternative meal acquisition methods. CO2 emissions generated by delivery processes were estimated, utilizing travel distances recorded by drivers. The traffic impact was assessed by calculating the road occupancy of motorcycles used for deliveries. Furthermore, the assessment of CO2 emissions and transport intensity considered hypothetical emission scenarios without such delivery services based on customer indications of their alternative means of meal acquisition. This analysis revealed that frequent short journeys by delivery motorcycles significantly contribute to urban traffic congestion and CO2 emissions, highlighting the environmental and traffic pressures resulting from the rise of food delivery services. Results from the customer survey revealed a profound dependence on these services, indicating that their availability prompts a significant number of additional trips, thereby exacerbating these impacts. The findings suggest recommendations for mitigating these adverse effects, including promoting the use of electric motorcycles for deliveries and managing demand by reassessing delivery fees to reflect the actual environmental costs.

Conversion Electric Motorcycle Business Strategy Using Swot Analysis and Business Model Canvas

Convertible electric motorbikes are the outcome of transforming motorbikes that run on fossil fuels into electric ones. The purpose of this conversion is to promote the growing adoption of electric vehicles, prolong the lifespan of motor vehicle products, and mitigate carbon emissions in the transportation sector. Hence, the objective of this study is to ascertain and evaluate the market position of the convertible electric motorbike industry through the utilisation of Strength, Weakness, Opportunities, Threats (SWOT) and Business Model Canvas (BMC) analysis. The collection of primary data involved conducting interviews and utilising brainstorming approaches with professionals from electric vehicle manufacturers and academia. Nine business strategy models for convertible electric motorbikes were derived from the investigation using the BMC technique. According to the examination of the Internal Factor examination Summary (IFAS) and External Factor Analysis Summary (EFAS) matrices, a growth strategy has been selected. This approach indicates that the business possesses strengths and opportunities. Therefore, it requires policies that promote rapid expansion through comprehensive, integrated, and diversified methods. Recommendations for further research include exploring company strategies that use a circular and sustainable approach.

Comparative Analysis of Naïve Bayes and K-Nearest Neighbors Algorithms in Predicting Public Interest in Electric Motorcycles

Concerns about global warming and the need for sustainable transport solutions have led to the emergence of electric vehicles as an alternative to conventional vehicles. These vehicles offer cleaner and more efficient transportation, especially in urban areas. However, high costs in some countries, such as Indonesia, hinder their adoption. Electric motorcycle companies use previously recorded data to predict customer interest in purchasing their products. Based on these forecasts, business owners make decisions about the quantity of goods to supply. Researchers are increasingly using machine learning algorithms to study consumer behavior and predict demand for a variety of products, including electric motorcycles. The researchers have used this data-based method to analyze large amounts of consumer data, including online survey responses and reviews, as well as other sources. The study aims to perform a comparative analysis of the performance of the Naïve Bayes and K-Nearest Neighbors algorithms to predict public interest in electric motorcycles in Labuhanbatu district. We will perform a comparative analysis based on the performance evaluation matrix (accuracy, precision, recall, and f1 score) to determine the most suitable algorithm. The phases of the research methods included: data collection, exploratory data analysis, data preprocessing, splitting data, implementation of naïve bayes and k-nearest neighbors, and evaluation. The results showed that Naïve Bayes achieved an accuracy of 31.13%, precision of 83.33%, recall of 4.63%, and f1-score of 8.77%. In contrast, K-Nearest Neighbors attains an accuracy of 71.52% and a precision of 71.52%, recall 100%, and f1 score of 83,40%. According to the research results, K-nearest neighbors showed much better results in terms of accuracy, recall, and F1 scores. These results demonstrate that K-nearest neighbor is more effective in detecting public interest in electric motorcycles, and it strikes a better balance between identifying all positive cases and avoiding predictive errors.

ELGO electric motorcycle: Design and analysis of trail-type electric motorcycle frame using finite element analysis

The frame is an important part of the motorcycle as it carries the load of the passenger. Therefore, it is necessary to design a frame that can withstand the load and is lightweight. A lighter motorcycle will reduce the power required to ride it. This research aims to redesign the frame of a trail-type ELGO electric motorbike by considering a reduction in frame weight. The research was carried out using finite element analysis. The data from the analysis were checked based on the maximum stress not exceeding the yield strength of the DIN 1.0030 material and the safety factor greater than 1. The results showed that the newly designed frame has a lighter weight, with a maximum stress less than the yield strength of the DIN 1.0030 material and a safety factor value greater than 1. The simulation results also indicate the potential for further research by showing the maximum and minimum stress areas on the frame.

Investment Decision-Making to Select Converted Electric Motorcycle Tests in Indonesia

Investment Decision-Making to Select Converted Electric Motorcycle Tests in Indonesia

A novel causative factor of injury: Severe burns related to fires and explosions of lithium-ion batteries of electric motorcycles

Severe burns related to fires and explosions of lithium-ion batteries of electric motorcycles have not been reported to date. We retrospectively studied 419 patients admitted to our burn intensive care unit from January 2016 to December 2021. Of these 419 patients, 26 (22 male, 4 female; median age, 42 years) had burns related to lithium-ion battery fires and explosions, and all of their injury characteristics were similar to those of traditional flame burns. Lithium-ion battery-related burns were the eighth most common cause of burn injuries among all hospitalized patients. The 26 patients comprised 10 unemployed and 16 employed individuals. Twenty-three patients were injured at home during the battery charging process, and three were injured outdoors (one by a fire while the electric motorcycle was stationary and the others two by a fire while riding the motorcycle). The burn sites were distributed over the whole body; the burn area ranged from 10 % to 100 % of the total body surface area, and the burn depth ranged from superficial second-degree burns to third-degree burns. Twenty-three patients had inhalation injuries, and ten underwent prophylactic tracheostomy and intubation. Multiple operations were required for wound repair. Although convenient, lithium-ion electric motorcycles can also cause severe burns. To prevent these injuries, we must increase public safety awareness and education, develop new battery energy storage systems and battery management systems, and ensure the safety of batteries. Consumers should be aware of the potential dangers of lithium-ion batteries and comply with related security measures.

User Adoption of Electric Motorcycles: An Investigation of UTAUT 2 Framework

Public concern about environmental sustainability has increased interest in electric motorcycles. This study used the Unified Theory of Acceptance and Use of Technology 2 (UTAUT 2) to explore what drives people to adopt electric motorcycles. Researchers reviewed 10 articles from Google Scholar and found that expectations of performance, effort, and social influence significantly encourage people to use electric motorcycles. However, support and resources available did not greatly affect their decision. Environmental awareness was a key factor in people choosing electric vehicles. This research helps understand what encourages the use of electric motorcycles and suggests areas for future study to increase their adoption.

The Impact of Social Media on Decision-Making in Electric Motorcycles in Indonesia: Online Social Network and Environmental Awareness

The expanding request for feasible transportation alternatives has impelled inquiry into the appropriation of electric cruisers in Indonesia. This think looks at the part of social media in improving natural mindfulness and advancing the selection of electric cruisers. Utilizing a story writing survey strategy, 25 articles were sourced from Google Researcher and the Measurements database. Of these, 10 articles were chosen based on the significance of their titles and abstracts and subjected to a comprehensive assessment. The discoveries uncover that social media introduction altogether impacts the selection of electric cruisers in Indonesia by spreading data, forming peer impact, and cultivating community bolster. Media presentation too considerably impacts buy eagerly, as more noteworthy media engagement can increase shopper mindfulness and cooperation in natural exercises. Be that as it may, the consideration is compelled by a few confinements, counting vague show validations and restricted information scope. Thus, future inquiry ought to center on observational and longitudinal ponders to prove these discoveries.

Analisis Pengaruh Kenyamanan dan Kualitas Produk Terhadap Kepuasan Konsumen pada Motor Listrik PT. Len Industi (Persero) di Lingkungan Militer TNI.

The research aims to analyze the perception of comfort in driving electric motorcycles among TNI military personnel at PT. Len Industri (Persero) to identify factors influencing comfort and measure the impact of PT. Len Industri (Persero) electric motorcycle product quality on interest and satisfaction in the military environment. The study adopts a quantitative method, employing purposive sampling with a total sample of 40 respondents. Data collection involves distributing Likert scale questionnaires to consumers or TNI military personnel using PT Len Industri (Persero) electric motorcycles. The author employs data analysis techniques such as validity testing, reliability testing, normality testing, t-test, F- test, and R-test. The data obtained are processed using SPSS 29. Based on the results of the t-test, the calculated r-value is 5.323 > r-table 1.686, and a significant value of 0.00 < 0.05 is obtained, indicating that the related variables are influenced by independent variables. Referring to the coefficient of determination test results, an R square value of 86.7% is obtained. Thus, based on the analysis conducted, it is concluded that customer satisfaction is influenced by rider comfort and product quality.

Management of battery waste recycling by electric motorbike workshops: A literature review

Battery waste needs to be managed immediately and thus electric vehicles (EVs) become more popular as a sustainable replacement for internal combustion engine vehicles. This review discusses waste battery recycling industry practices and emphasizes the need for authorized battery management services for electric motorcycles. This gap allows third-party businesses not affiliated with the primary manufacturer to emerge and potentially grow sustainably. The literature review methodology involves an in-depth search of relevant literature using the Scopus database with strategically selected keywords. The VoS Viewer collects, organizes, and graphically maps references to synthesize them orderly. The search initially yielded 138 relevant articles. This number was then narrowed to 66 articles published in English journals. Thirty-nine articles were found when the search was modified to focus on full-text availability. These findings indicate an important need for more targeted research to design effective management frameworks for recycling waste batteries in electric motorbike workshops. All critical components increased waste management awareness and expertise among workshop managers, government policies, funding support, and a well-coordinated supply chain. This assessment identifies barriers to developing a viable battery waste recycling sector and makes recommendations for future policy and research initiatives.