Winter-Safe Slip Prevention Rim for E-Scooter: Design to Production Lifecycle Analysis ^†

Abstract

Electric scooters (e-scooters) are becoming popular for short-distance urban transportation since they prioritize environmental sustainability. To enhance the rider’s safety in Alberta’s winter weather, this study entails incorporating real-time posture identification, tire pressure, and slip traction monitoring in e-scooters. This is achieved by deploying sensors from traction control and tire pressure monitoring systems into the rims of e-scooters. The suggested rim’s 3D CAD models, its whole manufacturing cycle, and a simulation-based production layout study are discussed in detail. The manufacturing process simulations reveal bottlenecks, followed by proposed optimizations exhibiting enhanced efficiency, and minimized waiting times.

1. Introduction

Electric scooters are becoming a more trendy and environmentally friendly type of transportation as urban mobility changes constantly. With its varied landscape and growing metropolitan areas, Alberta is a prime example of this development. Electric scooters have become a compelling substitute for short-distance urban transportation. However, using an electric scooter in cold weather poses many challenges. The slippery roads during rain and snow season make it tough for the rider to stand still on a fragile board of an e-scooter while maintaining proper balance. Moreover, traction becomes unstable on snow-covered roadways, increasing the risk to public safety. Safety concerns are exacerbated by limited visibility brought on by fewer daylight hours and bad weather, highlighting the necessity of improved lighting and reflective materials [1].

Electric scooters do not yet have specific safety features, so riders’ safety is at risk when riding one of these vehicles. The dangers of winter driving are increased when drivers struggle with decreased traction and slick roads, and there is no customized remedy. This motivating problem statement emphasizes how important it is to implement cutting-edge technological solutions to improve the safety of using electric scooters in the winter, ultimately providing people with a more dependable and safe means of transportation.

This paper proposes a novel sensor-based rim design that can limit the slipping and imbalance traction issues in e-scooters. The system offers real-time monitoring of posture, tire pressure, and slip traction via corresponding sensors, with alerts displayed through LED indicators. These sensors installed inside the rim of e-scooters will alert the rider about the presence of slipperiness or traction loss in real time, hence, stepping towards rider safety. Moreover, this study investigates and proposes improved production strategies to enhance the overall efficiency of the production line of the proposed sensor-based e-scooter rims. The subsequent sections discuss the 3D CAD models of the proposed rim, its complete production cycle, and a simulation-based production layout analysis. In this paper, Section 2 covers the literature review, Section 3 presents the proposed methodology, Section 4 analyzes the results and proposes improvements, and Section 5 concludes the paper, outlining future directions.

2. Related Work

There are many risks associated with the use of e-scooters. Trivedi et al. studied 249 patients to examine injuries linked to electric scooter (e-scooter) usage. The research revealed that 80% of incidents resulted from falls, while 11% involved collisions with objects [2]. Puzio T et al. delved into a detailed analysis of e-scooter risks, injuries, and the significance of protective gear and adherence to road laws [3]. Ma Q et al. extensively examined e-scooter riding characteristics, utilizing various sensors and a Raspberry Pi for data recording. The study highlighted the impact of different terrains and speeds on accidents [4]. A few literature reports discuss e-scooter features, usage, and some design challenges. Montgomery R et al. investigated the performance of mobility scooters in winter conditions and found that winter tires could improve the slip resistance of mobility scooters in winter conditions [1]. Yuniarto M et al. explained the challenges of shifting to electric-driven technology to reduce air pollution and how modeling and simulation can help in developing an e-scooter energy consumption mode [5]. Garcia-Vallejo D et al. provide information on the dynamics, control, and stability of e-scooters, covering topics such as ground vehicle dynamics [6]. Garman C et al. used a test course to simulate an urban environment to collect data and found that the primary factors affecting tip-over stability are rider inputs such as body positioning and foot placement [7]. Paudel M et al. discuss the need for design guidelines and mathematical models to evaluate the dynamic performance, stability, handling, and safety of e-scooters. The paper focuses on a specific aspect of e-scooter design and does not cover other important factors such as battery life, charging time, and durability [8]. Chen T et al. present a feedback-based traction control system for electric scooters that uses fuzzy logic to prevent slipping and improve safety [9].

When it comes to the production and life cycle of an electric scooter, Jeon S and Kim G’s survey simulation modeling techniques are used in production planning and control. It includes 131 papers from various fields and is organized into sections that describe simulation techniques, review production planning and control issues, and discuss simulation techniques for these issues [10]. Pham T et al. also show how Industry 4.0 and a circular economy can revolutionize manufacturing processes and promote sustainability. Their paper uses a case study of e-scooter sharing to illustrate the concepts in practice [11]. The paper by Severengiz I et al. shows the Life Cycle Assessment of e-scooter-sharing services, which compares their environmental impact to other modes of transport. The study concludes that e-scooters have the potential to reduce greenhouse gas emissions and improve urban mobility, but their environmental impact depends on the specific operating scenarios and sustainable practices and policies [12].

Mittal A et al. discuss the problems of production in their case study of rubber weather strips. Their study showed that their initial production process resulted in a high rate of rejection. By implementing Six Sigma DMAIC (Define, Measure, Analyze, Improve, and Control), the rejection rate was reduced to 3.08%, resulting in substantial cost savings and an improvement in the sigma level from 3.9 to 4.45 within three months [13]. This shows that the DMAIC method can help streamline the production method such that the product quality can be improved, which signifies a reduction in process variation and an increase in process capability.

Today’s era emphasizes adapting manufacturing optimizations and Industry 4.0 to improve a product’s production methods and overall life cycle. However, there is a research gap in the design and analysis of an e-scooter, which shows how features can be added to the e-scooter and how features affect the manufacturability and life cycle of the scooter. Therefore, this paper addresses this gap by implementing safety features to the e-scooter and discusses how adding these features affects its manufacturability.

3. Methodology: DMAIC

This study focuses on developing a manufacturing plan for an improved design of an e-scooter rim incorporating advanced sensors for slip prevention and enhanced safety. To achieve this, a Computer-Aided Design (CAD) model was developed using SolidWorks Education Edition-version 2023, integrating Tire Pressure Monitoring System (TPMS) and Traction Control System (TCS) sensors (Figure 1). The rim’s design is made from aluminum 6061alloy, which ensures ease of manufacturing and structural integrity. This alloy is also known for its excellent strength-to-weight ratio, good corrosion resistance, and weldability. It is widely used in the automotive and aerospace industries for these reasons [14].

The methodology employed in this study (Figure 2) uses the standard DMAIC approach which involves a sequential approach starting with the identification of improvements in e-scooter rim design for enhanced winter safety, which encompasses the integration of advanced sensor technology. Data-driven decisions are made by estimating and analyzing resource needs and production processes through 3D design simulations. This leads to strategic enhancements in the manufacturing layout, including the addition of parallel processing stations to streamline efficiency. The future work involves implementing a feedback loop for continuous refinement of the design and production process, leveraging real-time data acquired from integrated sensors.

To devise a comprehensive production plan, a Computer-Aided Manufacturing (CAM) simulation (using Siemens Tecnomatix Plant Simulation version 2302-Student Edition software) is employed to translate the CAD model into a manufacturable product. This simulation of the production process involves several stages, encompassing CNC machining, drilling, final finishing, and sensor assembly, to achieve the precise specifications of the rim (Figure 3). The production stations were strategically laid out to maximize efficiency, merging automated processes for sensor integration with manual assembly stages to achieve an optimal balance. The cycle and recovery times for each station were also estimated as given in

Table 1. Breakdown of the estimated lead time.

Station No.	Station Name	Process Time	Recovery Time
1	CNC Milling	30 min	5 min
2	Drilling	10 min	2 min
3	Assembly (TPMS)	15 s	1 min
4	CNC cutting/finishing	5 min	2 min
5	Assembly (TPMS + TCS)	30 s	1 min
6	Quality Control	30 s	1 min

, resulting in the estimated lead time of 51 min and 15 s to produce one rim assembly.

4. Results and Discussion

The initial simulation of the production workflow is obtained through the application of CAM software (Tecnomatix Plant Simulation 2302-Student Edition). This simulation outlines each step in the rim manufacturing process, integrating empirical data on cycle and recovery times across various stations, benchmarked against standard times for comparable parts and machinery. It serves as a predictive model for the production cycle, identifying critical points where resource allocation and process timing are crucial. The simulation enables the visualization of the entire manufacturing process, pinpointing potential inefficiencies and establishing a benchmark for future improvements. The resulting resource parameters (working, waiting, blocked, etc.) for each station are shown in Figure 4a, which displays the percentage of the total time each station was running (working) or waiting for the inventory from the upstream station (waiting), etc.

Upon analysis of the production simulations (Figure 4a), it was identified that CNC milling was a bottleneck due to extended waiting times of the stations subsequent to it (downstream stations). Various parameters were analyzed and optimized to rectify this issue and an improved version of the production line simulation is proposed, shown in Figure 5. The replacement of one CNC machine with four parallel CNC machining stations in an improved simulation version effectively resolves the bottleneck in the machining process and quadruples the productivity of the first station. Along with the above change, it was observed that the inspection stations have the longest waiting times in our first production line (Figure 4a). Hence, it is proposed that a centralized inspection station can be made, which can also be used to inspect other components of the e-scooter. These optimizations are anticipated to reduce the waiting time and increase the station’s productivity.

The resource utilization percentages after implementing the above improvements are shown in Figure 4b. As depicted, these adjustments significantly enhanced the process flow by substantially reducing the waiting time and increasing the working time (productivity) for the stations downstream to the CNC machine. The waiting times for the drilling station, assembly stations (front and rear), finishing station, and inspection stations (manual and automatic) were reduced by 30%, 8%, 20%, and 60%, respectively. This results in reducing the total idle time of the production stations from 35 min to 23 min, which is a 34.29% reduction in the total idle time. Furthermore, the total lead time for the production was cut from 51 min and 15 s to 34 min and 30 s, marking a 32.68% reduction in total lead time.

5. Conclusions

This research has successfully developed and optimized a production process for a winter-safe scooter rim with integrated safety sensors. Through a systematic approach involving CAD modeling, production simulation, and process refinement, we significantly improved manufacturing efficiency without compromising the quality and safety features of the rim. The application of the DMAIC methodology led to a substantial increase in productivity, particularly within the machining phase, where a 75% increase in its output was observed. Overall, the total idle time across all stations was curtailed by 34.29%, and the total lead time of the manufacturing process was reduced by 32.68%, demonstrating the effectiveness of the process improvements implemented. These advancements underscore the study’s success in optimizing the manufacturing line for the winter-safe scooter rim.

Looking ahead, further research should explore more advanced automation techniques and the integration of sensors at each production station, which provide real-time data analytics and feedback for continuous process improvement. Expanding the design to accommodate a wider range of scooter models and investigating the use of environmentally sustainable materials would also be advantageous in broadening the rim’s applicability and reducing its environmental impact.