Sport Utility Vehicle Research Articles

Impact of Vehicle Steering Strategy on the Severity of Pedestrian Head Injury.

In response to the sudden violation of pedestrians crossing the road, intelligent vehicles take into account factors such as the road conditions in the accident zone, traffic rules, and surrounding vehicles' driving status to make emergency evasive decisions. Thus, the collision simulation models for pedestrians and three types of vehicles, i.e., sedans, Sport Utility Vehicles (SUVs), and Multi-Purpose Vehicle (MPVs), are built to investigate the impact of vehicle types, vehicle steering angles, collision speeds, collision positions, and pedestrian orientations on head injuries of pedestrians. The results indicate that the Head Injury Criterion (HIC) value of the head increases with the increase in collision speed. Regarding the steering angles, when a vehicle's steering direction aligns with a pedestrian's position, the pedestrian remains on top of the vehicle's hood for a longer period and moves together with the vehicle after the collision. This effectively reduces head injuries to pedestrians. However, when the vehicle's steering direction is opposite to the pedestrian's position, the pedestrian directly collides with the ground, resulting in higher head injuries. Among them, MPVs cause the most severe injuries, followed by SUVs, and sedans have the least impact. Overall, intelligent vehicles have great potential to reduce head injuries of pedestrians in the event of sudden pedestrian-vehicle collisions by combining with Automatic Emergency Steering (AES) measures. In the future, efforts need to be made to establish an optimized steering strategy and optimize the handling of situations where steering is ineffective or even harmful.

Comparative assessment of global warming potential of gasoline, battery, and hybrid vehicles in India

This study reports a comparative analysis of life cycle greenhouse gas (GHG, CO2-eq.) emissions of three fuel-type vehicles, viz. internal combustion engine (ICEVs), battery electric (BEVs), and hybrid electric vehicles (HEVs), from an Indian perspective. Vehicles in hatchback and compact sports utility vehicles (SUVs) categories were analyzed. GHG emissions in fuel life cycle (well-to-tank and tank-to-wheel) and vehicle life cycle were estimated using data from websites of OEMs and ministries of Government of India, and the GREET software. The vehicle life cycle constitutes a smaller fraction (15–20 %) of total life cycle GHG emissions for ICEVs and HEVs compared to BEVs (∼35 %). The vehicle cycle emissions of BEVs are higher than ICEV and HEV in both categories (52–63 % for hatchback and approx. 45 % for compact SUV). The fuel cycle emissions of BEVs are smaller than ICEV and HEV in both categories (29–34 % for hatchback and 32–40 % for compact SUV). The total life cycle GHG emissions of BEVs were lower than ICEV and HEV (15–17 % in hatchback and 17–25 % in compact SUV category) for the 2023 energy mix in India with a significant share of thermal route. With anticipated doubling of renewable energy sources in India by 2030, the total life cycle GHG emission of BEV will decrease by approximately 20 %. However, sensitivity analysis revealed that BEVs produce higher GHG emissions than ICEVs and HEVs until a minimum lifetime travel distance. Thermal power plant efficiency and battery lifespan also influence BEVs lifetime GHG emissions.

Experimental Evaluation of Damping and Stiffness in Optimized Active Sport Utility Vehicle Suspension Systems

On a flat city road, the specificity of the suspension poses a challenge. Sport utility vehicles (SUVs) have less flexibility compared to regular cars, despite providing a firm grip on asphalt and concrete. Constant adjustments to the center of gravity of SUVs are necessary. Surprisingly, they are less reliable and stable in urban settings due to this characteristic. In this study, a mathematical model of the suspension system of SUVs based on the Newtonian approach is introduced and validated with data from experiments carried out by the MTS machine system on the mono-tube (oil/air) mixed damper element. The model accurately predicts the performance of this damper, commonly used in SUVs, across various operating conditions, including different frequencies. A coil spring element, serving as a passive suspension unit with this damper, is also tested experimentally under similar conditions. By integrating passive suspension elements with the active actuator, the proposed modified design reduces the power consumption needed for the actuator to function and ensures a certain level of reliability. The effectiveness and performance of the modified active suspension system in comparison to the traditional passive suspension system are assessed using three different strategies: hybrid PID-LQR, linear quadratic regulator (LQR), and proportional-integral-derivative (PID). The genetic algorithm is utilized to determine the optimal parameter values for each controller by minimizing a cost function, maximizing performance, and minimizing energy consumption. Simulation results demonstrate that the active suspension system controlled by the PID-LQR controller offers significantly improved ride comfort and vehicle stability compared to other systems. This suggests that the performance of the active suspension system is greatly enhanced by the combined application of the hybrid PID-LQR controller compared to other systems.

Validation of a generic finite element vehicle buck model for near-side crashes

Objective Finite element (FE) reconstructions of motor vehicle crashes using human body models are effective tools for developing a better understanding of occupant kinematics and injuries in real-world lateral crash conditions, but current near-side reconstruction methods are limited by the paucity of full-scale FE vehicle models. The objective of this study was to validate a generic vehicle model equipped with left-side airbags and intrusion capability by simulating a series of near-side crash tests for a range of vehicles and assessing model accuracy using objective evaluation methods. Methods Moving deformable barrier crash tests were reconstructed for five common vehicle classifications (compact passenger, mid-size passenger, sport utility vehicle, pickup truck, and van) using an updated version of a previously developed simplified vehicle model. Unknown vehicle and intrusion properties (pretensioner force, seatback airbag pressure, curtain airbag pressure, door panel stiffness, ratio of dynamic-to-static intrusion, intrusion velocity, and intrusion scaling factor) were estimated by parameterizing them across 224 simulations per crash test using a Latin hypercube design of experiments. Model accuracy was assessed for 13 anthropomorphic test device signals using the Correlation and Analysis (CORA) objective rating method and injury metric comparisons. Results Maximum ratings of 0.69, 0.67, 0.52, 0.52, and 0.62 were achieved for the compact passenger, midsize passenger, sport utility vehicle, pickup truck, and van classifications, respectively. On average, the abdomen displayed the most accurate behavior (0.51 ± 0.12), followed by the thorax (0.50 ± 0.10) and head (0.50 ± 0.07). The pelvis displayed the least accurate behavior (0.46 ± 0.18) of any region. Reconstructions overpraedicted injury metrics in all cases. Conclusions All vehicles achieved “fair” biofidelity ratings and the compact passenger and midsize passenger vehicles achieved “good” biofidelity ratings, validating them for kinematic evaluations with vehicle-to-vehicle nearside crash reconstructions. Regression models were developed for injuries and CORA ratings and can be used to optimize vehicle parameters in future studies.

Investigating factors affecting injury severity of single-vehicle run-off-road crashes

This study aimed to identify and investigate the contributing factors influencing injury severity in single-vehicle run-off-road (ROR) crashes, which are known for their high severity. The primary objective was to analyze and compare the impact of these factors across three distinct vehicle classes: passenger cars, sport utility vehicles (SUVs), and pickups. A mixed logit model with heterogeneity in mean and variance was developed to analyze the injury severity outcomes in ROR crashes for the three vehicle classes. The model accounted for the potential variations in the impact of contributing factors across different vehicle types. The study revealed several significant variables consistently influencing injury severity across all three vehicle classes. These included driver age, alcohol or drug usage, seatbelt utilization, airbag deployment, higher travel speeds, and the vehicle model year post-2010. Notably, as driver age increased, the impact on changes in injury severity outcomes was more pronounced for pickup drivers compared to those operating passenger cars and SUVs. Among the common findings was the highly effective role of seatbelt usage in mitigating injury severity in ROR crashes. Additionally, passenger cars were associated with increased injury severity, particularly at relatively higher travel speeds exceeding 75 mph when contrasted with SUVs and pickups traveling between 61 and 75 mph. The study highlights the importance of considering vehicle class-specific factors in analyzing injury severity in ROR crashes. Recommendations include further in-depth investigations into distinct factors contributing to injury severity within each vehicle class and utilizing more extensive crash datasets to gain additional insights for enhancing road safety.

Multi-Criteria Decision Making In The Selection Of Electric Sports Utility Vehicles: Integrated Critic–Copras Method

The aim of this study was to assess and determine the most suitable electric sports utility vehicle (e-SUV) alternatives for consumers who are considering buying an e-SUV that is sold in Turkey. Accordingly, 10 different vehicles were selected using specific criteria from the e-SUVs available through distributor sales in Turkey. CRITIC and COPRAS methods were used to rank these vehicles. The results of the CRITIC method for determining the weights of the criteria were the following three: fast charging time, energy consumption, and price. The results of the analyses conducted using the COPRAS method and these weights determined the e-SUV rankings. Based on these results, the top three alternatives were Subaru Solterra, New MG ZS EV, and BMW iX3 eDrive20.

Multilevel Hierarchical Bayesian Modeling of Cross-National Factors in Vehicle Sales

SUVs (sport utility vehicles), as a car segment, have become a foundation within the automotive industry due to their versatility, which is used by a wide range of customers. Recognising the complex interplay between geographical and economic conditions across countries, we delve into cross-national factors that significantly influence SUV sales. This article presents an analysis of the global sales of SUVs (sport utility vehicles) using multilevel hierarchical Bayesian modelling. We identify key predictors of SUV sales, including the effects of fuel prices, income levels and geographical aspects. We prepared four statistical models that differ in their probability distribution or hierarchical internal structure. The last presented model, with Student’s t-distribution and separate distribution for unique alpha parameter values, turned out to be the best one. Our analysis contributes to a deeper understanding of the automotive market dynamics, and it can also assist manufacturers and policymakers in designing effective sales strategies.

Optimal ventilation strategies for balancing carbon dioxide and suspended particulate matter concentrations in vehicle cabins

Maintaining safe levels of carbon dioxide (CO2) and particulate matter (PM) in vehicle cabins during driving is essential to ensure driver safety and minimize health risks. However, while the fresh-air ventilation mode reduces the CO2 level, it also allows the ingress of PM pollutants from the external environment and hence increases the PM concentration in the cabin. Thus, effective ventilation strategies are required to optimize the air quality by balancing the average levels of CO2 and suspended PM within the cabin. Accordingly, this study performed computational fluid dynamics simulations to explore the effects of different air-conditioning ventilation modes on the indoor air quality in the cabin of a commercial sports utility vehicle. The simulations commenced by examining the optimal switching times between the recirculation and fresh-air modes that yielded an average CO2 concentration of less than 1000 ppm for various numbers of occupants in the cabin. Further simulations were then performed to investigate the effect of the air filtration efficiency of the in-cabin filter on the PM concentration in the cabin under the optimal ventilation strategies determined for 1–5 occupants. The results indicated that, as the number of occupants increased, the required fresh-air mode usage time also increased. Furthermore, to mitigate the corresponding increase in the PM concentration, a higher filtration efficiency was required. Overall, the results showed that the optimal switching cycles achieved the required indoor air quality within the cabin by successfully balancing the CO2 and suspended PM concentrations.

Development of a recommended practice for evaluating rumble strip noise and vibration performance

As part of the NCHRP 15-68 project "Effective Low-Noise Rumble Strips" project, a recommended measurement program was developed for evaluating the noise and vibration performance of rumble strip designs. Two rumble strip designs located in Northern California, which included cylindrical indentations ground into the pavement at regular intervals and a continuous sinusoidal profile, were used to assess the input to a vehicle operator and the resultant exterior noise when the vehicle departed from the travel lane. Measurements were conducted on and off shoulder rumble strips at 60 and 45 mph with eight light vehicles ranging from small compact passenger cars to large sport utility vehicles. Six triaxial vibration measurements and six microphone locations were evaluated on the interior of the vehicle, and from these measurements, one primary and one (optional) secondary location for noise and vibration were selected for the recommended procedure. Vehicle pass-by measurements were also made following the procedures of AASHTO TP 98 Statistical Isolated Pass-By Method. While not required, on-board sound intensity (OBSI) testing is recommended to establish the off-strips pavement level. The recommended procedure is intended to be used by transportation agencies and other practitioners to assure comparable results when evaluating existing and alternative rumble strip designs.

The impact of ambient temperature and powertrains of SUVs on the environment in Slovakia during the use phase

This study compares different powertrains of sport utility vehicles (SUVs) with respect to ambient temperature and energy mix in Slovakia using the well-to-wheel (WTW) Life Cycle Assessment (LCA) method. Battery electric vehicles (BEV), plug-in hybrid electric vehicles (PHEV), and petrol and diesel vehicles were assessed and compared. The WTW study was conducted in SimaPro software assessing electricity/petrol/diesel production, transport, and use (energy conversion in the vehicle), with impact categories being climate change, particulates, NOx emissions, ionizing radiation, and fossil resource scarcity depending on the season (summer and winter). The results indicate that for Slovak conditions, BEV generally had the lowest environmental impact in both seasons studied. The only exceptions were ionizing radiation, which is clearly caused by the high share of nuclear power in the Slovak energy mix, and NOx emissions, which are caused by the combustion of biomass for electricity generation. The other impact categories were dominated by vehicles with an internal combustion engine. The results of emissions from fuel production are also given for each impact category. The transportation of fuel did not exceed the value of 1% for any impact category or for any powertrain. The conclusions of the study support the global trend in favour of vehicle electrification as an important way to reduce the negative environmental impacts of internal combustion engine vehicles in Slovakia.

Multi-objective optimization of vehicle body B-pillar lower joints based on crashworthiness analysis

The joints in an automobile’s body structure are crucial in bearing loads and transmitting stresses, thereby significantly affecting the body’s rigidity. To effectively improve body rigidity and crashworthiness, this study employed a sensitivity analysis to identify the critical joints among the nine joints of a specific sport utility vehicle (SUV) body. Following regulatory requirements, collision simulations were performed, revealing that the joint below the B-pillar exhibited the most significant deformation. Thus, using the material and thickness of the B-pillar’s lower joint as design variables, experimental samples were generated by the design of experiment (DOE). A multi-objective optimization for the B-pillar’s lower joint model was conducted using the response surface method and the simulated annealing algorithm to determine the final optimized solution. The optimization results showed a 9.31% increase in body bending stiffness, an 11.37% increase in torsional stiffness, and reduced intrusion at various points on the B-pillar, effectively enhancing the body’s rigidity and crashworthiness.

Many-objective optimization for overall performance of an electric sport utility vehicle under multiple temperature conditions based on natural gradient boosting model

In this study, significant efforts have been devoted to optimizing energy consumption and driving range of an electric sport utility vehicle (ESUV). Vehicle energy flow tests were conducted at different temperatures, and integrated simulation models were built and validated. Subsequently, Natural Gradient Boosting (NGBoost) model was constructed based on extensive datasets obtained by parallel simulation framework, and many-objective optimizations of ESUV design parameters were performed. The results show that energy consumption per 100 km under high and low temperatures increases by 19.3 % and 34.9 % compared to room temperature, while effective work of half-axis decreases by 12.9 % and 28 %, suggesting extreme environmental temperature significantly affects ESUV performance. Mean square errors (MSEs) of NGBoost model predictions are less than 0.12, indicating its reliable predictive capability for unknown data. Many-Objective Random Walk Grey Wolf Optimizer (MORW-GWO) algorithm achieves optimal or suboptimal results on 10 test problems, fully demonstrating its efficient convergence and excellent robustness. Battery recovered energy, effective work of half-axis and electricity consumption per 100 km of final optimization scheme are increased by 20.2 %, 9.0 % and 13.0 %, with the largest overall percentage improvement of 17.3 %. These findings can provide data support and directional guidance for optimization designs and performance improvements of ESUVs.

Quantifying delayed climate mitigation benefits in electric and hydrogen fuel cell vehicle deployment for sustainable mobility

In the fight against climate change, transitioning to green transport is imperative. Our study evaluates the Greenhouse Gas Emission Breakeven Time (GBET) for Electric Vehicles (EVs) and Hydrogen Fuel Cell Vehicles (HFCVs) in China, the largest auto market globally. Emissions vary significantly across Chinese provinces due to differences in grid carbon intensity, hydrogen production methods, and vehicle usage. EVs show more than a 50 % difference in Well-to-Wheel (WTW) emissions regionally, which is also true for HFCVs, although they exhibit less interprovincial variability, suggesting a steadier reduction trend. These findings stress the need for regional considerations in Life Cycle Assessment (LCA) and introduce the concept of “climate debt” to evaluate the delayed benefits of emission reductions. HFCVs, benefiting from cleaner hydrogen production advancements, can repay their climate debt about 60 % quicker than EVs, presenting a caveat: the risk of short-term emission spikes if the initial greenhouse gas surplus isn't efficiently compensated. We found that the average GBET for EV-Cars is about 3.39 years and for EV-SUVs (Sports Utility Vehicle), 4.23 years. Comparatively, HFCV-Cars and HFCV-SUVs have shorter average GBETs of roughly 1.39 and 1.43 years, respectively, indicating faster environmental paybacks. Conclusively, our study endorses regional assessment frameworks and highlights HFCVs' role in China's path to a low-carbon future. It is essential for sustainable mobility stakeholders to adapt vehicle technology adoption and related policies to local energy contexts to meet climate goals. Our research provides crucial insights into how green vehicles can achieve emission targets, advocating for a localized approach in sustainable transportation strategies.

An integrated data-driven framework for vehicle quality analysis based on maintenance record mining and Bayesian network

PurposeThe purpose of this paper is to present an integrated data-driven framework for processing and analyzing large-scale vehicle maintenance records to get more comprehensive understanding on vehicle quality.Design/methodology/approachWe propose a framework for vehicle quality analysis based on maintenance record mining and Bayesian Network. It includes the development of a comprehensive dictionary for efficient classification of maintenance items, and the establishment of a Bayesian Network model for vehicle quality evaluation. The vehicle design parameters, price and performance of functional systems are modeled as node variables in the Bayesian Network. Bayesian Network reasoning is then used to analyze the influence of these nodes on vehicle quality and their respective importance.FindingsA case study using the maintenance records of 74 sport utility vehicle (SUV) models is presented to demonstrate the validity of the proposed framework. Our results reveal that factors such as vehicle size, chassis issues and engine displacement, can affect the chance of vehicle failures and accidents. The influence of factors such as price and performance of engine and chassis show explicit regional differences.Originality/valuePrevious research usually focuses on limited maintenance records from a single vehicle producer, while our proposed framework enables efficient and systematic processing of larger-scale maintenance records for vehicle quality analysis, which can support auto companies, consumers and regulators to make better decisions in purchase choice-making, vehicle design and market regulation.

Experiment investigation for interconnected effects of driving cycle and ambient temperature on bidirectional energy flows in an electric sport utility vehicle

The constrained driving range has emerged as a pivotal quandary impeding the advancement of electric sport utility vehicles (ESUVs). In this study, energy flow tests were executed on an ESUV to investigate interconnected effects of driving cycle and ambient temperature on bidirectional energy flows throughout the entire driving range. The results reveal that air conditioner singularly imposes a substantial surge in energy consumption at high-temperature settings. Notably, energy utilization efficiency (ηuti) reaches its zenith under Fixed speed cycle at room temperature (FSR_R) and energy recovery efficiency (ηrec) attains its peak under World Light Vehicle Test Cycle at room temperature (WLTC_R). Nevertheless, ηuti and ηrec attain their acme at room temperature and reach their nadir under low-temperature conditions, regardless of the specific driving cycle employed. Battery average temperature exhibits an ascending trajectory at room temperature and displays cyclic fluctuations at high temperature, while first increasing rapidly and then decreasing gradually in low-temperature ambient. Battery output energy per cycle initially decreases and eventually stabilizes with increasing the cycle count, and the battery recovery energy shows an initial upward trend followed by a stabilization phase. These findings provide empirical substantiation and prescriptive insights for mitigating energy consumption and enhancing driving range of ESUVs.

Influence of car front-end designs on motorcyclists’ trajectory in head-on and side-on-head crashes

Motorcyclists are highly vulnerable road users, and cars are one of their primary crash opponents. This study investigates the influence of car front-end designs on motorcyclist trajectory in head-on and side-on-head crashes. The analysis is based on a dataset of 120 multi-body crash simulations conducted using MADYMO and post-processed with MATLAB. An analysis of 1412 real-world Powered Two-Wheeler (PTW) to car accidents was conducted to determine the most common crash configurations and the associated ranges of the variables, such as vehicle speeds and contact points. Three PTW styles (sport-touring, scooter, and sport) and four car front-end designs (Sport utility vehicle (SUV), Family Car/Sedan (FCR), Roadster (RDS), and Multi-purpose vehicle (MPV)) were considered.The study examined the riders’ thrown distance in both collision types. It was observed that, regardless of the collision type, the head was identified overall as the primary body region coming into contact with the opposing vehicle, followed by the chest and neck. In frontal collisions, an augmented bonnet height corresponded to an increased incidence of head contact, whereas a lower bonnet height resulted in a higher frequency of chest contact. Moreover, the thrown distance depended also on PTW speed, particularly for sport and sport-touring motorcycles. Notably, contact with the car windscreen was only observed at velocities exceeding 60 km/h, whereas impact with the bonnet leading edge occurred exclusively below this threshold. Due to the shielding effect of their PTW’s fairing, scooter riders predominantly experienced no contact with the opposing vehicle. Sport-touring motorcycles exhibited a more vertical trajectory upon ejection, leading to a greater likelihood of overturning and subsequent rearward head impact with the vehicle. In contrast, sport motorcycles tended to forward projections with a high likelihood of chest contact. In the case of lateral impacts, it was observed that vehicles with a more prominent profile, such as SUVs and MPVs, equipped with protruding bumpers, effectively restrained riders. In this case, vehicle speed did not exert a significant influence on the thrown distance. Additionally, the presence of a conspicuous fuel tank and the initial posture of the rider on the PTW played a crucial role in determining the final thrown distance. Due to their upright postures and the absence of a pronounced fuel tank, scooter dummies were thrown further than others, thus causing head contact with the windscreen.These findings highlight the importance of car front-end design and PTW fairings in mitigating riders’ injuries and provide valuable insights to vehicle manufacturers for developing tailored safety measures for riders.

Examining risky driving behaviours: A comparative analysis of SUVs and other car types

The aim of the study was to analyze whether the driving behaviour changes with different types of cars (Hatchback, Sedan and Sports Utility Vehicle (SUV)), particularly on expressways. To achieve the study's objective, an exclusive DBQ was designed. Self-reported frequency of instances related to violations, mistakes and lapses were gathered which can collectively be used to study driver's behaviour in different circumstances. A total of 546 samples were collected through an online survey from drivers across India who possessed a valid driving license. Results of exploratory factor analysis confirmed two factor solution (errors and violations) and the same was verified by confirmatory factor analysis. With the help of structural equation modelling, individual risky driving score of each driver was calculated. Based on the calculated scores, drivers were clustered into different risk levels using K-Means clustering technique. Further, ANOVA test revealed that SUV drivers performed riskier behaviour more compared to other types of cars. To compare the relationships among different risk levels with drivers' demographics and driving characteristics, two ordered logit models were developed for SUV and Standard car drivers. The results indicated that drivers' age, crash history, and speeding behaviour were statistically significant predictors of risky driving behaviour. Comparison of risk levels between SUV and Standard cars with respect to different predictor variables presented various interesting findings. These findings would be helpful in understanding the differences among risky driving behaviour performed by drivers of different types of cars and to identify the potential road safety countermeasures.

A numerical investigation of e-scooter-to-vehicle traffic accidents

Within the past decade, injuries caused by electric scooter (e-scooter) crashes have significantly increased. A common cause of fatalities for e-scooter riders is a collision between a car and an e-scooter. To develop a better understanding of the complex injury mechanisms in these collisions, four crashes between an e-scooter and a family car/sedan and a sports utility vehicle were simulated using finite element models. The vehicles impacted the e-scooter at a speed of 30 km/hr in a perpendicular collision, and at 15 degrees towards the vehicle, to simulate a rider being struck by a turning vehicle. The risks of serious injury to the rider were low for the head, brain, and neck, but femur/tibia fractures were observed in all simulations. The primary cause of head and brain injuries was found to be the head-ground impact in cases where such an impact occurred.

Vehicle-Type Recognition Method for Images Based on Improved Faster R-CNN Model.

The rapid increase in the number of vehicles has led to increasing traffic congestion, traffic accidents, and motor vehicle crime rates. The management of various parking lots has also become increasingly challenging. Vehicle-type recognition technology can reduce the workload of humans in vehicle management operations. Therefore, the application of image technology for vehicle-type recognition is of great significance for integrated traffic management. In this paper, an improved faster region with convolutional neural network features (Faster R-CNN) model was proposed for vehicle-type recognition. Firstly, the output features of different convolution layers were combined to improve the recognition accuracy. Then, the average precision (AP) of the recognition model was improved through the contextual features of the original image and the object bounding box optimization strategy. Finally, the comparison experiment used the vehicle image dataset of three vehicle types, including cars, sports utility vehicles (SUVs), and vans. The experimental results show that the improved recognition model can effectively identify vehicle types in the images. The AP of the three vehicle types is 83.2%, 79.2%, and 78.4%, respectively, and the mean average precision (mAP) is 1.7% higher than that of the traditional Faster R-CNN model.

ANALISIS SPESIFIKASI TEKNIS SISTEM PENGEMUDIAN ELEKTRIK PADA KENDARAAN LISTRIK BERBASIS BATERAI KOMERSIAL POPULER

The increase in the number of motor vehicles in Indonesia has elevated the negative impact on the environment, both in terms of air pollution and global warming. According to the Ministry of Transportation (Kemhub), the number of motor vehicles in Indonesia reached 141.99 million units in 2021. The electrification of motor vehicles is one of the solutions to reduce emissions from the motor vehicle sector. This is supported by Presidential Regulation No. 55/2019 to stimulate the growth of the national battery-based electric vehicle (BEV) industry. This paper reviews battery-based electric vehicle (BEV) technology worldwide, especially four-wheeled vehicles with a high total population. The research was conducted through online surveys in 2023 using international data sources and the Association of Indonesian Automotive Industries (GAIKINDO), resulting in a database of 33 battery-based electric vehicles. From this database, it was found that the majority of electric cars purchased by the public are hatchback and sport utility vehicle (SUV) types. In terms of electric drive systems, the power capacity range of electric motors used by these vehicles is between 20 - 150 kW for hatchbacks and 100 - 406 kW for SUVs. The most commonly used electric motor type, constituting about 64%, is the Permanent Magnet Synchronous Motor (PMSM). About 70% of these vehicles use lithium-ion battery technology. The technical specifications of these popular electric cars are expected to assist stakeholders in quickly understanding the technical specifications of electric drive systems in battery-based electric vehicles, serving as a benchmark for designing electric cars suitable to meet domestic needs.