Energy Efficiency Of Electric Vehicles Research Articles

Experimental study and exergy analysis of electric vehicle single-stage compression secondary throttling CO2 air-source heat pump system in cold climate

A novel CO2 air-source heat pump system (ASHPs) with a secondary throttling based on a single-stage compression was proposed to further improve the heating energy efficiency of electric vehicles (EVs) for heating in cold climates. The heating characteristics of the secondary throttling system (STS) and conventional throttling system (CTS) at different indoor air supply temperatures were compared. The STS performance characteristics were investigated under varying ambient temperature, outdoor air velocity, and exergy analysis was performed on the system and its components. A fitting correlation equation for optimal discharge pressure was obtained based on experimental data. The results demonstrate that implementing STS significantly reduces CO2 ASHPs energy consumption (Wcom), enhances the coefficient of performance (COP), and reduces exergy destruction. When the indoor air supply temperature reaches 45 °C, using STS can result in an 8.1 % reduction in Wcom, a 9.4 % increase in COP, and a 6.1 % improvement in the exergy efficiency (Exη) compared to that of CTS. Furthermore, the advantages of STS become more pronounced with higher indoor target air supply temperatures. As the ambient temperature decreases, the exergy efficiency of the STS reaching the target air supply temperature decreases. The velocity of outdoor air significantly affects the STS performance, and increasing outdoor air velocity can effectively enhance the Exη of STS. The optimal discharge pressure fitting equation for the STS was obtained through multiple linear regression analysis. These findings can provide ideas for architectural optimization and design of CO2 ASHPs for EVs, which are important for further improving system performance.

Fuzzy Logic-Based Energy Management System for Regenerative Braking of Electric Vehicles with Hybrid Energy Storage System

Electric vehicles (EVs), which are environmentally friendly, have been used to minimize the global warming caused by fossil fuels used in vehicles and increasing fuel prices due to the decrease in fossil resources. Considering that the energy used in EVs is obtained from fossil resources, it is also important to store and use energy efficiently in EVs. In this context, recovery from a regenerative braking system plays an important role in EV energy efficiency. This paper presents a fuzzy logic-based hybrid storage technique consisting of a supercapacitor (SC) and battery for efficient and safe storage of a regenerative braking system. First, the constraints of the battery to be used in the EV for fuzzy logic control are identified. Then, the fuzzy logic system is created and tested in the ADVISOR and Siemens Simcenter Flomaster programs in the New European Driving Cycle (NEDC) driving cycle. A SC was selected for primary storage to prevent the battery from being continuously charged from regenerative braking, thus reducing its lifetime. In cases where the vehicle consumes more energy than the average energy consumption, energy consumption from the battery is reduced by using the energy stored in the SC, and the SC energy is discharged, making preparations for the energy that will come from the next regenerative braking. Thus, the high current values transferred to the battery during regenerative braking are effectively limited by the SC. In this study, the current values on the battery in the EV with a hybrid storage system decreased by 29.1% in the ADVISOR program and 28.7% in the Simcenter Flomaster program. In addition, the battery generated 46.84% less heat in the hybrid storage system. Thus, the heating and capacity losses caused by this current on the battery were minimized. The presented method provides more efficient energy management for EVs and plays an important role in maintaining battery health.

Extend the EV Range through Dynamic Scheduling of Battery: Present and Future Techniques

The pursuit of extending the driving range and improving the energy efficiency of electric vehicles (EVs) is a critical objective in advancing sustainable transportation. Central to this pursuit is the battery management system (BMS), which ensures the operational integrity and optimal performance of the EV's battery. Traditional BMSs have largely been conservative, relying on static parameters and predefined rules, which often do not fully exploit the battery's capacity or adapt to the dynamic nature of driving conditions. This has resulted in EVs that do not optimize their range, either underutilizing their energy or risking premature depletion. This paper introduces a dynamic scheduling approach for battery usage in EVs, a paradigm shifts from traditional BMS algorithms that are deterministic and linear, to one that is adaptable and predictive. The proposed dynamic scheduling method utilizes data analytics, machine learning, and real-time monitoring to anticipate and adapt to varying driving conditions, traffic patterns, driver behavior, and route topography. The objective is not just to respond to the battery's current state but to manage the energy distribution proactively, optimizing the use of the stored energy and enhancing the vehicle's range on a single charge. The paper explores the technological advancements enabling dynamic scheduling, the benefits of such a system, and the challenges it may encounter. It is posited that dynamic scheduling represents a necessary evolution in battery management, capable of significantly boosting the range and desirability of EVs. Finally, the paper proposes a novel system and method that leverage real-time data and machine learning to implement an effective energy management strategy for dynamic scheduling, which could markedly improve the range of an EV. The implications of this approach suggest a future where EVs can meet and exceed the range expectations of consumers, thereby accelerating the transition to electric mobility.

Last Mile Humanitarian Aid Delivery Model with Electric Vehicles to Disaster Areas

Natural disasters that cause a lot of loss of life and property in Türkiye occur frequently due to reasons such as our country's geographical location, climatic conditions and landforms. For this reason, it is necessary to be extremely prepared for the consequences of disasters. It is also very important to quickly deliver the necessary materials and equipment to people affected by these disasters. For this reason, electric vehicles were included in the mathematical model proposed in the study due to the fuel problems that will be experienced during the disaster process. In this study, a 0-1 mixed integer last mile humanitarian aid distribution model that will be delivered to hospitals from the designated warehouse after a possible disaster is proposed. The proposed model determines routes that will minimize the total distance of electric vehicles, taking into account their charging and energy consumption. Considering the power outages after the earthquake, solar powered electric vehicle charging stations were preferred. The proposed model was applied for the post-earthquake aid distribution example in Kayseri province. Distance data and consumed energy amounts for the Kayseri province example were entered into the model and solved in the CPLEX program. With this study, an example application was carried out for aid distribution activities with electric vehicles after an earthquake in Kayseri. Additionally, the effectiveness of the use of energy-efficient electric vehicles in disaster management was evaluated in the article.

Application of Neural Network Feedforward in Fuzzy PI Controller for Electric Vehicle Thermal Management System: Modeling and Simulation Studies

The electric vehicle thermal management system (EVTMS) plays a crucial role in ensuring battery efficiency, driving range, and passenger comfort. However, EVTMSs still face unresolved challenges, such as accurate modeling, compensating for temperature variations, and achieving efficient control strategies. Addressing these issues is crucial for enhancing the performance, reliability, and energy efficiency of electric vehicles. Therefore, this study presents a cooling EVTMS model, considering both the battery pack temperature and the cabin comfort, and utilizes the prediction of neural network as a feedforward in a fuzzy PI controller to compensate for the model temperature variations. The simulation results reveal that, compared with PI controller and MPC, the neural network fuzzy PI (NN-Fuzzy PI) controller can well predict and compensate for the system’s nonlinear characteristics as well as the time-delay caused by heat transfer, achieving superior control performance and reducing energy consumption. The battery pack temperature and PMV fluctuations are effectively constrained within [−0.5, 0.5] and [−0.1, 0.1], reducing up to 150% and 164%, and the energy consumption of the pump and compressor are reduced by up to 0.23 and 100.1 KJ, with ranges of 18% and 2.68%. Meanwhile, the neural network feedforward also works effectively in different controllers. The findings of this research can provide valuable insights for TMS engineers to select advanced control strategies.

Optimal Predictive Torque Distribution Control System to Enhance Stability and Energy Efficiency in Electric Vehicles

This article presents a novel approach to address the critical issues of stable rotation and energy efficiency in electric vehicles (EVs). To achieve these objectives, we propose a comprehensive control system that leverages the power of optimization through optimal predictive control methods. The central idea revolves around minimizing the predicted tracking error for future time steps by intelligently determining control inputs. In this innovative approach, we emphasize the dynamic adjustment of weight coefficients and optimization of wheel torque to strike a delicate balance between energy consumption and enhanced vehicle stability. The result is an adept controller that not only ensures vehicle stability but also significantly reduces energy consumption. Given the inherent limitations of electric motors, especially in terms of torque during vehicle operation, and the growing importance of energy conservation, our method tailors weight coefficients to generate optimal wheel torque. This ensures that the electric motors operate within their power range, thereby minimizing energy consumption and extending the overall efficiency of EVs. The combination of stable rotation and energy efficiency offered by this control system represents a promising step forward in the realm of electric vehicles, making them more sustainable and environmentally friendly while maintaining the high standards of performance and safety that consumers expect.

Enhancing Energy Efficiency in Connected Vehicles for Traffic Flow Optimization

In urban settings, the prevalence of traffic lights often leads to fluctuations in traffic patterns and increased energy utilization among vehicles. Recognizing this challenge, this research addresses the adverse effects of traffic lights on the energy efficiency of electric vehicles (EVs) through the introduction of a Multi-Intersections-Based Eco-Approach and Departure strategy (M-EAD). This innovative strategy is designed to enhance various aspects of urban mobility, including vehicle energy efficiency, traffic flow optimization, and battery longevity, all while ensuring a satisfactory driving experience. The M-EAD strategy unfolds in two distinct stages: First, it optimizes eco-friendly green signal windows at traffic lights, with a primary focus on minimizing travel delays by solving the shortest path problem. Subsequently, it employs a receding horizon framework and leverages an iterative dynamic programming algorithm to refine speed trajectories. The overarching objective is to curtail energy consumption and reduce battery wear by identifying the optimal speed trajectory for EVs in urban environments. Furthermore, the research substantiates the real-world efficacy of this approach through on-road vehicle tests, attesting to its viability and practicality in actual road scenarios. In the proposed case, the simulation results showcase notable achievements, with energy consumption reduced by 0.92% and battery wear minimized to a mere 0.0017%. This research, driven by the pressing issue of urban traffic energy efficiency, not only presents a solution in the form of the M-EAD strategy but also contributes to the fields of sustainable urban mobility and EV performance optimization. By tackling the challenges posed by traffic lights, this work offers valuable insights and practical implications for improving the sustainability and efficiency of urban transportation systems.

A bibliometric review on electric vehicle (EV) energy efficiency and emission effect research.

Electric vehicles have received extensive attention due to their unique energy efficiency and good emission reduction effects. While a large-scale of electric vehicles are gradually replacing traditional fuel vehicles, it is necessary to ensure the energy efficiency of electric vehicles and the effectiveness of their emission reduction effects. This study conducted a bibliometric analysis of scientific publications on energy efficiency and emission reduction effects of electric vehicles from 2003 to 2022, using a variety of bibliometric tools such as R Studio, biblioshiny and VOSviewer. The results showed the gradual elimination of traditional energy vehicles, where electric vehicles play an important role in connecting energy efficiency and emission control. The results also showed the top publication outlets, citations trackers, authors with thematic evaluation of energy efficiency and emission reduction effects of electric vehicles. The contribution of the study is manifold. The academic contribution of the present study is the bibliometric analysis which will help academicians to get a quick overview of the most popular journals, top collaborators, documents, authors, and scientific knowledge structure. Secondly, policy makers, environmentalists, researchers, and academician will definitely get a pathway how they should go for future research. Finally, this study suggests more researches trend to focus on the sales of electric vehicles, automobile exhaust emissions, sensitivity analysis of electric vehicles, energy storage analysis to improve the energy efficiency of electric vehicles, and V2G related to the energy efficiency of electric vehicle clusters.

Collaborative thermal management of power battery and passenger cabin for energy efficiency optimization

Collaborative thermal management is a promising approach for improving the energy efficiency of electric vehicles by optimizing both the battery and passenger cabin temperatures. This study proposes a novel collaborative thermal management system that addresses key performance aspects such as battery safety, cabin comfort, and system efficiency. The system utilizes a vehicle speed prediction-nonlinear model predictive control strategy (SP-NMPC) based on genetic algorithm. Experimental testing validates the effectiveness and accuracy of the proposed method. Temperature variations in the battery and passenger cabin are compared at ambient temperatures of 30℃ and 40℃ using the SP-NMPC and prototype original control strategy. The Mean Square Error (MSE) values are calculated and compared. Results show that the original control strategy yielded MSE values of 0.0722 and 0.3334 for the passenger cabin temperature at 30℃ and 40℃, respectively. In contrast, employing SP-NMPC under similar conditions achieved significantly lower MSE values of 0.0513 and 0.0521. This highlights the superior accuracy and resilience of SP-NMPC in maintaining passenger cabin temperature, reducing fluctuations, and enhancing passenger comfort. Moreover, the system achieves energy savings, reducing thermal management energy consumption by 5.59% and 5.20% at ambient temperatures of 30 °C and 40 °C, respectively, thereby extending the vehicle's range.

Overview of the Potential of Energy Harvesting Sources in Electric Vehicles

Energy harvesting, a cutting-edge technology that captures wasted energy from vehicles, constitutes a means to improve the efficiency of electric vehicles. Dissipated energy can be converted into electricity using regenerative energy recovery systems and put to various uses. This study tenders a thorough examination into energy recovery technologies which could be applied to the various types of energy dissipated in electric vehicles. The paper investigates the possible sources of energy recoverable from an electric vehicle, as well as the various types of energy dissipated. It also examines the energy recovery technologies most frequently used in vehicles, categorizing them according to the type of energy and application. Finally, it determines that with further research and development, energy harvesting holds considerable potential for improving the energy efficiency of electric vehicles. New and innovative methods for capturing and utilizing wasted energy in electric vehicles can be established. The potential benefit of applying energy recovery systems in electric vehicles is a vital issue for the automobile industry to focus on due to the potential benefits involved. The ongoing progress currently being made in this field is expected to play a significant role in shaping the future of transportation.

Bimetallic CoMoO4 Nanosheets on Freestanding Nanofiber as Wearable Supercapacitors with Long-Term Stability

Currently, lightweight wearable energy storage devices are in great demand owing to their use in wearable electronics and energy-efficient electric vehicles. Freestanding carbon nanofibers replace the need for metal substrates while providing a rapid electrical network owing to their excellent electrical properties. Bimetallic oxides with multivalent oxidation states facilitate the rapid transfer of electrolytic ions owing to efficient Faradaic reactions, thereby enhancing the overall energy storage capability. In this study, CoOx@CNF was derived from ZIF-67 (zeolitic-imidazolate framework) @PAN-2MI fibers that were stabilized in air at 280°C and then annealed in argon at 900°C. Subsequently, Co was seeded on the annealed CoOx@CNF and subjected to a hydrothermal process in sodium molybdate dihydrate solution to grow CoMoO4 nanosheets, eventually forming bimetallic CoMoO4@CNF. The concentration of sodium molybdate solution was varied to determine the optimal growth conditions for CoMoO4 nanosheets. The energy density of the optimal bimetallic CoMoO4@CNF sample was 166.5 μWh cm-2 at a power density of 200 μW cm-2; this represented a nearly twofold increase compared to that of the single metallic CoOx@CNF. Powering humidity sensors using only one CoMoO4@CNF supercapacitor was demonstrated. The optimal sample remained stable during long-term galvanostatic charge and discharge cycles ( N cyc = 30,000 ) and retained 100% of its specific capacitance.

Event-Driven Energy-Efficient Driving Control in Urban Traffic for Connected Electric Vehicles

The traffic light in urban areas dominates the traffic flow, resulting in variation of energy consumption of the vehicles involved. To mitigate the impact of traffic bias on the energy efficiency of electric vehicles (EVs), this article proposes an event-driven energy-efficient driving control (EEDC) strategy based on a receding horizon two-stage control framework, which harnesses the Internet of Vehicles to incorporate the traffic light and preceding vehicle for adaption of different driving scenarios. At the core of the first stage are the vehicle driving event classification rules, which classified the urban traffic scenarios into four events. This article contributes to empirical solutions on the design of the traffic scenario classifier considering conflict goals, including driving efficiency and safety. In the second stage, the speed trajectory in each driving event is optimized using Pontryagin’s minimum principle to reduce vehicle energy consumption. A real-time solution for the energy-efficient driving problem is derived with the consideration of vehicle dynamics, control input, and speed limit constraints. Finally, extensive simulations and road tests are conducted to evaluate the effectiveness of the EEDC. The results show that the EEDC is excellent in energy efficiency improvement over two benchmark strategies in different traffic scenarios while satisfying the constraints in inter-vehicle driving safety and travel time. Moreover, the road tests demonstrate that the EEDC is capable of energy saving in real-world driving.

Analysis of Urban TITEN EV-2 Car Chassis Design on Dynamic Loading When Brakes

In the race conditions of the Energy Saving Car Contest, urban concept vehicles must comply with the special rules that have been set, namely 'stop & go driving'. Therefore, the designed car must have the toughness to accelerate and decelerate safely. Chassis is a fundamental part of the vehicle that functions to support the overall load that works on the car, keeps the car's condition rigid, does not experience excessive deformation and is safe when used. For that chassis design planning must be considered. The selection of a strong but still lightweight material was carried out, using 6061 aluminum material. In this research, an analysis will be carried out on the design of the TITEN EV-2 car chassis. TITEN itself is the name of the Jember University research team that focuses on developing energy-efficient electric vehicles. In this study the analysis is focused on the chassis that experiences dynamic loading when braking with variations in the shape of the chassis profile, namely profiles in the shape of a square and hollow rectangle and the braking distance is varied. from 20m, 15m, 10m. By using the Finite Element Method (FEM), the strength analysis of the chassis structure design can be determined by looking at the maximum stress and total deformation acting on the chassis. From the simulation results, the rectangular hollow chassis profile has a mass of 7.6 kg which is lighter than the square hollow chassis which has a mass of 9.4 kg. Rectangular hollow chassis has a maximum stress that is superior to square hollow chassis at loading distances of 20 m and 15 m, but at a braking distance of 10 m, square hollow chassis is superior in terms of total deformation and maximum stress

Commuting-pattern-oriented optimal sizing of electric vehicle powertrain based on stochastic optimization

Owing to the dynamic randomness of traffic flow, driving patterns stochastically change from time to time and person to person, which inevitably triggers significant variability in the energy efficiency of electric vehicle (EV) powertrains. For optimizing the expected energy efficiency of the EV powertrain in probable driving operations while reducing the variability in energy efficiency, this paper engineers an integrated stochastic optimization (SO) method for the design scheme and control strategy of EV powertrains. To evaluate the candidate design scheme and identify the superior design scheme in random driving operations, an instantaneous optimal control and a Monte Carlo simulation-aided iterative searching process are developed and utilized as critical components of the SO. According to simulation validation, by optimizing the energy consumption in extreme operations, the SO improves the expectation of the energy efficiency of the EV powertrain by 26.4% and reduces the variability in energy efficiency by 90.4%. Moreover, the proposed SO has no side-effect on the energy consumption in typical/frequent driving operations. Even operated in the driving cycle from which the deterministic optimization (DO) result is obtained, the increase in energy consumption of the SO result is less than 5% compared with the energy consumption of the DO result.

Optimization of the energy efficiency in Smart Internet of Vehicles assisted by MEC

Smart Internet of Vehicles (IoV) as a promising application in Internet of Things emerges with the development of the fifth generation mobile communication (5G). Nevertheless, the heterogeneous requirements of sufficient battery capacity, powerful computing ability and energy efficiency for electric vehicles face great challenges due to the explosive data growth in 5G and the sixth generation of mobile communication (6G) networks. In order to alleviate the deficiencies mentioned above, this paper proposes a mobile edge computing (MEC) enabled IoV system, in which electric vehicle nodes upload and download data through an anchor node which is integrated with a MEC server. Meanwhile, the anchor node transmitters radio signal to electric vehicles with simultaneous wireless information and power transfer technology so as to compensate the battery limitation of eletric vehicles. Moreover, the spectrum efficiency is further improved by multi-input and multi-output and full-duplex technologies which is equipped at the anchor node. In consideration of the issues above, we maximize the average energy efficiency of electric vehicles by jointly optimize the CPU frequency, vehicle transmitting power, computing tasks and uplink rate. Since the problem is nonconvex, we propose a novel alternate interior-point iterative scheme (AIIS) under the constraints of computing tasks, energy consumption and time latency. Results and discussion section verifies the effectiveness of the proposed AIIS scheme comparing with the benchmark schemes.

Assessing the Energy Consumption and Driving Range of the QUIET Project Demonstrator Vehicle

This article summarises the experimental testing campaign performed at the Joint Research Centre (JRC) on the demonstrator battery electric vehicle (BEV) of the European Union Horizon 2020 research project QUIET. The project, launched in October 2017, aimed at developing an improved and energy-efficient electric vehicle with increased driving range under real-world driving conditions, focusing on three areas: improved energy management, lightweight materials with enhanced thermal insulation properties, and improved safety and comfort. A heating, venting, and air conditioning (HVAC) system based on the refrigerant R290 (propane), a phase change material (PCM) thermal storage system, infrared heating panels in the near field of the passengers, lightweight materials for seat internal structures, and composite vehicle doors with a novel atomically precise manufacturing (APM) aluminium foam are all the breakthrough technologies installed on the QUIET demonstrator vehicle. All these innovative technologies allow the energetic request for cooling and heating the cabin of the demonstrator vehicle under different driving conditions and the weight of the vehicle components (e.g., doors, windshields, seats, heating, and air conditioning) to be reduced by about 28%, leading to an approximately 26% driving range increase under both hot (40 °C) and cold (−10 °C) weather conditions.

A comparative study of energy-efficient driving strategy for connected internal combustion engine and electric vehicles at signalized intersections

This paper takes into consideration of vehicle queues at the intersection and proposes an energy-efficient driving strategy to improve vehicle energy efficiency and overall traffic throughput in an urban traffic environment. The proposed strategy is applicable for both electric vehicle and internal combustion engine vehicle, and the control framework is formed by three sections, a vehicle queue discharge predictor, a spatial-domain optimal control strategy for energy consumption minimization, and a speed tracker with consideration of collision avoidance constraints. The former is based on the intelligent driver model, which predicts an accurate vehicle queue discharge time. Then the iterative dynamic programming is utilized to find the optimal solutions with fast computational speed. Finally, the optimal speed profile is followed by a Proportion-Integration controller while keeping a safe inter-vehicular distance. A Monte-Carlo simulation is designed to evaluate the energy efficiency of the proposed strategy in the stochastic traffic environment. Compared to the regular eco-approach and departure and constant speed strategies that lack awareness of the queue, significant energy saving can be achieved of the proposed strategy. In addition, three typical cases are selected to study the energy efficiency when the proposed strategy is applied to internal combustion engine and electric vehicles, respectively. The results show the energy efficiency of electric vehicles is less sensitive to the queuing effect at the intersection because of regenerative braking and the overall higher efficiency of the electric motor in contrast to the internal combustion engine, especially in stop-and-go scenarios.

Simulation of Multi-Motor Propulsion System for Energy Efficiency in Electric Vehicles

For a vehicle, the knowledge of components, characteristics, performances, and behaviors are some base elements for a successful simulation. Today's tools are offering instruments able to cover a such request and new possibilities appear. Choosing the right ones, representing the entire vehicle as a system of subsystems, integrating as much as possible the parameters of different components, are also few additional elements. For internal combustion engine vehicles, the generation and the transmission of the mechanical power to the wheels imply the usage of specific mechanic parts. From a single engine, the mechanical power is controlled to offer the requested torque and speed simultaneously to the vehicle wheels, passing by clutches and gearboxes. An electric propulsion, generating high torque at zero speed, and covering large speed area, implies less components for the mechanical transmissions. One single gear, a reducer, could cover the entire speed area request of the vehicle. On the other hand, it is possible to approach the generation of the mechanical power to the wheels, by using not only one electric motor, but one for each axle, or, even more, one electric motor for each wheel of the vehicle. This paper presents the usage of numerical simulation in such situations, emphasizing opportunities for onboard energy efficiency improvement, and opening new possibilities for optimization in multiple motor solutions.

Torque Vectoring Control of RWID Electric Vehicle for Reducing Driving-Wheel Slippage Energy Dissipation in Cornering

The anxiety of driving range and inconvenience of battery recharging has placed high requirements on the energy efficiency of electric vehicles. To reduce driving-wheel slip energy consumption while cornering, a torque vectoring control strategy for a rear-wheel independent-drive (RWID) electric vehicle is proposed. First, the longitudinal linear stiffness of each driving wheel is estimated by using the approach of recursive least squares. Then, an initial differential torque is calculated for reducing their overall tire slippage energy dissipation. However, before the differential torque is applied to the two side of driving wheels, an acceleration slip regulation (ASR) is introduced into the overall control strategy to avoid entering into the tire adhesion saturation region resulting in excessive slip. Finally, the simulations of typical manoeuvring conditions are performed to verify the veracity of the estimated tire longitudinal linear stiffness and effectiveness of the torque vectoring control strategy. As a result, the proposed torque vectoring control leads to the largest reduction of around 17% slip power consumption for the situations carried out above.

The Structure and Optimal Gear Tooth Profile Design of Two-Speed Transmission for Electric Vehicles

As environmental regulations have been strengthened worldwide since the Paris Climate Agreement, the automobile industry is shifting its production paradigm to focus on eco-friendly vehicles such as electric vehicles and hydrogen-battery vehicles. Governments are banning fossil fuel vehicles by law and expanding the introduction of green vehicles. The energy efficiency of electric vehicles that use a limited power source called batteries depends on the driving environment. Applying a two-speed transmission to an electric vehicle can optimize average speed and performance efficiency at low speeds, and achieve maximum speed with minimal torque at high speeds. In this study, a two-speed transmission for an electric vehicle has been developed, to be used in a compact electric vehicle. This utilizes a planetary gear of a total of three pairs, made of a single module which was intended to enable two-speed. The ring gear was removed, and the carrier was used in common. When shifting, the energy used for the speed change is small, due to the use of the simple method of fixing the sun gear of each stage. Each gear was designed by calculating bending strength and surface durability, using JGMA standards, to secure stability. The safety factor of the gears used in the transmission is as follows: all gears have been verified for safety with a bending strength of 1.2 or higher and a surface pressure strength of 1.1 or higher. The design validity of the transmission was verified by calculating the gear meshing ratio and the reference efficiency of the gear. The transmission to be developed through the research results of this paper has a simple and compact structure optimized for electric vehicles, and has reduced shift shock. In addition, energy can be used more efficiently, which will help improve fuel economy and increase drive range.