A comprehensive research study on powertrain parameter matching and regenerative braking control has outlined a detailed path toward higher efficiency for the battery electric vehicle. The work focuses on an electric vehicle equipped with a two-speed wet dual-clutch transmission, or DCT, and combines high-fidelity simulation, multi-objective genetic optimization, fuzzy logic control, and standard driving-cycle validation. The central finding is that careful co-design of the electric motor, battery, transmission ratios, and braking energy recovery strategy can deliver meaningful gains in energy economy without compromising dynamic performance or braking safety.
The research arrives as the global automotive industry continues to shift toward electrification. The electric vehicle is increasingly viewed as a central solution for reducing tailpipe emissions, improving energy security, and supporting carbon-neutral transport goals. At the same time, range anxiety, battery cost, and the efficient use of stored energy remain practical barriers. The study addresses these barriers by treating the electric vehicle as an integrated system rather than a collection of separate components. Its results indicate that the two-speed DCT electric vehicle can benefit from optimized gear ratios, refined component sizing, and an intelligent regenerative braking strategy that coordinates motor braking with hydraulic friction braking.
1. Research Context: Why Electric Vehicle Efficiency Still Matters
The electric vehicle has moved from a niche alternative to a mainstream product category in many markets. However, the research notes that two technical issues remain critical for further performance improvement: whether the powertrain parameters are matched rationally, and whether braking energy recovery is optimized effectively. These issues are especially important for a pure electric vehicle because every unit of stored battery energy must support propulsion, auxiliary loads, thermal management, and vehicle dynamics. Small inefficiencies in the motor, inverter, transmission, or braking system can accumulate into a noticeable reduction in driving range.
The study identifies range anxiety as a persistent challenge for the electric vehicle. Battery technology continues to improve, but gains in cell energy density alone cannot solve all efficiency problems. The research therefore examines how the electric vehicle powertrain can be better matched to real operating conditions. It also investigates how regenerative braking can recover kinetic energy during deceleration and return it to the battery. In urban driving, where stop-and-go operation is common, the potential for energy recovery is particularly significant. The research argues that optimized braking feedback control can reduce battery energy consumption and extend the driving range of the electric vehicle.
The work also places the electric vehicle within the broader context of energy transition and carbon neutrality. The introduction highlights the environmental pressures associated with conventional internal combustion engine vehicles, including resource consumption and emissions. The electric vehicle is presented as a zero-emission alternative that aligns with long-term sustainability goals. Yet the research does not treat electrification as a simple substitution. Instead, it emphasizes that the electric vehicle must be engineered as a complete system in which powertrain matching, transmission architecture, and energy management work together.
2. Two-Speed DCT Electric Vehicle Architecture and Target Performance
The study uses a front-wheel-drive small passenger electric vehicle as its reference platform. The electric vehicle is equipped with a two-speed wet DCT, an electric motor, an inverter, and a battery pack. The DCT architecture is selected because it can provide high transmission efficiency while supporting rapid and smooth gear changes. In the electric vehicle, the motor is connected to the transmission through a dual-clutch module, and the controller selects the appropriate gear according to vehicle speed and driving demand. The battery pack supplies energy through the inverter, while the energy management system distributes power across the electric vehicle subsystems.
The research argues that a multi-speed transmission can help the electric vehicle keep the motor operating in a more efficient region. A single-speed reduction gear is simple and cost-effective, but it may force the electric vehicle motor to operate outside its high-efficiency area under certain speed and load conditions. By contrast, a two-speed DCT allows the electric vehicle to adjust the motor operating point, improve torque and speed matching, and reduce energy consumption. The study also notes that the DCT can support both dynamic performance and energy economy, making it a suitable choice for an electric vehicle that must satisfy varied driving conditions.
| Parameter | Symbol | Value | Unit | Performance target | Design value |
|---|---|---|---|---|---|
| Vehicle curb mass | mv | 1455 | kg | Maximum speed | 150 km/h |
| Frontal area | A | 2.593 | m2 | Gradeability I | 30% at 5 km/h |
| Dynamic wheel radius | RD | 0.273 | m | Gradeability II | 20% at 30 km/h |
| Aerodynamic drag coefficient | CD | 0.456 | – | Acceleration I | 0-100 km/h under 12 s |
| Rolling resistance coefficient | f | 0.012 | – | Acceleration II | 0-100 km/h under 10 s |
| Static front and rear load distribution | L1/L2 | 0.5 | – | Driving range | 150 km or more at 60 km/h constant speed |
| Wheelbase | L | 2490 | mm | – | – |
| Center of gravity height | hg | 510 | mm | – | – |
| Final drive ratio | i0 | 4.058 | – | – | – |
The electric vehicle performance targets are defined around maximum speed, gradeability, acceleration, and driving range. The research uses these targets as constraints for component sizing and later optimization. For the electric vehicle, the motor must deliver enough peak power and torque to meet acceleration and gradeability requirements, while the battery must supply sufficient energy and power for the intended driving range. The transmission ratios must allow the electric vehicle to achieve the target maximum speed while preserving climbing capability and efficient motor operation.
3. Initial Powertrain Matching for the Electric Vehicle
The initial matching process begins with vehicle dynamics theory. The research derives the longitudinal motion equation of the electric vehicle by considering driving force, rolling resistance, aerodynamic drag, grade resistance, and acceleration resistance. From this equation, the study calculates the power required for maximum speed, maximum gradeability, and target acceleration. The electric vehicle motor is then selected to satisfy all these requirements with an appropriate power reserve. An interior permanent magnet synchronous motor is chosen because of its high power density, wide field-weakening range, fast torque response, and strong efficiency characteristics.
For the electric vehicle battery, the study selects a lithium-ion chemistry with high energy density. The battery pack voltage is matched to the motor operating voltage, and the pack is configured from 21700 cells. Each cell has a rated voltage of 3.7 V, a nominal capacity of 5 Ah, and an internal resistance of 1.5 milliohm. The battery pack is designed with 87 cells in series and 23 cells in parallel, giving a rated voltage of 320 V and a rated capacity of 115 Ah. The research notes that battery voltage, capacity, and cell count must be coordinated with the electric vehicle motor and transmission to ensure both power delivery and energy storage.
The two-speed DCT ratios are matched to the electric vehicle performance requirements. The first gear is designed to satisfy acceleration and gradeability demands, while the second gear is designed to support maximum speed and efficient cruising. The study also applies a wheel-slip constraint so that the motor peak torque does not exceed the maximum torque that the tires can transmit under high adhesion conditions. This constraint is important for the electric vehicle because excessive torque at low speed could cause wheel slip and reduce effective traction.
| Item | Parameter | Item | Parameter |
|---|---|---|---|
| Motor peak power | 100 kW | DCT second gear ratio | 1.3 |
| Motor rated power | 50 kW | DCT first gear ratio | 1.8 |
| Motor peak speed | 8000 rpm | Battery pack rated capacity | 115 Ah |
| Motor rated speed | 4000 rpm | Battery cell series count | 87 |
| Motor peak torque | 238 Nm | Battery cell parallel count | 23 |
| Motor rated torque | 120 Nm | Battery rated voltage | 320 V |
The initial matching results are then validated through a vehicle performance simulation platform. The electric vehicle achieves a maximum speed of 163.69 km/h, a gradeability of 35.45% at 5 km/h, and a gradeability of 24.88% at 30 km/h. The 0-100 km/h acceleration time is 9.54 s. Under a 60 km/h constant-speed range test, the electric vehicle reaches 175.41 km. These results meet or exceed the target performance indicators, confirming that the initial electric vehicle powertrain parameters are reasonable and that the simulation platform has sufficient accuracy for further optimization.
| Performance item | Target | Simulation result | Unit |
|---|---|---|---|
| Maximum speed | 150 or more | 163.69 | km/h |
| Gradeability at 5 km/h | 30% or more | 35.45 | % |
| Gradeability at 30 km/h | 20% or more | 24.88 | % |
| 0-100 km/h acceleration time | Under 12 s | 9.54 | s |
| 0-100 km/h acceleration time | Under 10 s | 9.54 | s |
| 60 km/h constant-speed range | 150 km or more | 175.41 | km |
4. High-Fidelity Modeling of the Electric Vehicle Powertrain
The research builds a detailed simulation model of the electric vehicle to capture the behavior of the battery, inverter, motor, DCT, and longitudinal vehicle dynamics. For the battery, a second-order RC equivalent circuit model is selected. This model represents the electric vehicle battery with an open-circuit voltage source, an ohmic internal resistance, and two RC branches that describe electrochemical polarization and concentration polarization. The study uses open-circuit voltage tests and hybrid pulse power characterization tests to identify the battery model parameters. The model is validated against pulse discharge data, with a maximum deviation of 310 mV and an average deviation of only 6.7 mV. This level of accuracy supports the use of the model in electric vehicle energy management studies.
For the inverter, the research models conduction losses, switching losses, and diode reverse-recovery losses. The inverter is a key component in the electric vehicle because it converts direct current from the battery into alternating current for the motor. The study uses a reference inverter with insulated gate bipolar transistors and freewheeling diodes. The inverter efficiency is calculated from the motor input power and the sum of the loss terms. This approach allows the electric vehicle simulation to account for the real losses that occur during power conversion, rather than assuming an ideal inverter.
The motor model is based on an interior permanent magnet synchronous machine design tool. The model calculates the full-load characteristic and efficiency map from the motor geometry, winding design, permanent magnet arrangement, and electromagnetic parameters. The research uses a water-cooled machine with a V-shaped permanent magnet layout and a stator made of M800-50A steel. The efficiency model includes copper losses, iron losses, and mechanical losses. By combining the motor efficiency map with the DCT efficiency model, the electric vehicle simulation can evaluate how motor operating points and gear selection affect energy consumption.
The DCT model is especially detailed. The research divides transmission power losses into clutch drag losses, gear mesh losses, bearing and seal losses, shaft shear losses, and windage and churning losses. The model considers the state of the wet clutches, the oil film behavior between clutch plates, the gear mesh friction, the viscous shear between concentric shafts, and the oil churning caused by rotating gears. This detailed loss model is important for the electric vehicle because transmission efficiency is not constant. It varies with speed, torque, oil temperature, and gear ratio. The study shows that using a fixed transmission efficiency can underestimate the optimization potential of the electric vehicle powertrain.
The vehicle longitudinal dynamics model calculates the electric vehicle acceleration, gradeability, maximum speed, and energy consumption. The model uses a WLTP speed-time profile to calculate the energy consumption per 100 km. The research emphasizes that motor and transmission efficiency vary with operating conditions, so the electric vehicle simulation must evaluate efficiency at each time step rather than relying on constant values. This integrated modeling approach provides the foundation for the multi-objective optimization and braking control studies that follow.
5. Multi-Objective Optimization of Electric Vehicle Powertrain Parameters
After initial matching, the research applies a multi-objective genetic algorithm to optimize the electric vehicle powertrain. The optimization considers two objectives: energy consumption and powertrain cost. These objectives are in tension. A larger motor or battery may improve performance and efficiency, but it also increases cost. A smaller, lower-cost system may reduce cost but could force the electric vehicle motor to operate less efficiently or fail to meet performance targets. The study uses the NSGA-II algorithm to explore this trade-off and generate a Pareto front of candidate designs.
The optimization variables are the motor rated power, motor rated speed, motor rated voltage, first gear ratio, and second gear ratio. The constraints include maximum gradeability, maximum speed, acceleration time, and wheel-slip limits. The optimization is performed for two acceleration targets, one under 12 s and one under 10 s, to examine how dynamic performance requirements affect the optimal electric vehicle powertrain. The study also compares a fixed transmission efficiency model with a variable transmission efficiency model. The variable model uses the detailed DCT efficiency map, while the fixed model assumes a constant transmission efficiency of 0.96.
The Pareto results show a clear trade-off between cost and energy consumption. At one end of the front, lower-cost designs use smaller motors or different gear ratios but consume more energy. At the other end, higher-cost designs achieve lower energy consumption. The research identifies a turning point where small changes in cost produce large changes in energy consumption, and beyond which small changes in energy consumption require large cost increases. The optimal candidate is selected around this balanced region. The study reports that the variable-efficiency model achieves lower energy consumption than the fixed-efficiency model, demonstrating that detailed transmission efficiency modeling is necessary for electric vehicle powertrain optimization.
| Transmission efficiency model | Acceleration target | Energy consumption per 100 km | Powertrain cost | Motor rated power | Motor rated speed | Motor rated voltage | First gear ratio | Second gear ratio |
|---|---|---|---|---|---|---|---|---|
| Fixed efficiency | 12 s | 12.96 | 29040 yuan | 40 kW | 6000 rpm | 300 V | 3.44 | 1.72 |
| Variable efficiency | 12 s | 12.48 | 29048 yuan | 41 kW | 6000 rpm | 300 V | 1.75 | 1.17 |
| Fixed efficiency | 10 s | 12.89 | 29149 yuan | 51 kW | 6000 rpm | 300 V | 3.53 | 1.72 |
| Variable efficiency | 10 s | 12.48 | 29121 yuan | 48 kW | 6000 rpm | 300 V | 1.75 | 1.17 |
The optimization results show that the variable-efficiency electric vehicle design reduces energy consumption by 3.7% for the 12 s acceleration target and by 3.3% for the 10 s target, compared with the fixed-efficiency design. The study also finds that the cost of the electric vehicle powertrain is sensitive to the acceleration target, because a shorter acceleration time requires a higher-power and higher-torque motor. However, the effect of acceleration target on energy efficiency is smaller. The optimized electric vehicle achieves an energy consumption of about 12.5 kWh per 100 km under the WLTP cycle, according to the study summary. The two-speed DCT electric vehicle therefore demonstrates a stronger balance between cost, dynamic performance, and energy economy than a fixed-efficiency or single-speed reference approach.
The research further explains why lower gear ratios appear in the optimal electric vehicle design when variable transmission efficiency is considered. The DCT efficiency map shows that lower gear ratios can provide higher transmission efficiency in the low- and medium-speed regions that dominate the WLTP cycle. The electric vehicle therefore benefits from a transmission design that matches the actual operating conditions of the test cycle. At the same time, the motor power and torque distributions show that higher motor power can reduce energy consumption without a dramatic change in motor efficiency. This means that a higher-power motor paired with a lower gear ratio can improve energy economy, but the cost penalty must be managed. The Pareto front provides a practical tool for electric vehicle designers to select a configuration that matches their cost and efficiency priorities.
6. Braking Force Distribution and Regenerative Braking Boundaries for the Electric Vehicle
The second major part of the research focuses on braking energy recovery for the electric vehicle. During braking, a conventional vehicle converts kinetic energy into heat through friction brakes. An electric vehicle can recover part of that energy by using the motor as a generator. The recovered energy is converted by the inverter and stored in the battery. The challenge is to recover as much energy as possible while maintaining braking stability, steering control, and regulatory compliance. The study therefore begins with a detailed analysis of front and rear braking force distribution.
The research reviews the ideal braking force distribution curve, often called the I-curve, which represents the condition where the front and rear wheels lock simultaneously. It also reviews the ECE R13 regulation line, which defines the legal requirements for braking force distribution. In addition, the study examines the F-line and R-line groups, which describe the braking force relationships when the front wheels lock first or the rear wheels lock first. The electric vehicle must avoid rear-wheel lock before front-wheel lock, because that condition can cause yaw instability and loss of directional control.
Based on these theoretical boundaries, the study proposes a segmented braking force distribution strategy for the electric vehicle. When braking intensity is below 0.22, the rear axle produces no braking force and the front axle provides the required braking force. This region allows the electric vehicle to use motor braking as much as possible, maximizing energy recovery. When braking intensity is between 0.22 and 0.53, the braking force follows the ECE R13 regulation line to maintain safety while recovering energy. When braking intensity is between 0.53 and 0.7, the braking force follows the F-line for a road adhesion coefficient of 0.7, balancing safety and energy recovery. When braking intensity exceeds 0.7, the electric vehicle enters emergency braking and the hydraulic friction braking system provides the braking force according to the I-curve, prioritizing safety over energy recovery.
The research also defines constraints for regenerative braking in the electric vehicle. Regenerative braking is limited when the battery state of charge is above 0.95 to prevent overcharging. It is also limited at low vehicle speed, with a practical lower speed threshold of 10 km/h, because motor back electromotive force decreases at low speed. In addition, when braking intensity exceeds 0.7, the electric vehicle disables regenerative braking and relies on hydraulic braking to ensure a rapid and reliable emergency response. These constraints are integrated into the braking control strategy so that the electric vehicle can recover energy only within safe operating boundaries.
7. Fuzzy Logic and Genetic Algorithm Optimization of Electric Vehicle Braking Feedback
The study designs a fuzzy logic controller to allocate braking force between the electric motor and the hydraulic friction brakes. The controller has three inputs: demanded braking intensity, battery state of charge, and vehicle speed. The output is a regenerative braking control coefficient that determines the share of motor braking in the front axle braking force. The electric vehicle uses a Mamdani-type fuzzy inference system with 27 rules. The membership functions are selected according to the characteristics of each input. Demanded braking intensity uses normal distribution functions, vehicle speed and battery state of charge use trapezoidal functions, and the output coefficient uses a combination of triangular and trapezoidal functions.
The fuzzy controller is intended to handle the nonlinear and time-varying nature of electric vehicle braking. Braking demand changes quickly, battery state of charge affects charging limits, and vehicle speed influences motor back electromotive force. A rule-based fuzzy controller can coordinate these inputs without requiring an exact mathematical model of the entire braking system. However, traditional fuzzy controller design relies heavily on expert experience. To reduce subjective bias and improve robustness, the research applies a genetic algorithm to optimize the membership function parameters. The optimization covers 47 parameters across the input and output membership functions. The genetic algorithm searches for a parameter set that improves the regenerative braking control coefficient and, ultimately, the energy recovery efficiency of the electric vehicle.
The optimized fuzzy controller is then integrated into the electric vehicle braking control system. The controller calculates the motor regenerative braking torque reference from the front axle braking demand, the transmission ratio, and the transmission efficiency. A torque management module applies safety limits: the battery state of charge must remain below 0.95, vehicle speed must be above 10 km/h, braking intensity must be below 0.7, and the regenerative torque must not exceed the motor maximum regenerative torque. If any condition is not met, the electric vehicle disables regenerative braking and relies on hydraulic braking. The motor torque control module then determines whether the electric vehicle motor is in driving mode or braking mode and applies the appropriate torque command.
8. Simulation Validation Under NEDC and WLTP
The research validates the optimized braking strategy using two standard driving cycles: the New European Driving Cycle and the Worldwide Harmonized Light Vehicles Test Procedure. The NEDC cycle includes urban and suburban segments, with frequent starts, stops, accelerations, and decelerations. The WLTP cycle includes low-speed, medium-speed, high-speed, and extra-high-speed segments, and is designed to represent a wider range of real-world driving conditions. The electric vehicle simulation uses an initial battery state of charge of 0.95 and records the state of charge variation, recovered energy, and vehicle speed tracking performance.
The results show that the optimized fuzzy braking strategy improves vehicle speed tracking. Under the NEDC cycle, the electric vehicle achieves an average speed tracking accuracy of 98.6%, which is 2.4 percentage points higher than the unoptimized strategy. Under the WLTP cycle, the electric vehicle achieves an average tracking accuracy of 98.2%, which is 1.5 percentage points higher than the unoptimized strategy. The tracking error remains within plus or minus 0.3 m/s, and the overshoot is less than 5%. These results indicate that the optimized braking controller supports stable and accurate vehicle speed control while recovering energy.
The state of charge results show a clear advantage for the genetic-algorithm-optimized fuzzy strategy. Under the NEDC cycle, the optimized electric vehicle ends with a higher state of charge than the basic fuzzy strategy and the ideal braking force distribution strategy. The study reports that the optimized strategy raises the final state of charge by 4.46 percentage points compared with the basic fuzzy strategy and by 9.75 percentage points compared with the ideal distribution strategy. In terms of recovered braking energy, the optimized strategy improves energy recovery by 27.71% compared with the ideal distribution strategy and by 2.41 percentage points compared with the basic fuzzy strategy. These gains are attributed to better coordination between motor regenerative braking and hydraulic friction braking, especially in the frequent stop-and-go conditions of the NEDC cycle.
| Control strategy | NEDC reported state of charge endpoint or low point | NEDC recovered energy comparison | WLTP reported state of charge endpoint or low point | WLTP recovered energy comparison |
|---|---|---|---|---|
| Ideal braking force distribution | About 0.81 | Reference case | 0.704 | Reference case |
| Basic fuzzy control | 0.851 | Lower than optimized case | 0.756 | Lower than optimized case |
| Genetic-algorithm-optimized fuzzy control | 0.889 | 27.71% higher than ideal distribution and 2.41 percentage points higher than basic fuzzy | 0.818 | 7.1% higher than ideal distribution and 6.45 percentage points higher than basic fuzzy |
Under the WLTP cycle, the optimized electric vehicle also performs better than the comparison strategies. The final state of charge is 0.818 for the optimized fuzzy strategy, 0.756 for the basic fuzzy strategy, and 0.704 for the ideal braking force distribution strategy. The study reports that the optimized strategy improves the final state of charge by 8.24% and 16.12% compared with the basic fuzzy and ideal distribution strategies, respectively. In terms of recovered braking energy, the optimized strategy improves energy recovery by 7.1% compared with the ideal distribution strategy and by 6.45 percentage points compared with the basic fuzzy strategy. These results confirm that the genetic-algorithm-optimized fuzzy controller can improve the energy management performance of the electric vehicle across different driving cycles.
The research also compares the optimized strategy with a single-speed transmission reference. The two-speed DCT electric vehicle shows advantages in keeping the motor in a high-efficiency operating range and in recovering braking energy. The study concludes that the optimized two-speed DCT electric vehicle achieves lower energy consumption and better braking energy recovery than single-speed reference configurations. This finding supports the use of a multi-speed transmission in an electric vehicle when the transmission efficiency is modeled accurately and the gear ratios are optimized together with the motor and battery parameters.
9. Implications for Electric Vehicle Design and Energy Management
The research provides several practical implications for electric vehicle development. First, powertrain parameter matching should not be treated as a standalone task. The electric vehicle motor, battery, transmission, and braking system interact strongly, and their parameters should be optimized together. The study shows that a two-speed DCT electric vehicle can benefit from lower gear ratios when the transmission efficiency map is considered. It also shows that motor power and torque should be selected with attention to both energy consumption and cost, because the Pareto front reveals a clear trade-off between these objectives.
Second, transmission efficiency modeling matters for electric vehicle optimization. The study finds that a fixed transmission efficiency assumption can underestimate the potential of the electric vehicle powertrain. When the variable DCT efficiency map is used, the optimized electric vehicle achieves lower energy consumption and a different optimal gear ratio set. This result suggests that electric vehicle designers should use detailed loss models for the transmission, especially when evaluating multi-speed architectures. The DCT clutch drag, gear mesh, bearing, shaft shear, and churning losses all influence the final energy consumption of the electric vehicle.
Third, regenerative braking control should be coordinated with braking safety. The electric vehicle cannot recover energy at any cost. The study defines clear boundaries based on battery state of charge, vehicle speed, and braking intensity. It also uses a segmented braking force distribution strategy that respects the ECE R13 regulation and avoids unstable braking conditions. The fuzzy controller then allocates braking force within these boundaries. The genetic algorithm improves the membership functions so that the electric vehicle can recover more energy without compromising braking stability. This combination of safety constraints and intelligent optimization is a key contribution to electric vehicle energy management.
Fourth, standard driving cycles are essential for evaluating electric vehicle efficiency. The study uses both NEDC and WLTP to test the optimized braking strategy. The WLTP cycle is more representative of real-world driving because it includes a wider range of speeds and more dynamic operation. The electric vehicle performs well under both cycles, but the absolute state of charge and energy recovery values differ. This highlights the importance of validating electric vehicle control strategies across multiple cycles rather than relying on a single test procedure.
10. Outlook: Integrated Thermal Management, Learning-Based Control, and Real-World Validation
The research outlines several directions for future work on the electric vehicle. One direction is integrated optimization of the motor, battery, transmission, and thermal management system. The electric vehicle generates heat in the motor, inverter, battery, and brakes, and thermal conditions affect efficiency and component life. A multi-physics optimization approach could improve overall energy efficiency by coordinating cooling, heating, and power distribution. For the electric vehicle, thermal management is not a secondary concern; it directly affects battery power capability, motor efficiency, and braking energy recovery limits.
Another direction is the deeper application of intelligent algorithms. The study uses a genetic algorithm to optimize fuzzy membership functions, but future electric vehicle controllers could use deep learning or reinforcement learning to adapt to complex driving conditions. A learning-based electric vehicle braking controller could adjust regenerative braking in real time based on traffic, road grade, battery temperature, and driver behavior. However, such methods would require extensive validation to ensure safety and predictability. The electric vehicle braking system must remain reliable under all conditions, so any adaptive algorithm must be constrained by robust safety rules.
Real-world validation is also necessary. The research relies on high-fidelity simulation, but simulation cannot capture every real-world effect. Future work should include vehicle tests to validate the electric vehicle powertrain model and braking controller. Different driving styles, road surfaces, ambient temperatures, and battery aging conditions could affect the performance of the optimized strategy. Online calibration and dynamic adjustment could help the electric vehicle maintain efficient braking energy recovery as conditions change. The study therefore provides a strong simulation-based foundation for future electric vehicle development, while acknowledging that real-world testing will be needed before production deployment.
Overall, the research presents a coherent engineering approach for improving electric vehicle efficiency. It combines two-speed DCT architecture, detailed component modeling, multi-objective genetic optimization, safety-constrained braking force distribution, fuzzy logic control, and genetic algorithm tuning. The electric vehicle benefits from a powertrain that is matched to its operating conditions and a braking system that recovers energy within safe limits. The reported results show that the optimized electric vehicle can achieve lower energy consumption and higher braking energy recovery than reference strategies. For the electric vehicle industry, the study offers a practical framework for balancing dynamic performance, cost, and energy economy in the next generation of electric vehicle designs.