The Principle of Resistance Simulation
When the vehicle drives on the road, resistance is determined as follows [
23]:
where rolling resistance is
Ff, air friction is
Fw, gradient resistance is
Fi, acceleration resistance is
Fj, the mass of the vehicle is
m, the coefficient of rolling resistance is
f, the coefficient of air resistance is
CD, the vehicle windward area is
A, the speed of vehicle running is
ua, the ramp angle is
i,
du and
dt is velocity and time of EV respectively. The vehicle rotating mass reduction coefficient is
δ.
The force on the wheels that the traction motor transmits is as follows [
24]:
where the traction motor load torque is
Tz, the gear box transmission ratio is
ig, the final ratio is
i0, transmission efficiency is
η, and the wheel radius is
r. Instead of (2) and (3), the traction motor load torque is expressed as the Formula (4).
The relationship between the traction motor speed
n and the vehicle running speed
u is as follows [
25]:
The meaning of ig, i0, and r are shown in Formula (3).
Wheel power (
Pw) is determined by the product of the tensile force acting on the wheels (
Fw) and the speed of the vehicle [
13].
The determination of the total power available to the wheels is
The relationship between velocity and acceleration time is defined as
W =
FD is acceleration work, and
F = ma is acceleration force, where the distance (D) moved is in meters [
13].
This study analyzes electric vehicle performance using three testing methods: simulation, a dynamometer (dino), and actual road testing. The simulation, conducted in MATLAB/SIMULINK R2016a, follows the RITA drive cycle, covering 7.64 km in 11.25 min with an average speed of 60.7 km/h. The dino test measures power output and torque using a dynamometer, while actual road testing assesses the vehicle’s performance and efficiency in real-world conditions. All three situations will be compared in terms of driving distance, power, and torque.
Figure 15 shows the experimental design of the Jaguar XJ40 electric vehicle transformation.
Figure 16 displays the simulated speed profiles of the EV transformation for a single drive cycle. During this cycle, the vehicle accelerated to an initial constant speed of about 61.08 km/h, which it maintained for 137.8 s before decelerating to an idle state. The second cycle began 40 s later, where the vehicle achieved and held a steady speed of approximately 69.6 km/h for 175 s before entering the deceleration phase. After another 40 s interval, the third and fourth cycles featured speeds of around 90 km/h (for 210 s) and 125 km/h (for 230 s), respectively. The final cycle involved accelerating to a constant speed of 138 km/h and maintaining it for 280 s.
Figure 17 presents the efficiency map shapes and their corresponding loss contours for the PMSM, while
Figure 18 shows the performance simulation curves of the PMSM. In a battery management system (BMS), displaying the battery cell balance information, as shown in
Figure 19, can provide insight into the performance and condition of the battery pack. The BMS demonstrates effective cell balancing across all battery cells. From the image, it is evident that each cell has a value of 3.8, indicating that the battery is in good condition. This also shows that the BMS is functioning efficiently, with proper cell balancing contributing to system stability. It prevents the system from shutting down during driving and also indicates the internal resistance status of the battery. These parameters confirm that the battery can be charged according to standards. Furthermore,
Figure 20 shows the efficiency of a 3.3 kW multi-charging system. The graph indicates that the stability of the charging systems across all four batteries demonstrates similar efficiency in operation. A multi-charging system provides flexibility and convenience for EV users, enabling them to charge their vehicles from various sources without needing multiple separate systems.
Figure 21 shows 90.47% SOC degradation over the driving cycles of the simulation, dino, and actual road testing. The rate of change of SOC, denoted by ΔSOC, is obtained by taking the difference between the current sample and the previous sample for the SOC signal.
Figure 22 and
Figure 23 show the power during the charging process in normal mode and acceleration mode, respectively. They also present a comparison of power and torque in BEVs using different testing methods such as dynamometer (dino) simulation and actual road tests. These comparisons offer valuable insights into the vehicle’s performance under various conditions. Here is an overview of these testing methods and their implications: Simulation is useful for early-stage design and optimization but may not fully reflect real-world performance. Dino testing provides a good balance between accuracy and control, useful for tuning and validating the powertrain [
26]. Actual on-road testing is essential for final validation and understanding real-world performance and driver experience [
27]. In results, the lower power and torque observed during actual on-road testing reflect the numerous real-world variables and inefficiencies that are either controlled or idealized in simulations and dino tests. This makes on-road testing an essential step in evaluating the true performance of BEVs under everyday driving conditions as shown in
Figure 24.
In
Figure 25, there is a comparison of the driving range of BEVs in different testing methods: simulation, dynamometer (dino), and actual road testing. The simulation is for one drive cycle covering 7.64 km that takes 11.25 min, covering a driving distance of up to 282.14 km. Dino uses a chassis dynamometer to simulate driving conditions while the vehicle is stationary; the driving distance covers 264.68 km. Actual on-road tests conducted on public roads show a driving distance of 259.09 km. Real-world driving conditions include variations in speed, road types, weather, and traffic. The study found that the real-world energy consumption of EVs was generally higher than the values predicted by standardized driving cycles and simulations.