Final Project: Design of an Electric Vehicle

AIM

The aim of this project is to create a MATLAB model of an electric car utilizing a battery and a DC motor using the Powertrain Block set.

OBJECTIVE

The objective is to develop a MATLAB model of an electric car employing the Powertrain Block set, and subsequently, prepare a comprehensive report encompassing the following aspects:

1. System-level configurations

2. Model parameters

3. Results

4. Conclusion

DESIGN CALCULATIONS

To construct the electric vehicle (EV) model, several parameters such as power, energy, range, motor rating, battery capacity, etc., need to be calculated. We will refer to the specifications of the TATA NEXON EV for vehicle dimension data, as outlined below:

TATA NEXON EV – Specifications

· Power: 95 kW

· Length (mm): 3993

· Width (mm): 1811

· Height (mm): 1606

· Tire Dimension: 664.4 mm

· Kerb Weight (Kg): 1400

· Battery Capacity: 30.2 kWh

· Motor Ratings (3-phase PMSM): 97.46 kW, 245 Nm

· Range: 312 km

To determine the motor ratings, battery capacity, maximum power, torque, and energy required by the vehicle, a model must be created initially for the electric vehicle design.

Drive Cycle

The Worldwide harmonized Light vehicles Test Procedure (WLTP) is a standardized test procedure used to measure pollutants, CO2 emissions, and fuel consumption of traditional, hybrid, and electric vehicles. The WLTP procedure incorporates various driving cycles, including:

1. Class 1: Low power vehicles with PWr <= 22

2. Class 2: Vehicles with 22 < PWr <= 34

3. Class 3: High-power vehicles with PWr > 34

Where PWr [kW/Tonne] denotes the power-to-weight ratio, calculated as the ratio between the rated engine power and kerb weight.

Since we are modeling the TATA Nexon EV, parameters from the provided specifications will be used. With Pmax = 95 kW and Kerb Weight = 1.4 Tonnes, the power-to-weight ratio (PWr) is calculated as:

PWr = Pmax / mv = 95 / 1.4 = 67.85

As the vehicle's PWr exceeds 34, the WLTC Class 3 driving cycle will be utilized to compute energy consumption.

WLTC Class 3

The WLTC Class 3 consists of four sub-parts, each with a different maximum speed:

1. Low: up to 56.5 km/h

2. Medium: up to 76.6 km/h

3. High: up to 97.4 km/h

4. Extra-high: up to 131.3 km/h

These driving phases simulate urban, suburban, rural, and highway scenarios, with an equal division between urban and non-urban paths (52% and 48%).

Force Calculation

The forces acting on the vehicle include:

1. Rolling Resistance

2. Aerodynamic Drag

3. Grade Resistance

4. Acceleration Resistance

The total force is the sum of all the above forces, and power, torque, and energy can be calculated accordingly.

The Vehicle Model in Simulink will be translated into a Simulink model, as illustrated below.

By dividing the last calculated value of the total energy by the total length of the WLTC drive cycle (23.266 km), we can obtain the average energy consumption of the vehicle, which is 160 Wh/km.

The power curve of the vehicle, as depicted Above, illustrates the power required at different speeds during the drive cycle. From this plot, it is evident that to attain the maximum speed of 131 km/hr, the motor should ideally be rated at 140 kW. However, due to size and cost constraints, selecting a high-rated motor may not be feasible. Therefore, the motor should be chosen to operate at maximum efficiency during average speeds.

The torque curve, shown below, indicates that the maximum required torque is 222 Nm.

Motor Capacity Selection

Based on the torque and power plots, a suitable motor for the Electric Car can be selected. Since we are modeling the TATA Nexon, a BLDC motor with the following specifications can be chosen:

· Rated Power: 95.8 kW

· Rated Torque: 279 Nm

· Rated voltage: 460V

· No load Speed: 3280 rpm

· Rated Speed: 4000 rpm

· Armature Inductance: 2mH

Battery Capacity Calculations

The average energy consumption for propulsion (Ep) is calculated as 160 Wh/km on the WLTC drive cycle. In addition to the energy needed for propulsion, the high voltage battery must supply energy for the vehicle’s auxiliary devices (Eaux) such as the 12V electrical system, heating, and cooling. We also need to consider the efficiency of the powertrain (ηp) during the conversion from electrical energy to mechanical energy.

The average electrical loads, including headlights and multimedia, consume approximately 430 W of electrical power. Over the duration of the WLTC cycle (1800 s or 0.5 h), this results in an energy consumption of 215 Wh for auxiliary loads. Dividing this by the length of the WLTC driving cycle (23.266 km) yields an average energy consumption for auxiliary loads (Eaux) of 9.241 Wh/km.

The overall average energy consumption (Eavg) is calculated using Equation (1), taking into account both propulsion and auxiliary loads. Considering an efficiency (ηp) of 0.9, we arrive at an average energy consumption of 188.045 Wh/km.

The battery pack is designed for a vehicle range of 250 km, resulting in a total energy requirement (Ebp) of 47.011 kWh. Following this, the number of battery cells connected in series (Ncs) is calculated, and subsequently, the energy content of a string (Ebs) and the total number of strings (Nsb) are determined.

SIMULINK MODELLING OF COMPLETE ELECTRIC VEHICLE

Main Simulation Diagram:

The Complete model of Electric vehicle in Simulink is as shown below.

COMPONENTS:

The main components of the EV are:

i. Wheels/tires

ii. Battery

iii. Motor/generator

iv. Motor controllers

v. Power converters

vi. Transmission system

The main simulation diagram for the electric vehicle model consists of several interconnected components designed to mimic the behavior of a real vehicle. Below is a detailed explanation of the key components and their functions:

Inputs:

Battery Block:

Vehicle Body Block:

Vehicle Body Subsystem:

Subsystem Connections:

Tire Block:

Simple Gear Block:

Motor and Controller Subsystem:

DC Motor:

H-Bridge:

Controlled PWM Voltage:

Controlled Voltage Source:

Battery Subsystem:

Battery:

Controlled Current Source:

Current Sensor:

SOC (State of Charge) Estimate Subsystem:

Speed Measurement:

Distance Measurement:

Longitudinal Driver and Drive Cycle:

All the mentioned above parameters are explained briefly in the attachments. Do refer it.

Drive Cycle:

A drive cycle is a dataset that records the velocity and time data, representing the vehicle's motion. It typically mirrors real-world driving conditions and is essential for modeling vehicle behavior accurately. In this project, two drive cycles were utilized: the "New York Bus Cycle" and the "Artemis Motorway of 150 km/h." Drive cycles serve various testing purposes, including mileage, pollution tests, and acceleration and top speed evaluations.

Block Parameters:

The Artemis Motorway 150 km/h drive cycle graph was input into the vehicle model. This data provides the velocity profile for the vehicle's operation.

Artemis Motorway 150kmph Drive cycle graph is given as input to the vehicle model:

Results:

After running the drive cycle for the electric vehicle (EV) model, outputs from speed, current, and state of charge (SOC) scopes were collected. The distance traveled by the vehicle during the drive cycle was also measured using a display block. The simulation runtime was adjusted to match the duration of the drive cycle, which was 1068 seconds.

Speed Scope:

The speed scope displays two signals: the blue signal represents the vehicle's actual speed, while the yellow signal represents the Artemis drive cycle data. The longitudinal driver successfully maintains the vehicle's speed according to the reference speed provided by the drive cycle data. The graph effectively overlaps during acceleration and deceleration phases.

Current Scope:

The current scope illustrates how the current flowing from the battery to the motor varies throughout the drive cycle. This information is crucial for monitoring power consumption and ensuring the battery operates within its limits.

SOC Scope:

The SOC scope depicts the variation in the battery's state of charge over the duration of the drive cycle. The battery model begins with an SOC of 100%, and as the simulation progresses, the SOC decreases. Notably, during braking periods, regenerative braking replenishes the battery charge, resulting in an uplift in the SOC graph. At the end of the simulation, the SOC stabilizes at around 75%.

Battery Parameters:

The nominal voltage of the battery is set to 460V, consistent with the motor's rated voltage. The battery's specifications are derived from Table 3 data, providing critical information for modeling its behavior accurately.

Battery SOC Estimation:

To estimate the state of charge of the battery, the Coulomb counting method is employed. This method subtracts the charge taken from the battery from the initial SOC. The equation for SOC estimation is modeled in Simulink to track the battery's state throughout the drive cycle.

Vehicle Model:

The vehicle model subsystem encompasses the vehicle body, tire, and gear blocks. These components work together to simulate the vehicle's motion and performance accurately.

Vehicle Dynamics:

The Vehicle Body block represents a two-axle vehicle body with adjustable mass, geometry, and drag properties, facilitating longitudinal motion. It accounts for mass, aerodynamic drag, road incline, and weight distribution between axles during acceleration and road profile changes. This block, sourced from Simscape/Driveline/Tires & Vehicles library, is configured based on parameters such as mass, frontal area, and drag coefficient, sourced from Table 1, TATA NEXON specifications.

Tire Modeling:

The Tire (Magic Formula) block models tire longitudinal behavior using the Magic Formula, employing four fitting coefficients. It accurately captures tire dynamics under various pavement conditions. Load-dependent magic formula coefficients are chosen for this project. The tire parameters, including inertia and rolling radius, are sourced from Table 1, TATA NEXON specifications.

Gear Transmission:

The Simple Gear block simulates a gearbox, constraining base and follower gears to co-rotate with an adjustable gear ratio. It offers options for friction losses and fault triggering. This block, accessed from Simscape/Driveline/Gears library, provides precise control over gear behavior in the driveline.

Simulation Results:

The distance covered by the vehicle in WLTP Class 3 drive cycle is 23.27km:

The vehicle's performance during the Artemis Motorway 150 km/h drive cycle is analyzed. Integration of vehicle velocity provides the distance covered, revealing a total of 23.27 km traveled. The comparison between drive cycle speed and vehicle speed indicates alignment, with minor deviations observed during sudden braking, attributed to tire inertia.

The above figure shows the drive cycle speed and vehicle speed plot. We can see that vehicle follows drive cycle in all regions. Slight variations in the curve are in the regions of sudden braking. This may be due to inertia of the tires.

Acceleration and Deceleration:

The longitudinal driver generates acceleration and brake signals, crucial for controlling the vehicle's motion. The plot illustrates the commands issued during acceleration and deceleration phases.

PWM Signal Analysis:

The PWM signal to the H-Bridge varies with speed requirements, reaching maximum magnitude during acceleration and zero during braking.

Battery State of Charge (SOC):

The SOC plot demonstrates battery discharge during acceleration and recharge during regenerative braking. By the end of the simulation, the SOC stabilizes at 82%, indicating efficient energy management.

Results:

The EV model was subjected to the specified drive cycle, and outputs from speed, current, and SOC scopes were analyzed. Additionally, the distance covered by the vehicle during the drive cycle was measured using the display block. Before initiating the simulation, the runtime was adjusted to match the drive cycle duration of 1068 seconds.

From the simulation results obtained, the distance traveled by the vehicle was determined to be 8.306 kilometers within the 1068-second simulation period. Furthermore, the analysis revealed that the battery charge remained at 75% by the end of the simulation.

This outcome provides valuable insights into the vehicle's performance, indicating its capability to cover a significant distance while maintaining a substantial portion of its battery charge. Such findings contribute to the overall assessment of the EV model's efficiency and suitability for real-world applications.

Conclusion:

In conclusion, the project on the design of an electric vehicle (EV) using MATLAB and Simulink has provided valuable insights into the intricacies of electric vehicle engineering and simulation. Through this endeavor, several critical aspects of EV design, simulation, and analysis have been explored and understood.

The constructed MATLAB model, incorporating components such as the vehicle body, battery, motor, controller subsystems, and drive cycle analysis, accurately captures the dynamics and performance of an electric vehicle. Simulation results from various scopes and subsystems, including speed, current, SOC, and distance measurements, offer a comprehensive overview of the EV's behavior under different conditions.

Analysis of the simulation outcomes indicates that the EV model adheres well to the specified drive cycle, with efficient energy utilization and appropriate vehicle performance. Integration of regenerative braking enhances battery charging efficiency, contributing to sustainable energy management.

Furthermore, the project underscores the importance of design optimization, considering factors like motor selection, battery capacity calculations, and efficiency improvements to meet performance requirements while minimizing costs and energy consumption. Real-world application considerations, such as cost, size, weight, and efficiency trade-offs, have been considered throughout the design process.

Overall, the project serves as a testament to the capabilities of MATLAB and Simulink in modeling complex systems like electric vehicles and analyzing their performance. The insights gained from this project are invaluable for future endeavors in electric vehicle design, development, and deployment, contributing to the ongoing evolution of sustainable transportation solutions.