DESIGN OF DATA ACQUISITION SYSTEM FOR ELECTRIC BIKES TO DEVELOP DRIVE CYCLE FOR PESHAWAR

Project Title

DESIGN OF DATA ACQUISITION SYSTEM FOR ELECTRIC BIKES TO DEVELOP DRIVE CYCLE FOR PESHAWAR

Project Area of Specialization Electrical/Electronic EngineeringProject Summary

The interest in electric vehicles has increased rapidly over the past few years. International energy outlook report 2012 reported that more than 100,000 hybrid and all-electric vehicles are sold globally, and sales figures are approximately doubling each year. And, according to the international energy outlook report 2015, alternative vehicle technologies are replacing conventional vehicles. These alternative vehicle technologies, which include hybrid electric vehicles (HEVs), Plug-in HEVs (PHEVs), and Electric Vehicles (EVs), have gained popularity because of their advantages, such as being more environmentally friendly, less noisy, and more efficient. Electric Ikes are gaining popularity in Pakistan. The primary reason for it is that they do not need expensive fossil fuels for driving. They can be charged off renewable energy.

The system architecture of an electric bike (e-bike) is shown in Fig.1.1. There are two main parts in an e-bike, the Battery and Traction Motor Driver. The Battery is the only source of energy in an EV. The Battery Management System (BMS) or the Cell Equalizer continuously monitors the cells inside the battery and makes sure the battery is operated in the safest conditions. E-bikes may have on-board chargers or off-board chargers. The traction motor is responsible for moving the bike. It takes energy from the battery and converts it into a rotating magnetic field, thus moving the bike.

The driving range of an e-bike depends on the energy storage capacity of the battery. For a longer range, a high capacity battery is required which increases the cost of the e-bike. It is very important to utilize the energy stored in the battery in the most efficient way. The energy consumption by e-bikes depends on the driving terrain (flat surface, uphill, or downhill) and driving habits (weight on the bike, acceleration, and speed). Because of the range anxiety, it is hard to replace fossil fuel-based bikes with e-bikes. In this project, a system is developed that can measure a number of parameters while the bike is driving. These parameters are used to develop a model that can accurately predict the range of e-bikes in km. If the Eco-mode is activated in the e-bike, it will make sure the e-bike has the maximum possible range. The block diagram of the system is shown below,

Project Objectives

The goal of this project is to design a sensor network for EV applications that can store the measured data locally. The proposed project will mainly focus on the following objectives:

1. Develop a sensor network that can measure all the parameters of an e-bike. The measurements can be stored locally.

2. Apply Machine Learning and Artificial Intelligence algorithms to the measured data to develop a model that can predict the range of the e-bike for the given operating conditions.

3. Develop an Eco-mode for the e-bike that makes sure the range of the e-bike is maximized.

Project Implementation Method

This project will be implemented in the following steps.

1. First a simulation model will be built in Proteus and PSIM to select the type and number of sensors.

2. From the simulation model, the minimum sampling frequency for each measurement is determined. From this information, the total storage capacity of the memory can be calculated.

3. The sensors and microcontroller are identified and procured.

4. The sensors are tested one by one.

5. The sensors network with all the sensors is tested.

6. The memory storage is interfered with by the microcontroller and is tested.

7. The developed system was tested on the e-bike while it is driven on campus.

8. The developed system was tested on the e-bike while it is driven on roads in Peshawar.

9. Machine Learning and Artificial Intelligence algorithms are applied to the gathered sensor data to develop the model.

10. The developed model is tested on the road and is finely tuned to predict the outcome accurately.

11. The Eco-mode is developed based on the model developed using Machine Learning and Artificial Intelligence algorithms.

Benefits of the Project

To reduce the dependence on fossil fuels and to reduce environmental pollution, many countries including Pakistan are trying to develop electric vehicles like electric bikes, electric rickshaws and electric cars. These electric vehicles have electric motors that need to be driven off the energy stored in the batteries. These batteries have limited storage capacity and the energy stored in them must be utilized in the most efficient way. In this project, a system is developed that can predict the range of e-bikes in km very accurately. Also, an Eco-mode is developed for an e-bike that can make sure the e-bike has the maximum possible range.

Technical Details of Final Deliverable

The final deliverables are both Hardware and Software.

A. Hardware deliverables:

1. Voltage Sensors

2. Current Sensors

3. Speed sensor

4. Motion Sensors (Accelerometer, Magnetometer, and Gyroscope)

5. GPS

6 Temperature sensors

7. Printed Circuit boards

8. Microcontroller

9. Memory

B. Software:

1. Firmware in Microcontrollers

Final Deliverable of the Project Hardware SystemCore Industry Energy Other Industries Transportation Core Technology Artificial Intelligence(AI)Other Technologies Internet of Things (IoT), Clean TechSustainable Development Goals Affordable and Clean Energy, Industry, Innovation and Infrastructure, Climate ActionRequired Resources

Item Name	Type	No. of Units	Per Unit Cost (in Rs)	Total (in Rs)
			Total in (Rs)	80000
Microcontroller	Equipment	5	5000	25000
Sensors	Equipment	3	10000	30000
Memory Card	Equipment	5	2000	10000
Enclosure and packaging	Equipment	2	2500	5000
Traveling/surveying/testing	Miscellaneous	1	10000	10000