AI is at the heart of computer science and is used in almost every industry, from healthcare and finance to design and education, helping to make data-driven decisions and actions for repetitive or computationally intensive tasks.
Many existing technologies use artificial intelligence to enhance capabilities. We see it in smartphones with AI assistants, e-commerce platforms with recommendation systems and vehicles with autonomous driving abilities.
AI also helps protect people by piloting fraud detection systems online as well as leading research in healthcare and climate initiatives.
What is an Electric Vehicle (EV)?
Electric Vehicles (EVs) are vehicles that operate using electric-drive technologies, such as battery electric vehicles, plug-in hybrid electric vehicles, and fuel cell electric vehicles, offering low to zero carbon emissions, high efficiency, and flexibility in grid integration.
Different Categories of EVs
- Battery Electric Vehicle (BEV): Battery Electric Vehicles, also called BEVs and more frequently called EVs, are fully electric vehicles with rechargeable batteries and no gasoline engine. Examples: Tesla Model S, Nissan Leaf, Renault Zoe.
- Hybrid Electric Vehicle (HEV): It is a type of vehicle that combines a traditional Internal Combustion Engine (ICE) with an electric motor to improve fuel efficiency and reduce emissions. Examples: Toyota Prius, Toyota Yaris Cross, , Chevy Bolt, and Nissan Leaf.
- Plug-in Hybrid Electric Vehicle (PHEV): Plug-in Hybrid Electric Vehicles, have both an engine and electric motor to drive the car. Like regular hybrid, they can recharge their battery through regenerative braking. They differ from regular hybrids by having a much larger battery, and being able to plug into the grid to recharge. Examples: Toyota Prius Prime, Chevrolet Volt, Mitsubishi Outlander PHEV.
- Fuel Cell Electric Vehicle (FCEV): It uses hydrogen fuel cells to produce electricity. The most common one is the Toyota Mirai FCEV Example: Toyota FCHV, Honda FCX, Toyota Sora.
Compared to gas-powered cars, EVs are more efficient. They have lower operating costs and produce no tailpipe emissions, making them better for the environment.
The EV Control System
The EV control system plays a significant role in the energy consumption of EV equipment, and thus can indirectly affect the driving range of EVs. Optimal use of the EV control system can maximize vehicle speed while reducing the energy consumption of EV equipment such as power steering, regenerative braking, and indoor environmental control such as HVAC (heating, ventilation, air conditioning). In particular, the Regenerative Braking System (RBS) installed in most electric vehicles provides the function of generating energy to increase the driving range.
The RBS converts kinetic energy into useful electrical energy when the vehicle slows down, increasing the driving range of the EV. Driving technique, system temperature, and temperature affect the efficiency of energy extracted from the RBS.
EV control architecture and process flowcharts of some related controllers:
- EV control architecture.
- Conventional (PID) controller.
- Swarm optimization (PSO) based controller.
- AIC (fuzzy-logic based) controller.
Artificial Intelligence in Hybrid Electric Vehicles
EV Charging Technologies
The term ‘charging technology’ defines the method and technology used to restore energy to a rechargeable device. Rechargeable cells, commonly called batteries, are used in electric vehicles. There are various types of batteries, including nickel cadmium, nickel metal hydride, and lithium ion. Lithium ion (Li-ion), including lithium-ion polymer and lithium-ion phosphate batteries, are the most suitable for electric vehicle applications.
Because of their high energy density, these batteries are lighter in weight. Various types of charging technologies have been implemented in practice. Extensive literature explains that charging electric vehicles requires single-phase or three-phase unidirectional or bidirectional power flow. However, conductive and inductive charging technologies are important types. Conductive technology is well developed, while other technologies such as inductive charging and new technologies like battery swapping, vehicle-to-grid (V2G), and smart charging are interesting topics for researchers.
- Inductive Charging: Inductive charging is a wireless energy transfer method for charging Electric Vehicles (EVs). The technology uses the principles of electromagnetic fields to transfer energy from a charging station to a car battery without physical connectors or cables.
- Capacitive Charging: Capacitive Charging is a wireless method of charging that uses electric fields to transfer energy between two capacitive plates or electrodes, without the need for physical connectors or wires. The process involves transferring energy from a charging station to an Electric Vehicle (EV) by creating an electric field that can transfer energy across the air gap between the vehicle and the charging source.
Smart Charging Infrastructure and Grid Integration
AI can help optimize charging infrastructure, ensure efficient use of renewable energy sources, and minimize the environmental impact of power generation.
- AI-based Smart Charging: AI can determine the most efficient charging time for electric vehicles based on electricity demand, cost, and renewable energy availability through a smart charging network. This helps balance the grid and encourages the use of renewable energy sources such as solar and wind, which are often variable.
- Vehicle-to-Grid (V2G) integration: AI plays a key role in facilitating V2G technology, where electric vehicles feed power back into the grid during peak demand periods, helping stabilize the grid and support renewable energy integration. This reduces our dependence on fossil fuels and minimizes carbon emissions into the power grid.
- Battery Swapping: Battery swapping is an innovative charging solution for Electric Vehicles (EVs) that aims to overcome some of the challenges posed by conventional charging methods, such as the long charging time and high procurement and management cost of EV batteries. In this technique, EV owners to swap their discharged batteries with fully charged ones at a swapping station while waiting for the battery to be recharged.
Role of Artificial Intelligence in Electric Vehicle Performance
Artificial Intelligence (AI) is revolutionizing the automotive industry, especially in optimizing the performance of Electric Vehicles (EVs). AI applications in EVs are making these vehicles smarter, safer, and more energy efficient. AI is driving efficiency, safety, and user experience across a wide range of EV technology with innovative capabilities. From predictive maintenance to autonomous driving features, energy management systems, smart charging infrastructure, and personalized user experiences, AI is driving EV innovation and optimization. AI can analyse vast amounts of data, identify patterns, and make informed decisions in real time. AI is driving groundbreaking advances in EV technology. Here are some of the key roles AI plays in EVs.
- AI-based Advanced Driver Assistance Systems: AI-powered Advanced Driver Assistance Systems (ADASs) use artificial intelligence, cameras, sensors, and radar to assist drivers with tasks like navigation, parking, lane-keeping, and collision avoidance. By processing real-time data, AI helps detect obstacles, pedestrians, and hazards, providing alerts and assistance such as automatic emergency braking or steering control. ADAS enhances safety, reduces human error, and optimizes driving decisions, making it a crucial component of modern vehicles.
AI for Driver Assistance Systems
A. Anti-lock braking systems
B. High beam protection system
C. Provides predictive maintenance alerts.
D. Traffic lights traction control recognition.
E. Improves vehicle safety through real-time monitoring.
F. Optimizes driving with adaptive cruise control (ACC)
- Autonomous Driving and Safety Features: Autonomous Electric Vehicles (AEVs) are self-driving cars powered entirely by electricity. These vehicles combine the technology of autonomous driving with electric propulsion systems. Using a combination of sensors, cameras, lidar, radar, and Artificial Intelligence (AI), AEVs can navigate, detect obstacles, and make driving decisions without human intervention.
AEVs can follow traffic rules, avoid collisions, and even make complex decisions, such as changing lanes or stopping at traffic signals, by analysing real-time data from their surroundings. The integration of AI allows these vehicles to learn and adapt to different driving conditions, improving over time.
The primary benefits of autonomous electric vehicles include enhanced safety, reduced human error, and environmental sustainability, as they contribute to cleaner air and lower carbon emissions. As technology advances, autonomous electric vehicles have the potential to revolutionize transportation, making it safer, efficient, and more sustainable.
- The Benefits of Autonomous Electric Vehicles: i) Enhanced Safety, ii) Improved Mobility and iii) Society and Economics Benefits.
- Battery Research and Development: Battery research and development (R&D) is essential for the development of Electric Vehicles (EVs) technology, renewable energy storage devices, and portable electronic devices. Higher energy density allows for longer driving ranges for electric vehicles and longer usage times for electronic devices. Another important area of R&D is increasing the life and durability of batteries, aiming to increase the number of charge and discharge cycles a battery can endure without losing capacity.
Continuous research and development of electric vehicle batteries is of paramount importance because batteries are expensive and also have a substantial impact on vehicle performance.
- Energy Management and Real-Time Optimization: In an effort to build energy management systems into existing vehicles, researchers and manufacturers have developed Battery Electric Vehicles (BEVs), Hybrid Electric Vehicles (HEVs), Plug-in Hybrid Electric Vehicles (PHEVs), and Fuel Cell Vehicles (FEVs), all designed with sustainability in mind.
However, the basic mechanisms of these vehicles rely on Internal Combustion Engines (ICEs) and battery power, depending on the vehicle mode. Various energy management systems and strategies are mainly implemented for different types of EVs. Similarly, AI-based algorithms are being used to solve complex problems in energy management systems. Several experiments have been conducted to test the effectiveness of various methods, including recurrent models, Q-Learning (QL), Artificial Neural Networks (ANNs), Deep Learning (DL), reinforcement learning, and their integration with EVs.
- Predictive Maintenance for Electrical and Mechanical Faults: Predictive maintenance is an advanced strategy that uses data analysis, machine learning, and Artificial Intelligence (AI) to predict and prevent electrical and mechanical faults in equipment before they occur.
By collecting real-time data from sensors embedded in machines, vehicles, or systems, predictive maintenance continuously monitors key parameters such as temperature, vibration, and pressure. This data is then analysed to identify patterns or anomalies that could indicate impending failures, such as wear and tear on mechanical parts or electrical issues in components like motors, batteries, or power electronics. The system uses predictive algorithms to forecast when a failure might happen, allowing maintenance to be scheduled proactively, before a breakdown occurs.
This helps reduce unplanned downtime, minimize costly repairs, and extend the lifespan of equipment. In industries like manufacturing, electric vehicles, and energy, predictive maintenance can optimize resource use by ensuring that maintenance activities are performed only when necessary. The result is improved safety, lower operational costs, and enhanced system reliability.
- Environmental Impacts of AI-Assisted EV Technologies: The environmental impacts of AI-assisted Electric Vehicle (EV) technologies are significant, AI can greatly improve energy efficiency, reduce carbon emissions, and optimize battery life. AI also enhances Battery Management Systems (BMS), prolonging battery life and reducing waste by optimizing charging and discharging cycles. Additionally, AI-powered route optimization and driving assistance systems help minimize energy consumption by recommending the most efficient paths and driving techniques, leading to lower emissions and reduced environmental impact.
Overall, AI-assisted EV technologies have the potential to significantly reduce emissions and improve energy efficiency, their environmental benefits depend on how these systems are powered and managed.
Conclusion
AI is transforming the electric vehicle industry by making EVs smarter, more efficient, and environmentally friendly. From improving battery management and optimizing energy use to enabling autonomous driving and creating smarter charging infrastructure, AI is helping EVs reach their full potential as a sustainable alternative to traditional fossil fuel-powered vehicles.
The expansion of EVs is an effective strategy for moving towards sustainable development. The improvement of charging technologies and the integration of emerging trends have transformed future mobility.
All the stakeholders involved in this EV adoption and infrastructure chain must work collaboratively to fulfill user expectations by focusing on net zero emissions. In recent years, significant research has depicted the suitability of available EV types, charging methods, vehicle emissions, and costs that can help transition fuel-based vehicles to electric vehicles. This review covers an overview of the charging technologies and the role of AI in developing intelligent electric vehicles to address the knowledge gaps.
Improving and standardizing charging infrastructure and technologies is crucial for accelerating the adoption of Electric Vehicles (EVs), as policies and development strategies rely heavily on the current state of EVs.
Megha Sen is a PhD Scholar in Electrical Department, College of Technology and Engineering, MPUAT, Udaipur.
Dr. Vikramaditya Dave is an Associate Professor & Head of the Electrical Engineering Department, College of Technology and Engineering, MPUAT, Udaipur.
