Chapter 13: Autonomous and Connected Electric Vehicles

Prof.(Dr.) D G Mahto, Founder & CEO -

Abstract:

Autonomous and Connected Electric Vehicles (ACEVs or CASE vehicles: Connected, Autonomous, Electric, Shared) merge self-driving AI with electric power and constant data exchange (V2X) for safer, greener, more efficient transport, reducing emissions, optimizing traffic, and offering new shared mobility models, though high costs and complex tech integration remain challenges, say futuremobilitymedia.events, i GET IT by Tata Technologies, NobleProg Nepal, GovTech, SWITCH - Street WITCHer. These vehicles use sensors and networks to "see" and communicate, making driving decisions without human input for better urban mobility and sustainability. 
Key Components & How They Work
  • Autonomous (Self-Driving): AI and sensors (cameras, lidar, radar) perceive the environment, enabling the car to navigate, steer, brake, and accelerate without human intervention, progressing through levels of automation.
  • Connected (V2X): Vehicles communicate with each other (V2V), infrastructure (V2I), pedestrians (V2P), and the cloud (V2C) via 5G/cellular, sharing real-time data on traffic, hazards, and conditions, say NobleProg Nepal, Caltrans, and futuremobilitymedia.events.
  • Electric (EV): Battery power provides consistent energy for complex systems and offers instant torque, lower emissions, and reduced operating costs over time.
  • Shared: Often integrated with ride-sharing, these vehicles can operate as fleets, maximizing usage, reducing parking needs, and offsetting high initial costs, according to GovTech and SWITCH - Street WITCHer. 
Benefits & Impact
  • Safety: Reduces accidents through faster reaction times and 360-degree awareness.
  • Efficiency: Optimizes traffic flow, reduces congestion, and lowers energy consumption.
  • Environment: Decreases reliance on fossil fuels and lowers pollution.
  • Mobility: Enhances accessibility and frees up urban space. 
Challenges
  • Cost: High initial cost of technology.
  • Infrastructure: Need for smart road infrastructure and 5G networks.
  • Cybersecurity: Protecting connected systems from threats
  • Regulation: Evolving legal frameworks. 
The Future (ACES)
This convergence (ACES) is transforming transportation, creating new business models and ecosystems, impacting everything from vehicle design to urban planning, say i GET IT by Tata Technologies, futuremobilitymedia.events, and SAE International. 

Here is a complete, comprehensive, and future-oriented Chapter 13, written in a clear textbook style and aligned with the previous chapters of the Electric Vehicles book.

**Chapter 13

Autonomous and Connected Electric Vehicles**

13.1 Introduction

The convergence of electric mobility, automation, and digital connectivity is transforming the future of transportation. Autonomous and Connected Electric Vehicles (ACEVs) integrate electric powertrains with advanced sensing, artificial intelligence, communication technologies, and control systems. These vehicles promise safer roads, improved traffic efficiency, reduced emissions, and enhanced user experience.

This chapter explores the fundamentals of autonomous driving, vehicle connectivity, enabling technologies, applications, benefits, challenges, and future prospects of autonomous and connected electric vehicles.

13.2 Evolution of Autonomous and Connected Vehicles

  • Early driver-assistance systems (ABS, cruise control)

  • Advanced Driver Assistance Systems (ADAS)

  • Fully autonomous vehicles

  • Vehicle connectivity and smart transportation systems

13.3 Levels of Vehicle Autonomy

The Society of Automotive Engineers (SAE) defines six levels of driving automation:

LevelDescription
Level 0No automation
Level 1Driver assistance
Level 2Partial automation
Level 3Conditional automation
Level 4High automation
Level 5Full automation

13.4 Key Components of Autonomous Electric Vehicles

13.4.1 Sensors

  • Cameras

  • LiDAR

  • Radar

  • Ultrasonic sensors

13.4.2 Control Units

  • Electronic Control Units (ECUs)

  • Centralized vehicle computers

13.4.3 Actuators

  • Steering

  • Braking

  • Acceleration

13.5 Role of Artificial Intelligence and Machine Learning

  • Object detection and classification

  • Path planning and decision-making

  • Predictive behavior modeling

  • Reinforcement learning for control

13.6 Vehicle Connectivity Technologies

13.6.1 Vehicle-to-Vehicle (V2V)

  • Collision avoidance

  • Cooperative driving

13.6.2 Vehicle-to-Infrastructure (V2I)

  • Traffic signal communication

  • Smart road systems

13.6.3 Vehicle-to-Everything (V2X)

  • Integration with pedestrians, grid, and cloud

13.7 Communication Protocols and Networks

  • Dedicated Short Range Communication (DSRC)

  • Cellular V2X (C-V2X)

  • 4G LTE and 5G networks

13.8 Autonomous Driving Architecture

  1. Perception

  2. Localization and mapping

  3. Planning

  4. Control

13.9 Autonomous EV Power Management

  • Energy-efficient route planning

  • Regenerative braking optimization

  • Intelligent battery management

13.10 Safety in Autonomous and Connected EVs

  • Redundancy in sensors and systems

  • Functional safety (ISO 26262)

  • Cybersecurity protection

  • Fail-safe and fallback mechanisms

13.11 Human–Machine Interface (HMI)

  • Driver monitoring systems

  • Visual and auditory alerts

  • User trust and acceptance

13.12 Applications of Autonomous and Connected EVs

  • Robotaxis

  • Autonomous buses

  • Logistics and delivery vehicles

  • Industrial and campus mobility

13.13 Benefits of Autonomous and Connected EVs

  • Reduced accidents

  • Improved traffic efficiency

  • Lower operating costs

  • Enhanced accessibility

13.14 Challenges and Limitations

  • High development costs

  • Complex regulatory approval

  • Ethical decision-making

  • Infrastructure readiness

13.15 Ethical and Legal Considerations

  • Responsibility in accidents

  • Data privacy

  • Algorithmic fairness

13.16 Global Developments and Pilot Projects

  • Smart cities initiatives

  • Autonomous EV trials

  • Public-private partnerships

13.17 Case Study: Autonomous Electric Robotaxi (Illustrative)

A robotaxi operating in a smart city:

  • Uses LiDAR, cameras, and V2X

  • Reduces energy consumption by optimized routing

  • Improves passenger safety and convenience

13.18 Future Trends

  • Level 4 and Level 5 autonomy

  • Integration with smart grids

  • AI-powered predictive maintenance

  • Autonomous EV fleets

13.19 Role of Government and Policy

  • Autonomous driving regulations

  • Data governance

  • Infrastructure investment

13.20 Conclusion

Autonomous and Connected Electric Vehicles represent a paradigm shift in transportation. By combining electric propulsion with automation and connectivity, these vehicles have the potential to revolutionize mobility systems. While technological, regulatory, and ethical challenges remain, continued innovation and collaboration will pave the way for safer, cleaner, and smarter transportation.

Prof. (Dr.) D. G. Mahto: A Visionary Director, Professor, Leader & Educator With over 25 years of excellence, Prof. (Dr.) D. G. Mahto is a Director, Professor, Philanthropist, HEI Builder, writer, consultant, reviewer, and career advisor at "Career Education for Success—Discover, Apply, Succeed!" He has empowered individuals, brands, and institutions through insightful content, innovation, and mentorship. Holding a Ph.D. in Engineering from NIT Jamshedpur, he has pursued Post-Doctoral Programs at IITs, NITs, and IIITs, reinforcing his expertise. A member of prestigious global organizations like NPA (USA), IET (UK), ASME, IAENG, and MRSI, he actively contributes to engineering, research, and education. An accomplished writer and researcher, he shares knowledge via Career Education Success Now, Career Edus, and Discover Knowledge Wisely. His vision integrates technology, innovation, and leadership, bridging gaps between aspirations and achievements. Connect with him for insights into education, research, and professional growth.

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