Call for Chapters: Artificial Intelligence Applications in Battery Management Systems and Routing Problems in Electric Vehicles


ASHOK KUMAR LOGANATHAN, PSG College of Technology, India

Call for Chapters

Proposals Submission Deadline: July 29, 2022
Full Chapters Due: August 28, 2022


The present world is looking for feasible alternatives to conventional fossil fuels. Integrating renewable energy sources with the existing grid has proved to be a viable solution. As the renewable sources are inherently intermittent, energy storage systems are essential to provide reliable power to the consumers. The controllers play a vital role in providing the stable power output to the loads by reducing the variations between the expected and actual parameters. Smart grid is a promising and self-sufficient system which is based on digital automation technologies to monitor, control & provide effective solutions to the utilities and consumers. Now-a-days, to reduce the carbon dioxide gas emission from the motor vehicles and to save mother nature, the electric vehicles are becoming more practical. This book chapter is mainly aimed to focus on the areas of integration of renewable energy sources with existing grid, to introduce power exchange scenario in the prevailing power market, to control the voltage and frequency along with power management in hybrid micro grid, to expand the use of electric vehicle market for creating cleaner and transformative energy, to optimize the control variables with artificial intelligence techniques. The chapters will be discussed the algorithms involved, methodology used, solution techniques and its implementation, results with its discussion. In the generation of climate-conscious people, traditional Internal Combustion (IC) engine based vehicles are rapidly replaced by eco-friendly Electric Vehicles (EV). Electric motor based transportation systems are employed in all forms of transport like land (e-Bikes, Cars, light good transport), air (Drones) and sea (Electric ferry boats). Due to the usage of high amount of electricity for charging of electric vehicles, conventional grid technologies may fail, which leads to catastrophic failures. Smart grid integrated with the renewable energy sources can effectively balance the load using integrated and intelligent monitoring and control systems. Vehicle-to-grid technology is used to balance the energy in the smart grid. Battery Management System (BMS) monitors performance of the battery packs present in the electric vehicle. BMS evaluates the State of Charge (SoC) of the battery. It estimates SoC by measuring different parameters like current, voltage and temperature of the battery. It predicts the battery level, which the critical parameter in deciding remaining charge state, especially in electric vehicles. It helps in maximizing the efficiency of the battery and also improves the charging/discharging performance. BMS also estimate state of state of health and depth of discharge by measuring various parameters of the battery. It is integrated with vehicle management system. BMS system consists of both hardware and software components. BMS hardware components include automobile grade sensors and controllers. BMS performs additional functionalities like protection, diagnosis and data management. Electric Vehicle Routing Problem is an optimization problem that deals with the framing of effective route plans for electric vehicle while fulfilling a set of batter power related constraints. This problem aims to find the optimized schedule for Electric Vehicles (EV) while considering the set of operational constraints like geography of charging stations, vicinity of charging station, number of charging points available in particular station, charging speed and time, power drawn from grid, and vehicle’s battery capacity and state of charge. Routing Problem in electric vehicles can be considered as a special case of traditional traveling salesman problem with different operational constraints. Operational constraints include recharging policy, charging technology used, and availability of battery swapping stations. EV’s characteristic parameters like travel distance, speed, vehicle tonnage, and gradient of road are also considered for optimized route plan. Routing problem is well utilized in EV based logistic delivery, public transportation system and EV based shuttle services. Artificial Intelligence (AI) techniques like machine learning, deep learning and reinforcement learning algorithm plays a major role in forecasting of demand and supply of electrical energy in smart grid. In Electric Vehicles, AI techniques are used in self-driving autonomous cars, intelligent battery monitoring systems and prognostics of faults in EV prime movers. This book provides a curated content in the applications of AI techniques in EV and smart grid. It delivers the peer reviewed and well tested results that can be directly used by researchers and industry professionals to develop new concepts and products. It deals with recent developments in the niche areas like energy forecasting from renewable energy sources, active load balancing in the smart grids integrated with solar and wind power and block chain based peer to peer energy transactions. It also deals with the topics in the developing fields like Vehicle-to-Grid (V2G), where there is bidirectional energy flow between vehicle and smart gird. V2G is gaining popularity, due to its ability to both charge and receive power from EV during peak loads. Artificial intelligence techniques applied in power system sector makes the prediction of renewable power source generation and demand in an efficient and effective way. Moreover, these techniques are more powerful for solving the complexity of various energy sources having large size of data. By integrating clustering algorithms, the solar power generation can be estimated for effective analysis. The forecasting methods may be very-short, short, medium and long term depending on the characteristics of the systems developed. This book chapter contents are focusing on these artificial intelligence techniques for the evolving power system filed, electric vehicle market, energy storage elements and renewable energy source integration as distributed generators. The current trend of electric vehicle battery management system, vehicle routing problems will be elaborated in the proposed book chapters.


Conventional transport system is the primary consumer of fossil fuels and highest source of greenhouse gas emission in the world. Electric vehicles are found to be the best alternate to the conventional transport system and reduce the (Carbon di-Oxide) CO2 emissions thereby promising eco-friendly vehicle. The power to run vehicle is provided by the energy stored in the battery which is the heart and art of the EV. The battery management system has to be focused much to improve the life time of the battery, to make more efficient and more safety to the users. Driving range, charging time, power capability are few main factors in the battery management systems. Vehicle Routing Problem (VRP) is finding the optimal set of routes to deliver the products to the consumers with reduced transport cost. It has been originated from Travelling Salesman Problem (TSP) and VRP has various dimensions that includes time windows (TW), Team Orienteering Problem (TOP), Capacitated Team Orienteering Problem (CTOP), Vehicle Routing Problem with Pickup and Delivery (VRPPD), Inventory Routing Problem (IRP), Multiple Trips (MT), Multi-Depot (MDVRP), Open Vehicle Routing Problem (OVRP) etc. Electric Vehicle routing Problem (E-VRP) are emerging as interested topic in VRP optimization techniques considering the objective functions as vehicle costs, transit costs, battery swapping costs and charging costs with various constraints. The conventional optimization techniques are not often used in finding the solution due to its limitations such as slowness and less number of orders. Heuristic algorithms are widely used in VRP with different constraints. Artificial intelligent techniques are effective in finding the solutions for the complex problems, for the prediction of the battery level, finding the optimal solution for the battery management system, charging time reduction, performance analysis of various batteries in vehicle applications. The primary objective of this book is to apply artificial intelligence and machine learning techniques with the objectives of • State-of-the-art in battery management in EV application, various modes of power transfer, Electric vehicle routing problems, artificial intelligence applications in Electric Vehicles. • Performance analysis of batteries in Electric Vehicles. • Prediction of battery level in Electric Vehicles. • Battery Management Systems (BMS) in Electric Vehicle (EV) applications. • Integration of renewable energy sources for battery charging in electric vehicle stations. • Electric Vehicle Routing Problems (E-VRP) with various objective functions and constraints. • Grid to Vehicle (G2V) and Vehicle to Grid (V2G) power transfer mode analysis. • Control circuits design for G2V, V2G and V2V operation of Electric Vehicles. • Optimization techniques for Electric Vehicle Routing Problems. This book gives platform for the young researchers • To provide a curated content in the applications of AI techniques in Electric Vehicles and smart grids. • To deliver the peer reviewed and well tested results that can be directly used by researchers and industry professionals to develop new concepts and products. • To deal with recent developments in the niche areas like energy forecasting from renewable energy sources, active load balancing in the smart grids integrated with solar and wind power and block chain based peer to peer energy transactions. • To discuss the topics in the developing fields like Vehicle-to-Grid (V2G), where there is bidirectional energy flow between vehicle and smart gird.

Target Audience

This book is intended for electrical engineers, mechanical engineers, environmental engineers, computer science engineers, distributors of electric vehicle, power system engineers, energy policy makers, agents in power trading, manufactures of battery and research scholars working in the field of electric vehicle, artificial intelligence, data science, machine learning, deep learning, post graduate and under graduate students of various branches such as electrical, mechanical, computer science, artificial intelligence specialization etc.

Recommended Topics

Contributors are welcome to submit their chapters on the following topics related to electric vehicle, battery management systems, routing problems and artificial intelligent techniques and applications. Topics of interest include, but are not limited to: • Electric Vehicles • Renewable Energy systems • Smart Grids • Battery management systems • Artificial Intelligent techniques • Data Analytics • Optimization techniques • Intelligent controllers • Electric Vehicle Routing Problems • Controller design • Hybrid micro-grids • Sustainable development

Submission Procedure

Researchers and practitioners are invited to submit on or before July 29, 2022, a chapter proposal of 1,000 to 2,000 words clearly explaining the mission and concerns of his or her proposed chapter. Authors will be notified by August 8, 2022 about the status of their proposals and sent chapter guidelines.Full chapters are expected to be submitted by August 28, 2022, and all interested authors must consult the guidelines for manuscript submissions at prior to submission. All submitted chapters will be reviewed on a double-blind review basis. Contributors may also be requested to serve as reviewers for this project.

Note: There are no submission or acceptance fees for manuscripts submitted to this book publication, Artificial Intelligence Applications in Battery Management Systems and Routing Problems in Electric Vehicles. All manuscripts are accepted based on a double-blind peer review editorial process.

All proposals should be submitted through the eEditorial Discovery® online submission manager.


This book is scheduled to be published by IGI Global (formerly Idea Group Inc.), an international academic publisher of the "Information Science Reference" (formerly Idea Group Reference), "Medical Information Science Reference," "Business Science Reference," and "Engineering Science Reference" imprints. IGI Global specializes in publishing reference books, scholarly journals, and electronic databases featuring academic research on a variety of innovative topic areas including, but not limited to, education, social science, medicine and healthcare, business and management, information science and technology, engineering, public administration, library and information science, media and communication studies, and environmental science. For additional information regarding the publisher, please visit This publication is anticipated to be released in 2023.

Important Dates

July 29, 2022: Proposal Submission Deadline
August 8, 2022: Notification of Acceptance
August 28, 2022: Full Chapter Submission
October 11, 2022: Review Results Returned
November 22, 2022: Final Acceptance Notification
December 6, 2022: Final Chapter Submission




PSG College of Technology


Science and Engineering; Education; Computer Science and Information Technology; Environmental, Agricultural, and Physical Sciences
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