The main goal of this book is to show the potential of connected and automated vehicles (CAVs) for increasing energy efficiency, reducing environmental impact, decreasing car accidents, and improving quality of life and mobility for older populations and people with disabilities.

The book’s structure is very adequate. The way it is written is understandable for beginners, and the concepts are illustrated with suitable examples. Many theoretical concepts are formally formulated and explained with sufficient concreteness, making it possible to follow the book without having to use supplementary texts. Each chapter contains helpful references.

It’s an adequate text for a course on efficient energy mobility with support in robotics systems. All related aspects are considered in the book’s nine chapters, from the basics of vehicle modeling to traffic considerations, covering problems like environment perception and routing and energy-efficient driving (range, recharging time, and the ability to regenerate energy during deceleration are especially important when considering electric vehicles).

The problem is well motivated in chapter 1, and justified based on an adequate set of references. Three pillars are considered--minimal-energy routing and anticipative and cooperative driving--in order to improve traffic flow, increase safety, and reduce energy consumption. Vehicles will adjust routes and speeds to improve the energy efficiency and the energy consumption of conventional vehicles as well. They can cooperate to achieve a common goal or only share information for particular goals.

To implement a solution to this problem, technologies for transmitting traffic signal information to/from vehicles and for interchanging information between vehicles are necessary, that is, vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) bidirectional connectivity. These aspects are taken into account in chapter 3.

Chapter 2 presents an energy model for the vehicle based on three conversion steps: grid-to-tank, tank-to-wheels, and wheel-to-distance. The book focuses on the third step. So, to analyze the minimization of energy consumed by the vehicle’s components, models of powertrain components are introduced here. Mathematical models for combustion engines and electric and hybrid electric vehicles are introduced in an intuitive way, considering energy consumption/recovery in traction and braking stages. Also, for completeness, human-powered vehicles are considered, taking the metabolic energy spent by a cyclist into account; they are not treated elsewhere in the book, however.

Chapter 3 provides an overview of automated vehicle localization, perception, planning, and control. The base supporting these topics is the communications V2X, and the two main technologies supporting them are wireless local area network (WLAN) and cellular network technologies. These two technologies are briefly described. The typical components of a perception system are enumerated and briefly presented in a very intuitive way. The algorithms for perception and localization, in the context of CAVs, are accessible to beginners. A logical scheme of planning and control after the perception and localization layers is introduced in general, taking into account key aspects, from decision trees to proportional–integral–derivative (PID) controllers with anti-windup mechanisms and optimal controllers. The classical pure pursuit algorithm used in mobile robotics is also introduced; without going into detail, it is still very clear.

Chapter 4 combines a roads network model with chapter 2’s vehicle model in order to predict energy consumption. Spatial data models for storing geographic data and key concepts for regulating traffic in intersections (road network topology, signal phasing and timing) are introduced. This chapter also considers traffic modeling. Simple mathematical models are presented, and a simple model for thermal comfort--“as a function of the ambient temperature and considered constant over the trip”--is introduced. All of these models are used to predict energy consumption for electric, hybrid electric, and combustion vehicles.

Chapter 5 introduces eco-routing algorithms to predict the maximal driving range of a vehicle. These algorithms find the minimum weight route between an origin and a destination, where the weights assigned to different links in the route represent the associated vehicle energy consumption. Negative values for these weights represent energy recovering due to regenerative braking in electric vehicles. Also, resource consumption is taken into account in the multi-objective optimization algorithm (time at stations, recharging time, and so on). Two common shortest path algorithms, Dijkstra and Bellman-Ford, are presented to solve the multi-objective optimization problem. Examples illustrate the use of these kinds of algorithms to solve the eco-routing problem; due to the large computational burden, a practical implementation based on cloud computing is also proposed.

Eco-driving is analyzed in chapter 6. The authors justify its predictive character, for example, the anticipation of slow-down or descents, the state of the traffic lights at intersections, traffic conditions, and so on. Eco-driving is managed as a constrained optimal control problem and solved using two classical numerical techniques, dynamic programming and Pontryagin’s minimum principle, which are briefly introduced. References to standard textbooks can extend the reader’s knowledge of these important techniques. The authors also consider wheel-to-distance and tank-to-wheel energy efficiency (for combustion, electrical, and hybrid vehicles). This is the most theoretical chapter, but all concepts are correctly illustrated.

Chapter 7 applies previously introduced general methods to several practical driving scenarios: acceleration, deceleration, road slopes, speed limit, intersection, traffic light, and car following. Numerical analyses of these scenarios are provided, followed by closed-form solutions to confirm the numerical results. Predictive and nonpredictive strategies are taken into account.

Chapter 8 considers different implementations for eco-driving. It first presents eco-coaching to assist human drivers with the energy-optimal speed profiles to follow. In the second step, this profile, taking into account 3D digital maps, is connected to an automatic control loop to ensure economic predictive cruise control (eco-PCC). An evolution of the eco-PCC is introduced next: economic adaptive cruise control (eco-ACC), where the speed of the vehicle ahead is taken into account to ensure a safe gap between both vehicles. Practical issues about speed and path recording, breakpoint detection, leader position, and traffic light prediction are also considered. The chapter ends with a section about on-board implementation that considers characteristics for human machine interfaces (HMI).

The last chapter presents five case studies from research results by other authors. Interesting results for both simulated and real tests are also included.