China 
Africa 
Explore chapters and articles related to this topic
Other technology aspects
Published in Hanky Sjafrie, Introduction to Self-Driving Vehicle Technology, 2019
In some SDV use cases, such as autonomous public shuttles or last mile delivery systems, multiple SDVs operate together to provide a service. Fleet management services ensure smooth, safe and efficient operation of all the SDVs. Typical fleet management services include location tracking of each SDV, service dispatching, dynamic route calculation, system health monitoring, and remote diagnostics as shown in Figure 6.20. The fleet management services might be performed manually by human operators in a control room, automated using fleet management software on the back-end servers, or a combination of both.
Electric vehicle energy consumption estimation for a fleet management system
Published in International Journal of Sustainable Transportation, 2021
Abbas Fotouhi, Neda Shateri, Dina Shona Laila, Daniel J. Auger
The number of electric vehicles (EVs) is increasing quickly and with new regulations on clean and sustainable transportation systems, this trend would continue. More use of EVs makes new opportunities and also new challenges. Private EV users have different expectations and usage patterns while they still have some kind of range anxiety. “Accurate” estimation of EVs range is a demanding task that depends on a number of uncertainties such as variations in weigh, road and weather conditions, driving and traffic conditions, and degradation or change in components, etc. In addition to private vehicles, fleets of taxis or commercial delivery vans and trucks can play a significant role in the transportation sector. Since the next generation of fleets is likely to be electrified, new strategies are needed for proper management of EV fleets energy demand and charging. Even for scheduling of the fleet, new algorithms are necessary in which the range of EVs and the charging time are considered. At the present time, a scheduling fleet management software does not have to consider range limits in conventional non-EVs nor the fueling time, as refueling is quick, and easily managed by the driver. In addition, all the existing fleet management algorithms are not necessarily optimal in terms of energy consumption. An EV fleet can be used optimally in a way to minimize the total fleet energy consumption to do a certain task. Summarizing the above discussion, an important part of an EV fleet management system (FMS) or a private EV trip planner is “energy consumption prediction” which is the topic of this research.
Digital technologies for energy efficiency and decarbonization in mining
Published in CIM Journal, 2023
A conceptual system to improve haul truck safety at surface mines comprises truck location and route mapping, cameras and AI algorithms for fatigue monitoring, and cameras for improving vision (blind spots, image-restoring algorithms in adverse climate conditions) integrated with warnings and alarms (Sun, Nieto, Li, & Kecojevic, 2010). Some of these technologies could lead to other fuel consumption, GHG emission, and productivity benefits. For example, in addition to helping avoid collisions, the vehicle location data could be used to enhance scheduling and limit vehicle idling, as was proposed by Quash (2019). Fleet management software integrating ML and cameras to capture data related to loading and hauling activities could also be beneficial.