20 by each work machine 10.

More specifically, the controller 30 may monitor a type of the work machine 10, e.g. its make, model, fuel type, powertrain, tractive device 14, etc.; a state of charge of the rechargeable battery 12; a location of the work machine 10; and one or more assigned tasks of the work machine 10. In some embodiments, the controller 30 may further monitor a state of health of the rechargeable battery 12; a priority of the assigned tasks; and yet other possible states, where no limitation is intended by the present disclosure. And in other embodiments where the work machine 10 is the fuel cell electric vehicle 10b, the controller 30 may monitor a fuel cell fuel level, e.g. a quantity or percentage of hydrogen gas remaining in the tank. Similarly, the fleet controller 30a may monitor the above states for each work machine 10 of the fleet 100. Furthermore, the controller 30 or fleet controller 30a may monitor one or more states of the trolley network 20, such as a count of the presently connected work machines 10, a power draw of each connected work machine 10, and a saturation of the trolley network 20.

And in yet other embodiments, the controller 30 or fleet controller 30a may further monitor one or more diesel-electric vehicles and/or other vehicles and machinery associated with the trolley network 20. In such embodiments, the states of the trolley network 20 may include a count of the presently connected diesel-electric vehicles 102 (or other connected vehicles); and/or a power draw of each connected diesel-electric vehicle 102 (or other connected vehicles). Indeed, no limitation is intended herein for the type or number of states of the trolley network 20 being monitored by the controller 30 or fleet controller 30a.

Finally, the controller 30 may manage the work machine 10 by optimizing for a number of predetermined criteria, which may include, without limitation, an energy efficiency, a productivity, and/or a depreciation of the work machine 10. Likewise, the fleet controller 30a may manage one or more work machines 10 and/or the fleet 100 by optimizing for a number of predetermined criteria, which may include, without limitation, an energy efficiency, a productivity, and/or a depreciation of each work machine 10, one or more work machines 10, and/or the fleet 100 as a whole. Various management strategies dictating the usage of the trolley network 20 by each work machine 10 and/or the plurality of work machines 10 are discussed in greater detail below.

INDUSTRIAL APPLICATION

The present disclosure may find industrial applicability toward work machines and their associated charging networks, and may be particularly useful toward fleets of zero-emission work machines being charged by a trolley network.

For example, the present disclosure may be applied to a work machine, such as a large mining truck, excavator, backhoe, bulldozer, grader, trencher, tractor, front loader, and yet other possibilities. The work machine may specifically be a zero-emission vehicle, such as one being powered exclusively by electric batteries or hydrogen fuel cells (when disconnected from external power sources). The work machine may operate independently, i.e. may self-manage its power usage of the trolley network; or the work machine may be centrally managed, i.e. by the controller.

Moreover, the present disclosure may be applied to a fleet of zero-emission work machines. For example, a plurality of work machines may be employed collectively on an open-pit mine, a road construction work zone, a farm or ranch, and yet other possible work sites. The fleet may include a plurality of zero-emission work machines being supplied electric power through the trolley network. Moreover, the present disclosure may be applied to a fleet which includes zero-emission vehicles, diesel-electric vehicles, and other vehicles capable of being supplied electric power through a trolley network. Advantageously, an existing management system for a fleet of diesel-electric vehicles may be modified with the systems and methods disclosed herein to include zero-emission vehicles.

Finally, the present disclosure may be applied to a trolley network, such as one employing overhead wires, ground-level power supplies, non-contact conductors or in-road inductive charging, railway electrification technologies, and yet other possible forms of live-electricity transfer. The trolley network may be powered through independent power generation mechanisms, and/or may transfer electric power from external sources.

Advantageously, the present disclosure may enable an optimized power usage of the trolley network by a single work machine and/or by the plurality of work machines; and may improve a productivity, an energy efficiency, and a longevity of each machine, one or more work machines, and/or the fleet as a whole.

First, the presently disclosed systems and methods may improve the productivity of a single work machine. By managing the supply of electric power through the trolley network, a work machine may reduce a quantity of time spent at charging stations, may increase an operating range, and may increase an operating uptime. The productivity of the work machine may further be improved by supplementing electrical power to the tractive device, thereby increasing the machine's travel speed and reducing a travel time between tasks.

Next, the presently disclosed systems and methods may improve the energy efficiency of a single work machine. Electric power supplied directly from the trolley network to the propulsion motors may avoid inefficiencies stemming from charging or discharging the battery, powertrain energy conversion, heat loss, etc. Moreover, electric power supplied by the trolley network may offer economies of scale and may be transported at advantageous ratings with regard to power loss. Finally, where fuel cell electric vehicles are concerned, consumption of electricity through the trolley network may be substantially cheaper than consumption of hydrogen fuel, and may provide increased range between refueling.

Further, the presently disclosed systems and methods may improve the battery health and/or longevity of a single work machine. The power usage of the trolley network may be managed such that the machine's battery remains within healthy state of charge limits, such that the battery is charged or discharged at favorable rates, such that the battery is operated within safe temperature ranges, and/or such that a lifetime number of charging cycles is reduced. With regard to component life, the work machine may be directed to travel at favorable speeds and/or may reduce powertrain usage when connected to the trolley network. Finally, where fuel cell electric vehicles are concerned, the fuel cell may be powered-off or idled when the machine is connected to the trolley network, further lengthening component longevity.

Perhaps more importantly, the present disclosure may extend each of the above advantages toward a plurality of work machines or an entire fleet of work machines. By following the various methods disclosed herein, the fleet and trolley system can optimize for a productivity, an efficiency, and/or a longevity of a single work machine, of one or more work machines, or of the entire fleet as a whole. Individual work machines within the fleet may be prioritized, such as by receiving greater allocations of power to their batteries or to their tractive device.

Finally, the present disclosure may improve the logistical operations of the fleet and the trolley network. By managing the schedule of each work machine and its speed of travel across the trolley network, the present disclosure may reduce ‘congestion’ along the route of the trolley network, may better align the schedule of each machine with its assigned tasks, may guarantee that each machine is sufficiently charged for its assigned tasks, may improve collaboration between machines having shared tasks, may stabilize the power output or saturation of the trolley network, and yet other possibilities.

Turning now to FIG. 4, a method of charging a work machine via a trolley network is generally referred to by a reference numeral 400. It may be understood that the method 400 and FIG. 4 relate to a process of charging a single zero-emission work machine, while the method 500 and FIG. 5 relate to a process of charging a fleet of zero-emission work machines, which will be discussed further below. As seen in FIG. 4, the method 400 comprises monitoring a plurality of states of the work machine (step 410); scheduling a usage of the trolley network by the work machine (step 430); and supplying electric power from the trolley network to the work machine (step 460).

In some embodiments, each step of the method 400 may be operatively performed by the controller. In the same or other embodiments, the controller may be included on the work machine. Accordingly, the method 400 may be operatively performed by the work machine itself, which effectively self-manages its scheduling and power usage of the trolley network. In other embodiments, however, the controller may be located external to the work machine and may communicate with the work machine through the first communication module and the second communication module. In such embodiments, the method 400 may be operatively performed externally, e.g. by a central planner, which may or may not manage other work machines concurrently.

In step 410, a plurality of states of the work machine are monitored, including at least a type, a state of charge of its rechargeable battery, a location, and one or more assigned tasks. The type may include the make, model, or category of machine; its prime mover; its power train; its tractive device; and yet other defining characteristics, which may impact a charging strategy for the machine. In various embodiments, the assigned tasks may include basic logistical information, such as a location and start time of one or more jobs, but may include additional information, such as but not limited to expected energy consumption, expected duration, expected depreciation, etc. In other embodiments, the plurality of states may further include a state of health of the rechargeable battery, a priority of the assigned tasks, and/or a fuel cell fuel level (if the work machine is a fuel cell electric vehicle), although no limitation is intended herein for the total type or number of states being monitored.

Where applicable, the above plurality of states may be relayed to the controller through the first communication module of the work machine and the second communication module of the controller, for example, through a Wi-Fi connection, Bluetooth pair, radio communication, or other technology common to the art. In various embodiments, the monitored states may be transferred in real-time or at predetermined intervals, depending on applicational requirements.

In step 430, the work machine is scheduled to use the trolley network based on the plurality of states. The scheduled usage may include an entry time, an exit time, and, consequently, a speed of travel throughout the route of the trolley network. A number of exemplary strategies for the scheduling process are provided below.

In a first example, the work machine may be scheduled based on a function of state of charge of the battery. In an embodiment, a battery near depletion may be scheduled for immediate charging. And in other embodiments, a time until the scheduled usage may be proportional to the state of charge, may be a function of the state of charge, and/or may factor in an expected rate of energy consumption until the scheduled usage.

In a second example, the work machine may be scheduled based on a function of the location. In an embodiment, a time until the scheduled usage may be proportional to a distance between the work machine and the trolley network, may be a function of the distance, and/or may factor in an expected fuel consumption or energy consumption for traveling to the trolley network.

In a third example, the work machine may be scheduled according to the one or more assigned tasks. In an embodiment, the work machine may be scheduled to use the trolley network such that the exit time coincides with a start time of the next assignment. In another embodiment, a time until the scheduled usage may be inversely proportional to a priority of the assigned tasks.

And in other examples, some or all of the above considerations, and yet other possible considerations, may be used in combination in order to schedule the usage of the trolley network.

Moreover, the scheduled usage may also depend on the type of the work machine. In an embodiment, the work machine is a fuel cell electric vehicle having a rechargeable battery of relatively low capacity. According to a first strategy, the state of the charge may be easily repleted and thus take relatively low priority during the scheduling process, e.g. with respect to battery electric vehicles. According to a second strategy, the scheduled usage may prioritize one or more fuel electric vehicles based on their respective fuel cell fuel levels. In another embodiment, the work machine is a battery electric vehicle being having a rechargeable battery of relatively high capacity. According to a second strategy, the state of charge of the battery, the state of health of the battery, and/or an energy efficiency of the work machine make take relatively high priority. And in yet other strategies, a high capacity battery may receive less priority than a low capacity battery since it offers greater reserve power. In various embodiments, properties of the work machine, such as battery capacity, travel speed, powertrain efficiency, and other considerations may impact the scheduling process, where no limitation is intended by the present disclosure.

In step 460, electric power is supplied from the trolley network to the work machine, and may include allocating a power draw by the work machine (step 461) and distributing the power draw among the machine's functions (step 462). The step of allocating (step 461) may be measured as an instantaneous rate, i.e. in Watts, and/or as a total power draw throughout the duration of the route, i.e. in Watt-hours or Joules. Next, the power draw may be distributed (step 462) between charging the rechargeable battery and powering the propulsion motors, i.e. driving the tractive device. As with step 430 above, a number of exemplary strategies for step 460 are also provided below. It is worth noting that the instantaneous rate and the distribution of the power draw may not be constant throughout the duration of the route.

In a first example, the power draw may be fully maximized according to the specifications of the work machine and the trolley network. In other words, the battery may be supplied a maximum allowable or safe current; the propulsion motors and the tractive device may be supplied a maximum allowable or safe current; and any intervening components, such as the contact wire, the charging apparatus, the powertrain, etc. are each operating within allowable or safe limits.

In a second example, the power draw to the rechargeable battery may be optimized and/or prioritized. In various embodiments, the power draw may be allocated and distributed such that the rechargeable battery is charged as quickly as possible, such that the rechargeable battery is charged as fully as possible, such that the rechargeable battery is charged at an optimal rate for battery health, and/or some combination of the above. In the same or other embodiments, the power draw to the tractive device may be accordingly relegated, and a speed and/or energy efficiency of the work machine may be affected.

In a third example, the power draw to the tractive device may be optimized and/or prioritized. In various embodiments, the power draw may be allocated and distributed such that the work machine travels as quickly as possible, such that the work machine exits the trolley network at a time congruent with its assigned tasks, such that the machine travels at an optimal speed for energy efficiency, and/or some combination of the above. In the same or other embodiments, the power draw to the rechargeable battery may be accordingly relegated, and a state of charge and/or a productivity of the work machine may be affected.

In a fourth example, the work machine is specifically a fuel cell vehicle and the power draw may be allocated based on the fuel cell fuel level. In an embodiment, during the step of supplying electric power (460), a fuel cell of the work machine may be idled. In the same or other embodiments, the power draw to the work machine may be distributed such that the rechargeable battery is charged during the scheduled usage; and such that the tractive device is driven via electric power from the trolley network only. In some embodiments, the work machine may be supplied electric power such that the rechargeable battery is fully charged during the scheduled usage.

And in other examples, some or all of the above considerations, and yet other possible considerations, may be used in combination when supplying, allocating, and distributing electric power to the work machine. In an embodiment, the method 400 may further comprise optimizing for one or more predetermined criteria during the scheduled usage of the trolley network, during the course of the machine's assigned tasks, or even throughout the lifespan of the work machine (step 420). The criteria may include, among other possibilities, an energy efficiency of the work machine, such as a fuel efficiency or a cost of travel; a productivity of the work machine, e.g. as measured in widgets produced, freight transported, machine uptime, or other common units; and a depreciation of the work machine, such as a reduction in the state of health of the rechargeable battery.

In the same or other embodiments, the method 400 may further comprise optimizing for a reward function. For example, the reward function may assign a value to each of the above considerations, including but not limited to energy efficiency, productivity, and depreciation, and may consider other factors germane to the goals of the mine site. In various embodiments, the reward function may be predetermined, may be programmed by a human operator, and/or may be set according to specific applicational requirements. And in other embodiments, the reward function may be based on a machine learning algorithm, such as one employing reinforcement learning, supervised learning, or unsupervised learning techniques, which may or may not be updated in real-time

In some embodiments, the method 400 may further comprise navigating the work machine to an entry point of the trolley network (step 440) and/or connecting the work machine and, more specifically, the charging apparatus, to the trolley network (step 450). In the same or other embodiments, the controller may utilize the location of the work machine to provide navigational instructions toward an entry point of the trolley network.

Turning now to FIG. 5, a method of charging a fleet of work machines via a trolley network is generally referred to by a reference numeral 500. As seen in FIG. 5, the method 500 may comprise monitoring a plurality of states of the fleet (step 510); monitoring a plurality of states of the trolley network (step 520); scheduling usages of the trolley network by one or more work machines (step 540); and supplying electric power from the trolley network to the connected work machines (step 570). It may be appreciated that the method 500 is related to the method 400 in several ways, but may include additional strategies to accommodate for scheduling and charging multiple work machines concurrently.

In some embodiments, each step of the method 500 may be operatively performed by the fleet controller. It should be noted that, while the method 400 may be operatively performed by the machine itself (i.e. the controller is included on the work machine), the method 500 may require an external fleet controller in bi-directional communication with each machine within the fleet. In various embodiments, the fleet controller may be located in a management station, may be located on the trolley network, may be located on a distributed computer, may be located on a single work machine within the fleet, and yet other possibilities.

In step 510, a plurality of states of the fleet are monitored, including at least a type of each work machine, a state of each rechargeable battery, a location of each work machine, and one or more assigned tasks of each work machine. In some embodiments, the plurality of states of the fleet may further include a state of health of each rechargeable battery, a priority of each assigned task, a fuel cell fuel level (if the machine is a fuel cell electric vehicle), and other possible states, where no limitations are intended herein. Furthermore, unlike step 410 in the method 400, step 510 may further include aggregated states of the fleet, such as global average or global total states of charge, global average states of health, global average fuel cell fuel levels, roster requirements for assigned tasks, and other possibilities.

In step 520, a plurality of states of the trolley network are monitored, including a maximum power output from the trolley network, a count of the connected work machines, and a power draw of each connected work machine. Consequently, the instantaneous power output from the trolley network and/or a saturation of the trolley network may also be known and monitored. In some embodiments, the plurality of states of the trolley network may further include a speed of each connected work machine, an expected duration of travel, and yet other possibilities.

The plurality of states may be relayed to the fleet controller through the first communication module of each work machine, the second communication module of the trolley controller, and a communication module of the trolley network. In various embodiments, the monitored states of both the fleet and the trolley network may be transferred in real-time or at predetermined intervals, depending on specific applicational requirements.

In step 540, one or more work machines are scheduled to use the trolley. In some embodiments, each scheduling may include an entry time, an exit time, and, consequently, a speed of travel throughout the route for each machine. The scheduled usages may be based on the plurality of states of the fleet and the network, and may further include prioritizing one or more work machines (step 541) and relegating one or more work machines (step 542) in a scheduling queue. A number of exemplary strategies for the scheduling processes are provided below.