Patent Description:
Hybrid power systems are system comprising different power sources, typically an energy storage device such as a battery and a combustion engine. Hybrid power systems have successfully been implemented in vehicles e.g. cars, vessels, and trains to reduce fuel consumption and local emissions (e.g. in heavily populated areas).

As the capacity of most energy storage devices is limited, the combustion engine may be used as an electric-power generator to (re)charge the energy storage device. Depending on the system, the combustion engine may be e.g. a dedicated electric-power generator, typically a Diesel generator, for providing electrical power; or a propulsion engine that is coupled to e.g. an asynchronous motor which may be used to charge the energy storage device. In order to determine which power source to use for propelling the vehicle in question and, in particular, when to discharge and charge the energy storage device, a hybrid power system may comprise a controller that selects one or more power sources based on at least the current power demand.

Conventionally, these controllers are rules-based. More advanced systems may use an optimisation algorithm, e.g. an Equivalent Consumption Minimisation Strategy, which may result in a higher fuel efficiency than a rules-based controller. However, in order to apply an optimisation algorithm, an equivalent fuel consumption must be associated with the battery in order to make a meaningful comparison with the fuel consumption of the electric-power generator. Conceptually, when the battery is discharged by a certain amount, the associated equivalent fuel consumption represents the fuel required by the electric-power generator to recharge the battery, at some future point in time, by the same amount; and when the battery is charged by a certain amount, the associated equivalent fuel consumption represent the fuel saved, i.e., not consumed by a combustion engine, at some future point in time, by discharging the battery the same amount.

However, the amount of fuel required to charge a battery (or saved by discharging a battery) is generally not known at the time the battery is discharged (respectively charged), as it depends on the load of the electric-power generator, the charge rate of the battery, the state of charge (SOC) of the battery, and so on, at the point in time when the battery is charged (respectively discharged). Various solutions to this problem are known in the art.

For example, <NPL>, discloses a constant equivalence model, where the battery equivalent fuel consumption depends on a nominal fuel consumption of the electric-power generator. This model only provides a solution close to the global optimum if the battery usage is limited to a relative narrow SOC range. The same document also discloses a battery equivalent fuel consumption based on a predicted propulsive load, which prediction is based on historical data. This model may give a more realistic estimate of the equivalent fuel consumption for charging or discharging the battery, especially for vessels with a fairly predictable load pattern, such as ferries. However, the method does not take the (potential) presence of a multitude of power generators that may charge the battery into account. Furthermore, the effect of the battery's state of charge is only taken into account close to the minimum and maximum allowed state of charge.

<NPL> describes a method to minimize fuel consumption for a hybrid all-electric tugboat, comprising the determination of a discharging equivalent specific fuel consumption for the energy storage device proportional to the battery discharge power.

Therefore, there is a need in the art to provide a method for determining an energy strategy of a hybrid power system that further reduces fuel consumption, and in particular for a method for selecting a power source in a hybrid system based on an energy storage device equivalent fuel consumption.

It is an aim of the invention to eliminate, or at least reduce one or more of the drawbacks known in the art. It is furthermore an aim of the invention to provide a method to reduce fuel consumption of a hybrid power system.

In an aspect, the invention provides a method for determining a power distribution for a plurality of power sources of a hybrid power system. The plurality of power sources comprises an energy storage device, preferably a battery. The energy storage device is associated with a charge rate and/or a discharge rate, and with a state of charge. The plurality of power sources further comprises one or more electric-power generators electrically coupled to the energy storage device. Each electric-power generator is associated with a maximum amount of provided power, Pmax, and with a power-specific fuel consumption, sfc(P), defining a quantity of fuel consumed per quantity of energy provided as a function of provided power. The method comprises determining a discharging equivalent specific fuel consumption for the energy storage device based on the respective power-specific fuel consumptions of the respective one or more electric-power generators. The discharging equivalent specific fuel consumption defines an estimated amount of fuel that will be consumed by recharging the energy storage device as a function of the discharge rate of the energy storage device. The discharging equivalent specific fuel consumption for a discharge rate is obtainable by performing the steps of:.

The method may, additionally or alternatively, comprise determining a charging equivalent specific fuel consumption obtainable by determining the amount of fuel consumed according to the discharging equivalent specific fuel consumption at a discharge rate that is equal to a given charge rate. The method further comprises determining a power distribution based on at least the discharging equivalent fuel consumption and optionally the charging equivalent fuel consumption.

As used herein, an energy storage device may also refer to an energy storage system comprising a plurality of devices. The energy storage device is typically a device for storing electrical energy. The previously determined discharge power refers to the discharge power as it was before the updating step. A power distribution for a plurality of power sources may be understood to define, for each power source of the plurality of power sources, an amount of power provided by the power source in question.

The energy storage device being associated with a charge rate and/or a discharge rate may be understood as that the energy storage device is currently being charged at said charge rate and/or being discharged at said discharge rate. Similarly, the energy storage device being associated with a state of charge may be understood as that the energy storage device currently has that state of charge.

An electric-power generator being associated with a maximum amount of provided power may be understood as that that generator can at most provide said maximum amount of provided power.

It has been found that an energy management system selecting a power source based on this energy storage device equivalent fuel consumption defined in embodiments in this disclosure, is surprisingly more fuel efficient than one based on known methods such as a constant equivalence model. In general, this method guides the one or more electric-power generators towards running at or near their optimum power efficiency, while remaining flexible enough to adequately deal with varying circumstances.

It is a further advantage of this method that it only depends on known design parameters and momentaneous power demand, and does not depend on previously collected data. Thus, it can be used directly without having to spend a period collecting data. Moreover, the method may also be used for hybrid power systems with an irregular power demand.

In an embodiment, the proportionality factor may be based on the state of charge of the energy storage device. This way the system is guided to recharge the energy storage device when the state of charge is low, and to discharge the energy storage device when the state of charge is high, and vice versa.

In an embodiment, the method may further comprise controlling a power source to deliver power based on the determined power distribution.

In an embodiment, selecting a power source may comprise determining a minimum equivalent fuel consumption based on at least the respective power-specific fuel consumptions of the one or more electric-power generators, and the discharging equivalent fuel consumption and/or the charging equivalent fuel consumption. The method may further comprise selecting the power source or combination of power sources associated with the minimum equivalent fuel consumption.

The determined equivalent fuel consumption may be used in an optimisation algorithm, e.g. an Equivalent Consumption Minimisation Strategy, in order to minimise fuel consumption of a hybrid power system.

In an embodiment, the minimum equivalent fuel consumption may be determined using a Mesh Adaptive Direct Search algorithm. In other embodiments, different non-convex optimisation algorithms may be used.

In an embodiment, the hybrid power system may further comprise one or more main engines, preferably combustion engines, for providing mechanical power at a rotational speed. Each of the one or more combustion engines may be associated with a power-specific fuel consumption. Determining a minimum equivalent fuel consumption may further be based on the respective power-specific fuel consumptions associated with the one or more main engines, and, optionally, on the respective rotational speeds of the one or more main engines.

In a further aspect, the invention provides a controller comprising the features of claim <NUM>.

In a further aspect, the invention provides a hybrid propulsion system comprising the features of claim <NUM>.

In a further aspect, embodiments may relate to a vehicle, preferably a vessel, more preferably a marine vessel, comprising a hybrid propulsion system as described above.

In a further aspect, the invention provides a computer program product comprising the features of claim <NUM>.

Accordingly, aspects of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro-code, etc.) or an embodiment combining software and hardware aspects that may all generally be referred to herein as a "circuit," "module" or "system". Functions described in this disclosure may be implemented as an algorithm executed by a microprocessor of a computer.

More specific examples (a non- exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fibre, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can comprise, or store a program for use by or in connection with an instruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fibre, cable, RF, etc., or any suitable combination of the foregoing. Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including a functional or an object oriented programming language such as Java(TM), Scala, C++, Python or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer, or entirely on the remote computer, server or virtualized server.

Aspects of the present invention are described below with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. These computer program instructions may be provided to a processor, in particular a microprocessor or central processing unit (CPU), or graphics processing unit (GPU), of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer, other programmable data processing apparatus, or other devices create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.

The invention will be further illustrated with reference to the attached drawings, which schematically will show embodiments according to the invention. It will be understood that the invention is not in any way restricted to these specific embodiments.

Embodiments are described for determining an charging and/or discharging equivalent power-specific fuel consumption of an energy storage device in a hybrid power system. Based on the equivalent power-specific fuel consumption, an efficient power distribution may be determined.

<FIG> depicts a schematic overview of a hybrid propulsion system according to an embodiment of the invention. The hybrid propulsion system <NUM> comprises a main engine <NUM>, for example an internal combustion engine such as a Diesel engine or gas turbine, connected to a propeller <NUM> via a shaft <NUM>. The main engine is arranged to power the propeller and may comprise or be connected to a gearbox (not shown). The propeller is preferably a screw propeller and may be a fixed pitch propeller or a variable pitch propeller. Other embodiments may use different propulsion systems, e.g. paddles or pump jets.

An asynchronous motor or induction motor <NUM> is also coupled to the propeller and typically acts on the shaft <NUM>. The induction motor may be electrically connected to a switchboard <NUM> via one or more transformers and/or AC/DC converters <NUM><NUM>-<NUM>. The switchboard may further electrically connect an energy storage device <NUM>, e.g. a battery pack, one or more electric-power generators <NUM><NUM>-<NUM>, e.g. Diesel generators, and other electric loads, e.g. so-called hotel loads <NUM>. Hotel loads may refer to any electrical loads not used for propulsion, e.g. for lighting, climate control, or communication. The energy storage device may be connected to the switchboard via an AC/DC converter <NUM>.

Depending on the configuration, the one or more power generators may be arranged to provide hotel electric power, to charge the energy storage device, and/or to power the induction motor. In some embodiments, there may be no electric-power generators. In such and other embodiments, electric power for charging the energy storage device may (additionally) be provided by e.g. the main engine and absorbed by the induction engine coupled to the same shaft, and/or by absorbing breaking energy. In some embodiments, there may be no main engine, and the propeller may only be powered by the induction motor.

The hybrid propulsion system may comprise further mechanical and/or electrical components for further controlling the propulsion. In some embodiments, the hybrid propulsion system may also comprise one or more additional main engines and/or one or more additional propellers. In some embodiments, each propeller may be powered by a plurality of main engines.

A controller <NUM> is communicatively connected to one or more elements of the hybrid propulsion system to control one or more aspects of the propulsion system, such as a state, e.g. the rotational speed, of the main engine, the charging or discharging of the energy storage device, a state of the one or more electric-power generators, et cetera.

The controller may comprise a computer readable storage medium having computer readable program code embodied therewith, and a processor, preferably a microprocessor, coupled to the computer readable storage medium. Responsive to executing the computer readable program code, the processor may be configured to perform executable operations for predicting a load and/or determining a power distribution. The power distribution may define how much power one or more components of the power system (e.g. main engine, induction motor, energy storage device and electric-power generators) may provide and/or absorb.

<FIG> depicts a graph displaying a power-specific fuel consumption. The power-specific fuel consumption <NUM> defines the relation between the amount of fuel consumed per amount of energy provided, versus the amount of power (energy per unit time) provided. The depicted graph is typical for a Diesel generator, which has a very low efficiency at low loads, and a maximum efficiency at or close to maximum load, indicated by Pmax. For relatively high loads, the change in efficiency for a change in provided power is much smaller than for relatively low loads. The power-specific fuel consumption is sometimes known as the brake-specific fuel consumption.

The power-specific fuel consumption of an engine is typically provided by the manufacturer and may be known, at least approximate, during design of a vehicle. In some cases, a manufacturer may provide a power-specific fuel consumption only for a limited set of delivered power values. Other values may then be obtained by function fitting or other interpolation and/or extrapolation methods. In other cases, the power-specific fuel consumption may have to be obtained in other ways, e.g. by doing measurements.

In the example depicted in <FIG>, such a power-specific fuel consumption might be provided or obtained for each of the main engine <NUM> and the one or more electric-power generators <NUM><NUM>-<NUM>. If only the electric-power generators will be used to charge the energy storage device, it may be sufficient to only be provided or obtain the power-specific fuel consumptions of the electric-power generators.

<FIG> depicts a power supply system according to an embodiment of the invention. The power supply system comprises an energy storage device <NUM>, e.g. a battery pack, associated with a maximum charge and a current charge, a maximum charge rate, a maximum discharge rate, and a current charge/discharge rate. For a typically energy storage device, the maximum charge rate is equal in magnitude to the maximum discharge rate. For example, the energy storage device may have a maximum charge of <NUM> kWh and have a maximum discharge rate providing a maximum power Pmax = <NUM> kW.

In some embodiments, the maximum charge/discharge rate may be dependent on the current charge, but typically, the maximum charge/discharge rate is fairly constant over at least a large range of charge values.

The energy storage device may be arranged to be electrically coupled, for instance via a switchboard <NUM>, to one or more electric-power generators <NUM><NUM>-<NUM>. In the depicted example, there are four electric-power generators, but other embodiments may have more or less electric-power generators. It is typical that there is more than one electric-power generator.

Each of the electric-power generators may be associated with a maximum power Pmax and an power-specific fuel consumption sfc(P) <NUM><NUM>-<NUM>. In the depicted example, the power generators are all identical, and each electric-power generator is associated with a maximum amount of provided power Pmax = <NUM> kW, and is associated with an identical power-specific fuel consumption. In other embodiments, different numbers of electric power generators may be used. In some embodiments, some of the electric-power generators may be different from each other.

The one or more electric-power generators and the energy storage device are communicatively coupled to a controller <NUM>. The controller may be configured to control the charge or discharge rate of the energy storage device. The controller may further be configured to control the power provided by each of the one or more electric-power generators. The controller may comprise a memory for storing software for determining an equivalent power-specific fuel consumption according to an embodiment. The memory may also store one or more parameters associated with the electric-power generators, such as the maximum provided power and the power-specific fuel consumption. Alternatively, the controller may be communicatively connected to an external system and may be configured to request such parameters from the external system as needed.

<FIG> depicts a flow chart of a method for determining a discharging equivalent specific fuel consumption according to an embodiment of the invention. The method may e.g. be executed by a controller as depicted in <FIG> or <FIG> for controlling a system comprising an energy storage device and one or more electric-power generators Geni (i = <NUM>,. , n; n ≥ <NUM>) electrically coupled to the energy storage device.

In a first step <NUM>, an actual or potential discharge power of the energy storage device (Pdis) is determined, which may be referred to as a computational discharge power Pcdis (Pcdis = Pdis). The computational discharge power Pcdis may be based on a power demand, for example of a hybrid propulsion system, and/or on a discharge rate Cdis of the energy storage device.

In a next step <NUM>, a first (i = <NUM>) electric-power generator Geni is selected. If all electric-power generators are identical, any selection is mathematically equivalent. In an embodiment with different electric-power generators, the electric-power generator may be selected based on any suitabel selection method, e.g. based on cumulative run time (typically selecting the electric-power generator that has the lowest run time first), based on some quality metric (typically selecting the `best' electric-power generator first), or even at random.

The maximum delivered power Pimax associated with the selected electric-power generator is then determined, typically via a look-up. Subsequently, the computational discharge power Pcdis is compared <NUM> with the maximum delivered power of the selected electric-power generator Pimax. If the computational discharge power Pcdis is larger than the maximum delivered power Pimax associated with the selected electric-power generator (Pcdis > Pimax), then the computational discharge power Pcdis is reduced with the maximum delivered power Pimax: <MAT> that is, a (new or updated) computational discharge power Pcdis is determined <NUM> by subtracting the maximum delivered power Pimax associated with the selected electric-power generator from the (current) computational discharge power.

If there are any unselected electric-power generators (i < n), then a next (i = i + <NUM>) electric-power generator from the one or more electric-power generators is selected <NUM>. The method then returns to the comparison step <NUM>, comparing the (new) computational discharge power Pcdis with the maximum delivered power Pimax associated with the (newly) selected electric-power generator.

If the computational discharge power Pcdis is not larger than the maximum delivered power Pimax associated with the selected electric-power generator (Pcdis ≤ Pimax), then an (estimated) equivalent amount of fuel sfceq(Cdis) is determined <NUM> by multiplying a predetermined proportionality factor fprop > <NUM> and the amount of fuel consumed sfci (P) by the selected electric-power generator Geni when providing power equal to the maximum provided power of the selected electric-power generator minus the determined discharge power: <MAT>.

In an embodiment, the proportionality factor may be based on the state of charge of the energy storage device, preferably the proportionality factor being larger if the state of charge of the energy storage device is larger. The proportionality factor is preferably chosen to obey <NUM> < fprop ≤ <NUM>. For example, the proportionality factor may be given by: <MAT> where <NUM> ≤ α ≤ <NUM> and SOC denotes the relative state of charge of the energy storage device where <NUM> denotes a completely empty energy storage device and <NUM> denotes a completely charged energy storage device (so <NUM> ≤ SOC ≤ <NUM>). This way the system is guided to recharge the energy storage device when the state of charge is low, and to discharge the energy storage device when the state of charge is high, and vice versa. Depending on the type of energy storage device, the relation between fprop and SOC may be further adjusted to prevent the energy storage device from fully discharging and/or from fully charging.

In a typical embodiment, the total maximum delivered power of the electric-power generators together is larger than the maximum discharge power of the energy storage device, such that always i ≤ n. In other embodiments, the maximum discharge power may be larger than the total maximum power deliverable by the electric-power generators. In such embodiments, the equivalent power-specific fuel consumption may be based on a representative measure of the specific power consumption, preferably the median power-specific fuel consumption of the combined electric-power generators.

In an embodiment, the determined equivalent power-specific fuel consumption may be used to select a power source, based on the power-specific fuel consumptions of the one or more electric-power generators and the equivalent power-specific fuel consumption of the energy storage device, typically by minimizing an (equivalent) fuel consumption.

In order to further elucidate the method, <FIG>,B depict an example of a determination of an equivalent power-specific fuel consumption according to an embodiment of the invention. In this example, a hybrid power system comprises at least a first and a second electric-power generator <NUM><NUM>,<NUM>, each being associated with a respective maximum delivered power P<NUM>max = P<NUM>max = <NUM> kW and with a respective power-specific fuel consumption sfc<NUM>(P) <NUM>, and sfc<NUM>(P) <NUM><NUM>, as depicted. The hybrid power system further comprises an energy storage device <NUM> electrically connectable to the first and electric-power generators.

<FIG> depicts an enlarged version of the graph representing power-specific fuel consumption sfc<NUM>(P) associated with the second electric-power generator. According to the example, an equivalent power-specific fuel consumption for the energy storage device providing a power of Pdis = <NUM> kW is to be determined. Thus, the initial computational discharge power is Pcdis = <NUM> kW. The first electric-power generator is selected and the computational discharge power is compared to the maximum delivered power P<NUM>max associated with the first electric-power generator <NUM><NUM>: <MAT> As the computational discharge power is larger than the maximum delivered power P<NUM>max, the maximum delivered power P<NUM>max is subtracted from the computational discharge power Pcdis: <MAT> Subsequently, the second electric-power generator is selected and the (newly determined) computational discharge power Pcdis is compared to the maximum delivered power P<NUM>max associated with the second electric-power generator <NUM><NUM>: <MAT> Therefore, the computational discharge power Pcdis is subtracted from the maximum delivered power P<NUM>max: <MAT> and the power-specific fuel consumption sfc<NUM>(P) associated with the second electric-power generator is obtained for the determined value and, optionally, multiplied with a proportionality constant fprop to yield the desired equivalent power-specific fuel consumption: <MAT>.

The equivalent specific fuel consumption may be determined for a single point, e.g. a single discharge rate or single provided power amount, as in the previous example. A graph of the equivalent specific fuel consumption may be determined by evaluating the equivalent specific fuel consumption for a plurality, preferably a large number, of values for the discharge rate or amount of provided power.

<FIG> depict an alternative, equivalent description of the same computation as detailed above with reference to <FIG>,B. <FIG> depicts a graph <NUM> representing a power-specific fuel sfc<NUM>(P) associated with a first electric-power generator.

<FIG> depicts a 'reversed' graph <NUM>, which is determined based on the graph representing sfc<NUM>(P). The reversed graph may be obtained by 'mirroring' the original graph around P = ½ P<NUM>max. For P ≤ P<NUM>max, the equivalent fuel consumption may be proportional to the reversed power-specific fuel consumption of the first electric-power generator. This is mathematically equivalent to determining: <MAT> Here P<NUM>max denotes the maximum provided power associated with the first electric-power generator and P is a variable denoting provided power. Thus, the discharge equivalent power-specific fuel consumption is relatively low if the power delivered by the energy storage device is low, and increases as the delivered power approaches the maximum delivered power of the first electric-power generator.

As a consequence, battery discharging is seen by the optimization routine as inefficient around areas where the electric-power generators are efficient, and vice versa. This is advantageous, since any power supplied by the battery will reduce the load of the electric-power generators, essentially pushing them towards areas of less efficient operation. Instead, the equivalent power-specific fuel consumption is preferably defined such that the electric-power generators are operated at more efficient operating points, by either shutting down the batteries, or by recharging them if possible.

<FIG> depicts a 'concatenated' graph. The power-specific fuel consumptions for the other power generators are similarly reversed, and based on the reversed graphs a 'concatenated' graph <NUM> is determined. Concatenation of graphs or functions means that for two functions with a bounded domain, the second function is shifted such that a minimum value of the domain of the second function coincides with the maximum value of the first function. Thus, for a delivered power slightly larger than the maximum delivered power of a first electric-power generator, the equivalent power-specific fuel consumption is proportional to the power-specific fuel consumption of the second electric-power generator.

It may be noted that the order of reversing and concatenating is not important and provides the same result. However, the (reversed) concatenated power-specific fuel consumption of the first and second electric-power generators is different from the (reversed) combined power-specific fuel consumption of the first and second electric-power generators, that is, the amount of fuel consumed by the first and second electric-power generators together when together providing a certain power. For example, the combined power-specific fuel consumption of two identical electric-power generators providing more than the maximum power of a single electric-power generator is, in principle, twice the power-specific fuel consumption of a single electric-power generator providing half the power: <MAT> However, in the same situation, the concatenated power-specific fuel consumption is equal to the power-specific fuel consumption of the amount of power provided more than the maximum power of a single electric-power generator: <MAT>.

The discharge equivalent power-specific fuel consumption may then be obtained by multiplying the concatenated mirrored power-specific fuel consumption with an optional proportionality factor fprop, and may be described by: <MAT> where j is determined such that: <MAT>.

So, in the example depicted in <FIG>,B and 6A-C, for P = <NUM> kW, one may find j = <NUM>, as P<NUM>max = <NUM>, P<NUM>max + P<NUM>max = <NUM> + <NUM> = <NUM> kW, and <NUM> kW ≤ <NUM> kW < <NUM> kW, and therefore P<NUM>max ≤ P < P<NUM>max + P<NUM>max. The value may also be obtained directly by reading from the concatenated graph <NUM>.

<FIG> depicts a graph <NUM> of an equivalent power-specific fuel consumption of a hybrid power system comprising two identical electric-power generators, each capable of providing up to <NUM> kW of electric power. In this example, the discharge power of the energy storage may be larger than the total maximum power provided by the electric-power generators. A first part <NUM><NUM> of the graph represents a reversed power-specific fuel consumption of a first electric-power generator, and a second part <NUM><NUM> of the graph represents a reversed power-specific fuel consumption of a second electric-power generator. A third part <NUM><NUM> of the graph represents a statistically representative quantity of the power-specific fuel consumptions of the first and second electric-power generators, in this case the median.

In some embodiments, the discharge power of the energy storage may be larger than the total maximum power provided by the electric-power generators. In such embodiments, for a delivered power larger than the total maximum power, the equivalent power-specific fuel consumption may be based on a statistically representative quantity of the power-specific fuel consumptions of the electric-power generators, preferably based on a median value of the power-specific fuel consumptions. Thus, the equivalent power-specific fuel consumption may be given by: <MAT> where med(. ) denotes the median. In other embodiments, other quantities may be used, such as the average power-specific fuel consumption.

If the electric-power generators are identical, the median power-specific fuel consumption may be equal to the power-specific fuel consumption corresponding to a delivered power of half the maximum delivered power of one of the electric-power generators: <MAT>.

If the electric-power generators are identical, the selection which electric-power generator is the first and which one is the second, et cetera, has no effect on the determined equivalent power-specific fuel consumption. In other embodiments, however, the electric-power generators may be non-identical. In such embodiments, the order in which the electric-power generators are selected may be determined in various ways. For example, the electric-power generators may be selected in increasing order of running hours, the generators with the fewest running hours being selected first. This reflects the fact that it may be advantageous to balance use among all electric-power generators. The selection may also depend on further factors, e.g. an operation mode of a vessel which may have a 'travel mode' and a 'work mode', and which may affect which electric-power generators are predominantly used.

<FIG> depicts an equivalent power-specific fuel consumption for charging and discharging an energy storage device. In an embodiment, an equivalent power-specific fuel consumption may be determined for charging an energy storage device. The charging equivalent power-specific fuel consumption may be determined by determining the amount of fuel consumed according to the discharging equivalent specific fuel consumption at a discharging rate that is equal to a given charging rate. In other words: <MAT> where Pcharge is the amount of power used to charge the energy storage device and Pdischarge is the amount of power provided by discharging the energy storage device; or, alternatively: <MAT> where a power P > <NUM> denotes discharging the energy storage device and P < <NUM> denotes charging the energy storage device.

In principle, it is not necessary to determine a discharge equivalent power-specific fuel consumption in order to determine a charge equivalent power-specific fuel consumption. The charge equivalent power-specific fuel consumption may, for example, also be obtained by shifting a concatenated power-specific fuel consumption over the combined maximum provided power of all electric-power generators: <MAT> where n is the number of electric power generators and P < <NUM> denotes charging the energy storage device. The charge equivalent power-specific fuel consumption may then be obtained multiplying with the optional proportionality factor fprop, and may be described by: <MAT> where j is determined such that: <MAT>.

<FIG> depicts an example of an energy optimisation routine according to an embodiment of the invention. An energy optimisation routine typically receives operator input data <NUM>, such as parameters are set by a controller, e.g. a desired shaft speed nset. The energy optimisation routine may further receive system input data <NUM>, such as dynamically determined parameters representing a state of the power system, e.g. a state of charge of the energy storage device and an amount of electric power required by the hotel. These parameters may affect the desired output of the hybrid power system.

The energy optimisation routine may further have access to a data storage <NUM> comprising parameters and/or functions describing the hybrid power system, such as the number and types of engines, the power-specific fuel consumptions of the one or more main engines sfciME(P, n) and the electric-power generators sfcjDG(P), and the efficiency of the induction motor as a function of power and/or rotational speed. The data storage may further comprise a precomputed function or look-up table defining an equivalent power-specific fuel consumption of the energy storage device according to an embodiment of this enclosure, e.g. as described above with reference to <FIG>. Alternatively, the data storage may comprise the elements needed to construct an equivalent power-specific fuel consumption, e.g. the power-specific fuel consumptions of the electric-power generators, an optional proportionality factor, and relations defining equivalent power-specific fuel consumption of the energy storage device according to an embodiment of this enclosure, e.g. as described above with reference to <FIG>, allowing the equivalent power-specific fuel consumption to be constructed during runtime.

In some embodiments, the operator input data and/or the system input data may be pre-processed <NUM> by a pre-processor, which may provide derived input data <NUM> as output. For example, in a vessel with an adaptive pitch propeller, the pre-processor may determine a new propeller pitch which may affect the relation between required power and shaft speed, and thus the efficiency of the main engine. In some embodiments, the pre-processor may also determine a predicted power demand Ppred.

Based on the operator input data and/or the system input data and, optionally, the derived input data, as well as, optionally, on parameters or functions from the data storage, the optimiser may determine <NUM> one or more boundary conditions or constraints, limiting the solution space to ensure viable solutions and, preferably, decrease the computational burden. For example, the solutions may be limited such that the power provided by the main engine(s) and electric-power generator(s) does not exceed their respective maximum provided power. Thus, constraints may be used to prevent overloading, to ensure sufficient electric power for the hotel at a predetermined voltage and frequency, et cetera.

Based on the operator input data and/or the system input data and, optionally, the derived input data, as well as, optionally, on parameters or functions from the data storage, the optimiser may determine <NUM> a target function may be determined. Typically, the object of the optimiser is to minimise the (equivalent) fuel consumption of the hybrid power system while providing the desired power to satisfy the hotel needs and set shaft speed. In an embodiment, the object function may be formulated as: <MAT> where ṁf(t) is the total (equivalent) fuel consumption rate of the hybrid power system, which in a typical system is equal to the sum of fuel consumption rates of the main engines and the electric-power generators and the equivalent fuel consumption rate of the energy storage device. Here, ṁf,MEi(t) is the fuel consumption rate of the ith of the NME main engines, ṁf,DGj(t) is the fuel consumption rate of the jth of the NDG electric-power generators, and ṁf,BAT(t) is the equivalent fuel consumption rate of the energy storage device, based on the equivalent power-specific fuel consumption defined above. In general, the fuel consumption rate is proportional to the delivered power multiplied with the power-specific fuel consumption. In the example depicted in <FIG>, NME = <NUM> and NDG = <NUM>. In other embodiments, there may be a plurality of energy storage devices, in which case the last term in equation (<NUM>) would be a summation over all such devices.

An optimisation algorithm may then optimise <NUM>, for example minimise, the target function subject to the determined boundary conditions. In general, the optimiser may use any non-convex optimiser, for example an optimiser based on the Mesh Adaptive Direct Search algorithm. The Mesh Adaptive Direct Search algorithm is described in more detail in <NPL>.

Based on the output of the optimiser, one or more power distributions <NUM> may be determined, for instance a propulsive power distribution defining a power split between the main engine(s) and the induction engine(s), and/or an electric power distribution defining a power split between the energy storage device and the electric-power generator(s). One or more power sources may then be selected based on at least one of the power distributions.

As was explained above, the (long-term) optimal power distribution can only be determined with hindsight, as it depends on future events. A common way in the art to determine the quality of an optimisation routine is to determine one or more sample trajectories and determine the optimal (typically, minimal) fuel consumption taking the entire trajectory into account. This may be achieved using a method named Dynamic Programming. Actual optimisation routines may have knowledge of current conditions and of past conditions, but lack knowledge of future power demands. The quality of a routine may then be determined by comparing the fuel consumption according to the routine to the optimal fuel consumption according to dynamic programming. Similarly, other quantities such as state of charge of the energy storage device may be plotted.

<FIG> depicts a graph comparing the state of charge of an energy storage device as determined according to an embodiment with dynamic programming and with a rules-based method according to the state of the art. The example is based on an actual power demand of a maritime vessel. The solid line <NUM> represents the state of charge according to an energy optimisation method implementing an equivalent power-specific fuel consumption for the energy storage device, as discussed above (Equivalent Consumption Minimisation Strategy, ECMS). The dotted line <NUM> represents the dynamic programming (optimal) solution, and the dashed <NUM> line represents the state-of-the-art rules-based solution. It can be clearly seen that the ECMS solution is much more similar to the optimal dynamic programming solution than the rules-based method, generally deciding to charge and discharge the energy storage device during the same parts of the trajectory as the dynamic programming solution, but often at a slightly more conservative rate.

<FIG> depicts the corresponding cumulative fuel consumption. Again, the solid line <NUM> represents the solution according to an embodiment (referred to as ECMS), the dotted line <NUM> represents the (optimal) dynamic programming solution, and the dashed <NUM> line represents the state-of-the-art rules-based solution. Although the amount of fuel saving of the EMCS method relative to the rules-based method varies over the trajectory, the ECMS method is generally more fuel-efficient than the rules-based method. Compared to the state-of-the-art solution, the ECMS method overall achieves an almost <NUM>% reduction in fuel consumption.

Table <NUM> compares the amount of consumed fuel, the fuel savings, and the state of charge at the end of the simulated trajectory.

<FIG> is a block diagram illustrating an exemplary data processing system that may be used to carry out the invention. Data processing system <NUM> may include at least one processor <NUM> coupled to memory elements <NUM> through a system bus <NUM>. Further, processor <NUM> may execute the program code accessed from memory elements <NUM> via system bus <NUM>. In one aspect, data processing system may be implemented as a computer that is suitable for storing and/or executing program code. It should be appreciated, however, that data processing system <NUM> may be implemented in the form of any system including a processor and memory that is capable of performing the functions described within this specification.

Memory elements <NUM> may include one or more physical memory devices such as, for example, local memory <NUM> and one or more bulk storage devices <NUM>. Local memory may refer to random access memory or other non-persistent memory device(s) generally used during actual execution of the program code. The processing system <NUM> may also include one or more cache memories (not shown) that provide temporary storage of at least some program code in order to reduce the number of times program code must be retrieved from bulk storage device <NUM> during execution.

Input/output (I/O) devices depicted as input device <NUM> and output device <NUM> optionally can be coupled to the data processing system. Examples of input device may include, but are not limited to, for example, a keyboard, a pointing device such as a mouse, or the like. Examples of output device may include, but are not limited to, for example, a monitor or display, speakers, or the like. Input device and/or output device may be coupled to data processing system either directly or through intervening I/O controllers. A network adapter <NUM> may also be coupled to data processing system to enable it to become coupled to other systems, computer systems, remote network devices, and/or remote storage devices through intervening private or public networks. The network adapter may comprise a data receiver for receiving data that is transmitted by said systems, devices and/or networks to said data and a data transmitter for transmitting data to said systems, devices and/or networks. Modems, cable modems, and Ethernet cards are examples of different types of network adapter that may be used with data processing system <NUM>.

As pictured in <FIG>, memory elements <NUM> may store an application <NUM>. It should be appreciated that data processing system <NUM> may further execute an operating system (not shown) that can facilitate execution of the application. Application, being implemented in the form of executable program code, can be executed by data processing system <NUM>, e.g., by processor <NUM>. Responsive to executing application, data processing system may be configured to perform one or more operations to be described herein in further detail.

In one aspect, for example, data processing system <NUM> may represent a client data processing system. In that case, application <NUM> may represent a client application that, when executed, configures data processing system <NUM> to perform the various functions described herein with reference to a "client". Examples of a client can include, but are not limited to, a personal computer, a portable computer, a mobile phone, or the like.

Claim 1:
A method for determining a power distribution for a plurality of power sources of a hybrid power system, the plurality of power sources comprising:
an energy storage device (<NUM>), preferably a battery, the energy storage device being associated with a charge rate and/or a discharge rate, and with a state of charge; and
one or more electric-power generators (<NUM><NUM>,<NUM>) electrically coupled to the energy storage device, each electric-power generator being associated with a maximum amount of provided power, and associated with a power-specific fuel consumption defining a quantity of fuel consumed per quantity of energy provided as a function of provided power,
the method comprising:
determining a discharging equivalent specific fuel consumption for the energy storage device based on the respective power-specific fuel consumptions of the respective one or more electric-power generators, the discharging equivalent specific fuel consumption defining an estimated amount of fuel that will be consumed by recharging the energy storage device as a function of the discharge rate of the energy storage device, the discharging equivalent specific fuel consumption being obtainable by performing, for each of a plurality of discharge rates, the steps of:
a) determining (<NUM>) a discharge power associated with the discharge rate in question;
b) selecting (<NUM>) a first electric-power generator from the one or more electric-power generators, and;
c) IF the discharge power is smaller than the maximum amount of provided power associated with the selected electric-power generator,
THEN determining (<NUM>) the equivalent estimated amount of fuel by determining an amount of fuel consumed by the selected electric-power generator when providing power equal to the maximum provided power of the selected electric-power generator minus the determined discharge power and, optionally, multiplying the determined amount of fuel with a predetermined proportionality factor larger than <NUM> and smaller than or equal to <NUM>, to obtain the discharging equivalent specific fuel consumption;
ELSE updating (<NUM>) the discharge power by subtracting the maximum amount of provided power associated with the selected electric-power generator from the previously determined discharge power, selecting (<NUM>) a next power generator from the one or more electric-power generators, and repeating step c;
and
determining a power distribution based on at least the discharging equivalent fuel consumption.