Patent Application
20220203844
The present disclosure generally relates to power generation systems and methods for controlling one or more power sources, including a battery, a fuel cell, and/or a supercapacitor.
Power generation in hybrid electric vehicles (HEV), such as fuel cell electric vehicles (FCEV) or battery electric vehicles (BEV) requires optimal efficiency of the system. Engine transients reduce the efficiency of hybrid electric vehicles (HEV). However, targeting higher efficiency of any HEV necessitates higher energy storage transients.
The need for higher energy storage transients of HEVs may be met by using a battery. For example, a lithium-ion (Li-ion) battery has limited life and transient capabilities. A typical battery in any battery electric vehicle (BEV) application is sized to meet life, range, and C-rate limitation specifications or targets. C-rate is defined as a measure of the rate at which a battery is discharged relative to its maximum capacity.
Depending on the specific application and architecture, a battery may have one or more of these parameters as the limiting factor. If life of the battery is a limiting factor, the battery throughput can be reduced to improve the battery size or life. If range of the battery is a limiting factor, the battery life can be enhanced to improve its total cost of ownership (TCO). Alternatively, the charging frequency of the battery can be manipulated to change the limiting factor of the battery from range to battery life. When C-rate is a limiting factor, the effective C-rate can be reduced to improve the battery size.
Similarly, high transient power loadings on a fuel cell can negatively affect the fuel cell life of a FCEV. As is in the case of any HEV, this may be mitigated by using a Li-ion battery. Additionally, cycle aging limits in both cases may be addressed by absorbing the transient power loads. Power transients tend to be better regulated in BEVs, where energy storage is sized based on range rather than based on life. However, even in BEVs, increasing the total cycle life of the system is beneficial, as it influences the effective TCO.
In all of these instances, the limited life and transient capabilities of a battery or fuel cell may be significantly impacted by absorbing transient power loads via supercapacitors. For example, in electrified battery powertrain systems where the onboard Li-ion energy storage system is sized based on life requirements, it is conceivable that usage of one or more supercapacitors may reduce the amount of Li-ion battery needed to achieve the same life and transient response targets. Furthermore, use of a supercapacitor may also reduce the overall energy storage weight, cost, and volume needed to achieve the same target battery life and transient response.
Similarly, for electrified fuel cell powertrain systems, the life of the fuel cell in part depends on the ability of the fuel cell controller to manage the internal operating conditions, such as the airflow, coolant, and hydration levels. This is increasingly difficult during transients, and results in greater membrane electrolyte assembly (MEA) or bi-polar electrolyte plate wear or lower fuel cell life. The life of the fuel cell may be extended by reducing the transient demand off the fuel cell and shifting that effort to another component, such as the energy storage system. In parallel, the energy storage system may also be leveraging supercapacitors to extend the life of the Li-ion cells.
The transient power loading and total energy throughput on the battery or fuel cell may be reduced by using supercapacitors in combination with a Li-ion battery or fuel cell for the energy storage system of a vehicle. Doing so may extend the battery and/or fuel cell life. Moreover, an intelligent system and method is needed to control the source of power (e.g., from a battery, fuel cell, supercapacitor, or combinations thereof) used by any electrified powertrain system in real time.
Embodiments of the present invention are included to meet these and other needs. In one embodiment, a method of intelligently controlling one or more power sources of a powertrain system (“powertrain”) to maximize the life of the powertrain comprises measuring and/or estimating in real time a power loading requirement of the powertrain, identifying at least two variables that determine a power demand split in the powertrain, splitting power between the one or more power sources of the powertrain based on the identified variables, monitoring the life of the one or more power sources of the powertrain, and controlling power of the one or more power sources to maximize life of the powertrain.
The one or more power sources of the powertrain comprises at least one fuel cell and an energy storage system. In some embodiments of the present method, the energy storage system further comprises a battery. In some embodiments of the present method, energy storage system further comprises a supercapacitor.
The at least two variables that determine the power demand split between the one or more power sources of the powertrain comprises a power split variable (β) and a fuel cell transient loading variable (γ). In some embodiments of the present method, the at least two variables further comprise a fuel cell load following factor, a. Embodiments of the method may further comprise correcting the value of the at least two variables to compensate for the expected impact on life of the energy source system.
In some embodiments, the method further comprises determining the life of the one or more power sources. In some embodiments of the present method, the energy storage system comprises at least one battery and at least one supercapacitor. The power split variable (β) determines the power split between the battery and the supercapacitor.
Some embodiments of the present method comprises identifying variables that determine power demand split in the powertrain. These method steps may also comprise using data generated offline. In some embodiments, the data generated or incorporated in the method offline is based on a Look Up Table. In some embodiments, the data generated or incorporated offline comprises incorporating predictive mapping or routing information. In some embodiments, the predictive mapping or routing data is acquired from electronic or online routing sources.
In some embodiments of the present method, the powertrain is comprised in a vehicle. The vehicle may be an electric vehicle. The electric vehicle may be a fuel cell electric vehicle (FCEV) or a battery electric vehicle (BEV).
These and other features, aspects, and advantages of the present invention will become better understood when the following detailed description is read with reference to the accompanying drawings, in which like characters represent like parts throughout the drawings, wherein:
Supercapacitors (SC), also called ultracapacitors, are high-capacity power sources with a capacitance value much higher than other types of capacitors. Typically, supercapacitors have about 10 to about 100 times more energy per unit volume or mass compared to electrolytic capacitors. However, supercapacitors have lower voltage limits compared to other capacitors.
Supercapacitors can accept and deliver charge much faster than batteries. They can achieve a high power density up to 50 times of that which is achieved by batteries. Supercapacitors can also tolerate more charge and discharge cycles compared to rechargeable batteries.
In addition, supercapacitors can sustain about 1 million charge cycles and have an operating temperature that ranges from about −50° C. to about 70° C. The manufacturing process of supercapacitors does not involve harmful materials or toxic metals, which allows them to be certified as a disposable component. Furthermore, supercapacitors are often more efficient than batteries, and require minimal maintenance compared to batteries.
Supercapacitors are constructed in a manner similar to electrolytic capacitors, and have a liquid or wet electrolyte between their electrodes. Based on their chemical compositions, supercapacitors can be characterized as aqueous electrolytic supercapacitors, or as ionic liquid supercapacitors. Types of supercapacitors may include, but are not limited to, hybrid supercapacitors or organic electrolytic supercapacitors. Different supercapacitor compositions have different working characteristics and specifications.
Supercapacitors do not have a dielectric. Both plates in a supercapacitor are soaked in an electrolyte, and separated by a very thin insulator. A supercapacitor insulator is made of material, such as carbon, paper, or plastic, and acts as a separator.
When supercapacitor plates are charged, an opposite charge forms on either side of the insulator, creating an electric double-layer. This double-layer may be about one molecule thick unlike a dielectric in a conventional capacitor that might range in thickness from a few microns to a few millimeters. Thus, supercapacitors are often referred to as double-layer capacitors or electric double-layer capacitors.
Electric double-layer capacitors (EDLCs) use carbon electrodes or derivatives with much higher electrostatic double-layer capacitance than electrochemical pseudo-capacitance. Electrochemical pseudo-capacitors use metal oxide or conducting polymer electrodes with a high amount of electrochemical pseudo-capacitance. Hybrid capacitors, such as the Li-ion capacitors, use electrodes with differing characteristics: one exhibiting mostly electrostatic capacitance and the other exhibiting mostly electrochemical capacitance.
Hybrid capacitors may have an aqueous, organic, and/or ionic electrolyte. An aqueous electrolyte is water based and may be acidic, alkaline, or have salts. An aqueous electrolyte typically has a low functional temperature range. An organic electrolyte is more expensive, but has a wider functional temperature range. An ionic electrolyte comprises liquid salts.
Supercapacitors have an inherently long cycle life, which may comprise millions of charge/discharge cycles. This is true, particularly when supercapacitors are compared to Li-ion batteries that may only have a cycle life of about several thousand charge/discharge cycles. Additionally, supercapacitors have inherently short response times (e.g., about 40-300 milliseconds, ms) due to a low equivalent series resistance compared to Li-ion batteries (e.g., about 500 ms).
Various energy storage system (ESS) architecture systems and method embodiments of the present disclosure may be used in a vehicle 100 or a powertrain system (“powertrain”) 200. In one embodiment, a powertrain 200 system of the present disclosure may be used or comprised in any application including, but not limited to on or off roads or highways, underwater, high altitudes, sub-Saharan, mobile, stationary, and/or industrial applications. In one embodiment, the powertrain 200 system may be comprised by a machine, an equipment, or an industrial facility or a method of producing or operating the same. In another embodiment, a powertrain 200 system may be separate and distinct from a vehicle 100.
A vehicle 100 may be any standard, recreational, or industrial vehicle or automobile, including, but not limited to a car, a truck, a boat, a train, a plane, a helicopter, a submarine, etc. In one embodiment, the vehicle 100 is an electrified vehicle. In one embodiment, a powertrain 200 system may be comprised in or by a vehicle or an electrified vehicle 100. In some embodiments, a powertrain 200 may be configured to be comprised by (e.g., inside, within, beside, atop, and/or underneath) the vehicle 100.
Provided herein are various ESS architecture systems and methods utilized by or in a vehicle 100 (e.g., 120, 130, and 140) and/or a powertrain 200 (e.g., 202, 204206, and 208). For example,
In some vehicle 100 or powertrain 200 embodiments, the DC/DC converter 110 may be located next to the battery 112 (only) side. In some embodiments, the DC/DC converter 110 may be located next to the supercapacitor 108 (only) side. In additional embodiments, the DC/DC converter 110 is located between the battery 112, the supercapacitor 108, and/or the motor 114 (see
The power circuit 230 shows an active architecture with an active battery 112 comprised in a vehicle 100 or a powertrain 206. This power circuit 230 embodiment may comprise a battery 112 in series with a DC/DC converter 110, a motor 114, and a supercapacitor 108. Another circuit 240 shows an active architecture with an active battery 112 and an active supercapacitor (SC) 108 comprised in a vehicle 100 or a powertrain 208. This embodiment 240 may comprise a battery 112 in series with a DC/DC converter 110, a supercapacitor 108 in series with a second DC/DC converter 110, and a motor 114 (see
All variants and variations of electric vehicle 100 or powertrain 200 architectures using a battery 112 have limitations in terms of maximum power and energy density, charging and discharging time, maximum range of currents and voltages, and regenerative power management. If these limitations are exceeded, it may result in a need for an oversized battery 112, untimely degradation of the battery, reduced overall performance, reduced battery life with a need for frequent replacements, or a high overall life cycle cost. A hybrid ESS that includes two or more storage elements (e.g., energy sources) of different technologies can mitigate these shortfalls to varying degrees.
An optimized combination of the two or more storage elements is determined in view of the vehicle 100 specification and/or powertrain 200 architecture, systems, or methods 300 of the present disclosure. The vehicle 100 or powertrain 200 specifications, in some embodiments, are combined with route targets or specifications that are setup in a simulation or real world environment. Once the optimized combination of routing and vehicle/powertrain specifications are selected (e.g., by a user, an operator, or a computer algorithm), the vehicle 100 or powertrain 200 can be intelligently controlled in real time by a system or method 300 in order to provide the necessary power to one or more power sources and/or storage elements for efficient and optimal performance of all, particularly a fuel cell 102.
In one embodiment, there may be more than two storage elements. In one embodiment, there may be two storage elements. In some embodiments, the two storage elements comprise a supercapacitor 108 and a battery 112.
In some embodiments, the battery 112 is a Li-ion battery. The battery 112 or supercapacitor 108 life may be modelled as a function of a chemistry. A supercapacitor life model may be set up based on an assumed end-of-life (EOL) capacity.
A split factor variable is determined based on the structure and the design of an experiment (DoE) set up for a combined system. This split factor variable determines the power demand split between the two or more storage elements (e.g., power sources). To develop an architecture that makes use of the properties of a supercapacitor 108 to minimize transient power impact on Li-ion battery 112 life, the range of the power demand split may be based off a series of models simulating various conditions depending on the desired applications and corresponding or related duty cycles of the vehicle 100 or powertrain 200.
In another embodiment, a fuel cell 102 is coupled with a supercapacitor 108 and a Li-ion battery 112. Fuel cell 102 failure cases illustrate localized cell component degradation, primarily caused by flow-field dependent non-uniform distribution of reactants. These spatiotemporal variations in fuel cell 102 state variables under transient load cycles can result in performance degradation. Often suboptimal or poor fuel cell 102 performance under such conditions is related to platinum loss and consequent reduction in electrochemical active surface area of the fuel cell 102. Such a cyclic operation leads to multiple degrading side reactions, eventually rendering the fuel cell 102 unable to provide the requested power demand and catastrophic fuel cell failure.
To avoid such fuel cell 102 operational performance failures, and to preserve the life of the fuel cell 102 (and other power sources or storage components), the fuel cell life may be assessed. In addition, the degree to which the fuel cell 102 is able to provide the needed vehicle 100 or powertrain 200 power in comparison to that provided by the ESS (e.g., the battery 112 and/or supercapacitor 108) may be determined during and/or by the intelligent control method 300 of the present disclosure. In some embodiments, a constraint is based on the assumption that the system is charge sustaining (i.e. energy storage system SoCmission start≈SoCmission end). In some embodiments, this constraint is relaxed, such as in a range extended FCEV (REEV; REFCEV).
The flowchart shown in
Once the physical system is set up based on those power source selections in step 306, the intelligent control method 300 of the system is executed in real time. The power loading requirements of the vehicle 100 or powertrain 200 are measured in step 308. These system requirements depend on the power needs of the vehicle 100 or powertrain 200 as based on its operational mission.
The power demand is split between fuel cell (FC) 102, supercapacitor (SC) 108, and Li-ion battery 112, and is calculated based on selected parameters in step 310. These parameters may be defined as the energy storage system (ESS) power split variable (β) and the fuel cell 102 transient loading variable (γ). The power demand split is determined by creating a weighted function that splits the power demand between the various power producing sources (102, 108, 112) by adjusting the weights and shifting the power demand between the different sources (102, 108, 112). The optimal weights for the power demand split are determined by assessing a cost function.
The power from fuel cell 102 is adjusted based on charge sustaining or range extending needs of the vehicle 100 or powertrain 200 in step 312. The variable a is the fuel cell 102 load following factor and may be uniquely determined to achieve a charge sustaining conditions. The life of the power sources (102, 108, 112) is monitored based on the mission in step 314. The parameter values are corrected to compensate for errors in the expected life when compared to the real life in step 316. The control of the system in real time is directly related to the values selected for the variables β and γ as well as on the charge sustaining or range extending needs of the powertrain 200.
A prime mover effort is defined as the primary source of power of the vehicle 100 or powertrain 200 and of the effort that the vehicle 100 or powertrain 200 applies. In some embodiments, real time control will not be able to determine the variable α, and a more typical “rubber band” controller may be employed where the prime mover effort is a function of the deviation of the energy storage system (ESS) net instantaneous state of charge (SoC) from the SoCstart or SoCtarget. Thus, the further the deviation in SoC, the more the effort by the prime mover to return to target, which is accomplished by increasing or by decreasing its power output.
Since real life degradation may be different compared to the targeted degradation, the specific pairing for β and γ can be adjusted based on data generated, determined, and/or gathered offline and/or online. Offline data and/or information may originate from an offline design of experiment (DoE). In some embodiments, the data gathered is based on predictive mapping or routing information. In some embodiments, the data may be in the form of a Look Up Table (LUT).
In some embodiments, a LUT is a simple deterministic table where given certain inputs, a calibrated value of a variable from the table can be determined offline, looked up (or interpolated) and to control the system. For example, β and γ may be the inputs to a LUT that is calibrated offline to create a value for life loss of a Li ion battery.
If a specific life loss of a Li ion battery LLi-ion is being targeted but a different value ΔLLi-ion is observed over the test period, to correct this discrepancy going forward β and γ parameters will need to be adjusted.
To determine those parameters (e.g., α, β, and γ), a LUT may be used. Illustratively, use of a LUT is often the simplest and most efficient way to determine the values of α, β, and γ parameters. Alternatively, any other method known in the art that is able to generate the α, β, and γ values and/or parameters may also be used in the present method 300 and systems.
In some embodiments, life loss (AL) may be described as:
[ΔLFC, ΔLSC, ΔLLi-ion]=fn(β, γ).
In some embodiments, life loss (ΔL) may be described as:
[δβ, δγ]=F(ΔLFCTgt−ΔLFCReal,ΔLSCTgtΔLSCReal, ΔL/LiIonTgt−ΔLLiIonReal )
where β is an output of a LUT with inputs of ΔLFCTgt−ΔLFCReal,ΔLSCTgtΔLSCReal, ΔL/LiIonTgt−ΔLLiIonReal, and γ is similar in form. In some embodiments, LUTs may be more complex such as a dependent function.
Any process, method, calculation, formula, and/or algorithm to determine life of a battery 112, a fuel cell 102, and/or a supercapacitor 108 may be used by the present method 300, including but not limited to any physics or stochastic based methods. In an illustrative embodiment, a throughput based life model is used for determining the life of the supercapacitor 108, the fuel cell 102, and/or the battery 112. In some embodiments, a model different from a throughput based life model is used to determine the life of the supercapacitor 108, the fuel cell 102, and/or the battery 112.
In some embodiments, it is assumed that the supercapacitor 108 has good capacity retention up to a cell temperature of about 40° C. In some embodiments, an end-of-life (EOL) capacity of the supercapacitor 108 at about 80% is assumed. In some embodiments, the supercapacitor 108 has a life of about 1 million cycles and can perform up to a cell temperature of about 40° C.
In some embodiments, a supercapacitor 108 life modelling method was employed. An exemplary life modelling method of the supercapacitor 108 measures supercapacitor voltage, RMS current, and/or temperature. For example, the life of a supercapacitor 108 is shown in
In some embodiments, a throughput based life model 300 is an intelligently controlled method 300 that is used for determining the life of a Li-ion battery 112. Such a throughput based life model 300 uses the value of the number of charge or discharge cycles that may be performed on the battery 112 before the ability to move energy to or from the battery 112 reaches zero. This variable is dependent on battery 112 chemistry and represents the total energy throughput for a battery 112 of any given size.
Any further cycle activity will gradually consume the total energy allowance and the decay will represent the State of Health (SOH). The SOH is a measure of how much capacity or throughput energy is left in a battery 112 or supercapacitor 108. In some embodiments, a model different from a throughput based life model 300 is used to determine the life of the Li-ion battery 112.
For example, a micro-cycling/pulsewise degradation method for typing a first chemistry (e.g., Type 1) of Li-ion batteries 112 may be employed. An exemplary life modelling method of the Li-ion Type 1 battery 112 is based on Type 1 chemistry.
In some embodiments, a micro-cycling/pulsewise degradation method for a second chemistry (e.g., Type 2) of Li-ion batteries 112 is employed. An exemplary life modelling method of the Li-ion Type 2 battery 112 is based on Type 2 chemistry.
In some embodiments, a micro-cycling/pulsewise degradation method for a third chemistry (e.g., Type 3) of Li-ion batteries 112 is employed. An exemplary life modelling method of the Li-ion Type 3 battery 112 is based on Type 3 chemistry.
The specific pairing for β and γ can be adjusted based on the data gathered from the offline design of experiment (DoE), as mentioned earlier. In one embodiment, the offline DoE may be set up and processed based on certain conditions or parameters as discussed below, such as the total power demand
The total power demand (PDMD) is the sum of and is split between the fuel cell (FC) power (PFC) and the power of the ESS (PESS). Power of the ESS (PESS) is the sum of PESS1, power obtained from one or a first component of the energy storage system (e.g., the battery 112) and PESS2, power obtained from a second component of the energy storage system (e.g., the supercapacitor 108).
P
DMD
=P
FC
+P
ESS
In some embodiments, PFC=α·(γ·PDMDi+(1−γ)·PFCi−1).
This is only one of several possible functional representations of PFC where the transient loading variable y, may be critical to aging.
P
ESS
=P
ESS1
+P
ESS2
(LiIon)PESS1β·PESS
(SC)PESS2=(1−β)·PESS
In some embodiments, the loss of life of the fuel cell is determined as described below.
Life loss of the fuel cell (ΔLFC) is given by:
ΔL
FC
=F(Temp, time, Vcell, xO
Life loss of the supercapacitor (ΔLSC) is given by:
ΔL
SC
=G(Temp, time, (1−β)·PESS,ΔV,SoC).
Life loss of the Li-ion battery (ΔLLi-ion) is given by:
ΔL
Li-ion
=H(Temp, time, —·PESS,Crate,Ah,SoC).
Where xO
A design of experiment (DoE) is performed to explore values of variables β and γ. This DoE may be performed offline. The variable α may be uniquely determined based on γ, such that there exists a charge sustaining solution.
This may be relaxed if a range extender solution is needed as is in the case of BEVs. Each unique combination of ESS elements as a function of β and γ will have a unique total TCO. In some embodiments, the optimal control may depend on the TCOmin solution. With each setting of β and γ, the life loss of the fuel cell 102, supercapacitor 108, and Li-ion battery 112 devices may be determined.
The performance of a DoE depends on the setup of the ESS. Referring now to
In one embodiment, the DoE is simulated using a moderate fidelity model. In some embodiments, the variable μ is the current split factor that determines the current split between a battery 112 and a capacitor 108. The variable μ is modeled as a function of the battery 112 range kWh, the supercapacitor 108 kWh, the supercapacitor 108 instantaneous state of charge (SoC), and the battery 112 chemistry. The variable μ is determined by carrying out simulations comprising the battery 112 range and the supercapacitor 108 range for a specific chemistry.
The split factor controller decides the split factor between the battery 112 and the supercapacitor 108. In some embodiments, the controller decides the split based on rule-based splitting. In rule-based splitting, the controller decides the split factor as a function of the battery 112 and supercapacitor 108 SoC with the supercapacitor 108 having a limit of 100° C. In some embodiments, the controller decides the split based on fixed splitting. In fixed splitting, the controller targets a constant power split between the battery 112 and the supercapacitor 108 at all times with the supercapacitor 108 having a limit of 100° C.
In some embodiments, other variants may be utilized. In some embodiments, the controller may decide the power split based on eHorizon or forward-looking data. In some other embodiment, the intelligent control of the power split may involve other storage elements.
In one embodiment, the Type 1 chemistry battery 112 range is about 100 kWh, the supercapacitor 108 range is about 1 kWh and the split factor is about 0.5. Referring now to
In one embodiment, a DC/DC converter 110 is included in the system architecture. As the efficiency of the DC/DC converter 110 is a function of its input voltage, which drops off rapidly at lower input voltages, the supercapacitor 108-DC/DC 110 subsystem efficiency drops as well. Therefore, maintenance of the voltage (related to SOC state of charge) of the DC/DC converter 110 at optimal conditions is important.
The supercapacitor 108 needs to be managed appropriately for good efficiency during usage, and cannot be discharged completely. The supercapacitor 108 efficiency is dependent on the voltage (SoC) and the current (power). In some embodiments, the supercapacitor 108 may operate in the range of about 40% of SoC to about 100% of SoC, including any specific or range of voltage (SoC) comprised therein, in order to avoid poor efficiency operation or performance
A post-processing step 318 in the present method 300 takes the output of the DoE runs, and finds the optimal battery 112/capacitor 108 combination that meets the selection criteria during real time control. In some embodiments, the post-processing 318 is done offline to LUTs that may be used during this intelligent control method 300 of the power split. Post-processing 318 of the DoE is used to determine the battery 112 life, supercapacitor 108 life, and to evaluate TCO calculations in order to identify optimum solutions for each vehicle 100 or powertrain 200 application depending on the route, conditions, and/or load demands
Factors considered during post-processing 318 comprise expected capacitor 108 life, expected battery 112 life, and battery 112 C-rate. Additionally, certain throughput based life assumptions may be made during post-processing 318. Post-processing 318 assumptions may also comprise power source (102, 108, 112) usage based on number of days per year and the number of hours per day as identified in the vehicle 100 or powertrain 200 mission. The battery 112 and capacitor 108 assumptions are modifiable inputs and are capable of variation over time. In some embodiments, LUTs may be created by running simulations based on post-processing 318 assumptions comprising battery 112 life, supercapacitor 108 life, battery 112 chemistry, supercapacitor 108 chemistry, or vehicle 100 or powertrain 200 routes or conditions.
A 3-factor DoE is executed with a battery 112 size of 1 kWh, a supercapacitor 108 size of 0.1 kWh, and a split factor of 0.1. Battery 112 assumptions include 80% usage range, 96% efficiency, and 2500 base cycles. Supercapacitor 108 assumptions include a C-rate of 100° C., 60% usable range, 96% efficiency, and 1 million base cycles. DC/DC 110 assumptions include 96% efficiency. The simulation splits this power profile between the battery 112 and supercapacitor 108 while respecting supercapacitor 108 limits such as SoC range at any given time and at any given power rate. The split factor governs how much power at any moment is sourced from the battery 112 and how much is sourced from the supercapacitor 108. For instance, a split factor of 0.3 implies 30% is sourced from the supercapacitor 108 and 70% is sourced from the battery 112. The result of such a simulation is a power profile for both the battery 112 and the supercapacitor 108, from which life and performance can be determined.
In some embodiments, the variables β and γ are constant. In some embodiments, β and γ can change. In some embodiments, the intelligent control system and method 300 may make use of eHorizon information to determine power change or the values of the control variables β and γ. This method 300 may be used to plan a look ahead based life management strategy. If specific loading conditions are expected, the system power devices or sources may be pre-conditioned to anticipate the maneuvers and optimize life consumption. In some embodiments, supercapacitors 108 may be replaced with any high cycle life, high power devices, like flywheel motors or pneumatic systems.
The intelligent control system and method 300 described here is applicable to all vehicles 100 , powertrains 200, and/or industrial markets and applications that can make use of a fuel cell 102 or hybrid or BEV powertrains 200. This method 300 has the potential to improve maintenance costs, increase change intervals, or result in less downtime. In some embodiments, the need for Li-ion battery 112 within a FCEV 100 may be eliminated, and the fuel cell 102 may be supported only by a supercapacitor 108.
In some embodiments, environment variations may also be factored into the intelligent control method 300. For example, startup of fuel cells 102 can be challenging in low temperatures, requiring low or high voltage (e.g., 12 or 24 V) batteries 112 to support fuel cell 102 heaters as the Li-ion battery 112 may not be able to support them. In such instances, supercapacitors 108 systems may provide a compelling and alternative option to batteries 112 that may further reduce system cost and complexity.
Typically, fuel cells 102 require some form of boosted fresh air to operate under high altitude conditions. At higher altitudes, blowers or compressors may fall short of providing the needed air through power transients as effectively as they can at lower altitudes, which is required for optimal fuel cell 102 performance and operations. A supercapacitor 108 system may be effective at compensating for this performance shortfall without significant impact to overall life of the fuel cell 102 or other power source components (e.g., battery 112).
The following numbered embodiments are contemplated and are non-limiting.
As used herein, an element or step recited in the singular and proceeded with the word “a” or “an” should be understood as not excluding plural of said elements or steps, unless such exclusion is explicitly stated. Furthermore, references to “one embodiment” of the presently described subject matter are not intended to be interpreted as excluding the existence of additional embodiments that also incorporate the recited features. Specified numerical ranges of units, measurements, and/or values comprise, consist essentially or, or consist of all the numerical values, units, measurements, and/or ranges including or within those ranges and/or endpoints, whether those numerical values, units, measurements, and/or ranges are explicitly specified in the present disclosure or not.
Unless defined otherwise, technical and scientific terms used herein have the same meaning as is commonly understood by one of ordinary skill in the art to which this disclosure belongs. The terms “first,” “second,” “third” and the like, as used herein do not denote any order or importance, but rather are used to distinguish one element from another. The term “or” is meant to be inclusive and mean either or all of the listed items. In addition, the terms “connected” and “coupled” are not restricted to physical or mechanical connections or couplings, and can include electrical connections or couplings, whether direct or indirect.
Moreover, unless explicitly stated to the contrary, embodiments “comprising,” “including,” or “having” an element or a plurality of elements having a particular property may include additional such elements not having that property. The term “comprising” or “comprises” refers to a composition, compound, formulation, or method that is inclusive and does not exclude additional elements, components, and/or method steps. The term “comprising” also refers to a composition, compound, formulation, or method embodiment of the present disclosure that is inclusive and does not exclude additional elements, components, or method steps.
The phrase “consisting of” or “consists of” refers to a compound, composition, formulation, or method that excludes the presence of any additional elements, components, or method steps. The term “consisting of” also refers to a compound, composition, formulation, or method of the present disclosure that excludes the presence of any additional elements, components, or method steps.
The phrase “consisting essentially of” or “consists essentially of” refers to a composition, compound, formulation, or method that is inclusive of additional elements, components, or method steps that do not materially affect the characteristic(s) of the composition, compound, formulation, or method. The phrase “consisting essentially of” also refers to a composition, compound, formulation, or method of the present disclosure that is inclusive of additional elements, components, or method steps that do not materially affect the characteristic(s) of the composition, compound, formulation, or method steps.
Approximating language, as used herein throughout the specification and claims, may be applied to modify any quantitative representation that could permissibly vary without resulting in a change in the basic function to which it is related. Accordingly, a value modified by a term or terms, such as “about”, and “substantially” is not to be limited to the precise value specified. In some instances, the approximating language may correspond to the precision of an instrument for measuring the value. Here and throughout the specification and claims, range limitations may be combined and/or interchanged. Such ranges are identified and include all the sub-ranges contained therein unless context or language indicates otherwise.
As used herein, the terms “may” and “may be” indicate a possibility of an occurrence within a set of circumstances; a possession of a specified property, characteristic or function; and/or qualify another verb by expressing one or more of an ability, capability, or possibility associated with the qualified verb. Accordingly, usage of “may” and “may be” indicates that a modified term is apparently appropriate, capable, or suitable for an indicated capacity, function, or usage, while taking into account that in some circumstances, the modified term may sometimes not be appropriate, capable, or suitable.
It is to be understood that the above description is intended to be illustrative, and not restrictive. For example, the above-described embodiments (and/or aspects thereof) may be used individually, together, or in combination with each other. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the subject matter set forth herein without departing from its scope. While the dimensions and types of materials described herein are intended to define the parameters of the disclosed subject matter, they are by no means limiting and are exemplary embodiments. Many other embodiments will be apparent to those of skill in the art upon reviewing the above description. The scope of the subject matter described herein should, therefore, be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled.
This written description uses examples to disclose several embodiments of the subject matter set forth herein, including the best mode, and also to enable a person of ordinary skill in the art to practice the embodiments of disclosed subject matter, including making and using the devices or systems and performing the methods. The patentable scope of the subject matter described herein is defined by the claims, and may include other examples that occur to those of ordinary skill in the art. Such other examples are intended to be within the scope of the claims if they have structural elements that do not differ from the literal language of the claims, or if they include equivalent structural elements with insubstantial differences from the literal languages of the claims.
While only certain features of the invention have been illustrated and described herein, many modifications and changes will occur to those skilled in the art. It is, therefore, to be understood that the appended claims are intended to cover all such modifications and changes as fall within the true spirit of the invention.
This nonprovisional application claims the benefit and priority, under 35 U.S.C. § 119(e) and any other applicable laws or statutes, to U.S. Provisional Patent Application Ser. No. 63/131,998 filed on Dec. 30, 2020, the entire disclosure of which is hereby expressly incorporated herein by reference.
| Number | Date | Country | |
|---|---|---|---|
| 63131998 | Dec 2020 | US |
