Method and system for controlling a powertrain in a hybrid vehicle

Abstract

A method and system for powertrain power management in a vehicle having an electric motor and an engine are disclosed. The method and system involve a powertrain operatively coupled to the engine and the electric motor, and an optimizer module operatively coupled to the powertrain. The optimizer receives operator information about a route from a remote management module, receives current route information for the route from a mapping application in response to the operator information, measures current vehicle state information of the hybrid vehicle, and determines a power management strategy for the vehicle based on the current route information and the current vehicle state information.

Description

Technical Field

[0001] The present disclosure relates generally to hybrid vehicles and, more particularly, to controlling a powertrain of a hybrid vehicle. Background Art

[0002] Recently, there has been an increasing demand for vehicles with hybrid powertrains (i.e., hybrid vehicles with multiple forms of motive power) to meet standards such as improved fuel economy and reduced emissions, maintaining optimal performance for the user. Such hybrid vehicles equipped with an electric powertrain with a battery and a range extender typically include controls for determining which of the available onboard power sources supplies power to the electric powertrain. For example, the controls can select between the battery and the range extender as the power source for the powertrain and can select the amount of energy to be provided from each energy source. These controls can include target state-of-charge profiles to manage this power distribution (including starting and ending states of charge and trajectory), as well as targets or decisions regarding how and when to operate the range extender. The reference used for the powertrain can have a significant impact on factors such as fuel economy, performance, emissions, and component life. However, controls currently known in the art use a single, globally selected set of calibrations or references for many or all use cases for a given vehicle. That is, the control is pre-set with a predetermined set of rules that are used by the control to determine which of the power sources to use to obtain energy and how much energy to obtain from each power source. Because there are many different factors that affect the aforementioned factors of the vehicle, it is difficult for the control to make a decision that is optimal for all of the following: fuel economy, performance, emissions, and component life.

[0003] In view of the above examples, a need exists for a control system that can more flexibly and dynamically control the hybrid powertrain in a hybrid vehicle so that the operation of the electric motor and the engine is performed in a manner that optimizes the fuel economy, performance, emissions, and component life of the hybrid vehicle.

[0004] Government support clauses

[0005] This invention was made with Government support under Grant No. DE-EE0007514 awarded by the Department of Energy. The Government has certain rights in this invention. Summary of the Invention

[0006] Various embodiments of the present disclosure relate to a system for controlling a powertrain of a hybrid vehicle including an engine and an electric motor. In one embodiment, a hybrid vehicle drive system is provided, comprising: an engine, an electric motor electrically coupled to an energy storage device, a powertrain operatively coupled to the engine and the electric motor, and an optimizer module operatively coupled to the powertrain. The optimizer module is configured to receive operator information regarding a route from a remote management module, receive current route information for the route from a mapping application in response to the operator information, measure current vehicle status information of the hybrid vehicle, and determine a power management strategy for the vehicle based on the current route information and the current vehicle status information.

[0007] In one embodiment, the powertrain is configured to control at least one of an engine, an electric motor, or an energy storage device of the vehicle based on a power management strategy. In one embodiment, the current vehicle status information includes at least one of the following: vehicle type and architecture, vehicle availability, vehicle mass, vehicle mileage, state of charge (SOC) of the energy storage device and the amount of time to fully recharge the energy storage device, state of health (SOH) of the energy storage device, the amount of fuel in a fuel tank fluidly coupled to the engine and the amount of time to refill the fuel tank, or the vehicle's total range based on the SOC or the amount of fuel.

[0008] In one embodiment, the optimizer module is further configured to provide powertrain-specific information of the vehicle to the remote management module after the vehicle completes the route of travel. In one embodiment, the powertrain-specific information includes at least one of the following: fuel and energy efficiency information, component life information, fault conditions, or the possibility of vehicle de-rating. In one embodiment, the current route information includes at least one of the following: speed limit information, road grade information, gas station location information, charging station location information, traffic information, weather information, terrain information, and zoning information. In one embodiment, the optimizer module determines the power management strategy by using online learning based on historical and predicted data. In one embodiment, the drive system further includes a plurality of sensors operable to measure current vehicle state information, wherein the power management strategy is further determined using a digital twin that monitors the plurality of sensors.

[0009] In one embodiment, a method of operating a hybrid vehicle is provided. The hybrid vehicle has an engine, an electric motor, a powertrain operatively coupled to the engine and the electric motor, and an optimizer module operatively coupled to the powertrain. The method includes the steps of: receiving, by the optimizer module, operator information about traveling a route from a remote management module; receiving, by the optimizer module, current route information of the route from a mapping application; measuring current vehicle state information of the hybrid vehicle; and determining, by the optimizer module, a power management strategy for the vehicle based on the current route information and the current vehicle state information.

[0010] In one embodiment, the method further includes the step of controlling, by the powertrain, at least one of an engine, an electric motor, or an energy storage device coupled to the electric motor of the vehicle based on a power management strategy. In one embodiment, the current vehicle status information includes at least one of the following: vehicle type and architecture, vehicle availability, vehicle mass, vehicle mileage, state of charge (SOC) of an energy storage device coupled to the electric motor and the amount of time it takes to fully recharge the energy storage device, state of health (SOH) of the energy storage device, the amount of fuel in a fuel tank coupled to the engine and the amount of time it takes to refill the fuel tank, or the vehicle's total range based on the SOC or the amount of fuel.

[0011] In one embodiment, the method further comprises the step of providing, by the optimizer module, powertrain-specific information about the vehicle to the remote management module after the vehicle completes traveling the route. In one embodiment, the powertrain-specific information includes at least one of the following: fuel and energy efficiency information, component life information, fault conditions, or potential for vehicle derating. In one embodiment, the current route information includes at least one of the following: speed limit information, road grade information, gas station location information, charging station location information, traffic information, weather information, terrain information, or zoning information.

[0012] In one embodiment, a fleet management system is provided. The management system includes: a plurality of hybrid vehicles, each hybrid vehicle including an engine fluidly coupled to a fuel tank, an electric motor electrically coupled to an energy storage device, a powertrain operatively coupled to the engine and the electric motor, and an optimizer module operatively coupled to the powertrain. The management system also includes a remote management module operable to receive powertrain-specific information for each of the plurality of hybrid vehicles from the optimizer module, determine which of the plurality of hybrid vehicles is instructed to travel a route based on the powertrain-specific information, and transmit operator information for traveling the route to the determined hybrid vehicle. The optimizer module of the determined vehicle is configured to receive current route information for the route from a mapping application in response to the operator information, measure current vehicle state information of the hybrid vehicle, and determine a power management strategy for the vehicle based on the current route information and the current vehicle state information. The powertrain of the determined vehicle is further configured to control at least one of the engine or the electric motor of the vehicle based on the power management strategy.

[0013] In one embodiment, the power management strategy is determined by an optimizer module using online learning based on historical and predicted data. In one embodiment, the power management strategy is further determined using a digital twin that monitors multiple sensors that are operable to monitor current vehicle state information. In one embodiment, the optimizer module is an on-board optimizer module located in a hybrid vehicle. In one embodiment, the on-board optimizer module is electrically coupled to a processing unit within the powertrain. In one embodiment, the optimizer module is an off-board optimizer module accessible via wireless communication. In one embodiment, the off-board optimizer module is accessible via a cloud network. In one embodiment, current route information is included as part of the operator information. In one embodiment, the operator information also includes operator request information.

[0014] While multiple embodiments are disclosed, other embodiments of the present disclosure will become apparent to those skilled in the art from the following detailed description, which shows and describes illustrative embodiments of the present disclosure. Accordingly, the drawings and detailed description are to be regarded as illustrative in nature and not restrictive. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The embodiments will be more readily understood in view of the following description taken in conjunction with the following drawings, wherein like numerals represent like elements. These depicted embodiments are to be understood as illustrative of the present disclosure and not limiting in any way.

[0016] Figure 1is a block diagram of an example of a drive system in a full hybrid electric vehicle (FHEV) architecture employing a specific layout according to one embodiment;

[0017] Figure 2 is a block diagram of an example of a hybrid powertrain system with a fleet management module and a mapping application according to one embodiment;

[0018] Figure 3 is a block diagram of an example of a hybrid powertrain fleet management system having a fleet management module for controlling a fleet of hybrid powertrains according to one embodiment;

[0019] Figure 4 is a flowchart illustrating a method of controlling a hybrid powertrain according to one embodiment;

[0020] Figure 5 According to one embodiment, Figure 1 A block diagram of an example of a powertrain control module is shown;

[0021] Figure 6 is a block diagram of an example of a hybrid powertrain system with a fleet management module and a mapping application according to one embodiment.

[0022] While the present disclosure is susceptible to various modifications and alternatives, specific embodiments have been illustrated by way of example in the drawings and are described in detail below. However, it is not intended to limit the present disclosure to the particular embodiments described. On the contrary, the present disclosure is intended to cover all modifications, equivalents, and alternatives falling within the scope of the present disclosure as defined by the appended claims. DETAILED DESCRIPTION

[0023] In the following detailed description, reference is made to the accompanying drawings, which form a part of this detailed description and in which are shown, by way of illustration, specific embodiments in which the present disclosure may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the invention, and it is understood that other embodiments may be utilized and structural changes may be made without departing from the scope of the present disclosure. Therefore, the following detailed description is not to be construed in a limiting sense, and the scope of the present disclosure is defined solely by the appended claims and their equivalents.

[0024] References throughout this specification to "one embodiment," "an embodiment," or similar language are intended to mean that a particular feature, structure, or characteristic described in conjunction with that embodiment is included in at least one embodiment of the present disclosure. The phrases "in one embodiment," "in an embodiment," and similar language appearing throughout this specification may, but need not, all refer to the same embodiment. Similarly, use of the term "implementation" is intended to mean an implementation having a particular feature, structure, or characteristic described in conjunction with one or more embodiments of the present disclosure, however, an implementation may be associated with one or more embodiments in the absence of a clear correlation to indicate otherwise. Furthermore, the features, structures, or characteristics of the subject matter described herein may be combined in any suitable manner in one or more embodiments.

[0025] like Figure 1 As shown, a hybrid powertrain 100 with a parallel hybrid architecture typically includes an engine 102 powered by a fuel (such as a gasoline or diesel engine) and an electric motor 104 controlled by a power electronics (PE) module 106 and powered by a battery 108. A powertrain control module (PCM) 110 controls the operation of engine 102, PE module 206, battery 108, and an automated manual transmission (AMT) 116. A clutch 112 is located between engine 102 and electric motor 104, and another clutch 114 is located between electric motor 104 and AMT 116, which is also controlled by PCM 110. Other parallel hybrid architecture configurations are also applicable. Engine 102 can be any suitable fuel-powered engine, such as an internal combustion engine (ICE), and engine types include gasoline engines, diesel engines, gas turbines, and the like. The motor 104 may be any suitable electric motor-generator capable of converting between electrical and mechanical energy, including an AC brushless motor, a DC brushless motor, a DC brushless motor, a direct-drive rotary motor, a linear motor, and the like. Furthermore, the motor 104 may be an auxiliary power unit, such as a range extender that charges the battery 108 when the battery is depleted. The range extender may be any one or more of the following: a diesel generator set, a gasoline generator set, a natural gas generator set, a fuel cell, and the like.

[0026] The layout of the hybrid powertrain 100 is a full hybrid vehicle (FHEV) architecture, enabling the vehicle to be driven in various hybrid modes. For example, in electric-only mode, the clutch 112 is open, disabling the engine 102 from providing power. Instead, the battery 108 provides energy to power the electric motor 104. Consequently, electrical energy flows from the battery 108 to the electric motor 104, and mechanical energy flows from the electric motor 104 to the driveshaft 118. In hybrid / electric-assist mode, the clutch 112 is closed, allowing both the engine 102 and the electric motor 104 to power the automatic transmission mechanism 116. Consequently, mechanical energy flows from the engine 102 to the electric motor 104, then to the driveshaft, and electrical energy flows from the battery 108 to the electric motor 104, where it is converted into mechanical energy and then flows to the driveshaft 118. In battery charging mode, clutch 112 is closed, and engine 102 provides all power to AMT 116 while also providing mechanical energy to motor 104, enabling it to convert this mechanical energy into electrical energy, which is then stored in battery 108. Thus, mechanical energy flows from engine 102 to motor 104, whereupon it is directed to driveshaft 118 and electrical energy is directed to battery 108. Finally, in regenerative braking mode, clutch 112 is open, and no power is provided to AMT 116 from either engine 102 or motor 104, ultimately bringing the vehicle to a stop. While the vehicle is in motion, mechanical energy from driveshaft 118 is converted into electrical energy by motor 104, which is then stored in battery 108. Thus, mechanical energy flows from driveshaft 118 to motor 104, and electrical energy flows from motor 104 to battery 108.

[0027] In some embodiments, the hybrid powertrain 100 may have Figure 1For example, a hybrid powertrain can have parallel, series, and hybrid series / parallel designs, all of which are collectively referred to as "power split architectures." In the parallel and hybrid series / parallel designs, the internal combustion engine charges the battery and is also mechanically connected to the vehicle's wheels to provide traction. In the series design, the internal combustion engine is used solely for the purpose of powering the battery or electric drive motor by driving a generator to generate electricity. In another example, a four-mode hybrid electric vehicle (HEV) includes an internal combustion engine and two electric motors to provide four operating modes: (1) electric vehicle (EV) mode, (2) range-extended (RE) mode, (3) hybrid mode, and (4) engine mode. The different modes have different characteristics, and the four-mode HEV has the advantage of adjusting these modes to suit different situations. In some embodiments, the powertrain is a battery electric vehicle (BEV) having only one or more electric motors and no engine, and is therefore used in all-electric vehicles. In some embodiments, the generator set associated with the powertrain is powered by diesel, gasoline, propane, natural gas, or solar energy. In some embodiments, the powertrain 100 uses a fuel cell that converts the chemical energy of a fuel, typically hydrogen, and an oxidant, typically oxygen, into electricity through a redox reaction.

[0028] Figure 2 A hybrid powertrain control system 200 is shown, which includes the powertrain 100 (more specifically, the PCM 110 of the powertrain 100) and a mapping application 202, a fleet management module 204, and an onboard optimizer module 206. Each of the mapping application 202 and the fleet management module 204 is located remotely from the powertrain 100 and is accessed wirelessly or wired via any suitable digital communication means (such as via the Internet, a local area network (LAN), a controller area network (CAN) bus, a cloud network, etc.). The onboard optimizer module 206 is located in the PCM 110, for example, coupled to the processing unit 500 of the PCM 110, as shown. Figure 5 As shown. Mapping application 202 is any software application that enables a user to obtain information about a route to be traveled. In one example, mapping application 202 collects information about any one or more of the following: traffic, weather forecasts, road conditions, terrain conditions, zero emission areas, toll road locations, geofenced areas, etc. Fleet management module 204 is any computing device or system of computing devices that is capable of managing data about proprietary information about each vehicle in a hybrid fleet and storing up-to-date information about each vehicle in the fleet. For example, module 204 can be implemented as a command center that has the authority to issue instructions to each vehicle in the fleet, wherein each vehicle includes a similar Figure 1The hybrid powertrain of the powertrain 100 shown. The information stored in the fleet management module 204 includes, but is not limited to, powertrain capabilities, such as possible driving range, ability to meet driving missions without derating, ability to meet zero-emission requirements, etc. In addition, in some examples, the information includes the current battery and / or fuel status of the vehicle and various system health states, such as battery degradation, fault conditions, etc.

[0029] In one example, the fleet management module 204 receives a command, such as from a user, that any of the available hybrid vehicles in the fleet needs to perform a trip. In response to the command, the module 204 receives current powertrain-specific information 208 from the onboard optimizer module 206 via wired or wireless digital communication as mentioned above. In this case, the specific information includes at least one of the following: the current state of charge (SOC) of the battery, the estimated full range of the electric vehicle, the time to fully charge and refuel, the health / degradation of component systems, the powertrain type / architecture (e.g., battery electric vehicle (BEV), extended-range electric vehicle (REEV), extended-range battery electric vehicle (BEVx), etc.), mileage, vehicle category, autonomy level and status, vehicle availability for the mission, and the physical location where the vehicle is parked.

[0030] After receiving proprietary information 208 about the vehicles in the fleet, the fleet management module 204 determines which of the vehicles is best suited for the mission. For example, this decision may depend on one or more of the following factors: performance, efficiency, emissions requirements, component life optimization, and a balance between these factors. Performance factors may include whether the vehicle can smoothly travel the route and provide the best driving experience for the user. This can be achieved by having higher horsepower or a larger engine size for faster response. Efficiency factors may include energy efficiency, fuel economy (e.g., miles per gallon), weight-to-weight efficiency, etc. Such factors may place a greater burden on the cost of completing the trip (e.g., fuel costs and the cost of hiring a driver for a specific trip). Emission requirements factors include the efficiency of the vehicle's aftertreatment system and how much power can be provided by the electric motor rather than the engine. Emission requirements may play a significant role in some regions because such regions have more stringent emissions requirements than others, or in some cases require vehicles to be emission-free (e.g., zero-emission regions). Component life optimization factors include the state of health (SOH) of the vehicle's battery, engine, electric motor, and transmission. Of particular importance is the SOH of the battery, since the amount of energy that can be stored in the battery decreases after prolonged use.

[0031] When the fleet management module 204 makes a decision about which vehicle in the fleet is best suited for the mission, it sends operator information 210 to that vehicle, or more specifically, to the vehicle's PCM 110. The PCM 110 then sends a route information request 212 to the mapping application 202, which responds by sending current route information 214 to the PCM 110. In one example, instead of the PCM 110 requesting route information, the fleet management module 204 can send the request 212 to the mapping application 202 at the same time as sending the operator information 210 to the PCM 110, after which the mapping application 202 forwards the resulting route information 214 to the PCM 110. In some examples, the operator information 210 includes not only the route information for the trip, but also the operator request, which is a specific request that the fleet management module 204 has for the powertrain 100 of each vehicle. For example, if there is insufficient time to fully charge the battery 108 before the next trip, the fleet management module 204 may send a request indicating that the battery 108 must be at a specific charge level by the end of the day so that there is still enough power for the powertrain 100 to operate the next day. In another example, the powertrain 100 may have a set of different preset modes in which the powertrain 100 can operate, where each preset mode achieves different results than another preset mode. For example, in one preset mode, greater emphasis may be placed on optimizing fuel economy, while in another preset mode, greater emphasis may be placed on optimizing the overall performance, emissions levels, and / or component life of the powertrain 100. Thus, if the fleet management module 204 intends to place greater emphasis on one of fuel economy, performance, emissions, and component life, the fleet management module 204 may send the operator information 210 including the preferred preset mode.

[0032] In some examples, the operator request includes a fleet performance preference based on whether the operator wants to adjust powertrain system operation to optimize vehicle performance, efficiency, emissions reduction, component life, or a balance between any of these factors. In some examples, the operator request enables one or more range extenders to charge the vehicle's battery to a specified state of charge, or targets the powertrain to a specified state of charge at the end of a mission.

[0033] The PCM 110 uses the information 214 to determine a power management strategy and controls the engine 102 and electric motor 104, as well as other components in the powertrain 100, according to the power management strategy. The means for doing this are discussed further herein. After the mission is completed, the PCM 110 performs measurements and calculates updated powertrain-specific information 208 based on the measurements at the end of the mission or trip. The onboard optimizer module 206 of the PCM 110 then sends the updated powertrain-specific information 208 to the fleet management module 204 to overwrite the old powertrain-specific information stored therein. This allows the fleet management module 204 to use the updated powertrain-specific information 208 for future decisions, replacing the old and outdated powertrain-specific information 208 that is no longer relevant. This way, the fleet management module 204 always has access to the latest specific information to help it make decisions about which vehicles to direct.

[0034] Figure 3 A hybrid powertrain fleet management system 300 is shown, which utilizes Figure 2The hybrid powertrain control system 200 is similar to that of FIG. 3 , but is applied to a fleet 302 in which each vehicle is numbered from (1) to (N). Each vehicle has a hybrid powertrain and therefore a PCM independent of the other PCMs, and each PCM can receive operator information 210 from the fleet management module 204, receive current route information 214 from the mapping application 202, and send updated powertrain-specific information 208 to the fleet management module 204 after each trip. In the system 300 , the fleet management module 204 has not just one but multiple tasks or trips to complete and therefore needs to decide which of the vehicles are to be directed for those trips. In one example, a group of vehicles is selected from the fleet 302 such that each vehicle in the group receives one set of operator information 210. In another example, one or more of the vehicles can receive two or more sets of operator information 210 because doing so is optimal compared to sending instructions to different vehicles. For example, when all but one of the vehicles have a low SOC in their batteries, and the one vehicle has a sufficiently high SOC in its battery that it alone can complete all trips before the batteries of the other vehicles are recharged, the fleet management module 204 may instruct the single vehicle with the high SOC to perform all trips. It should be noted that this operator information 210 will be determined by the fleet management module 204 using multiple factors, not just the SOC of the vehicle's battery. It should also be noted that when the fleet management module 204 makes this determination, the vehicles may be parked or traveling at different locations. Thus, the availability of the vehicles may also be considered, such as whether they are already running an errand or are not within a reasonable distance of the destination for the trip.

[0035] exist Figure 3 In the example shown, the fleet management module 204 sends operator information 210A to the first PCM 110, operator information 210B to the second PCM 304, and operator information 210C to the third PCM 110 based on the powertrain-specific information 208. NPCMs 306 are included. In some examples, fleet 302 includes multiple types of vehicles, resulting in different specifications for one vehicle than another. For example, one vehicle may have a larger engine, better fuel economy, a battery with a better state of health (SOH) and therefore more energy capacity, tires better suited for harsh terrain or inclement weather conditions, and / or a larger capacity to accommodate more passengers, compared to another vehicle in fleet 302. For example, the vehicles may utilize different powertrain architectures or run on different types of fuel (gasoline, diesel, ethanol, biodiesel, natural gas, etc.). These specifications are also included in proprietary information 208 for use by management module 204 in making decisions. After each of PCMs 110, PCM 304, and PCM 306 completes its assigned trip, it sends updated powertrain-specific information 208A, 208B, and 208C, respectively, to fleet management module 204 to keep the data in fleet management module 204 up to date.

[0036] In one example, the management module 204 performs prognostics on individual vehicles in the fleet 302 to detect any component degradation and then determines and schedules maintenance on those vehicles if such degradation is detected. Figure 1 Proactive diagnostic testing of components of the powertrain system 100 is shown. The management module 204 then recommends appropriate vehicles for different missions and routes based on the powertrain system status detected from analyzing the powertrain-specific information 208 .

[0037] Figure 4A method 400 for managing a hybrid powertrain as disclosed herein, according to an embodiment, is shown. In a first block 402, an optimizer module (e.g., optimizer module 206) receives an instruction to travel a route (e.g., operator information 210). Then, in a second block 404, the optimizer module receives current route information (e.g., current route information 214). In one example, the current route information includes at least one of traffic information, weather information, terrain information, and zoning information. In a third block 406, the optimizer module measures current vehicle state information of the hybrid vehicle. In one example, the current vehicle state information includes the state of charge (SOC) of the energy storage device, the state of health (SOH) of the energy storage device, and the amount of fuel in the fuel tank. In a fourth block 408, the optimizer module determines a power management strategy for the vehicle based on the current route information and the current vehicle state information, for example, by using online learning based on historical and predicted data. In some examples, the powertrain controls the engine and / or electric motor (e.g., engine 102 and electric motor 104) based on the power management policy after receiving it from the optimizer module. In one example, after the vehicle completes traveling the route, the optimizer module provides powertrain-specific information (e.g., updated powertrain-specific information 208) to a remotely located fleet management module (e.g., fleet management module 204). In one aspect of this example, the powertrain-specific information includes fuel and energy efficiency information for the vehicle.

[0038] Figure 5Components of the PCM 110 according to one embodiment are shown. The PCM 110 shown includes a processing unit 500, a memory unit 502, and a wireless receiver 504. The processing unit 500 may be any suitable processor, such as a system on a chip, a central processing unit (CPU), or the like. The memory unit 502 may be any suitable memory, such as static or dynamic random access memory (SRAM or DRAM), flash memory, or the like. The wireless receiver 504 may be any suitable digital wireless communication module that enables access to the internet and / or an intranet, or the like. As previously described, after the wireless receiver 504 receives the operator information 210 from the management module 204 and the current route information 214 from the mapping application 202, the receiver 504 transmits the received data to the processing unit 500. The processing unit 500 also accesses the memory 502 to obtain the updated powertrain-specific information 208 for the vehicle that was recently transmitted to the fleet management module 204. In one example, the onboard optimizer module 206 in the processing unit 500 determines a power management strategy based on the current route information 214 and the powertrain-specific information 208. In another example, the fleet management module 204 receives measurement data regarding the current vehicle state from a plurality of sensors coupled to various components within the powertrain 100. For example, a sensor coupled to the battery 108 may measure the current state of charge (SOC) of the battery, and a sensor coupled to the engine 102 may measure the current temperature of the engine 102 and an aftertreatment system (not shown) attached to the engine. This measurement data is referred to as vehicle component information 506 and is collected by the optimizer module 206 to determine a power management strategy.

[0039] The power management strategy includes control information for a target SOC profile and, if power distribution between the engine 102 and the electric motor 104 is desired, starting / ending SOC levels and a trajectory toward the target SOC. Furthermore, if the engine 102 can be replaced with a range extender (such as a microturbine or a fuel cell), the control information also includes how and when the range extender should operate. In another example, the powertrain management strategy includes at least one of the following: a battery SOC target profile, range extender operation (torque / speed operation, on / off target profile, etc.), a target ending SOC, and a target recharge amount for the battery (taking into account the starting state of charge). The processing unit 500 of the PCM 110, via a controller 508 coupled to the processing unit 500, then controls one or more of the vehicle's engine 102, PE 106, battery 108, and AMT 116 according to the powertrain management strategy.

[0040] The power management strategy is determined by processing unit 500 using methods such as online learning, which considers historical and predicted data on accessory load, the driver's driving style, predicted traffic, and environmental conditions. Historical data can be stored in memory unit 502, and predicted data can be derived based on current route information 214. For example, terrain and weather information can be used to determine predicted information about the potential load to be applied to the vehicle. If the battery SOC is low, traffic information can be used to determine whether it is possible to use the engine to power the battery. This combination of information can be used to predict the approximate time or distance to travel before the battery runs out of energy and requires recharging at a charging station or range extender. In one example, processing unit 500 uses a digital twin to form a digital representation of the physical components of the powertrain and calculates the total range that can be traveled using the electric vehicle components, as well as fuel economy and the predicted time to charge or refuel the vehicle. Furthermore, measurement data provided by sensors can be used to update the digital twin of the vehicle in real time. The power management strategy also determines when to switch between different modes or settings. In one example, the power management strategy determines when to apply the electric-only mode, hybrid / electric-assisted mode, battery charging mode, and regenerative braking mode described above to the powertrain system 100 to achieve desired operating characteristics. The power management strategy can determine when to request the range extender to charge the battery 108 to a specified SOC and when to request the powertrain 100 to target the specified SOC of the battery 108 at the end of a mission.

[0041] Furthermore, the power management strategy can be adjusted to achieve different results to suit certain requirements. For example, the power management strategy can be adjusted to optimize performance so that the engine 102 and electric motor 104 operate in a manner that optimizes the driving experience for the vehicle driver. This strategy can involve locating refueling and charging stations so that the driver always has sufficient fuel or SOC to continue driving until the next refueling or charging station along the way. Alternatively, the power management strategy can be adjusted to optimize efficiency and minimize the cost of completing a task or operation, especially for long-distance trips where fuel efficiency plays a major role in keeping costs low. In another example, the power management strategy can be adjusted to be ultra-green so that emissions from the engine 102 are minimized, if not completely eliminated, in some uses, which can become an important factor when the vehicle travels to zero-emission regions. Additionally, the power management strategy can focus on optimizing the life of vehicle components, such as the battery 108. In the case of a battery, extended use reduces the battery's maximum capacity, so that even when fully charged, the battery provides less energy than when it was newly installed. However, additional factors such as operating at low temperatures further reduce the battery's maximum capacity, which is known as derating. Again, in this case, the power management strategy can be adjusted to minimize the battery's total throughput and keep the battery at a relatively warm temperature to avoid this derating. Finally, the power management strategy can be adjusted to achieve a more balanced outcome with respect to the aforementioned optimization.

[0042] The following lists some examples of how the processing unit 500 of the PCM 110 optimizes power management strategies. In one example, the processing unit 500 uses weather information to adjust the battery's target SOC to account for weather's impact on battery capacity and accessory load. In another example, the processing unit 500 uses weather information to adjust range extender operation, such as by operating the range extender for longer periods during cold weather to keep the vehicle's aftertreatment system warm and reduce emissions. In another example, the processing unit 500 optimizes cost efficiency based on the cost of fuel energy versus electricity from the grid. Thus, the processing unit 500 determines the SOC level to which the battery should be charged before commencing a mission. In another example, the processing unit 500 optimizes the route by taking into account the number and location of refueling or charging stations along the vehicle's intended route. In another example, the processing unit 500 optimizes cost efficiency by applying vehicle velocity management based on real-time traffic and fleet preferences, particularly in heavy traffic along the route, to improve vehicle safety and fuel economy. In one example, current route information 214 includes the location of fast charging stations along the route, including charging duration, C-rate, and type of charging station. In one example, processing unit 500 adjusts the power management strategy based on the amount of load delivered at each stop along the route to improve the accuracy of energy estimation and thereby optimize powertrain energy management. In one example, processing unit 500 adjusts base regenerative braking and brake blending levels based on weather and environmental conditions to achieve desired braking performance, for example, reducing regenerative braking when ambient temperatures are high. In one example, the target state of charge (SOC) of the battery, determined at the start of a mission, is adjusted to allow for headroom for regenerative braking. In one example, the amount of regenerative braking is adjusted as a training mode for new drivers of electric vehicles. In one example, if the range extender being used is a diesel range extender, processing unit 500 uses the vehicle's stop duration and location to determine whether and when diesel particulate filter (DPF) regeneration is necessary to de-soot the DPF. In one example, processing unit 500 determines when to automatically switch to the hybrid powertrain's electric-only mode based on geofence or location information included in route information 214. In one example, processing unit 500 incorporates "smart charging" by analyzing weather and battery information. For example, charging power is adjusted in cold weather to keep the battery warm at the start of a mission, and charging power is adjusted based on battery temperature to keep the battery temperature within an acceptable range.

[0043] Upon completing a trip or mission, as previously mentioned, the PCM 110 provides updated powertrain-specific information 208 to the fleet management module 204 to assist the fleet management module 204 in making future intelligent fleet logistics decisions. The powertrain-specific information may include: the mission or trip completed, the charging time and refueling cost required to meet the trip requirements, fuel economy during the trip, trip time, the ability to meet zero-emission zones during the trip, the potential for de-rating during the trip, and the expected SOC at the end of the trip. Trip information may also include engine stop and start timings and the duration of the trip.

[0044] Figure 6 A hybrid powertrain control system 600 is shown, according to an embodiment, and includes a PCM 604, as well as a mapping application 202, a fleet management module 204, and an off-vehicle optimizer module 602. Each of the mapping application 202, the fleet management module 204, and the off-vehicle optimizer module 602 is remotely located from the powertrain 604 and is accessed wirelessly or wired via any suitable digital communication means, such as via the Internet, a local area network (LAN), a controller area network (CAN) bus, a cloud network, etc. In some examples, the off-vehicle optimizer module 602 is a cloud computing source, such as a remote server or computing device accessible via a cloud network, so that the PCM 604 does not need to rely on the processing power of its on-board processing unit to perform the calculations and / or simulations necessary to determine the powertrain management strategy. Alternatively, the off-vehicle optimizer module 602 may serve as a cloud service provider for computing resources based on data provided by the fleet management module 204, the PCM 604, and the mapping application 202.

[0045] Thus, in this embodiment, each of the management module 204, the PCM 604, and the mapping application 202 communicates with the off-board optimizer module 602 to provide and receive different types of data. For example, the fleet management module 204 sends operator information 210 to the optimizer module 602 and receives powertrain-specific information 208 in return. The mapping application 202 receives a route information request 212 from the optimizer module 602 and sends current route information 214 in return. The PCM 604 uses sensors associated with various components in the vehicle to collect vehicle component information 506 and sends it to the optimizer module 602, and receives a power management strategy 606 in return.

[0046] There are numerous advantages in implementing the aforementioned optimization methods and systems. In one example, optimization can be customized based on various factors to suit the varying needs of users, such as performance, efficiency, environmental impact, component lifespan, and the overall balance between these factors. Furthermore, the ability of a management module or control center to access up-to-date information allows the control center to make intelligent decisions regarding which vehicles are best suited for certain operations and tasks, thereby providing a more reliable and efficient network for fleets that would otherwise be operated and managed independently. Data sharing between different groups or entities (e.g., between the control center, database, and powertrain control module) ensures consistency between the analysis performed by the control center regarding the predicted performance of individual vehicles and the actual resulting performance of such vehicles.

[0047] The following describes some examples of how the power management strategy 606 can be used to influence the PCM 110 or 604. In one example, an optimizer module (which can be the onboard optimizer module 206 or the offboard optimizer module 602) uses a digital twin of the hybrid powertrain 100 to accurately estimate the per-mile energy demand, total energy demand, and / or power trajectory demand of the hybrid powertrain 100 using historical and / or predicted data. The historical or predicted data includes factors such as accessory load, the vehicle operator / user's driving style, predicted traffic, weather conditions, vehicle component efficiency, load delivery, and the likelihood of regenerative braking. Additionally, key performance metrics such as fuel consumption, the impact of various tasks on vehicle component life, route time, time to fully recharge and refuel the vehicle, and / or the distance the vehicle can travel using electric power alone (also known as electric range) can be estimated. This data estimated by the optimizer module 206 or 602 is input into the PCM 110 or 604 as part of the power management strategy 606. In some examples, this data can also be incorporated into the powertrain-specific information 208 that is input into the fleet management module 204. In some examples, the PCM 110 or 604 can use this data to allow emergency battery use (i.e., use of the battery 108 at or below the manufacturer's stated minimum SOC limit) when providing immediate power to the vehicle is more critical than maintaining the component life of the battery 108.

[0048] In some examples, the aforementioned estimation results are combined with operator information 210, which may include any one or more of the fleet performance preference data described previously, to enable the optimizer module 206 or 602 to optimize the operation of the range extender (e.g., the engine or range extender 102) and any electrical accessories (e.g., the traction motor used to maneuver the vehicle). In one example, the operation of the range extender 102 is adjusted during cold weather to operate longer to keep the aftertreatment system coupled to the range extender 102 sufficiently warm, thereby reducing cold-start emissions. In another example, the power distribution strategy between the range extender 102 and the battery 108 is optimized based on information about the location of refueling or charging stations along the way. This information may also include the duration or length of time the fuel or battery can last before being completely depleted, as well as the battery's C-rate. In another example, the aforementioned estimation results are combined with operator input regarding the desired target state of charge (SOC) of the battery 108 that the operator prefers to have at the end of a mission, to adjust the degree to which the range extender 102 is operated to achieve that SOC. For example, if the operator prefers a higher SOC at the end of discharge, the range extender 102 may be used more frequently to not only drive the vehicle but also charge the battery 108 in the process.

[0049] Adjusting the final target SOC of the battery 108 allows for more margin for implementing regenerative braking if the route's grade is known, or if the vehicle will be traveling uphill or downhill in the route. If it is known that the vehicle will be traveling downhill at the start of the next mission (or relatively soon after starting the mission), the optimizer module 206 or 602 can utilize regenerative braking to charge the battery 108 during the start of the mission without risking depleting the battery 108 below an operational SOC. This allows the SOC to remain relatively low at the end of the current mission while still enabling the battery 108 to provide power during the next mission. Furthermore, if it is determined that the next mission will require less than a full charge of the battery 108, the final target SOC for the current mission can be lowered accordingly to aid battery life.

[0050] As previously mentioned, energy provided to battery 108 can also come from the grid, but relying on the grid to provide all the required energy can be expensive, depending on electricity prices (e.g., the cost per kilowatt-hour of electricity) and fuel prices (e.g., the cost per gallon of fuel). Therefore, to reduce costs, range extender 102 can be utilized to reduce the amount of energy provided by the grid. For example, before a mission begins, optimizer module 206 or 602 can decide to only charge battery 108 from the grid until the battery 108's SOC reaches a predetermined value, after which range extender 102 can be used to provide energy during the mission. In some examples, battery 108 can be charged to a specific SOC level based on operator request.

[0051] In some examples, vehicle acceleration rate management can be based on real-time traffic information provided by the mapping application 202 and fleet preference information provided by the fleet management module 204. Managing vehicle acceleration rates can improve vehicle safety and fuel economy, such as by limiting the amount of acceleration rates allowed during moderate to heavy traffic conditions, during which the optimizer module 206 or 602 determines that the vehicle does not need to accelerate beyond a certain limit. In addition, the base regenerative braking and brake blending levels can be adjusted to achieve the desired braking performance determined by the optimizer module 206 or 602. Specifically, in one example, regenerative braking can be adjusted based on weather, environmental, or road conditions. In another example, regenerative braking can be adjusted to train drivers who are novice to electric vehicles because some electric vehicles with regenerative braking systems use a single pedal to accelerate and stop the vehicle.

[0052] In some examples, the optimizer module 206 or 602 can use the vehicle's stop duration and location to determine when to perform diesel particulate filter regeneration (DPF regeneration) of the diesel fuel range extender 102. DPF regeneration occurs when particulates trapped within the diesel range extender 102 are heated enough to combust and transform into ash. The resulting accumulated soot is then removed as gaseous carbon dioxide via passive regeneration (soot burns as quickly as it is produced), active regeneration (soot accumulates while idling or moving slowly during heavy traffic), or forced regeneration (when soot levels rise to a point where they must be forced out of the system). Understanding the vehicle's stop duration and location allows the optimizer module 206 or 602 to determine when DPF regeneration can occur and whether to perform passive or active regeneration. Furthermore, geofencing or location information can be used by the optimizer module 206 or 602 to determine when to switch between hybrid and electric-only modes to enable the vehicle to meet emissions requirements set by local jurisdictions.

[0053] In some examples, information about weather and battery SOC can be used to efficiently charge battery 108. For example, during cold weather, the vehicle's charging power can be adjusted so that battery 108 remains charged until the mission begins, in order to keep battery 108 at or above a certain temperature. Furthermore, the charging power can be adjusted based on the battery temperature so that the battery temperature remains within an acceptable temperature range.

[0054] The present subject matter may be specifically implemented in other specific forms without departing from the scope of the present disclosure. The embodiments are only as shown and are considered in all aspects without limitation. Those skilled in the art will recognize that other implementations consistent with the disclosed embodiments are possible. The above detailed description and examples described herein have been presented for the purposes of illustration and description only and not for limitation. For example, the described operations may be performed in any suitable manner. These methods may be performed in any suitable order while still providing the described operations and results. Therefore, it is contemplated that the present embodiment covers any and all modifications, variations or equivalents that fall within the scope of the basic principles disclosed above and claimed herein. Moreover, although the above description describes hardware in the form of a processor executing code, hardware in the form of a state machine, or dedicated logic that can produce the same effect, other structures may also be considered.

Claims

1. A drive system for a hybrid vehicle, the drive system comprising: a powertrain comprising an engine, an electric motor, and an energy storage device electrically coupled to the electric motor; Multiple sensors; as well as an optimizer module operatively coupled to the powertrain, the optimizer module configured to: receiving operator information traveling a route from a remote management module; receiving current route information for the route from a mapping application in response to the operator information; measuring current vehicle state information of the hybrid vehicle using the plurality of sensors; updating the digital twin generated by the optimizer module in real time based on the current vehicle state information to form a digital representation of the physical components of the powertrain; as well as The digital twin is used to determine a power management strategy for the vehicle based on the current route information and the current vehicle state information.

2. The drive system according to claim 1, wherein: The optimizer module is configured to control at least one of the engine, the electric motor, or the energy storage device of the vehicle based on the power management strategy.

3. The drive system according to claim 1, wherein: The current vehicle status information includes at least one of the following: vehicle type and architecture, vehicle availability, vehicle mass, vehicle mileage, the state of charge (SOC) of the energy storage device and the amount of time to fully recharge the energy storage device, the state of health (SOH) of the energy storage device, the amount of fuel in a fuel tank fluidly coupled to the engine and the amount of time to refill the fuel tank, or the total range of the vehicle based on the SOC or the amount of fuel.

4. The drive system of claim 1 , wherein the optimizer module is further configured to provide the vehicle's powertrain-specific information to the remote management module after the vehicle completes traveling the route, wherein The powertrain-specific information includes at least one of: fuel and energy efficiency information, component life information, fault conditions, or a de-rating potential for the vehicle.

5. The drive system according to claim 1, wherein: The current route information includes at least one of the following: speed limit information, road slope information, gas station location information, charging station location information, traffic information, weather information, terrain information, and zoning information.

6. The drive system according to claim 1, wherein: The optimizer module determines the power management strategy by using online learning based on historical and predicted data.

7. A method of operating a hybrid vehicle having a powertrain including an engine and an electric motor, a plurality of sensors, and an optimizer module operatively coupled to the powertrain, the method comprising the steps of: receiving, by the optimizer module, operator information traveling a route from a remote management module; receiving, by the optimizer module, current route information for the route from a mapping application; measuring current vehicle state information of the hybrid vehicle using the plurality of sensors; updating the digital twin generated by the optimizer module in real time based on the current vehicle state information to form a digital representation of the physical components of the powertrain; as well as The optimizer module uses the digital twin to determine a power management strategy for the vehicle based on the current route information and the current vehicle state information.

8. The method according to claim 7, further comprising the steps of: At least one of the engine, the electric motor, or an energy storage device coupled to the electric motor of the vehicle is controlled by the optimizer module based on the power management strategy.

9. The method according to claim 7, wherein: The current vehicle status information includes at least one of the following: vehicle type and architecture, vehicle availability, vehicle mass, vehicle mileage, the state of charge (SOC) of an energy storage device coupled to the electric motor and the amount of time to fully recharge the energy storage device, the state of health (SOH) of the energy storage device, the amount of fuel in a fuel tank coupled to the engine and the amount of time to refill the fuel tank, or the total range of the vehicle based on the SOC or the amount of fuel.

10. The method according to claim 7, further comprising the steps of: The optimizer module provides powertrain-specific information of the vehicle to the remote management module after the vehicle completes traveling the route, wherein the powertrain-specific information includes at least one of the following: fuel and energy efficiency information, component life information, fault conditions, or a potential for derating of the vehicle.

11. The method according to claim 7, wherein: The current route information includes at least one of the following: speed limit information, road slope information, gas station location information, charging station location information, traffic information, weather information, terrain information, or zoning information.

12. A fleet management system, comprising: a plurality of hybrid vehicles, each hybrid vehicle including a powertrain having an engine, a fuel tank fluidly coupled to the engine, an electric motor, a plurality of sensors, an energy storage device electrically coupled to the electric motor, and an optimizer module operatively coupled to the powertrain; A remote management module, wherein the remote management module is operable to: receiving powertrain-specific information for each of the plurality of hybrid vehicles from the optimizer module, wherein the powertrain-specific information includes at least one of: fuel and energy efficiency information, component life information, fault conditions, or a de-rating potential for the vehicle, determining which hybrid vehicle of the plurality of hybrid vehicles to instruct to travel a route based on the powertrain-specific information, and transmitting operator information for traveling the route to the determined hybrid vehicle; The optimizer module for the determined vehicle is configured to: receiving current route information for the route from a mapping application in response to the operator information, measuring current vehicle state information of the hybrid vehicle using the plurality of sensors, updating the digital twin generated by the optimizer module in real time based on the current vehicle state information to form a digital representation of the physical components of the powertrain, using the digital twin to determine a power management strategy for the vehicle based on the current route information and the current vehicle state information, and At least one of the engine or the electric motor of the vehicle is controlled based on the power management strategy.

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