Multi-engine switching system and performance optimization method

Abstract

To provide a suitable multi-engine switching system and a method for optimizing performance. [Solution] A multi-engine switching system and method for achieving overall optimization of performance criteria, taking into account variables and conditions throughout the entire driving cycle. The system's control device is adapted to perform overall optimization in that it determines the optimal distribution of engine load over the entire drive or driving cycle, as opposed to sequentially determining an optimized solution for a given time point and localized set of current conditions. In one embodiment in which the control system provides overall optimization, the control device receives driving information via a driving planning tool. The control device uses the driving information to generate a driving cycle and further incorporates this information into an optimization process performed by the control device to determine the optimal solution for the entire drive.

Description

[Technical Field]

[0001] Cross-references to related applications This application claims priority to U.S. Provisional Patent Application No. 62 / 881,759, filed on 1 August 2019, and U.S. Provisional Patent Application No. 62 / 988,334, filed on 11 March 2020, the disclosures of which are incorporated herein by reference as if they were fully described herein.

[0002] The systems and methods of the present invention generally relate to the field of multi-engine and multi-generator systems. More specifically, the systems and methods of the present invention described herein relate to multi-engine and multi-generator systems comprising control devices and related components that provide optimized performance and efficiency. [Background technology]

[0003] Systems and methods for controlling systems with multiple engines are known in the art. However, existing systems have significant drawbacks that prevent them from operating in a truly optimized and efficient manner.

[0004] For example, many existing systems typically rely on sequential local optimization for a given time point and a given set of current or instantaneous conditions to determine the load each engine should receive. Such local optimization for a given time point has a major problem in that it does not consider conditions and variables relevant to the entire journey. As a result, these systems are unable to prepare for an overall and efficient allocation of load on the engines, and optimization at one operating point may later lead to overheating. Furthermore, within the scope of existing multi-engine systems that do consider temperature, the consideration is generally limited to ensuring that engine temperatures remain within a predetermined range, thus resulting in a local binary decision. Moreover, existing systems do not integrate the optimization of energy regeneration (i.e., braking) in the case of electric transport equipment into their optimization solutions.

[0005] In addition, many of these methods and systems require transmissions to transmit torque loads between engines. These transmissions introduce mechanical losses into the system, hindering the efficient operation of multi-engine systems. It is highly desirable to provide multi-engine or multi-generator systems and methods that avoid these major drawbacks while achieving optimized performance. [Overview of the project] [Means for solving the problem]

[0006] Provided herein are multi-component optimization control systems and methods for overall optimizing the torque load distribution across an engine and a generator. By considering the entire task (for example, in the case of electric transport equipment, information related to the entire journey, including, in some embodiments, energy generation through braking), and by considering constraints such as maintaining the engine and generator temperatures below critical levels or within levels that ensure efficient operation of the engine and the entire system, the control systems of the present invention can provide more efficient solutions than systems that rely on “local” optimization based on a temporal snapshot of the current conditions. In this way, the systems and methods of the present invention overcome the shortcomings of existing systems, namely, the use of a predetermined static efficiency curve or efficiency range, which considers only a series of independent local decisions and only considers constraints such as engine and generator temperatures in a binary and non-optimized manner.

[0007] In one embodiment, the optimized control system is a highly efficient engine drive system that optimizes the allocation of drive loads across multiple engines. By doing so, the system achieves an efficiency level higher than the efficiency level of any individual engine in the system. The engine system disclosed herein is a multi-engine system controlled by a control device configured for efficiency optimization, the control device communicating with a plurality of sensors that provide inputs, and based on these inputs, the control device may generate the necessary drive signals to the multiple engines to achieve efficient operation, in particular, efficient operation in terms of energy consumption or regeneration (by braking) throughout the entire journey.

[0008] The optimization system disclosed in this specification can be adapted not only to existing engines but also to newly designed engines. The existing or newly designed engines can be in the form of multiple disks on the same axis (same speed) or in the form of completely independent engines, each maintaining its own operating characteristics with respect to both torque and speed and being independent of other engines. In the latter form, a gear device, such as a differential, can be used to transmit torque from different engines to the wheels.

[0009] The engine optimization system is designed to control the contribution of multiple engines to driving the loads within the system in such a way as to obtain an optimized performance of the system. In one embodiment of the system, the system reduces battery usage and the resulting energy losses by dynamically allocating driving tasks to multiple engines based on the efficiency levels at each required point of torque and power. The control device determines the torque percentage of each engine within the system while obtaining the goal of minimizing battery usage according to the real-time operating conditions and the engine power curve, and generates a driving signal for the control of the engine accordingly. In other embodiments, the system can control multiple engines within the system for the purpose of meeting a combination of performance goals other than efficiency improvement, such as maximizing acceleration or driving range, or maximizing the driving range assuming a minimum allowable acceleration.

[0010] The system's control unit is adapted to perform global optimization in that it determines the optimal distribution of engine load over the entire journey (or driving cycle), which is optimal in terms of maximizing energy efficiency or other driving objectives. This is in contrast to sequentially determining an optimized solution for a given point in time and for a given set of current conditions (i.e., local optimization), without considering the entire journey or driving cycle. In one embodiment where the control system provides global optimization, the control unit receives journey information (such as route, total distance traveled, speed limit information, and journey time information) through a journey planning tool (such as a GPS-equipped device). This journey information may further include environmental and meteorological information (e.g., temperature, humidity, wind speed and direction, etc.) that may be provided by a weather application communicating with the journey planning tool, as well as other journey factors and information provided by the journey planning tool, such as driving conditions (e.g., highway driving, urban or "stop and go", hilly terrain, flat terrain, etc.). The control unit generates driving cycles using driving information and incorporates this information into optimization processes performed by the control unit to determine the optimal solution for the entire drive. In addition, the control unit may use information provided by sensors on the vehicle, such as ambient temperature, engine heat including internal engine temperature, shaft rotation speed, engine, engine current, and torque requirements, to verify the validity of the overall optimization. For any given moment, the solution obtained from overall optimization is not necessarily optimal compared to the solution obtained from local optimization. However, when considering the entire drive, it is always true that overall optimization is equal to or better than the sum of multiple sequential local optimizations.

[0011] In another embodiment, the optimization control system, either alone or in combination with multiple engines, is applied to a generator, resulting in an efficient generator system that optimizes the allocation of available torque across multiple generators. By doing so, this system achieves an efficiency level higher than the efficiency levels of the individual generators within the system. In the generator system disclosed herein, the efficiency optimization executed by the control device is configured based on inputs from a plurality of sensors providing inputs, from which the control device may generate the necessary signals to the plurality of generators to achieve efficient operation across the entire operating range.

[0012] The generator optimization system is designed to control the contributions of the multiple generators within the system in such a way that the optimized performance of the system is obtained. In one embodiment of the system, the system dynamically assigns tasks to the multiple generators based on the efficiency levels at each point of the available torque in a given cycle, thereby maximizing current generation and, as a result, energy conversion losses.The control device determines the available torque percentage of each generator within the system while maximizing current generation according to real-time conditions. In other embodiments, the system can control the multiple generators within the system for purposes other than efficiency improvement.

[0013] These and other aspects and advantages will become apparent to those skilled in the art by reading the following detailed description while referring appropriately to the accompanying drawings. Furthermore, it is desirable to understand that the foregoing summary is merely illustrative and is not intended to limit in any way the scope or breadth of the legally entitled equivalents of the appended claims. This specification also provides, for example, the following items. (Item 1) A multi-engine system capable of performing global optimization to determine the optimal contribution of torque from an engine in a system based on a given torque requirement and achieving a desired performance goal over an operating cycle while remaining within the range of one or more constraint conditions: Multiple engines mechanically coupled to a shaft; The system comprises a database for storing efficiency data related to each of the plurality of engines, a control device for receiving operating cycle information, and a control device for generating torque commands to the plurality of engines; The control device generates torque commands for the multiple engines by performing an optimization analysis over all time periods of the operating cycle for all possible combinations of torque contributions from the engines, and the optimization analysis is performed as follows: Based on the aforementioned operating cycle information, the shaft speed and torque spanning all time periods are calculated; Based on the efficiency data, calculate a cost function spanning all time periods related to the performance target for each possible combination of torque contributions from the multiple engines; From the set of possible combinations of torque contributions, identify one or more optimal combinations of torque contributions that are optimized for a desired performance target based on the relevant cost function and remain within the range of one or more constraints; For each time period in the aforementioned operating cycle, a torque command signal is generated for each of the plurality of engines, and based on one or more optimal combinations of the torque contributions, the plurality of engines are driven to drive the shaft, thereby achieving the desired performance objective throughout the entire operating cycle while remaining within the range of one or more constraint conditions. A system that includes this. (Item 2) The system described in item 1, wherein the performance objective is to maximize efficiency, and the cost function for each possible combination of torque contributions by the engine is the amount of energy used. (Item 3) The system according to item 2, wherein the control device estimates the energy consumed for each time period of the operating cycle, and the control device outputs the estimated total power consumption for the operating cycle. (Item 4) The system according to item 1, wherein the one or more constraint conditions include the temperature of each engine. (Item 5) The system according to item 1, wherein the optimization analysis performed by the control device further includes calculating the efficiency of each of the plurality of engines in the next time period in the operating cycle for each time period in the operating cycle, based on the efficiency data and one or more transient phenomena. (Item 6) The system according to item 5, wherein the one or more transient phenomena include a thermal transient, and the optimization analysis performed by the control device further includes calculating the thermal transient to determine the estimated temperature of each of the plurality of engines at the end of the next time period. (Item 7) The system according to item 1, which is housed in an electric transport device, wherein the plurality of ignitions are electric motors. (Item 8) The system according to item 1, further comprising a driving planning tool, wherein the driving planning tool generates the driving cycle information. (Item 9) The system according to item 2, wherein the optimization analysis performed by the control device further includes integrating the optimization of energy recovery from braking. (Item 10) A method for overall optimization that determines the optimal contribution of torque from an engine in a system based on a given torque requirement, thereby achieving a desired performance target over an operating cycle while remaining within the range of one or more constraints: To store efficiency data related to each of the aforementioned multiple engines; Receiving driving cycle information; The process includes generating torque commands for the multiple engines by performing an optimization analysis over all time periods of the operating cycle for all possible combinations of torque contributions from the engines, wherein the optimization analysis is: Based on the aforementioned operating cycle information, the shaft speed and torque are calculated across all time periods; Based on the efficiency data, calculate a cost function spanning all time periods related to the performance target for each possible combination of torque contributions from the multiple engines; To identify one or more optimal combinations of torque contributions from the possible sets of torque contributions that are optimized for a desired performance target based on the relevant cost function and remain within the range of one or more constraints; For each time period in the aforementioned operating cycle, a torque command signal is generated for each of the plurality of engines, and based on one or more optimal combinations of the torque contributions, the plurality of engines are driven to drive the shaft, thereby achieving the desired performance objective throughout the entire operating cycle while remaining within the range of one or more constraint conditions. A method that includes this. (Item 11) The method according to item 10, wherein the performance objective is to maximize efficiency, and the cost function for each possible combination of torque contributions by the engine is the amount of energy used. (Item 12) The method according to item 11, further comprising estimating the energy consumed for each time period of the operating cycle and outputting the estimated total power consumption for the operating cycle. (Item 13) The method according to item 10, wherein the one or more constraint conditions include the temperature of each engine. (Item 14) The method according to item 10, further comprising calculating the efficiency of each of the plurality of engines in the next time period in the operating cycle, based on the efficiency data and one or more transient phenomena, for each time period in the operating cycle. (Item 15) The method according to item 14, wherein the one or more transient phenomena include a thermal transient, and the optimization analysis further comprises calculating the thermal transient to determine the estimated temperature of each of the plurality of engines at the end of the next time period. (Item 16) The method according to item 10, which is performed by a control device housed in an electric transport device, wherein the plurality of ignitions are electric motors. (Item 17) The method according to item 10, wherein the system further generates the driving cycle information using a driving planning tool. (Item 18) The method according to item 11, wherein the optimization analysis performed by the control device further includes integrating the optimization of energy recovery from braking.

[0014] The present invention is described below in relation to the following illustrative figures, where: [Brief explanation of the drawing]

[0015] [Figure 1] Figure 1 is a top-level block diagram of a multi-engine system used in a vehicle according to one embodiment of the present invention; [Figure 2] Figure 2 is a block diagram illustrating the operation of the control device and the process used by the control device to optimize the torque commands for each engine, according to an embodiment of the present invention; [Figure 3] Figure 3 is a block diagram showing steps performed by a control device in relation to processing related to the determination of an engine torque command signal, according to an embodiment of the present invention; [Figure 4] Figure 4 is a block diagram illustrating a raster scan technique used to determine the load sharing between engines and optimize efficiency for a given shaft speed and torque, according to an embodiment of the present invention; [Figure 5] Figure 5 shows an exemplary 4x4 raster in two-dimensional space that can optimize three variables (when optimizing load sharing across three engines) according to an embodiment of the present invention; [Figure 6a] Figures 6a-6c show the power curves and efficiency contours of three large engines in Case 1, which is a typical example of the operation of a multi-engine system according to an embodiment of the present invention; [Figure 6b] Same as above; [Figure 6c] Same as above; [Figure 7a] Figures 7a-7c show the power curves and efficiency contours of three large engines in Case 2, which is a typical example of the operation of a multi-engine system according to an embodiment of the present invention; [Figure 7b] Same as above; [Figure 7c] Same as above; [Figure 8a] Figures 8a-8c show the power curves and efficiency contours of three small engines in Case 1, which is a typical example of the operation of a multi-engine system according to an embodiment of the present invention; [Figure 8b] Same as above; [Figure 8c] Same as above; [Figure 9a] Figures 9a-9c show the power curves and efficiency contours of three small engines in Case 2, which is a typical example of the operation of a multi-engine system according to an embodiment of the present invention; [Figure 9b] Same as above; [Figure 9c] Same as above; [Figure 10a] Figure 10a is a graph showing the speed versus time of the UDDS operating cycle used in the test of the embodiment of the present invention; [Figure 10b] Figure 10b is a graph showing the speed versus time of the HWFET operating cycle used in the test of the embodiment of the present invention; [Figure 10c] Figure 10c is a graph showing the speed versus time of the US06 operating cycle used in the test of the embodiment of the present invention; and [Figure 11] Figure 11 is a block diagram illustrating the operation of a control device and the processes used by the control device to perform overall optimization of the torque commands for each engine, according to an embodiment of the present invention. [Modes for carrying out the invention]

[0016] While the present invention can be embodied in various forms, for the purpose of simplification and illustration, the principles of the invention are described by reference to several embodiments. However, it will be understood that this disclosure is to be considered representative examples of the claimed subject matter and is not intended to limit the appended claims to the specific embodiments described herein. It will be obvious to those skilled in the art that the invention may be practiced without being limited to these specific details. In other examples, well-known methods and structures have not been described in detail so as not to unnecessarily obscure the invention.

[0017] The multi-engine switching systems disclosed herein can be adapted to optimize the performance of any system in which the load is driven by an electric motor. For illustrative purposes, some embodiments considered herein describe multi-engine switching systems incorporated into a vehicle in which the load is the vehicle's drive axle and wheels. It will be readily apparent that the multi-engine systems described herein are adaptable to a variety of other systems in which multiple engines can be used to drive the load.

[0018] In addition, the multi-motor switching systems disclosed herein can be used with any type of electric motor. For illustrative purposes, some embodiments considered herein describe the use of DC electric motors. It will be readily apparent that AC electric motors or other types of electric motors may also be used with the multi-motor systems described herein.

[0019] In addition, by using the multi-engine switching systems disclosed herein to control the contribution of the engines to the drive load, desired levels of system performance can be achieved for various criteria of performance objectives. For illustrative purposes, some embodiments considered herein generally aim to achieve maximum efficiency and minimum power consumption by controlling the contribution of the engines. It will be readily apparent that the systems disclosed herein can optimize other criteria or performance objectives, such as maximum drivability or acceleration, or a combination of criteria or performance objectives, such as maximum drivability assuming a minimum required level of acceleration. It will also be apparent that the systems disclosed herein can achieve a given threshold, such as a target efficiency (or target average energy consumption) which may be equal to or less than the maximum possible efficiency of the system, or ensure that performance objectives are met within the bounds of one or more constraints, such as engine temperature, total time, and / or maximum acceleration.

[0020] In addition, in many of the embodiments disclosed herein, the number and types of engines are assumed to be determined a priori. It will be readily apparent that the methods and processes described herein for selecting the optimal contribution of engines to achieve specific criteria can be used to determine which engines will be included in a multi-engine switching system, and that this may involve based on the driving patterns observed by the motor vehicle driver (such as driving behavior, general road conditions, and general driving characteristics), and factors relating to the performance and efficiency of the engines, while controlling for other factors such as the cost, size, and other characteristics of the engines. Thus, the methods and systems described herein can select a partial set of engines from a larger set of engines, or create a group of engines, to best satisfy the driving objectives of a given motor vehicle driver.

[0021] Description of the overall system implementation

[0022] The control unit determines the torque percentage of the engines to supply the required output. As further described herein, the systems described herein determine the torque percentage of each engine while minimizing battery consumption, according to the requirements of the operating cycle and engine power curve. For example, in a system with three engines, the system operates such that one engine, two engines, or each of the three engines provides a torque percentage that minimizes battery consumption, according to the requirements of the operating cycle and power curve of the three small engines.

[0023] Figure 1 is a top-level block diagram of a multi-engine system used in a vehicle according to one embodiment of the present invention. The multi-engine system functions to optimize the performance of a given set of engines to achieve the best efficiency for given speed and torque requirements by the operator of a multi-engine vehicle.

[0024] The multi-engine system has three engines designated MA, MB, and MC, 102a, 102b, and 102c, which drive the same engine shaft 104 connected to a gear system 106. The gear system transmits power from the engines through the engine shaft to a drive shaft 108, which then powers the vehicle's wheels 110. Although the system shown has three engines, the system may include two, three, or more engines.

[0025] Each of the engines 102a, 102b, and 102c is coupled to its respective drive unit 118a, 118b, and 118c. These drive units are then coupled to a high-voltage power bus 128, which connects the drive units to a voltage converter 130. In the embodiment shown in Figure 1, the engines are DC motors, and therefore the converter is a DC / DC converter, and the high-voltage power bus is a DC high-voltage power bus. In other embodiments where the engines are AC motors, the system components are adapted to AC power, including the use of an AC / DC converter and an AC high-voltage power bus. The voltage converter 130 is coupled to a rechargeable power source in the form of a battery or a group of batteries 132. Power within the system moves in both directions relative to the engines and batteries: power is supplied by batteries 132 to engines 102a, 102b, and 102c to drive the engine shafts and drive shafts, and power generated by engines 102a, 102b, and 102c through braking or from the free rotation of the engine shafts relative to the engine coils is supplied to batteries 132.

[0026] When supplying power from the battery 132 to the engine, the voltage converter 130 converts the voltage of the power from the battery 132 to a level suitable for the drive units 118a, 118b, and 118c, and supplies the converted voltage and power from the battery 132 to the drive units via the power bus 128. The drive units 118a, 118b, and 118c then selectively supply the current to drive their respective engines 102a, 102b, and 102c.

[0027] When power is supplied from the engines to the battery, the current generated by engines 102a, 102b, and 102c is supplied to the respective drive units 118a, 118b, and 118c. Each of the drive units 118a, 118b, and 118c transmits power from this current onto the high-voltage power bus 128, and this power is converted by the converter 130 to a voltage level suitable for recharging the battery 132. In one embodiment, the control unit also utilizes an engine efficiency map for energy generation to determine the torque load supplied to each engine in order to achieve optimal energy generation.

[0028] The system operator 126, for example, the vehicle driver, provides input not only in the form of a request for an increase in speed or torque Td, for example via the accelerator pedal 124, but also in the form of a decrease in speed, for example via the brake pedal. Thus, it can be said that the operator 126 plays a role in inputting information related to the vehicle's driving cycle into the system. While the vehicle is in operation, the operator will indicate the requested speed via the accelerator pedal 124, where the requested speed corresponds to a certain point in the driving cycle. If the given vehicle speed is not equal to the requested speed, the operator inputs a request for an increase in speed, corresponding to a request for an increase in torque from the engine.

[0029] In one embodiment, the system includes multiple sensors that enable it to determine how to handle requests from an operator for increased speed and torque. An engine shaft sensor 122 measures and reports the speed n of the engine shaft. The system also includes current sensors 112a, 112b, and 112c that measure and report the amounts of current iA, iB, and iC supplied to the respective engines 102a, 102b, and 102c. A torque monitor 114 reads the sensed engine currents iA, iB, and iC and uses this information to calculate the engine torque. An energy monitor 116 reads the calculated torque from the torque monitor 114 and the engine shaft speed n to calculate the amount of energy EM that is either being supplied to the engine or generated by the engine instead. The system further includes a current sensor and a voltage sensor 134 that determine the amount of current ib and voltage Vb being supplied to or supplied by the battery 132. The engine shaft speed n, the energy EM to / from the engine, along with the requested torque Td are sent to the control unit 120. The system also includes engine temperature sensors 140a, 140b, and 140c associated with each engine, which provide the temperature, T' for each engine. In addition, the system provides ambient temperature T' through an ambient temperature sensor 142. aThe system measures the following. The information collected from these system sensors is used as input for various system functions. In one embodiment, the control device 120 generates torque commands TA, TB, and TC for each engine, which are sent to the engine drive units 118a, 118b, and 118c, based on the measured engine shaft speed and a given torque request, taking into account the temperature gradient and its effect on engine efficiency. In another embodiment, the control device uses the collected information, including the measured engine shaft speed, a given torque request, and other measured conditions, to optimally meet driving objectives, providing feedback to the vehicle operator regarding deviations from optimal conditions and making suggestions to the vehicle operator regarding modifications to driving behavior. In yet another embodiment, the collected information is used to determine the driving patterns of individual users and to create driving pattern data.

[0030] Control device operation and engine load optimization

[0031] The control device 120 optimizes the torque commands to each engine in order to minimize the energy drawn from the battery. Figure 2a is a block diagram illustrating the operation of the control device 120 and the process of causing the control device to optimize the torque commands TA, TB, and TC for each engine, according to one embodiment of the present invention. As described above, the control device 120 continuously receives from the operator the engine shaft speed n required by a given operating point in the operating cycle and the current torque request Td. In step 202, the control device also retrieves engine data from the database 204, including efficiency data, etaA, etaB, and etaC, corresponding to the efficiency of each engine in the system. The efficiency data for each engine represents the engine's efficiency across possible combinations of speed and torque, including efficiency across different operating temperatures. Figures 6-9 give examples of efficiency data for various large and small engines. The engine efficiency data also includes boundary parameters related to torque, power, and temperature, which can be derived from the engine specifications. The engine data stored in database 204 may also include other information regarding the operation and characteristics of the engines, such as the engine's operating temperature boundary; thermal response parameters for each engine in the system (i.e., engines A, B, and C), such as the thermal conductivity of the engine steel and other factors relating to heat generation and dissipation by the engines; and the initial engine model resistance of each engine in the system.

[0032] Figure 2 is a block diagram illustrating the operation of the control device 120 and the process of causing the control device to optimize the torque commands TA, TB, and TC for each engine according to one embodiment of the present invention. After extracting the engine efficiency data, the control device proceeds to steps 208 and 212 to perform an analysis of the engine efficiency information to determine the optimal contribution of each engine to prepare for the minimum energy consumption from the battery. To perform this optimization, the control device performs a raster scan technique and then determines the optimal portion, i.e., percentage, of the torque requirement Td that will be given by each individual engine. The results and outputs of the control device analysis may drive one, two, or all three engines to have a useful contribution to the torque requirement at a particular operating point in the operating cycle. In step 214, the control device generates engine torque command signals, TA, TB, and TC, for each engine drive unit 118a, 188b, and 118c, corresponding to the portion, i.e., percentage, of the overall torque requirement Td determined by the optimization analysis. For example, if, based on engine efficiency data and current speed and torque requirements, it is determined that the most efficient energy usage can be obtained from engine A supplying 20% ​​of the torque requirement, engine B supplying 30% of the torque requirement, and engine C supplying 50% of the torque requirement, the control unit may output engine torque command signals TA, TB, and TC to their respective engine drive units, such that TA is equal to 20% of Td, TB is equal to 30% of Td, and TC is equal to 50% of Td. Or, if it is determined that the most efficient energy use can be obtained from engine A, which supplies 100% of the torque requirement, and engines B and C, which each supply 0%, the control unit may output engine torque command signals TA, TB, and TC to the respective engine drive units, such that TA is equal to 100% of Td and TB and TC are equal to 0% of Td, or alternatively, TA is equal to 50% of Td, TB is equal to 50% of Td, and TC is equal to 0% of Td.

[0033] The operation of the control device 120 is performed in real time or near real time throughout the driving cycle, by having the control device continuously adjust the engine torque command signal based on torque requirements and changes in vehicle speed. In particular, the control device goes through the processing described in relation to Figure 2 continuously, in a fixed section or in a continuous loop of operation, from the beginning to the end of the vehicle's operation. Since this analysis is performed in real time or near real time, the processor of the control device ideally has sufficient speed and bandwidth to perform the optimization calculations (e.g., raster scan analysis) in steps 208 and 212 in real time or near real time.

[0034] In another embodiment, instead of or in addition to this, the control unit generates an engine torque command signal based on a non-real-time decision, for example, through the use of preloaded rules. In such an embodiment, the preloaded rules are determined based on optimization calculations performed in advance on a given set of engine characteristics and variables. For example, optimization calculations may be performed separately and used to pre-determine an optimal engine torque command to satisfy a given requirement (or combination of requirements) across a set of possible combinations of torque requirements and shaft speeds (based on engine characteristics, e.g., efficiency data). The results of these optimization calculations are then used to construct a lookup table that is stored in a memory accessible to the control unit, for example, memory 204. During operation, in steps 208 and 212, the control unit may then refer to the lookup table using a given torque requirement and shaft speed to determine the relevant engine torque command, which has been pre-calculated as optimized for a requirement or combination of requirements, including ensuring that temperature constraints are met. In one embodiment, based on driving pattern data and the driver's objectives (such as maximizing drivable distance or minimizing driving time), the control device can suggest to the operator how to modify the operator's driving behavior in order to optimally meet the driving objectives.

[0035] In another embodiment, alternatively or additionally, the control unit uses a heuristic method to determine a suboptimal but sufficient contribution of each engine to satisfy a requirement or set of requirements. For example, in one such alternative embodiment in step 208 of Figures 2a and 2b, the control unit uses a heuristic method to determine a suboptimal but sufficient division value of the torque requirements between the engines to accommodate the minimum energy consumption from the battery. By employing a heuristic method and determining a suboptimal solution, the control unit can perform operations more quickly while determining a division value of the load sharing between the engines that is sufficient for a given requirement or set of requirements (e.g., minimizing energy consumption, maximizing driving range).

[0036] Figure 3 is a block diagram showing the steps performed by a control device in relation to determining an engine torque command signal according to an embodiment of the present invention. First, the control device compares the relative efficiencies of each engine at a given speed and torque (n,T) at an operating point in the operating cycle and determines the engine with the highest efficiency at that point. In the case of a system with three engines, this comparison and determination includes steps 302a and 302b, in which the efficiency of each engine is compared with the efficiency of each other engine in the system. More specifically, in step 302a, the efficiency etaA of engine A is compared with the efficiencies etaB and etaC of engines B and C and it is determined whether it is greater. Similarly, in step 302b, the efficiency etaB of engine B is compared with the efficiencies etaA and etaC of engines A and C and it is determined whether it is greater. In the case of a system with a larger number of engines, the control device may go through further similar comparison steps to determine the engine with the highest efficiency. Alternatively, the control unit may use other algorithms to determine which of two, three, or more engines has the highest efficiency.

[0037] Once the engine with maximum efficiency is found, the control unit tests the first condition and checks the torque level to determine whether the torque requirement Td is greater than the torque boundary of the engine with maximum efficiency. Referring to Figure 3, in steps 304a, 304b, and 304c, the control unit compares the torque requirement Td with the torque boundary of the engine that was determined in the previous step to have maximum efficiency at a given speed and torque in the operating cycle. For example, if engine B is determined to have the maximum engine efficiency at a given point in the operating cycle, in step 304b, the torque requirement Td is compared with the torque boundary TbB of engine B to determine through steps 302a and 302b whether it exceeds that boundary. If the torque requirement exceeds the torque boundary of the engine with maximum efficiency, that engine alone cannot satisfy that particular torque requirement, so the control unit implements a distribution of the torque requirement load across multiple engines. As a result, the control unit proceeds to step 208, which implements a multi-engine optimization analysis.

[0038] However, if the torque requirement does not exceed the torque boundary and the torque boundary is being met, the control unit tests a second condition to determine whether the engine can supply the required power or whether it exceeds the engine power boundary. In steps 306a, 306b, and 306c, the required engine power is calculated for an engine that has maximum efficiency at a given point in the operating cycle. The engine power is calculated as the product of the torque requirement (e.g., in Newton-meters (Nm)) and the engine shaft speed (e.g., in revolutions per minute (rpm)). After the required engine power has been calculated, the control unit then, in steps 308a, 308b, and 308c, compares the engine power to the power boundary of the relevant engine to determine whether it exceeds the power boundary of that engine. For example, if in steps 302a and 302c it is determined that engine C has the maximum engine efficiency at a given point in the operating cycle, and further in step 304c it is determined that the torque requirement Td does not exceed the torque boundary TbC of engine C, then the control device compares the required engine power PC with the power boundary PbC of engine C. If the required engine power exceeds the power boundary of the relevant engine, it is determined that that engine alone cannot meet the specific power requirement, and the control device implements a distribution of the torque requirement load across multiple engines. As a result, the control device proceeds to step 208, which implements multi-engine optimization analysis.

[0039] However, if both conditions are met and the torque request and required power do not exceed the engine boundary, the control unit does this by directly sending the torque request to the engine determined to have the highest efficiency at a given engine shaft speed and torque (n,T) for the operating cycle, and making that engine's engine torque command the entire torque request Td. For example, if, through step 302a, engine A is determined to have the highest efficiency among the engines, and in step 304a, the torque boundary condition for engine A is determined to be met, and then, through steps 306a and 308a, the power boundary condition for engine A is determined to be met, the control unit generates an engine A torque command TA equal to the entire torque request Td. However, if either the torque boundary condition or the power boundary condition is not met, the control unit switches to multi-engine operation so that the torque request is distributed across multiple engines, and the control unit performs multi-engine optimization analysis.

[0040] Figure 4 is a block diagram illustrating a raster scan technique used to determine the load sharing among engines at the operating point of the operating cycle and to optimize efficiency for a given shaft speed and torque. As described above, the control unit receives the engine speed n and torque request Td at a given operating point of the operating cycle. If the control unit determines that load sharing among multiple engines is required, it implements the following raster scan technique to determine the percentage of torque request that each engine will provide, such that the sum of the engine torque requests transmitted by the control unit to the engine drive unit equals the total torque request. For example, in the system shown with three engines, for a given torque request Td, the control unit 120 generates engine torque commands TA, ZB, and TC such that TA = Td*x, TB = Td*y, and TC = Td*z, where 0 <= x <= 1, 0 <= y <= 1, 0 <= z <= 1, and 1 = x + y + z. When performing a raster scan analysis, in steps 402 and 404, the control unit first defines a raster in m-dimensional space, where m is a number less than or equal to the number of engines. In addition, the control unit defines the raster to have a defined number of points or intervals in each dimension of the m-dimensional space, where the number of intervals corresponds to the desired level of precision for the optimization calculation, where more intervals result in higher precision. As a representative example, Figure 5 shows a 4x4 raster in two-dimensional space that can optimize three variables (optimizing load sharing across three engines). In the example shown, the 4x4 point raster in two-dimensional space is defined in constant intervals where x and y are assumed to have values ​​of 0, 0.333, 0.667, and 1, respectively. At each point, the value of z is known to be z = 1 - xy. In step 406, a set of test torques is defined for each engine, and the test torque at each point is equal to the product of the torque requirement and the value in the raster corresponding to the particular engine.In the example shown in Figure 4, the value of x in the x-dimension corresponds to the percentage of the torque requirement (TA) assigned to engine A, the value of y in the y-dimension corresponds to the percentage of the torque requirement (TB) assigned to engine B, and the value of z (defined by x and y) corresponds to the percentage of the torque requirement (TC) assigned to engine C, such that TA = Td*x, TB = Td*y, and TC = Td*z. In step 408, the engine efficiency is calculated for each point in the raster scan based on the efficiency data stored for each engine. In step 410, the cost function is calculated for each point in the raster scan using the efficiency calculated in step 408 as input. The cost function is derived from the minimum battery energy at that point. For a raster scan of a three-engine system, the cost function takes the form F(x,y) = x / etaA + y / etaB + z / etaC. In steps 412 and 414, the control unit determines the minimum cost function Fmin that spans each of the calculated cost functions and identifies the point in the raster scan where the minimum cost function occurs. This point corresponds to the percentage of torque requirement that will be assigned to each engine to give the system the most efficient operation. For three engines, the minimum cost function Fmin occurs at the point (xmin, ymin, zmin). In step 416, the torque command for each engine is calculated based on the minimum value calculated in steps 412 and 414, such that each engine is assigned a torque corresponding to the product of the torque requirement for that engine in the raster and the point of the minimum cost function Fmin. For example, for three engines, the calculated torque command is calculated by the control unit as follows: TA = Td * xmin, TB = Td * ymin, and TC = Td * zmin. In step 418, the calculated torque command is transmitted to the respective drive unit.

[0041] The control device 120 can implement this raster scan technique and optimize it for two, three, or more engines. For a relatively large number of engines, the control device may utilize other techniques for determining the optimal load sharing among the engines, such as the steepest descent method using the gradient of the cost function. For example, if more than five engines are used, the control device may use the steepest descent method to determine the optimal load sharing for given operating conditions. In other embodiments involving even more engines, the control device uses a heuristic method to determine a suboptimal but sufficient contribution from each engine to satisfy the requirements or set of requirements. By employing a heuristic method instead of an optimization technique, the control device can generate torque commands in near real time, even though the calculations in step 208 become more complex as the number of engines increases. In other embodiments involving even more engines, the control device generates engine torque command signals based on non-real-time decisions, for example, by using rules determined and preloaded based on prior optimization calculations assuming a set of variables.

[0042] Furthermore, although the above techniques are described as optimizing performance, it is understood that the same techniques can be used to determine a load distribution that satisfies a desired threshold rather than being the optimal solution. For example, if the user specifies that the control unit should control the load distribution between engines to achieve a specific driving range (e.g., 100 miles), the control unit can use the above analytical techniques to determine which combination of load distribution between engines will give the given driving range. From these combinations, the control unit can use the same analytical techniques to determine which combination to use based on a different metric, for example, optimizing efficiency or meeting a minimum acceleration threshold.

[0043] The above describes the multi-engine optimization process in terms of maximum efficiency and minimum energy consumption from the battery. However, the control device 120 may optimize the torque load sharing among the multiple engines based on various factors. For example, the control device may alternatively or additionally optimize for factors such as maximum driving distance, acceleration, or a combination of factors such as maximum driving distance and minimum acceptable acceleration.

[0044] Overall optimization including environment variables and temperature

[0045] System 100 can also utilize environmental information to perform a global optimization over the entire operating cycle. Such a global optimization over the entire operating cycle can be performed based on the fact that the nature of the operating cycle ensures that the operating points are correlated and not independent. For example, real-world constraints and physical limitations imposed on the engine and the entire system, including limits imposed on acceleration, ensure that the operating points do not diverge too much from each other (in terms of speed and torque) in a given operating cycle. Taking this relationship into account, a new global optimization is performed at each operating point, considering all past and future points. This global optimization goes beyond simply summing up the optimizations of sequential points, as it considers the influence that past operating points have on future operating points in the operating cycle. As an example, considering thermal transients, each operating point influences future operating points. In particular, heat may not dissipate immediately but rather accumulate over time, and this accumulation of heat based on past torque commands affects the torque distribution among multiple engines to prepare for optimal efficiency in the system, thereby protecting the future operating performance of the engines.

[0046] FIG. 11 is a block diagram illustrating the operation of the control device 120 and the processes used by the control device to perform a global optimization of the torque commands for each engine to meet a specific performance goal. In the embodiments described herein, the performance goal is to minimize energy. However, as noted above, it will be understood that other performance goals or combinations of performance goals may be used for the global optimization. It will also be recognized that the global optimization can be performed such that the performance goal is reliably met within one or more constraints, such as engine temperature, maximum time required for travel, maximum speed, and / or maximum acceleration bounds.

[0047] As noted above, the global optimization takes into account the influence of past operating points on future operating points in the driving cycle. In the example given herein, the global optimization takes into account thermal transient phenomena. However, other transient variables and conditions that can affect engine efficiency and performance may be considered in addition to, or instead of, engine temperature.

[0048] In step 1101, the control device receives input information from various system components and initializes variables to be used to perform the global optimization. The control device receives driving cycle information directly from the driving plan tool, or instead the driving plan tool provides the control device with the driving cycle information and other characteristics of the driving cycle as the driving information that serves as a basis for the control device to determine. The control device then determines the speed and torque data (n i =(t0,t1,...,t f ) at time t during the driving cycle, where t is time, i is the index of the time step in the driving cycle data, t di ,T di ), where t is time, i is the index of the time step in the driving cycle data, t i is the time at the i-th step in the driving cycle, and t i =(t0,t1,...,t f ) refers to the i-th time step, t0 is the initial step time, and t f is the final step time.

[0049] In addition, the control unit collects mechanical data related to the vehicle, such as the coefficient of air friction; thermal response parameters of each engine k in the system (i.e., engines A, B, and C), such as the thermal conductivity of the engine steel and other factors related to heat generation and dissipation; and initial engine model parameters R for each engine in the system. k (T' k0 The control unit receives or inputs the following from database 204 or other memory storage devices: the engine's electromechanical parameters, e.g., engine bus voltage; and any temperature constraints or other constraints (e.g., maximum acceleration). These values ​​are pre-calculated or measured and stored in database 204 for use by the control unit when performing the overall optimization. These values ​​may also be measured and calculated in real time to provide feedback to the control unit used to verify the validity of the overall optimization performed by the control unit.

[0050] In step 1102, the control unit initializes the time variable. For example, the control unit takes i=0 and sets t=t0=0.

[0051] In step 1103, the control device also (n di ,T di Based on this, the acceleration at the operating point in the operating cycle is calculated. In addition, the control device updates any transient variables that affect engine performance. In the embodiment shown, the control device calculates the temperature T' of each engine k at the i-th step in the operating cycle. ki Update the engine temperature T' at time t0=0. ki This may be determined through engine temperature sensors 140a, 140b, and 140c associated with each engine. Then, the engine temperature T' ki The ambient temperature T a (This is measured via the ambient temperature sensor 142) as well as being affected by heat generation and heat dissipation inside the engine. Engine temperature measurement T' ki The ambient temperature and other measured values ​​are provided to the control device.

[0052] In step 1104, the control unit initiates the optimization calculation. In the embodiments described herein, the system initiates the optimization over the ranges of x and y using the raster scan technique disclosed herein, where 0 <= x <= 1, 0 <= y <= 1, 0 <= z <= 1, 1 = x + y + z, where the value of x in the x dimension corresponds to the percentage of the torque requirement (TA) assigned to engine A, the value of y in the y dimension corresponds to the percentage of the torque requirement (TB) assigned to engine B, and the value of z (defined by x and y) corresponds to the percentage of the torque requirement (TC) assigned to engine C. For systems with further engines, the raster scan process includes further dimensions. At each value over the range of (x, y), the control unit performs a thermal calculation and determines the cost function as further described below.

[0053] In step 1105, the control unit performs thermal calculations to determine the temperature changes for the engines in order to make specific assignments of torque requirements between the engines. The control unit calculates the thermal transients, and i+1 For each engine k, the simulated temperature T' after time Δt at the end of the i-th step is k(i+1) To obtain this, the control unit uses the engine thermal response parameters for each engine k retrieved from database 204.

[0054] In step 1106, the control unit performs a cost function analysis on a specific allocation of torque requirements between engines with respect to the desired objective function. For each engine k, the control unit calculates the temperature T' k(i+1)、 and t (i+1) R is the vector of engine parameters for the kth engine. k (T' k(i+1) The control device calculates the operating point (n) for each engine k. d(i+1) ,T d(i+1) ), and time t (i+1) Efficiency η of engine k k(i+1) The control unit then calculates (x) which is related to the performance criteria. i ,y i Cost function F at the point )c (x i ,y i The cost function is calculated as follows: In an embodiment of the representation where the performance criterion is maximized with respect to efficiency, the cost function is the amount of energy used.

[0055] In step 1107, the control unit determines whether the optimization analysis has been performed over the entire range (x,y). If not, the values ​​of x and y are updated in step 1112, by which the control unit selects the next value of x within the range 0 <= x <= 1, selects the next value of y within the range 0 <= y <= 1, and calculates the next value of z such that z = 1 - xy, and then steps 1105 and 1106 are performed with respect to the updated values ​​of x and y. Once the optimization analysis has been performed over the entire range (x,y) and the cost function has been derived for such points, the control unit proceeds to step 1108.

[0056] In step 1108, the control unit determines the result of the optimization analysis and outputs the resulting torque command. The control unit then calculates the cost function F c The minimum value of (x,y) is F m This is calculated over the range (x,y). The control device is F m The corresponding optimal value (x,y)=(x opt ,y opt ) Let's assume time t i+1 So, (x opt ,y opt The control device outputs the optimal torque to the engine using ). Then the control device calculates Δt=t i+1 -t i Calculate the battery energy consumed during 0 <= t <= t i+1 Over the course of that period, all battery energy consumed up to that point will be replaced.

[0057] In step 1109, the control unit determines whether the end of the operating cycle has been reached. i+1 >=t f Determine whether or not the end of the operating cycle has not been reached. i+1 <tf If so, in step 1110, the time variable is incremented so that t becomes t i+1 The value is incremented so that i increments to i+1, and steps 1102 to 1108 are repeated based on the updated values ​​of t and i. The end of the operating cycle is reached, and t i+1 >=t f If this is the case, the control device proceeds to step 1111.

[0058] In step 1111, the control unit outputs the result of the overall optimization calculation, for example, the total battery power consumed for the operating cycle.

[0059] In one embodiment, throughout the operating cycle and the process of realizing the torque command generated by the overall optimization, the control device uses current measurements from sensors on the vehicle, such as ambient temperature, engine heat including internal engine temperature, shaft rotation speed, engine, engine current, and torque request, to verify the validity of the overall optimization.

[0060] In another embodiment, the control device frequently performs an overall optimization process throughout the entire operating cycle, taking into account updated driving information, updated operating cycle characteristics, and updated measured and calculated values ​​for variables such as engine temperature and ambient temperature.

[0061] Simulation of multi-engine system operation demonstrating improved efficiency

[0062] The multi-engine system described above can be applied to a variety of systems. For illustrative purposes, an example of its use in a system related to electric transport equipment is given. It will be understood that the multi-engine system and the control unit within it can be applied to systems that use electric motors to perform tasks.

[0063] This specification provides representative examples of the application of the disclosed systems, which relate to simulation studies of battery usage by sets of engines in a vehicle under different driving scenarios, represented by driving pattern data, i.e., “driving cycles.” Specific engines and driving cycles are described in the following sections.

[0064] In this typical example, load sharing across the engines is determined by the control unit in accordance with the optimization routine described herein. Battery output is the energy consumption. The efficiency improvement of the system disclosed herein is demonstrated by a comparison with the battery consumption of a system without load sharing across the engines.

[0065] The simulated tests described below examine two cases each for large and small engines, where the different cases (Case 1 and Case 2) correspond to sets of engines with different characteristics.

[0066] Tested engine

[0067] Simulated tests of the system were performed in relation to both large and small engines. Each engine in the system has a distinctly different power curve. For the purpose of testing, both large and small engines are represented by similar models with identical resistances. In the given example, a resistance matrix is ​​used to define the engine circuit model. The parameters of the circuit model are identical for both the large and small engines.

[0068] Figure 6 shows the power curves and efficiency contour lines of three large engines in Case 1, a typical example of the operation of a multi-engine system according to an embodiment of the present invention. Figure 7 shows the power curves and efficiency contour lines of three large engines in Case 2, a typical example of the operation of a multi-engine system according to an embodiment of the present invention. Figure 8 shows the power curves and efficiency contour lines of three small engines in Case 1, a typical example of the operation of a multi-engine system according to an embodiment of the present invention. Figure 9 shows the power curves and efficiency contour lines of three small engines in Case 2, a typical example of the operation of a multi-engine system according to an embodiment of the present invention.

[0069] For large engines, the efficiency peak region is optimally spread across the (n,T) space. For small engines, the power curve is constrained by boundaries (constraints), and therefore only (n,T) points within the boundaries are considered valid. In particular, the power loss boundary restricts the small engine. In this simulation, the small engine is prevented from operating beyond the power loss boundary. Since the power curve of a large engine has no boundaries, all (n,T) points are potentially usable by the large engine. In this simulation, the large engine is allowed to operate across the entire plane (defined by the (n,T) operating cycle points). Furthermore, while large and small engines may have the same efficiency distribution, they may also have different distributions of valid (n,T) points.

[0070] Engine losses were calculated from the values ​​of the nominal power curve. For the modeling and testing described in this book, the engine power loss values ​​were simplified by using an estimated overall efficiency of 0.96.

[0071] Tested driving cycle

[0072] The driving cycles used to test the systems described in this document are provided by the EPA (see https: / / www.epa.gov / vehicie-and-fuel-emissions-testing / dynamometer-drive-schedules). Each driving cycle is characterized by the required speed and torque at each point. Additional information regarding the basic driving cycles used to test this system is provided below.

[0073] The EPA Urban Dynamometer Driving Schedule (UDDS), commonly known as "LA4" or "city test," represents urban driving conditions. It is used for light-duty vehicle testing. UN / ECE Regulation 53 designates the EPA UDDS as "Test Equivalent to the Type 1 Test" (which demonstrates exhaust emissions after a cold start). Figure 10a is a graph showing speed versus time for the UDDS driving cycle.

[0074] The Highway Fuel Economy Driving Schedule (HWFET) represents highway driving conditions at speeds below 60 miles per hour (mph). Figure 10b is a graph showing speed versus time for the HWFET driving cycle.

[0075] The US06 operating schedule is a high-acceleration, aggressive operating schedule, often classified as a "Supplemental FTP" operating schedule. Figure 10c is a graph showing the speed versus time for the US06 operating cycle.

[0076] Results of the simulated test

[0077] Simulations of systems using two different sets of engines (Case 1 and Case 2) demonstrated that, using the systems and techniques described herein, a set of three small engines could achieve the same efficiency as three large engines, and even outperform any of the individual large engines. As a result, the systems and techniques described herein enable the realization of multi-engine systems consisting of smaller engines that are more efficient than large engine systems, leading to improved engine system performance and cost reduction.

[0078] Optimization of energy generation

[0079] As described above, the multiple engines in the system can also function as generators through the reverse process described above, in which case torque from the drive shaft is distributed to those engines in order to generate current for the battery. For example, when the vehicle is braking, the control unit may selectively engage the engine receiving the available torque with the drive shaft, thereby generating current. In such a scenario, each of the drive units 118a, 118b, and 118c acts as a rectifier system. Based on the torque command signals TA, TB, and TC from the control unit 120, each rectifier system acts as a power converter and controls the proportion of the available torque handled by its corresponding generator (and thus the proportion of the total current generated), as further described below. Each rectifier system controls the extent to which it engages its respective generator with the generator shaft 104 to handle a portion of the available torque. Each rectifier system adjusts the pulse-width modulation (PWM) duty cycle of its corresponding generator to control the amount of torque handled by that generator and the amount of current generated by that generator. Current sensors 112a, 112b, and 112c monitor the current generated by each generator, and this current is supplied to monitor 114, which calculates the electromagnetic torque T generated by the generator. A second monitor 116 takes the electromagnetic torque T and the generator shaft speed n and calculates the energy to be extracted from the generator EG. Each rectifier then supplies energy to the DC high-voltage power bus 128. The AC / DC converter 130 acts as an inverter, transferring the energy from the bus 128 to an external energy load, such as an energy grid or energy storage system.

[0080] Optimization of multi-engine system design

[0081] The selection of engines used to construct a multi-engine system, such as the three-engine system 100 described above, can also be optimized with respect to efficiency based on the optimization techniques described above. Given a set of engines, each having specific characteristics (e.g., efficiency characteristics, torque boundary, power boundary, etc.), and a desired number of engines (e.g., three engines), the optimal engine for a given driving cycle or set of cycles that most closely matches the observed driving pattern of a motor vehicle driver (driving behavior, general road conditions, driving characteristics, etc.) can be determined using the raster scan technique described above. It will be readily apparent that the methods and processes described herein for selecting the optimal contribution of engines to achieve specific criteria can be used to determine which engines to include in a multi-engine switching system, and that this involves factors relating to the performance and efficiency of the engines, while controlling for other factors such as engine cost, size, and other characteristics of the engines.

[0082] According to embodiments of the present invention, the following systems and processes are used to select engines to be included in a multi-engine switching system. First, driving pattern data is received by the engine selection system. The driving pattern data includes data similar to the exemplary “driving cycle” data described above and may be generated by similar processes. In one example, the driving pattern data is generated by recording the acceleration and torque conditions of an individual riding in a given test vehicle, recorded during one or more driving events by a specific individual, and the driving data is combined so that a final driving pattern data for engine selection processing is obtained as a result.

[0083] Once the driving pattern data is received, the engine selection system receives engine information data from the engine information database. This engine information data is stored in the database and includes data on the operating characteristics of multiple engines, such as the power curves and efficiency contours of each such engine; mechanical data of the vehicle, such as the air friction coefficient; thermal response parameters of each engine k in the system (i.e., engines A, B, and C), such as the thermal conductivity of the engine steel and other factors related to heat generation and dissipation; and the initial engine model resistance R for each engine k in the system. k (T' k0 ); and the engine's electromechanical parameters, such as engine bus voltage, which are stored by the system in the database. The engine information data may also include other information related to the engine, such as the cost, weight, and space of each such engine. The engine information database may be located locally in the engine selection system or it may be a remote database accessed via a network connection.

[0084] The engine selection system receives the number of engines to be selected as an input variable. The number of engines acts as a constraint imposed on the engine selection process, causing the engine system to output a predetermined number of engines (for example, if the number of engines input to the system is 3, only 3 engines will be selected to fit the vehicle).

[0085] The engine selection system performs a global optimization process to determine the optimal set of engines for a given driving pattern, using driving pattern data, engine information data, and the number of engines to be selected. In the global optimization process performed by the engine selection system, the driving pattern data corresponds to the driving cycle described in the global optimization process in Figure 11, the engine characteristics for optimization analysis correspond to the engine efficiency data (including engine parameters) used in the optimization process, and the dimensionality of the raster scan is set by the number of engines to be selected. The cost function can be defined based on desired criteria, such as minimizing energy consumption or maximizing acceleration. In addition to efficiency, additional constraints and boundaries, such as engine cost, weight, and space, can be considered in engine selection. Following the global optimization process, the engine selection system outputs a combination of engines that gives the optimal solution satisfying the desired criteria. In this way, the engine selection system can provide a set of engines individually tailored to an individual's driving pattern, and also provide the user with an optimized (e.g., most energy-efficient) set of engines.

[0086] In addition, based on driving pattern data and the driver's goals (such as maximizing drivable distance or minimizing driving time), the control system can suggest to the driver how to modify their driving behavior to more optimally achieve their driving goals.

[0087] In addition to the raster scanning technique described above, other techniques can be used to optimize engine selection for multi-engine systems, including the Nelder-Mead simplex algorithm, the steepest descent method, Newton's method, or the Newton-Raphson method.

[0088] While the present invention has been described in terms of several preferred embodiments, it should be understood that there are many modifications, substitutions, and equivalents that fall within the scope of the invention. It should also be noted that there are alternative methods for achieving both the process and the apparatus of the present invention. For example, the steps do not necessarily have to be performed in the order shown in the accompanying drawings and may be rearranged as appropriate. Therefore, the appended claims are intended to include all such modifications, substitutions, and equivalents that fall within the true spirit and scope of the invention.

[0089] The components of the above system can be implemented in digital electronic circuits, computer hardware, firmware, software, or a combination thereof. The components of this system can be implemented as computer program products, i.e., computer programs tangibly embodied in information carriers, for example in machine-readable memory devices, or in propagated signals, and can be executed by data processing devices, such as programmable processors, computers, or multiple computers, or control the operation of such data processing devices.

[0090] Processors suitable for executing computer programs include, for example, general-purpose and application-specific microprocessors, and any one or more processors in any type of digital computer. Generally, a processor will receive instructions and data from read-only memory, random-access memory, or both. Essential components of a computer are a processor that executes instructions and one or more storage devices that store instructions and data. Generally, a computer will also include one or more mass storage devices for storing data, such as magnetic disks, magneto-optical disks, or optical disks, or will be operablely coupled to them to receive data from them, transfer data to them, or both. Suitable information carriers for embodying computer program instructions and data include all forms of non-volatile memory, such as semiconductor memory devices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks, e.g., internal hard disks or removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks. Processors and memory can be accompanied by or incorporate application-specific logic circuits.

[0091] Where the terms “a,” “an,” and “the” and similar references are used in the context of this disclosure (particularly in the context of the following claims), they shall be construed to encompass both the singular and plural unless otherwise indicated herein or if there is an obvious inconsistency in the context. All methods described herein may be performed in any preferred order unless otherwise indicated herein or if there is an obvious inconsistency in the context. Any examples or illustrative language given herein (e.g., preferred, preferably, etc.) are intended only to further illustrate the disclosure and not to limit the claims. It is desirable that no language herein be construed as indicating any unclaimed component as essential to the practice of this disclosure.

[0092] This specification describes several embodiments. Variations of these disclosed embodiments will be apparent to those skilled in the art by reading the preceding disclosures. The inventors anticipate that those skilled in the art will appropriately adopt such variations (e.g., by modifying or combining features or embodiments), and the inventors intend that the invention will be carried out in ways other than those specifically described herein.

[0093] Accordingly, the present invention includes all modifications and equivalents of the subject matter described in the appended claims, as permitted by applicable law. Furthermore, any combination thereof in all possible variations of the above components is incorporated into the present invention unless otherwise indicated herein or there is an obvious inconsistency in the context.

Claims

1. A method for generating a multi-engine switching system having engines optimized for a desired performance standard within a range of one or more constraints, wherein the method is: Receiving driving pattern data, The system receives the number of engines (N) for the multi-engine system as input, The process involves receiving engine information data from a database, wherein the engine information data includes operating characteristics for each of a plurality of engines, the plurality of engines are electric motors, and the plurality of engines comprises more than N engines. For each combination of N engines within the plurality of engines, an optimization analysis is performed across all time periods in the operating pattern data, wherein the optimization analysis is performed Based on the aforementioned operating pattern data, the shaft speed and torque are determined across all time periods. Based on the engine information data, a cost function for the desired performance standard is calculated for each possible combination of torque contributions from the N engines, spanning all time periods in the operating pattern data. For each time period in the aforementioned driving pattern data, from among the possible combinations of torque contributions, identify a combination of torque contributions that is optimized for the desired performance standard based on the cost function while remaining within the range of one or more constraint conditions, Using the combination of torque contributions optimized for the desired performance criteria, the sum of the cost function for the combination of the N engines over all time periods is provided. This includes, The objective is to output the optimal combination of N engines used in the multi-engine system, wherein the optimal combination of N engines has a sum of the cost function that satisfies the desired performance criteria within the range of one or more constraints. Methods that include...

2. The method according to claim 1, wherein the desired performance criterion is the maximization of efficiency, and the cost function is the amount of energy used by the possible combinations of torque combinations by the combination of the N engines.

3. The method according to claim 1, wherein the result of the optimization analysis provides the total energy consumption for the combination of N engines.

4. The method according to claim 1, wherein the engine information data includes at least one of the cost, weight, and space of each of the plurality of engines, and the combination of the N engines is limited based on engine constraints related to at least one of the total cost, weight, and space.

5. The method according to claim 1, wherein the engine information includes the power curve and efficiency characteristics of each of the plurality of engines, the power curve and efficiency characteristics are used to identify the combination of torque contributions optimized for the desired performance criteria based on the cost function while remaining within the range of one or more constraints.

6. The method according to claim 5, wherein the optimization analysis further comprises calculating the efficiency of each of the plurality of engines for each time period in the operating pattern data based on the efficiency characteristics and one or more transient phenomena.

7. The method according to claim 6, wherein the one or more transient phenomena include thermal transient phenomena, and the optimization analysis further comprises calculating the thermal transient phenomena to determine the estimated temperature of each of the plurality of engines at the end of each time period.

8. The method according to claim 1, wherein the multi-engine system is for an electric transport device.

9. The method according to claim 1, further comprising integrating the optimization analysis with the optimization of energy recovery from braking.

10. The method according to claim 1, wherein the one or more constraint conditions include at least one of minimum acceleration, maximum acceleration, and maximum temperature.

11. A system for generating individually tuned multi-engine systems having engines optimized for desired performance criteria within a range of one or more constraints, wherein the system is: A database storing engine information data including the operating characteristics for each of a plurality of engines, wherein the plurality of engines are electric motors, and the plurality of engines comprises more than N engines. A control device for receiving driving pattern data and the number of engines (N) for the multi-engine system, wherein the control device is communicably coupled to the database for receiving the engine information, A processor of the control device that performs an optimization analysis over all time periods in the operating pattern data for each combination of N engines within the plurality of engines, wherein the optimization analysis is: Based on the aforementioned operating pattern data, the shaft speed and torque are determined across all time periods. Based on the engine information data, a cost function for the desired performance standard is calculated for each possible combination of torque contributions from the N engines, spanning all time periods in the operating pattern data. For each time period in the aforementioned driving pattern data, from among the possible combinations of torque contributions, identify a combination of torque contributions that is optimized for the desired performance standard based on the cost function while remaining within the range of one or more constraint conditions, Using the combination of torque contributions optimized for the desired performance criteria, the sum of the cost function for the combination of the N engines over all time periods is provided. Processors and It is equipped with, The control device outputs an optimal combination of N engines used in the multi-engine system, wherein the optimal combination of N engines has a sum of the cost function that satisfies the desired performance criteria within the range of one or more constraints.

12. The system according to claim 11, wherein the database is located locally on the control device.

13. The system according to claim 11, wherein the database is located remotely on the control device and accessed via a network connection.

14. The system according to claim 11, wherein the desired performance criterion is the maximization of efficiency, and the cost function is the amount of energy used by the possible combinations of torque combinations by the combination of the N engines.

15. The system according to claim 11, wherein the result of the optimization analysis generates the total energy consumption for the combination of the N engines, and the control device outputs the total energy consumption.

16. The system according to claim 11, wherein the engine information data includes at least one of the cost, weight, and space of each of the plurality of engines, and the combination of the N engines is limited based on engine constraints relating to at least one of the total cost, weight, and space.

17. The system according to claim 11, wherein the engine information includes the power curve and efficiency characteristics of each of the plurality of engines, the power curve and efficiency characteristics are used to identify the combination of torque contributions optimized for the desired performance criteria based on the cost function while remaining within the range of one or more constraints.

18. The system according to claim 17, wherein the optimization analysis further comprises calculating the efficiency of each of the plurality of engines for each time period in the operating pattern data, based on the efficiency characteristics and one or more transient phenomena.

19. The system according to claim 18, wherein the one or more transient phenomena include thermal transient phenomena, and the optimization analysis further comprises calculating the thermal transient phenomena to determine the estimated temperature of each of the plurality of engines at the end of each time period.

20. The system according to claim 11, wherein the multi-engine system is for electric transport equipment.

21. The system according to claim 11, wherein the optimization analysis further comprises integrating the optimization of energy recovery from braking.

22. The system according to claim 11, wherein the one or more constraint conditions include at least one of minimum acceleration, maximum acceleration, and maximum temperature.

Images