Performance-optimized multi-motor switching system and method

Abstract

A multi-motor switching system and method is disclosed for achieving a global optimization of performance criteria by considering variables and conditions across an entire drive cycle. The system's controller is configured to perform a global optimization, determining the optimal distribution of motor loads across an entire trip or drive cycle, rather than sequentially determining an optimal solution for a given point in time and a set of current localized conditions. In one embodiment, the control system provides global optimization, wherein the controller receives trip information from a trip planning tool. The controller utilizes the trip information to generate a drive cycle and further incorporates this information into an optimization process performed by the controller to determine the optimal solution for the entire trip.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to U.S. Provisional Patent Application No. 62 / 881,759, filed on August 1, 2019, and U.S. Provisional Patent Application No. 62 / 988,334, filed on March 11, 2020, which are incorporated herein by reference in their entirety and become a part of the present invention. Technical Field

[0003] The innovative systems and methods described herein generally relate to the field of multi-motor systems and multi-generator systems. More specifically, the innovative systems and methods described herein relate to multi-motor and multi-generator systems, including controllers and related components for optimizing performance and efficiency. Background Art

[0004] As is well known in the art, various systems and methods exist for controlling multi-motor systems. However, existing systems suffer from serious deficiencies that prevent them from operating in a truly optimized and efficient manner.

[0005] For example, at a given point in time and under a given set of current or transient conditions, many existing systems typically rely on sequential local optimization to determine the load that each motor should receive. This local optimization, targeted at a given point in time, suffers from the fact that it fails to account for conditions and variables relevant to the entire journey. Consequently, these systems fail to provide an overall efficient load distribution among the motors; for example, optimizing at one operating point may lead to overheating at a later time. Furthermore, while existing multi-motor systems do consider temperature to some extent, any consideration is typically limited to ensuring that motor temperatures remain within certain predefined ranges, making this consideration a local, binary decision. Furthermore, existing systems do not integrate optimization of energy regeneration (i.e., braking) in electric vehicles into their optimization solutions.

[0006] Furthermore, many methods and systems require transmissions to transfer torque loads between motors. These transmissions introduce mechanical losses into the system and hinder the efficient operation of multi-motor systems. It would be highly desirable to provide a multi-motor or motor system and method that optimizes performance while avoiding these major drawbacks. Summary of the Invention

[0007] The present invention provides a multi-component optimization control system and method capable of globally optimizing the torque load distribution between a motor and a generator. By taking into account the entire mission (e.g., information related to the entire range of the electric vehicle, including, in some embodiments, taking into account energy generation through braking) and constraints (e.g., maintaining the motor and generator temperatures below a critical level, or within a range that ensures efficient operation of the motor and the entire system), the control system of the present invention can provide a more efficient solution than systems that rely on "local" optimization and are based on a timely snapshot of the current situation. In this way, the system and method of the present invention overcome the shortcomings of existing systems that use predetermined static efficiency curves or efficiency ranges (in which only a series of independent local decisions are considered) without considering constraints such as motor and generator temperature beyond the binary non-optimization approach.

[0008] In one embodiment, the optimized control system is a high-efficiency drive motor system capable of optimizing the distribution of drive loads among multiple motors. In this case, the system can achieve higher efficiency levels than any single motor within the system. The motor system disclosed herein is a multi-motor system controlled by a controller that utilizes an efficiency-optimizing configuration. The controller communicates with multiple sensors providing inputs, and based on these inputs, generates the necessary drive signals for the multiple motors to achieve efficient operation, particularly in terms of energy consumption or regeneration (through braking) throughout the entire travel range.

[0009] The optimization system disclosed herein can be adapted for both existing and newly designed motors. These motors can be configured as multiple discs on a single shaft (rotating at the same speed) or as completely independent motors, each designed independently of the others to maintain its own torque and speed characteristics. In the latter configuration, a gear mechanism, such as a differential, allows the different motors to deliver torque to the wheels.

[0010] The motor optimization system is designed to control the distribution of drive loads across multiple motors in a manner that optimizes system performance. In one embodiment of the system, driving tasks are dynamically allocated to multiple motors based on efficiency levels at each torque and power demand point, reducing battery usage and resulting energy losses. The controller determines the torque percentage for each motor in the system while minimizing battery usage based on real-time driving conditions and motor power profiles, and generates drive signals to control the motors accordingly. In other embodiments, the system can control the multiple motors in the system to achieve performance goals other than improving efficiency, such as maximizing acceleration or driving range, or a combination of performance goals (e.g., maximizing driving range given a minimum acceptable acceleration).

[0011] The system controller is tuned to perform global optimization, determining the optimal distribution of motor loads (optimal for maximizing energy efficiency or other driving objectives) throughout the entire trip, rather than sequentially determining an optimal solution for a given point in time and a given set of current conditions (i.e., local optimization) without considering the entire trip or drive cycle. In one embodiment, the control system can provide global optimization by receiving trip information (e.g., route, total mileage, speed limit information, and trip time information) from a trip planning tool (e.g., a GPS-equipped device). This trip information may further include other trip factors and information provided by the trip planning tool, such as driving conditions (e.g., highway driving, urban or stop-and-go driving, hilly, flat terrain), as well as information related to environmental and weather conditions (e.g., temperature, humidity, wind speed, and direction) that may be provided by a weather application in communication with the trip planning tool. The controller uses the trip information to generate a drive cycle and further incorporates this information into the optimization process performed by the controller to determine the optimal solution for the entire trip. Furthermore, the controller may utilize information provided by on-board sensors (e.g., ambient temperature, motor thermals (including internal motor temperature), shaft speed, motor current, and torque demand) to validate the global optimization. At any given moment, the global optimization does not necessarily produce a better solution than the local optimization. However, when the entire journey is considered, the global optimization is always equal to or better than the sum of the multiple local optimizations in sequence.

[0012] In another embodiment, the optimized control system is applied to generators—either individually or in combination with multiple electric motors—to create a high-efficiency generator system that optimizes the distribution of available torque among the multiple generators. In this case, the system achieves efficiency levels that exceed the efficiency of any individual generator within the system. In the generator system disclosed herein, the efficiency optimization performed by the controller is configured based on inputs from multiple sensors. Based on these inputs, the controller generates the necessary signals for the multiple generators to achieve efficient operation across the entire operating range.

[0013] The generator optimization system is designed to control the allocation of tasks among the system's multiple generators in a manner that optimizes system performance. In one embodiment, the system dynamically allocates tasks among the multiple generators based on their efficiency levels at each point in the available torque cycle, maximizing both the current power generation and the resulting energy conversion losses. A controller determines the percentage of available torque for each generator in the system, maximizing the current power generation based on real-time conditions. In other embodiments, the system can control the multiple generators in the system for purposes other than improving efficiency.

[0014] By reading the following detailed description and referring to the accompanying drawings as appropriate, the above and other aspects and advantages will become apparent to those skilled in the art. In addition, it should be understood that the foregoing summary of the invention is only illustrative and is not intended to limit the scope of equivalents protected by the appended claims in any way. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The present invention will be described below with reference to the accompanying drawings, in which:

[0016] Figure 1 This is a top-level block diagram of a multi-motor system for a vehicle according to an embodiment of the present invention;

[0017] Figure 2 A block diagram of an embodiment of the present invention showing the operation of a controller and its optimization process for each motor torque command;

[0018] Figure 3 A block diagram illustrating steps taken by a controller in connection with various processes associated with determining a motor torque command signal according to an embodiment of the present invention;

[0019] Figure 4 A block diagram illustrating a raster scanning technique for determining load sharing between motors to optimize efficiency for a given shaft speed and torque is provided in accordance with one embodiment of the present invention;

[0020] Figure 5 shows an illustrative 4×4 raster in two-dimensional space according to one embodiment of the present invention (optimizing three variables (e.g., optimizing load distribution among three motors));

[0021] Figures 6A to 6C shows the power curves and efficiency contours of three large motors in an illustrative example of Operation Case 1 of a multi-motor system according to an embodiment of the present invention;

[0022] 7A to 7C shows the power curves and efficiency contours of three large motors in an illustrative example of Operation Case 2 of a multi-motor system according to an embodiment of the present invention;

[0023] Figures 8A to 8C shows the power curves and efficiency contours of three small motors in an illustrative example of Operation Case 1 of a multi-motor system according to an embodiment of the present invention;

[0024] Figures 9A to 9C The power curves and efficiency contour lines of three small motors of an illustrative example of Operation Case 2 of a multi-motor system according to an embodiment of the present invention are shown.

[0025] Figure 10AA graph showing speed versus time for a UDDS driving cycle used in testing of an embodiment of the present invention;

[0026] Figure 10B A graph showing speed versus time for a HWFET drive cycle used in testing of an embodiment of the present invention;

[0027] Figure 10C A graph showing the speed versus time of the US06 driving cycle used in testing of an embodiment of the present invention;

[0028] Figure 11 A block diagram illustrating the operation of a controller and its process for performing global optimization of torque commands for each motor according to one embodiment of the present invention. DETAILED DESCRIPTION

[0029] While the present invention has numerous embodiments, for simplicity and illustration, the principles of the invention are described with reference to several examples. However, it should be understood that this disclosure is intended as an illustrative example of the claimed subject matter and is not intended to limit the appended claims to the specific embodiments shown. It will be apparent to one skilled in the art that the invention may be practiced without limitation to these specific details. In other instances, well-known methods and structures have not been described in detail to avoid unnecessarily obscuring the present invention.

[0030] The multi-motor switching system disclosed herein can be adapted to optimize the performance of any system where the load is driven by an electric motor. For illustrative purposes, several embodiments described herein describe a multi-motor switching system integrated within a vehicle, where the loads are the vehicle's drive shaft and wheels. It will be readily understood that the multi-motor system described herein can be adapted to various other systems where multiple motors are used to drive the load.

[0031] Furthermore, the multi-motor switching system disclosed herein can be used with any type of motor. For illustrative purposes, several embodiments described herein utilize DC motors. It will be readily understood that the multi-motor system described herein can also utilize AC motors or other types of motors.

[0032] In addition, the multi-motor switching system disclosed herein can be used to control the allocation of motors to driven loads to achieve the system performance levels required by various performance target standards. For the sake of illustration, the several embodiments described herein generally focus on controlling the allocation of motors to achieve maximum efficiency and minimum power consumption. It is easy to understand that the system disclosed herein can optimize other criteria or performance targets (such as maximum range or maximum acceleration), or optimize a combination of criteria or performance targets (such as maximum range given a given minimum required acceleration level). It should also be understood that the system disclosed herein can control the allocation of motors to 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 targets are achieved within the boundaries of one or more constraints (such as motor temperature, total time, and / or maximum acceleration).

[0033] Furthermore, in many of the embodiments disclosed herein, it is assumed that the number and type of motors are predetermined. It will be readily appreciated that the methods and processes described herein for selecting an optimal motor allocation to meet certain criteria can be used to determine which motors to include in a multi-motor switching system, including factors related to the driver's observed driving patterns (e.g., driving behavior, common road conditions, and common trip characteristics), the motors' performance and efficiency, while also controlling for other factors such as the motors' cost, size, and other characteristics. Accordingly, the methods and systems described herein can be used to select a small group of motors from a larger group, or to create a group of motors, to optimally achieve a given driver's driving goals.

[0034] Description of the overall system embodiment

[0035] The controller determines the torque percentages for the motors to provide the necessary output. As further described herein, the system of the present invention determines the torque percentages for each motor while minimizing battery usage based on the drive cycle and the motor power profiles. For example, in a three-motor system, the system operates by allocating a torque percentage to each of one, two, or three motors to minimize battery usage based on the drive cycle and the power profiles of the three smaller motors.

[0036] Figure 1 This is a top-level block diagram of a multi-motor system for a vehicle according to one embodiment of the present invention. Given the speed and torque requirements of a multi-motor vehicle operator, the multi-motor system optimizes the performance of a given set of motors to achieve maximum efficiency.

[0037] The multi-motor system has three motors 102a, 102b, and 102c, designated MA, MB, and MC, respectively, driving a common motor shaft 104 connected to a gear system 106. The gear system transmits the motor power transmitted through the motor shafts to a drive shaft 108, which powers vehicle wheels 110. Although the system is shown with three motors, the system may include two, three, or more motors.

[0038] Each motor 102a, 102b, 102c is coupled to a corresponding driver 118a, 118b, 118c. The drivers are in turn coupled to a high voltage power bus 128 that connects the drivers to a voltage converter 130. Figure 1 In the illustrated embodiment, the motors are DC motors, so the converters are DC / DC converters, and the high-voltage power bus is a DC high-voltage power bus. In other embodiments employing AC motors, the system components are adapted to AC power, including through the use of an AC / DC converter and an AC high-voltage power bus. Voltage converter 130 is coupled to a rechargeable power source in the form of a battery or battery pack 132. Power in the system flows in two directions relative to the motors and batteries: battery 132 provides power to motors 102a, 102b, and 102c, driving the motor shafts and drive shafts, while power generated by braking motors 102a, 102b, and 102c or by free rotation of the motor shafts relative to the motor coils feeds battery 132.

[0039] When power is supplied from the battery 132 to the motors, the voltage converter 130 converts the power voltage from the battery 132 to a level suitable for the drivers 118 a, 118 b, and 118 c, and supplies the converted voltage and the power from the battery 132 to the drivers via the power bus 128. The drivers 118 a, 118 b, and 118 c then selectively supply current to drive the corresponding motors 102 a, 102 b, and 102 c.

[0040] When providing power from the motors to the battery, the current generated by the motors 102a, 102b, and 102c is provided to the corresponding drivers 118a, 118b, and 118c. The corresponding drivers 118a, 118b, and 118c transmit the power from this current to the high-voltage power bus 128, where it is then converted by the converter 130 to a voltage level suitable for charging the battery 132. In one embodiment, the controller also utilizes the motor's efficiency map with respect to energy production to determine the torque load to provide to each motor for optimal energy production.

[0041] System operator 126 (e.g., vehicle driver) provides input via accelerator pedal 124 (in the form of a desired increase in speed or torque Td) and via brake pedal (in the form of a desired decrease in speed). In this manner, operator 126 can be said to input information related to the vehicle's drive cycle into the system. During vehicle operation, the operator will indicate a desired speed via accelerator pedal 124, where the desired speed corresponds to a point in the drive cycle. If the provided vehicle speed is not equal to the desired speed, the operator may input an increased speed request, corresponding to an increased motor torque request.

[0042] In one embodiment, the system includes multiple sensors that enable the system to determine how to handle operator requests for increased speed and torque. A motor shaft sensor 122 measures and reports the motor shaft speed n. The system also includes current sensors 112a, 112b, and 112c, which measure and report the magnitude of the currents iA, iB, and iC delivered to the respective motors 102a, 102b, and 102c. A torque monitor 114 reads the sensed motor currents iA, iB, and iC and uses this information to calculate the motor torque. An energy monitor 116 reads the torque calculated by the torque monitor 114 and the motor shaft speed n and calculates the magnitude of the energy EM, either delivered to or generated by the motor. The system further includes a current sensor and a voltage sensor 134, which determine the magnitude of the current ib and voltage Vb supplied to or from the battery 132. The motor shaft speed n and the energy EM entering or exiting the motor are transmitted to the controller 120 along with the desired torque Td. The system also includes motor temperature sensors 140a, 140b, 140c associated with each motor, which provide the temperature T of each motor. In addition, the system also measures the ambient temperature T via an ambient temperature sensor 142. a '. The information collected from these system sensors serves as input to various system functions. In one embodiment, the controller 120 generates torque commands TA, TB, TC for each motor based on the measured motor shaft speed and the given torque demand, taking into account temperature gradients and the impact on motor efficiency, and sends them to the motor drivers 118a, 118b, 118c. In another embodiment, the controller uses the collected information (including the measured motor shaft speed and the given torque demand and other measured conditions) to provide feedback to the vehicle operator about deviations from optimal conditions and to make relevant recommendations to the vehicle operator to change driving behavior in order to achieve driving goals in a more optimized manner. In another embodiment, the collected information is used to determine the driving pattern of an individual user and create driving pattern data.

[0043] Optimization of controller operation and motor load

[0044] The controller 120 optimizes the torque commands sent to each electric machine to minimize battery energy output. Figure 2 This block diagram illustrates the operation of controller 120 and its optimization of torque commands TA, TB, and TC for each electric motor, according to one embodiment of the present invention. As described above, controller 120 continuously receives the motor shaft speed n and the operator's current torque request Td, as required at a given operating point in the drive cycle. In step 202, the controller also retrieves motor data from database 204, including efficiency data etaA, etaB, and etaC corresponding to each electric motor in the system. Each motor's efficiency data represents the efficiency of that motor at all possible speed and torque combinations, including the efficiency at various operating temperatures. 6A to 9C Examples of efficiency data for various large and small motors are provided. Motor efficiency data also includes boundary parameters for torque, power, and temperature, which can be derived from the motor's technical specifications. The motor data stored in database 204 may also include other information related to motor operation and characteristics, including the motor's operating temperature boundaries; thermal response parameters for each motor in the system (i.e., motors A, B, and C), such as the thermal conductivity of the motor steel and other factors related to motor heat generation and dissipation; and the initial motor model resistance for each motor in the system.

[0045] Figure 2This block diagram illustrates the operation of controller 120 and its optimization process for each motor's torque commands, TA, TB, and TC, according to one embodiment of the present invention. After retrieving motor efficiency data, the controller performs steps 208 and 212 to analyze the motor efficiency information and determine the optimal allocation for each motor (considering minimum battery energy usage). To perform this optimization, the controller performs a raster scanning technique and determines the optimal fraction or percentage of the torque demand, Td, that should be provided by each individual motor. The controller's analysis and output may result in driving one, two, or all three motors to allocate torque to maintain a specific operating point in the drive cycle. In step 214, the controller generates motor torque demand signals, TA, TB, and TC, for the respective motor drivers 118a, 118b, and 118c, corresponding to the fraction or percentage of the total torque demand, Td, determined by the optimization analysis. For example, if it is determined based on the motor efficiency data and the current speed and torque demand that the most efficient energy usage is produced by motor A providing 20% ​​of the torque demand, motor B providing 30% of the torque demand, and motor C providing 50% of the torque demand, the controller will output motor torque command signals TA, TB, TC to the corresponding motor drivers such that TA is equal to 20% of Td, TB is equal to 30% of Td, and TC is equal to 50% of Td. Alternatively, if it is determined that the most efficient energy usage is produced by motor A providing 100% of the torque demand, motor B providing 0% of the torque demand, and motor C providing 0% of the torque demand, the controller will output motor torque command signals TA, TB, TC to the corresponding motor drivers such that TA is equal to 100% of Td, TB and TC are equal to 0% of Td; or, another solution may be that TA is equal to 50% of Td, TB is equal to 50% of Td, and TC is equal to 0% of Td.

[0046] The controller 120 operates in real time or near real time throughout the entire drive cycle, allowing the controller to continuously adjust the motor torque demand signal based on changes in torque demand and vehicle speed. In particular, the controller continuously and step-by-step completes this process at fixed intervals or continuous operating cycles throughout the vehicle operation (combined with Figure 2 Because this analysis is performed in real time or near real time, ideally, the controller's processor 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.

[0047] In another embodiment, the controller may instead or additionally generate the motor torque command signal based on non-real-time decisions (e.g., by using preloaded rules). In one such embodiment, the preloaded rules may be determined based on previously performed optimization calculations (based on motor characteristics and a given set of variables). For example, the optimization calculations may be performed separately and used to determine in advance the optimal motor torque command to meet a given requirement (or combination of requirements) for a set of possible combinations of torque demands and shaft speeds (based on motor characteristics, such as motor efficiency data). The results of these optimization calculations are then used to construct a lookup table, which is stored in a memory accessible to the controller (e.g., memory 204). During operation, in steps 208 and 212, the controller then consults the lookup table using the given torque demand and shaft speed and determines the associated previously calculated motor torque command that optimizes the requirement or combination of requirements, including ensuring compliance with temperature constraints. In one embodiment, based on driving pattern data and the driver's goals (e.g., maximizing mileage or minimizing driving time), the controller may provide recommendations to the operator regarding changes in the operator's driving behavior to more optimally achieve the driving goals.

[0048] In another embodiment, the controller may instead or additionally employ heuristic methods to determine a suboptimal but sufficient allocation of power to each motor to meet a requirement or set of requirements. Figure 2 In one such alternative embodiment, in step 208 of , the controller employs a heuristic approach to determine a suboptimal, yet sufficient, split of torque demands among the motors to minimize battery energy consumption. By employing a heuristic approach and identifying a suboptimal solution, the controller can execute operations more quickly while still determining a split of load distribution among the motors that is sufficient to meet a given requirement or set of requirements (minimizing energy use, maximizing driving range, etc.).

[0049] Figure 3This block diagram illustrates the steps a controller takes to determine motor torque command signals, according to one embodiment of the present invention. First, for a given operating point in the drive cycle, the controller compares the relative efficiency of each motor at a given speed and torque (n, T) and then determines the motor with the highest efficiency at that point. For a system with three motors, this comparison and determination includes steps 302a and 302b, in which the efficiency of each motor is compared with the efficiency of each other motor in the system. More specifically, in step 302a, the efficiency of motor A, etaA, is compared with the efficiencies etaB and etaC of motors B and C to determine whether motor A's efficiency, etaA, is higher. Similarly, in step 302b, the efficiency of motor B, etaB, is compared with the efficiencies etaA and etaC of motors A and C to determine whether motor B's efficiency, etaB, is higher. For systems with a larger number of motors, the controller performs additional similar comparison steps to determine the motor with the highest efficiency. Alternatively, the controller may employ other algorithms to determine the most efficient motor among two, three, or more motors.

[0050] Once the most efficient motor is found, the controller tests the first condition and checks the torque level to determine if the torque demand Td is greater than the torque limit of the most efficient motor. Figure 3 In steps 304a, 304b, and 304c, the controller compares the torque demand Td with the motor torque boundaries determined in the previous step to achieve the highest efficiency at a given speed and torque in the drive cycle. For example, if steps 302a and 302b determine that motor B has the highest motor efficiency at a given point in the drive cycle, then in step 304b, the torque demand Td is compared with the torque boundary TbB for motor B to determine whether the torque demand Td exceeds this boundary. If the torque demand exceeds the torque boundary of the most efficient motor, the specified torque demand cannot be met by that motor alone, and the controller can implement torque demand load sharing among the multiple motors. Therefore, the controller proceeds to step 208 to perform multi-motor optimization analysis.

[0051] However, if the torque demand does not exceed the torque boundary and satisfies the torque boundary requirement, the controller tests the second condition and determines whether the motor can provide the required power or if it exceeds the motor power boundary. In steps 306a, 306b, and 306c, the motor power required for the most efficient motor at a given point in the drive cycle is calculated. The motor power is calculated as the product of the torque demand (e.g., in Newton-meters (Nm)) and the motor shaft speed (e.g., in revolutions per minute (rpm)). After calculating the required motor power, the controller compares the motor power to the power boundary of the relevant motor in steps 308a, 308b, and 308c to determine whether the motor power exceeds the power boundary of that motor. For example, if motor C is determined to have the highest motor efficiency at a given point in the drive cycle by steps 302a and 302c, and further determines in step 304c that the torque demand Td does not exceed the torque boundary TbC of motor C, the controller compares the required motor power PC with the power boundary PbC of motor C. If the required motor power exceeds the power limit of the relevant motor, it can also be determined that the motor itself cannot meet the specific power requirement, and the controller can implement torque demand load distribution among multiple motors. Therefore, the controller continues to execute step 208 to implement multi-motor optimization analysis.

[0052] However, if both conditions are met, and the torque demand and required power do not exceed the motor boundaries, the controller directly sends the torque demand to the motor determined to have the highest efficiency for the given motor shaft speed and torque (n, T) in the drive cycle. This is done by setting the motor torque command for that motor to the total torque demand, Td. For example, if motor A is determined to have the highest efficiency in step 302a, and if the torque boundary conditions for motor A are determined to be met in step 304a, and if the power boundary conditions for motor A are subsequently determined to be met in steps 306a and 308a, the controller will generate a motor A torque command, TA, equal to the total torque demand, Td. However, if both the torque boundary conditions and the power boundary conditions are not met, the controller switches to a multi-motor operating mode, distributing the torque demand among the multiple motors, and the controller implements a multi-motor optimization analysis.

[0053] Figure 4is a block diagram illustrating a raster scanning technique for determining load distribution among motors to optimize efficiency for a given shaft speed and torque at a given operating point in a drive cycle. As described above, a controller receives motor speed n and torque demand Td at a given operating point in a drive cycle. When the controller determines that load distribution among multiple motors is necessary, the controller determines the percentage of the torque demand to be provided by each motor by implementing the following raster scanning technique so that the combined motor torque demand sent by the controller to the motor drivers equals the total torque demand. For example, in the illustrated system with three motors, for a given torque demand Td, the controller 120 generates motor torque commands TA, TB, TC, where 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 the raster scanning analysis, in steps 402 and 404, the controller first defines a raster in m-dimensional space, where m is less than or equal to the number of motors. Additionally, the controller defines the raster as having a specified number of points or intervals in each dimension of the m-dimensional space, where the number of intervals corresponds to the level of accuracy required for the optimization calculations, with more intervals providing greater accuracy. Figure 5 A 4×4 raster in two-dimensional space is shown by way of illustrative example only (optimizing three variables (e.g., optimizing the load distribution among three motors)). In the example shown, a 4×4 raster of points in two-dimensional space is defined at regular intervals such that x and y can assume the values ​​0, 0.333, 0.667, and 1, respectively. At each point, the z value is known to be z = 1-xy. In step 406, a set of test torques for each motor is specified, where the test torque at each point is equal to the product of the torque demand and the value in the raster corresponding to the particular motor. Figure 4In the example shown, the x value in the x dimension corresponds to the percentage of torque demand (TA) allocated to motor A, the y value in the y dimension corresponds to the percentage of torque demand (TB) allocated to motor B, and the z value (defined by x and y) corresponds to the percentage of torque demand (TC) allocated to motor C. TA = Td*x, TB = Td*y, and TC = Td*z. In step 408, the motor efficiency at each point in the raster scan is calculated based on the stored efficiency data for each motor. In step 410, a 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 in time. For a three-motor raster scan, the cost function takes the form F(x, y) = x / etaA + y / etaB + z / etaC. In steps 412 and 414, the controller determines the minimum cost function Fmin from 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 demand allocated to each motor to achieve the highest system operating efficiency. For three motors, the minimum cost function Fmin occurs at point (xmin, ymin, zmin). In step 416, the torque demand for each motor is calculated based on the minimum values ​​calculated in steps 412 and 414, so that each motor receives an allocated torque corresponding to the product of the torque demand and the point in the raster corresponding to the minimum cost function Fmin. For example, for three motors, the calculated torque command is calculated by the controller according to the following formula: TA = Td*xmin, TB = Td*ymin, TC = Td*zmin. In step 418, the calculated torque command is sent to the corresponding drive.

[0054] The controller 120 may perform a raster scanning technique to optimize two, three, or more motors. If the number of motors is large, the controller may utilize other techniques to determine the optimal load distribution among the motors, such as using a steepest descent method of the gradient of a cost function. For example, in the case of using more than five motors, the controller may use a steepest descent method to determine the optimal load distribution for given driving conditions. In other embodiments involving a larger number of motors, the controller uses a heuristic method to determine a suboptimal but sufficient distribution for each motor to meet one or a set of requirements. Although the additional motors in step 208 increase the computational complexity, by using a heuristic method instead of an optimization technique, the controller is able to generate torque commands in near real time. In other embodiments involving a larger number of motors, the controller generates motor torque command signals based on non-real-time decisions, such as using pre-loaded rules based on a priori optimization calculations for a given set of variables.

[0055] Furthermore, while the above techniques are described as optimizing performance, it should be understood that they can be used to determine a load distribution that achieves a desired threshold, rather than the optimal solution. For example, if a user specifies that the controller control the load distribution between the motors to achieve a specific range (e.g., 100 miles), the controller uses the above analysis techniques to determine which load distribution combinations between the motors can provide the given range. Based on these combinations, the controller can then use the same analysis techniques to determine which combination to use based on another metric (e.g., optimizing efficiency or achieving a minimum acceleration threshold).

[0056] While the multi-motor optimization process has been described above with respect to maximum efficiency and minimum battery energy usage, the controller 120 may optimize the load distribution among the multiple motors based on a variety of factors. For example, the controller may optimize for maximum range or acceleration instead of, or in addition to, optimizing for a combination of factors, such as the minimum level of acceptable acceleration for maximum range.

[0057] Global optimization (including environmental variables and temperature)

[0058] System 100 can also utilize environmental information to perform global optimization throughout the drive cycle. This global optimization is performed across the entire drive cycle, based on the fact that the operating point is related to the nature of the drive cycle, not independently of the other. For example, within a given drive cycle, real-world constraints and physical limitations on the motors and the entire system (including acceleration limits) ensure that the differences between operating points (in terms of speed and torque) are not too large. To illustrate this relationship, a new global optimization is performed at each operating point, taking into account all past and future points. Because the global optimization considers the impact of past operating points on future operating points in the drive cycle, it is not simply the sum of optimizations performed at a series of points. For example, considering thermal transients, each operating point affects future operating points. Specifically, heat does not dissipate immediately but accumulates over time. This heat accumulation, based on past torque commands, influences the torque distribution among the multiple motors, providing optimal efficiency within the system and protecting the future performance of the motors.

[0059] Figure 11 This block diagram illustrates one embodiment of the present invention, illustrating the operation of controller 120 and its process for performing a global optimization of torque commands for each motor to achieve a specified performance target. In the embodiment described herein, the performance target is energy minimization. However, as mentioned above, it should be understood that the global optimization may employ other performance targets or combinations of performance targets. It should also be understood that the global optimization may be performed to ensure that the performance target is achieved within the bounds of one or more constraints, such as motor temperature, maximum travel time, maximum speed, and / or maximum acceleration.

[0060] As mentioned above, global optimization considers the impact of past operating points on future operating points during the drive cycle. In the example presented here, global optimization takes into account thermal transients. However, other transient variables and conditions that may affect motor efficiency and performance can also be considered in addition to or as an alternative to motor temperature.

[0061] In step 1101, the controller receives input information from various system components and initializes the variables that will be used to perform the global optimization. The controller directly receives the driving cycle information from the trip planning tool; alternatively, the trip planning tool provides the trip information to the controller, and the controller determines the driving cycle information and other driving cycle characteristics based on this information. The controller then determines the time t during the driving cycle. i = (t0, t1, ..., t f ) when the speed and torque data (n di , T di ), where t represents time, i represents the index of the time step in the driving cycle data, and t i represents the time of step i in the driving cycle, t i = (t0, t1, ..., t f ) refers to the i-th time step, t0 represents the initial time step, t f Indicates the last step time.

[0062] In addition, the controller also receives vehicle-related mechanical data from the database 204 or other memory storage device or inputs vehicle-related mechanical data, such as the air friction coefficient; thermal response parameters of each motor k in the system (i.e., motors A, B, and C), such as the thermal conductivity of the steel used in the motor and other factors related to heat generation and heat dissipation; initial motor model parameters R for each motor k in the system k (T' k0 ); and electromechanical parameters of the motor, such as the motor bus voltage; and any temperature constraints or other constraints (such as maximum acceleration). These values ​​need to be calculated or measured in advance and stored in database 204 for use by the controller when performing global optimization. These values ​​can also be measured and calculated in real time to provide feedback to the controller for verification of the global optimization performed by the controller.

[0063] In step 1102, the controller initializes the time variable. For example, the controller sets i = 0 and sets t = t0 = 0.

[0064] In step 1103, the controller also calculates the driving cycle according to (n di , T di) calculates the acceleration at the operating point. In addition, the controller also updates any transient variables that affect the performance of the motor. In the embodiment shown, the controller updates the temperature T' of each motor k at step i in the driving cycle ki At time t0 = 0, the motor temperature T' can be determined by the motor temperature sensors 140a, 140b, 140c associated with each motor. ki After that, the motor temperature T' ki Affected by ambient temperature T a ' (measured by ambient temperature sensor 142) and the influence of heat generation and heat dissipation inside the motor. Provide the motor temperature measurement value T' to the controller ki and ambient temperature measurements.

[0065] In step 1104, the controller initiates optimization calculations. In the embodiment illustrated herein, the system initiates optimization using the disclosed raster scanning technique within the ranges x and y, where 0 <= x <= 1, 0 <= y <= 1, 0 <= z <= 1, and 1 = x + y + z. The x value in the x dimension corresponds to the specified torque demand percentage (TA) for motor A, the y value in the y dimension corresponds to the specified torque demand percentage (TB) for motor B, and the z value (bounded by x and y) corresponds to the specified torque demand percentage (TC) for motor C. For systems with more motors, additional dimensions are included in the raster scanning process. For values ​​within the range (x, y), the controller performs thermal calculations and determines a cost function, as described below.

[0066] In step 1105, the controller performs a thermal calculation to determine the temperature change of the motor under the specified torque demand distribution between the motors. The controller calculates the thermal transient and, after a period of time Δt, at the end of step i at t i+1 Get the simulated temperature T' of each motor k k（i+l） For this calculation, the controller uses the motor thermal response parameters for each motor k retrieved from the database 204 .

[0067] In step 1106, the controller performs a cost function analysis for the specific torque demand distribution among the motors relative to the desired objective function. For each motor k, the controller calculates R k (T' k（i+l） ), that is, temperature T' k（i+l） and t （i+l） The motor parameter vector for the next k-th motor. For each motor k, the controller calculates η k（i+l） , that is, the working point (n d（i+l） , T d（i+l） ) and time t （i+i） The efficiency of motor k at . Then, the controller calculates (xi ,y i ) Cost function F associated with the performance criterion at point c (x i ,y i In the illustrated embodiment, the performance criterion is efficiency maximization, and the cost function is energy usage.

[0068] In step 1107, the controller determines whether the optimization analysis has been performed for the entire range (x, y). If not, the x and y values ​​are updated in step 1112. The controller selects the next x value in the range 0 <= x <= 1 and the next y value in the range 0 <= y <= 1, and calculates the next z value, where z = lxy. Steps 1105 and 1106 are then performed for the updated x and y values. Once the optimization analysis has been performed for the entire range (x, y) and the cost function has been derived for these points, the controller proceeds to step 1108.

[0069] In step 1108, the controller determines the results of the optimization analysis and outputs the torque command obtained thereby. The controller calculates F in the range (x, y) m , that is, the cost function F c The controller recognizes the minimum value of (x, y). m The corresponding optimal (x, y) = (x 最佳 ,y 最佳 ). At time t i+l When the controller uses (x 最佳 ,y 最佳 ) outputs the optimal torque to the motor. Then, the controller calculates Δt = t i＋l – t i The battery energy consumed during the period and updated 0 <= t <= t i＋l The total battery energy consumed during the period so far.

[0070] In step 1109, the controller determines whether the drive cycle has ended. i＋l >= t f If the driving cycle has not yet ended, t i＋l < t f , then in step 1110, the time variable is incremented so that t i Increment to t i＋l , i increments to i+1, and steps 1102 to 1108 are repeated based on the updated t and i values. If the driving cycle has ended, t i＋l >=t f , the controller enters step 1111.

[0071] In step 1111 , the controller outputs the result of the global optimization calculation, such as the total battery power consumed during the driving cycle.

[0072] In one embodiment, during the drive cycle and implementation of the torque command generated by the global optimization, the controller uses current measurements from on-board sensors (e.g., ambient temperature, motor thermals (including internal motor temperature), shaft speed, motor, motor current, and torque demand) to validate the global optimization.

[0073] In another embodiment, the controller continuously performs global optimization throughout the drive cycle, taking into account updated trip information, updated drive cycle characteristics, and updated measured and calculated values ​​of variables (eg, motor temperature, ambient temperature).

[0074] Simulations of multi-motor system operation show improved efficiency

[0075] The multi-motor system described above can be applied to various systems. For illustrative purposes, this article provides examples of its use in electric vehicle systems. It should be understood that the multi-motor system and controller described herein can be applied to systems that use electric motors to perform work.

[0076] Illustrative examples of the application of the system disclosed herein are provided by combining simulation studies of battery usage for multiple motors in a vehicle under different driving scenarios, represented by driving pattern data or "drive cycles." The following sections describe specific motors and drive cycles.

[0077] In an illustrative example, the controller determines the load distribution between the motors according to the optimization procedure described herein. The battery output represents energy usage. Comparing battery usage to a system without load distribution between the motors demonstrates improved efficiency in the disclosed system.

[0078] In the simulation tests described below, two motor cases were explored for each of the large and small motors, where the different cases (Case 1 and Case 2) corresponded to groups of motors with different characteristics.

[0079] Testing the motor

[0080] A system simulation test was conducted combining large and small motors. Each motor in the system has a unique power curve. For testing purposes, both large and small motors were represented using similar models, including identical resistors. In the example provided, a resistor matrix was used to define the motor circuit model. The circuit model parameters were identical for both large and small motors.

[0081] Figures 6A to 6C The power curve value lines of three large motors of an illustrative example of Operation Case 1 of a multi-motor system according to an embodiment of the present invention are shown. 7A to 7C The power curves and efficiency contour lines of three large motors of an illustrative example of Operation Case 2 of a multi-motor system according to an embodiment of the present invention are shown. Figures 8A to 8C The power curves and efficiency contour lines of three small motors of an illustrative example of Operation Case 1 of a multi-motor system according to an embodiment of the present invention are shown. Figures 9A to 9C The power curves and efficiency contour lines of three small motors of an illustrative example of Operation Case 2 of a multi-motor system according to an embodiment of the present invention are shown.

[0082] For large motors, the peak efficiency region is optimally distributed across the (n, T) space. For small motors, the power curve is limited by boundaries (constraints), so only the (n, T) points within the boundaries are considered valid. In particular, the power loss boundary imposes restrictions on smaller motors. In simulations, smaller motors cannot operate with power losses above the boundary. The power curve for large motors is unbounded, so every (n, T) applies to the large motor. In simulations, the large motor can operate across the entire plane (defined by the (n, T) drive cycle points). Furthermore, while the efficiency distribution for large and small motors may be the same, their valid (n, T) points may differ.

[0083] Motor losses were calculated based on nominal power curve values. For the modeling and testing described in this article, motor power loss values ​​were calculated using an estimated overall efficiency of 0.96 to simplify the calculations.

[0084] Test drive cycle

[0085] The EPA drive cycle used to test the systems described in this article (see http: / / www.epa.gov / vehicle-and-fuel-emissions-testing / dynamometer-drive-schedules). Each drive cycle is characterized by the required speed and torque at each point. The following provides information about the basic drive cycle used to test the system.

[0086] The EPA Urban Dynamometer Drive Schedule (UDDS), commonly referred to as "LA4" or the "City Test," represents urban driving conditions. This schedule is used for testing light-duty vehicles. United Nations Economic Commission for Europe (UN / ECE) Regulation No. 53 defines the EPA UDDS as "a test equivalent to a Type 1 test (verification of emissions after cold start)." Figure 10A Figure 2 shows a speed versus time graph for the UDDS drive cycle.

[0087] The Highway Fuel Economy Driving Program (HWFET) represents highway driving conditions below 60 mph. Figure 10B Figure 2 shows a speed versus time graph for the HWFET drive cycle.

[0088] The US06 driving program is a high-acceleration aggressive driving program, often referred to as a "supplemental FTP" driving program. Figure 10C Figure 2 shows a speed versus time graph for the US06 driving cycle.

[0089] Results of the simulation test

[0090] Simulations of systems using two different sets of motors (Case 1 and Case 2) show that, using the systems and techniques described in this article, a set of three small motors performs as efficiently as three large motors and outperforms any single large motor. Therefore, using the systems and techniques described in this article, it is possible to implement a multi-motor system composed of small motors that are more efficient than a system of large motors, thereby improving motor system performance and reducing costs.

[0091] Optimizing energy production

[0092] As described above, through a process reversed from the one described above, the multiple motors in the system can also function as generators, where the torque generated by the drive shaft is distributed to each motor so that they can generate the current needed by the battery. For example, when the vehicle is braking, the controller can selectively engage the motors to receive the available torque from the drive shaft, thereby generating current. In this case, the corresponding drivers 118a, 118b, and 118c function as rectifier systems. As described below, based on torque command signals TA, TB, and TC from controller 120, each rectifier system acts as a power converter, controlling the proportion of available torque handled by its corresponding generator (and, therefore, the proportion of total current generated). Each rectifier system processes a portion of the available torque by controlling the degree to which its respective generator is engaged with the generator shaft 104. Each rectifier system controls the amount of torque handled by the generator, thereby controlling the amount of current generated by the generator, by adjusting the pulse width modulation (PWM) duty cycle of the corresponding generator. Current sensors 112a, 112b, and 112c monitor the current generated by each generator and provide the current to monitor 114, which calculates the electromagnetic torque T generated by the generator. Second monitor 116 receives electromagnetic torque T and generator shaft speed n and calculates the energy EG output by the generator. Each rectifier then supplies energy to a DC high-voltage power bus 128. DC / AC converter 130 acts as an inverter, transferring energy from bus 128 to an external energy load, such as a power grid or energy storage system.

[0093] Optimizing multi-motor system design

[0094] When selecting motors for building a multi-motor system (such as the three-motor system 100 described above), efficiency optimization can also be performed using the aforementioned optimization techniques. Given a set of motors, each with a specific set of characteristics (e.g., efficiency characteristics, torque limits, power limits, etc.), and a desired number of motors (e.g., three motors), the aforementioned raster scanning technique can be used to determine the optimal motor for a given drive cycle or set of drive cycles (one that best matches the observed driver driving pattern (driving behavior, common road conditions, trip characteristics, etc.)). It will be readily appreciated that the methods and processes described herein for selecting the optimal motor allocation to meet specific criteria can be used to determine which motors are included in a multi-motor switching system, including those based on motor performance and efficiency-related factors, while also controlling for other factors such as motor cost, size, and other characteristics.

[0095] According to an embodiment of the present invention, the following system and process are utilized to select motors for inclusion in a multi-motor switching system. First, the motor selection system receives driving pattern data. This driving pattern data includes data similar to the exemplary "drive cycle" data described above and can be generated through a similar process. In one example, the driving pattern data is generated by recording the acceleration and torque conditions experienced by a specific individual in a given test vehicle during one or more driving events. The driving data is then combined to generate the final driving pattern data for the motor selection process.

[0096] Once the driving mode data is received, the motor selection system receives motor information data from the motor information database. The motor information data is stored in the database and includes data related to the operating characteristics of multiple motors, including the power curve and efficiency contour characteristics of each motor; vehicle mechanical data, such as the air friction coefficient; thermal response parameters of each motor k in the system (i.e., motors A, B, and C), such as the thermal conductivity of the steel used in the motor and other factors related to heat generation and heat dissipation; and the initial motor model resistance R of each motor k in the system. k (T' k0 ); and motor electromechanical parameters stored by the system in a database, such as motor bus voltage. The motor information data may also include other motor-related information, such as the cost, weight, and space requirements of each motor. The motor information database can be local to the motor selection system or a remote database accessed via a network connection.

[0097] The motor selection system receives the number of motors to be selected as a variable input. The number of motors acts as a constraint on the motor selection process, causing the motor system to output a fixed number of motors (e.g., if three motors are input to the system, only three motors are selected for installation in the vehicle).

[0098] The motor selection system uses the driving mode data, motor information data, and the number of motors to be selected to perform a global optimization process to determine a set of optimized motors suitable for this driving mode. In the global optimization process performed by the motor selection system, the driving mode data corresponds to the driving cycle, Figure 11 The global optimization process in is described according to this driving cycle; the motor characteristics used for the optimization analysis correspond to the motor efficiency data (including motor parameters) used in the optimization process; the number of dimensions in the raster scan is set by the number of motor inputs to be selected. A cost function can be defined based on the desired criteria, such as minimizing energy consumption or maximizing acceleration. In addition to efficiency, motor selection can further consider additional constraints and boundaries, such as motor cost, weight, and space. After the global optimization process, the motor selection system outputs the motor combination that provides the best solution that meets the desired criteria. In this way, the motor selection system can provide a set of motors customized for individual driving styles, providing the user with an optimized set of motors (e.g., with the highest energy efficiency).

[0099] Furthermore, based on driving pattern data and the driver's goals (such as maximizing mileage or minimizing driving time), the controller can provide the driver with recommendations on how to improve driving behavior in order to achieve the driving goals in a more optimized manner.

[0100] In addition to the raster scanning technique described above, other techniques can be used to optimize motor selection for multi-motor systems. These include the following: Nelder-Mead simplex algorithm, gradient descent, Newton's method, or Newton-Raphson method.

[0101] While the present invention has been described with respect to several preferred embodiments, it should be understood that numerous modifications, permutations, and equivalents are also within the scope of the present invention. It should be noted that there are alternative ways to implement both the process and apparatus of the present invention. For example, the steps do not necessarily need to be performed in the order shown in the accompanying drawings, and the steps may be rearranged as appropriate. Accordingly, the appended claims are intended to encompass all such modifications, permutations, and equivalents within the true spirit and scope of the present invention.

[0102] The system components described above may be implemented in digital electronic circuitry, computer hardware, firmware, software, or a combination thereof. The system components may be implemented as a computer program product (i.e., a computer program tangibly embodied in an information carrier), for example, in a machine-readable storage device or a propagated signal, for execution by, or to control the operation of, a data processing apparatus (e.g., a programmable processor, a computer, or multiple computers).

[0103] Processors suitable for executing a computer program include general-purpose and special-purpose microprocessors, and any one or more processors of any kind of digital computer. Generally, a processor will receive instructions and data from read-only memory or random-access memory, or both. The essential elements of a computer are a processor that executes instructions and one or more memory devices that store instructions and data. Generally, a computer will also include one or more mass storage devices (e.g., magnetic, magneto-optical, or optical disks) for storing data, or be operatively coupled to receive data from or transfer data to one or more mass storage devices for storing data, or both. Suitable information carriers for embodying computer program instructions and data include all forms of nonvolatile memory, including, for example, 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 ROMs and DVD-ROMs. The processor and memory may be supplemented by, or incorporated in, special-purpose logic circuitry.

[0104] In the context of this disclosure (especially in the context of the claims), the use of the terms "a," "an," "the," and similar referents should be construed to include both the singular and the plural, unless otherwise indicated herein or clearly contradicted by context. All methods described herein can be performed in any suitable order unless otherwise indicated herein or clearly contradicted by context. The use of any and all examples or exemplary language (e.g., preferably, preferably) provided herein is intended merely to further illustrate the present disclosure and does not limit the scope of the claims. No language in the specification should be construed as indicating any non-claimed element essential to the practice of the present disclosure.

[0105] Various embodiments are described herein. Variations of the disclosed embodiments will become apparent to persons skilled in the art upon reviewing the foregoing disclosure. The inventors intend that such variations (e.g., altering or combining features or embodiments) will be appropriately utilized by those skilled in the art, and the inventors contemplate practicing the invention otherwise than as specifically described herein.

[0106] Accordingly, to the extent permitted by applicable law, the present invention includes all modifications and equivalent variations of the subject matter described in the claims appended hereto. In addition, unless otherwise indicated herein or clearly contradicted by context, the present invention encompasses any combination of the above elements in all possible variations thereof.

Claims

1. A multi-motor system capable of performing global optimization to determine an optimal torque distribution among motors in the system based on a given torque demand to achieve a desired performance target over a drive cycle while maintaining the desired performance target within one or more constraints, the system comprising: a plurality of motors mechanically coupled to the shaft; a database for storing efficiency data associated with each of the plurality of motors; a controller for receiving drive cycle information and generating torque commands to the plurality of electric motors; The controller generates torque commands for the plurality of motors by performing an optimization analysis of potential torque distribution combinations for the motors in all time periods of a driving cycle, the optimization analysis comprising the following steps: Calculate shaft speed and torque at all time periods based on driving cycle information; calculating, based on the efficiency data, a cost function associated with a performance target for each potential combination of torque distribution for the plurality of motors over all time periods; determining one or more optimal torque distribution combinations from a set of potential torque distribution combinations, the one or more optimal torque distribution combinations being optimized for a desired performance goal according to an associated cost function and maintained within one or more constraints; and At each time period in the drive cycle, a torque command signal is generated for each electric motor so that the multiple electric motors drive the shaft according to one or more optimal torque distribution combinations to achieve a desired performance target throughout the entire drive cycle while maintaining the desired performance target within one or more constraints.

2. The system according to claim 1, wherein: The performance objective is to maximize efficiency, and the cost function for each potential combination of torque distribution between the motors is the amount of energy used.

3. The system according to claim 2, wherein: The controller estimates energy consumption for each time period during a driving cycle, wherein the controller outputs the estimated total power consumption for the driving cycle.

4. The system according to claim 1, wherein: The one or more constraints include a temperature of each of the motors.

5. The system according to claim 1, wherein: The optimization analysis performed by the controller further includes, for each time period in the drive cycle, calculating an efficiency of each of the plurality of electric machines for a next time period in the drive cycle based on the efficiency data and the one or more transients.

6. The system according to claim 5, wherein: The one or more transients include a thermal transient, wherein the optimization analysis performed by the controller further includes calculating the thermal transient to determine an estimated temperature of each of the plurality of electric machines for a next time period.

7. The system according to claim 1, wherein: The system is installed inside an electric vehicle, wherein the multiple motors are electric motors.

8. The system according to claim 1, wherein: The system further includes a trip planning tool, wherein the trip planning tool is operable to generate drive cycle information.

9. The system according to claim 2, wherein: The optimization analysis performed by the controller further includes integrating brake energy regeneration optimization.

10. A method for performing global optimization to determine an optimal torque distribution among electric machines in a system based on a given torque demand to achieve a desired performance target over a driving cycle while maintaining the desired performance target within one or more constraints, the method comprising: storing efficiency data associated with each of the plurality of motors; receiving driving cycle information; The torque commands for the plurality of electric machines are generated by performing an optimization analysis of potential combinations of torque distribution for the electric machines during all time periods of a driving cycle, the optimization analysis comprising the following steps: Calculate shaft speed and torque at all time periods based on driving cycle information; calculating, based on the efficiency data, a cost function associated with a performance target for each potential combination of torque distribution for the plurality of motors over all time periods; determining one or more optimal torque distribution combinations from a set of potential torque distribution combinations, the one or more optimal torque distribution combinations being optimized for a desired performance goal according to an associated cost function and maintained within one or more constraints; and At each time period in the drive cycle, a torque command signal is generated for each electric motor so that the multiple electric motors drive the shaft according to one or more optimal torque distribution combinations to achieve a desired performance target throughout the entire drive cycle while maintaining the desired performance target within one or more constraints.

11. The method according to claim 10, wherein: The performance objective is to maximize efficiency, and the cost function for each potential combination of torque distribution between the motors is the amount of energy used. 12 . The method of claim 11 , further comprising estimating energy consumption for each time period in the drive cycle and outputting an estimated total power consumption for the drive cycle.

13. The method according to claim 10, wherein: The one or more constraints include a temperature of each of the motors.

14. The method according to claim 10, wherein: The optimization analysis further includes, for each time period in the driving cycle, calculating an efficiency of each of the plurality of electric machines in a next time period in the driving cycle based on the efficiency data and the one or more transients.

15. The method according to claim 14, wherein The one or more transients include a thermal transient, wherein the optimization analysis further includes calculating the thermal transient to determine an estimated temperature of each of the plurality of electric machines for a next time period.

16. The method according to claim 10, wherein The method is executed by a controller installed in the electric vehicle, wherein the plurality of motors are electric motors.

17. The method according to claim 10, wherein The system further utilizes a trip planning tool to generate drive cycle information.

18. The method according to claim 16, wherein The optimization analysis performed by the controller further includes integrating brake energy regeneration optimization.

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