System and method to maximize the energy efficiency of motor units in real-time

Abstract

A control system for motor units (11) comprises a plurality of devices (23, 24, 30, 31) for determining operating values of each motor unit (11) and at least one local controller (20) for collecting data from the devices (23, 24, 30, 31) of at least one motor unit (11). The local controller (20) processes and dynamically communicates the collected data to a remote cloud-based computing management and control platform 31 which continuously analyses the characteristics of the motor units (11) utilizing artificial intelligence and transmits control data to the local controller (20), the local controller (20) being configured to adjust operating characteristics of the motor units (11) in response to control data in real time. The disclosed control system and associated control method can thus independently or universally maximize the energy efficiency of a plurality of motor units (11), such as the motor units of electric road vehicles (10), building HVAC installations (10'), marine vessels (10'') or factories (10''').

Description

[0001] SYSTEM AND METHOD TO MAXIMIZE THE ENERGY EFFICIENCY OF MOTOR UNITS IN REAL-TIME

[0002] BACKGROUND

[0003] 1. Field of the Invention

[0004] This invention relates to a control system and method to maximize the energy efficiency of motor units in real-time.

[0005] 2. Related Background Art

[0006] Motorized devices such as electric vehicles, and heating, ventilating and air- conditioning (HVAC) installations comprise a motor unit. One such motor unit may comprise an electric motor and a control circuit having an inverter or the suchlike which modifies the voltage waveform applied to the electric motor, so as to alter its speed and torque characteristics.

[0007] A problem is that motor units often do not function at their optimal level in terms of energy consumption and operational parameters, such as vector currents and inductances. Furthermore, when updates are made to alter certain parameters of a motor unit, the updates have to be applied individually to each motor.

[0008] In order to address this issue, there is a need for an efficient and intelligent system that has the ability to optimize energy requirements and operational parameters in realtime across motor units.

[0009] Accordingly, we have now devised an intelligent control system for motor units which manipulates and configures the motor units in real-time for energy optimization and longevity of the motor unit and its components.

[0010] SUMMARY

[0011] In accordance with the present invention there is provided a control system to maximize the energy efficiency of a plurality of motor units in real-time, the system comprising: a plurality of devices for determining operating values of each motor unit; a local controller for receiving data from said devices, the local controller having a transmitter for transmitting motor data and a receiver for receiving control data; a remote cloud-based computing management and control platform having a receiver for receiving said motor data transmitted by said local controller, a processor for analyzing said motor data and for generating said control data, and a transmitter for transmitting said control data to the local controller, the local controller being configured to adjust operating characteristics of at least one said motor unit in response to said control data in real-time.

[0012] The present invention provides a centralized system to intelligently learn, control and manage different motor units situated in different locations, which results in optimal performance compared with existing arrangements. The cloud-based computing management and control platform optimizes energy efficiency requirements, predicts the health of the motor units, and improves the life of the components by altering the parameters of the connected motor units in real-time.

[0013] Disclosed are a system and method for monitoring and dynamically implementing the energy requirements of the connected systems via the Assisted Intelligence Computing (AIC), which alters the parameters of the systems in real-time to achieve optimal energy usage and performance.

[0014] The processor of the remote cloud-based computing management and control platform may be an artificial intelligence controller adapted to determine the optimal mode of operation of each motor unit.

[0015] The remote cloud-based computing management and control platform may comprise a database provided in a standalone or integrated cloud-based storage device, the database being adapted to store historic data relating to the motor units, the processor being arranged to generate the control data by comparing the received motor data with the historic data.

[0016] A predictive analysis module may be adapted to analyze ageing parameters and / or health of the motor units and to update the local controller with analysis information. The local controller may be communicatively coupled to collect said analysis information from the predictive analysis module and to transmit said analysis information to the remote cloud-based computing management and control platform.

[0017] The predictive analysis module may be arranged to transmit said analysis information to the remote cloud-based computing management and control platform.

[0018] The plurality of devices for determining operating values of each motor unit may comprise devices selected from devices for determining Id / lq currents in the motor unit, devices for determining voltage in the motor unit, devices for determining the resistance of a winding in the motor unit, devices for determining the insulation of a winding in the motor unit, devices for determining the Ld / Lq inductances of a winding in the motor unit, devices for determining the temperature of a winding in the motor unit, devices for determining the magnetic flux linkage of a winding in the motor unit, and devices for determining the torque generated by the motor unit. The artificial intelligence controller may utilize machine learning algorithms to analyze the received motor data to detect health and anomalies in any motor unit, and to identify problems, such as component wear or impending failures.

[0019] The processor may comprise a database for storing user information regarding a user to be associated with each motor unit and a server for communicating with remote users.

[0020] The server may be adapted to transmit an SMS, email or other electronic alert message to a user when the processor detects an issue with a motor associated with the user. A user can update the server to customize the alert messages, for example to allow a user to control the types of alerts and notifications that they receive.

[0021] The server may comprise an interface (such as an application programming interface API) which allows a user to remotely manage and oversee (for example over the internet) the functionality of the control system pertaining to a motor associated with that user.

[0022] The remote cloud-based computing management and control platform may be adapted to transmit and receive secure end-to-end data to and from the or each local controller. The remote cloud-based computing management and control platform and the or each local controller may be assigned IP addresses and arranged on a local IP network, thereby allowing users to have complete control and enhanced security. Such a system can operate within a secure intranet network without cloud access. All primary features and remote functionality remain available in this setup. Instead of a web application, remote control and monitoring are handled through a local IP application.

[0023] The artificial intelligence controller may be adapted to generate the control data by determining the rate of change of operating values of each motor unit.

[0024] Data received and generated by the remote cloud-based computing management and control platform may be transmitted externally of the system for analysis by a third party.

[0025] An energy management module may be adapted to collect energy-related information from various components and to update the local controller with energy information.

[0026] The local controller may be communicatively coupled to collect said energy information from the energy management module and to transmit said energy information to the remote cloud-based computing management and control platform.

[0027] The energy management module may be arranged to transmit said energy information to the remote cloud-based computing management and control platform.

[0028] A plurality of local controllers may be provided for receiving data from devices of respective motor units.

[0029] A single local controller may be provided for receiving data from devices of a plurality of motor units.

[0030] Also, in accordance with the present invention, there is provided a method of controlling motor units, the method comprising: determining operating values of each motor unit; transmitting motor data from a local controller to a remote cloud-based computing management and control platform; analyzing said motor data at said remote cloud-based computing management and control platform and generating control data; transmitting said control data to the local controller; and adjusting operating characteristics of at least one said motor unit in response to said control data in real-time.

[0031] The method may comprise storing historic data relating to the motor units at said remote cloud-based computing management and control platform and generating the control data at said remote cloud-based computing management and control platform by comparing the received motor data with the historic data.

[0032] The method may comprise utilizing a predictive analysis module to analyze ageing parameters and / or health of the motor units and to update the local controller with analysis information.

[0033] The determined operating values of each motor unit may comprise values selected from Id / lq current values in the motor unit, voltage values in the motor unit, resistance values of a winding in the motor unit, insulation values of a winding in the motor unit, Ld / Lq inductance values of a winding in the motor unit, temperature values of a winding in the motor unit, magnetic flux linkage values of a winding in the motor unit, and values of torque generated by the motor unit.

[0034] The method may comprise utilizing machine learning algorithms to analyze the received motor data to detect health and anomalies in any motor unit, and to identify problems, such as component wear or impending failures.

[0035] The method may comprise generating the control data by determining the rate of change of operating values of each motor unit.

[0036] The method may comprise transmitting data received and generated by the remote cloud-based computing management and control platform to an external third party for analysis by a third party. The method may comprise utilizing an energy management module adapted to energy-related information from various components and to update the local controller with energy information.

[0037] The said system applies to any transportation applications, building management applications, industrial plants, and other such environments where energy utilization needs to be optimized. The details of the operation may be elaborated in embodiments.

[0038] BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Embodiments of the present invention will now be described by way of examples only and with reference to the accompanying drawings, in which:

[0040] Figure 1 is a schematic diagram of an embodiment of control system for motor units in a plurality of electric road vehicles;

[0041] Figure 2 is a block diagram of modules of the system of Figure 1 ; and

[0042] Figure 3 is a schematic diagram of an alternative embodiment of control system for motor units in a plurality of motorized devices;

[0043] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0044] Referring to Figures 1 and 2 of the drawings, there is shown a control system and method to maximize the energy efficiency of motor units in real-time, wherein the motor units 11 in each of a plurality of respective electric road vehicles 10, such a vehicle fleet operated by a courier or ride-hailing company. Each road vehicle 10 comprises a motor unit 11 comprising an electric motor 102 and a control circuit 103 having an inverter or the suchlike which modifies the voltage waveform applied to the electric motor 102, so as to alter its speed and torque characteristics, for example in response to input from a driver of the vehicle. Each road vehicle 10 further comprises a local controller 20 configured to establish communication between the vehicle 10 and a cloud-based computing management and control platform in the form of a remote artificial intelligence controller (AIC) 31 based in the cloud 12. The local controller 20 acts as a data transceiver unit, which gathers information of the vehicle 10 and transmits it to and from the AIC 31 in the cloud 12.

[0045] The AIC 31 is communicatively connected with the local controller 20 of connected vehicles 10 and operates autonomously, in receiving, analyzing, and determining the optimal mode of operation of each vehicle 10. The AIC 31 comprises a database 33 provided in a standalone or integrated cloud-based storage device 33, the database 33 being adapted to store historic data of the connected vehicle(s) 10.

[0046] The local controller 20 is communicatively coupled to collect information from a predictive analysis module 21 and energy management module 22 of the vehicle 10 and to send it to the AIC 31. It should also be noted that the local controller 20 can function as a transceiver or each of the modules 21 , 22 can independently transmit and receive data from the AIC 31. This transmission and reception of data are in realtime (in milliseconds) for seamless data transfer.

[0047] The local controller 20 reads information, such as data obtained from devices for determining the Id / lq currents 23, voltage 24, motor winding resistance 30, motor winding insulation 31 , Ld / Lq inductances 28, torque 25, temperature 26, flux linkage 27, sensors 29 and various other data from the motor unit 11 and consolidates it with the information received from the predictive analysis module 21 and energy management module 22. The information is dynamically communicated to the AIC 31 , where the AIC 31 reads the input values to continuously monitor the characteristics of the motor unit 11 .

[0048] The predictive analysis module 21 collects information such as ageing parameters or health of the components of the vehicle and updates the local controller 20 in real-time. This information may also be transmitted to the AIC 31 in real-time. By analyzing data from the various sensors and devices 23 - 31 in motor unit 11 , proactive maintenance and optimization of the system’s performance can be achieved, resulting in improved reliability and reduced downtime of the vehicles 10. The AIC 31 may use machine learning algorithms to analyze the data to detect health and anomalies in motor unit 11. These algorithms can then be used to identify potential problems, such as component wear or impending failures.

[0049] One such failure may involve motor winding Insulation: this is a critical parameter that affects the performance, efficiency, and lifespan of the motor unit 11. Stator motor winding insulation plays a critical role in the reliable operation of motors and drives. Excessive temperature can permanently damage the stator motor winding and reduce the life of the motor unit 11 and can cause complete motor winding insulation breakdown and failure. For every 10°C that the motor temperature exceeds its rated insulation temperature, the insulation life is reduced by 50%.

[0050] In one example, the predictive analysis module 21 is initially fed with a pre-configured motor winding temperature value such as 160°C which may be 20°C below the rated motor winding temperature of 180°C. In addition to that, the predictive analysis module 21 also measures the motor winding temperature as a reference value at the start of motor operation and dynamically measures and monitors the temperature from different sensors distributed around the winding of the motor 102 at regular intervals.

[0051] If we consider a situation where the vehicle’s bus voltage is 400V for example, this 400V may cause an 800V spike in the supply to the motor 102 and a control circuit 103. Assuming that the winding of motor 102 is rated at 180°C, the winding insulation may have a breakdown voltage of 6,000V, and the winding of the motor 102 has a standard life of 20,000 hours. The life of the winding of the motor 102 and the breakdown voltage capability will diminish with time. As the breakdown voltage approaches 800V after 20,000 hours, the spike voltage equals the breakdown voltage and arcing occurs, causing short circuit in the winding of the motor 102. However, if the temperature exceeds 180°C, the insulation ages at an increased rate. For every 10°C increase in temperature above the rated temperature, the insulation life is halved. Therefore, at 190°C, 200°C and 210°C the life of the winding of the motor 102 would be 10,000, 5,000 and 2,500 hours respectively. This shows that running the motor 102 above rated temperature will dramatically reduce the life of the winding of the motor 102. The AIC 31 not only compares the measured values with the historic data but also reads the rate of change of temperature to ensure that the winding of the motor 102 does not normally exceed the rated temperature and allows for temperature adjustments to meet the warranty period of the motor 102. By utilizing advanced neural algorithms, the AIC 31 makes system-level decisions to optimize the performance of motor unit 11. The AIC 31 promptly adjusts the local controller 20 module, regulating the temperature below the threshold to prolong the life of the winding of the motor 102. For example, it may automatically activate restricted mode settings in each vehicle 10 to avoid further deterioration.

[0052] All recorded temperature measurements and related data from the AIC 31 are consolidated and may be sent to respective maintenance centers. This comprehensive data set provides valuable information for evaluation and analysis purposes, enabling the centers to assess the performance and condition of motor units 11 . This information can also be made accessible to any third parties on a paid subscription basis.

[0053] Failure in bearings can also be identified by analyzing the noise / vibration signature of the bearings using neural networks to impend failures.

[0054] The energy management module 22 monitors and collects energy-related information from various components. The energy parameters include but are not limited to the kWh / km, battery pack state of charge etc. The collected parameters are then transmitted to the local controller 20 for analysis and control purposes. Additionally, the energy management module 22 maintains pre-configured values for each component of the motor unit 11 , that are used to evaluate the received data from the components.

[0055] The energy management module 22 information is transmitted to the AIC 31 in realtime. The AIC 31 computes the information against the pre-configured values. If the analyzed value falls below a specified threshold level, the AIC 31 takes immediate action to address supply and demand issues. It achieves this by dynamically adjusting the settings of the motor unit 11 , allowing the system to operate within its operational capabilities and meet the required performance. All the change that is configured with respect to the energy management module 22 is saved in the AIC 31 module and is available for the user / driver to access and utilize these saved settings in the future, allowing for convenience and customization according to their specific requirements. Users can access the data and control the motor using various interfaces such as mobile devices and desktops. The AIC 31 provides a monitoring interface where users can visualize real-time data and perform necessary actions.

[0056] Referring to Figure 3 of the drawings, there is shown an alternative embodiment of control system for controlling motor units 11 in each of a plurality of respective electric road vehicles 10, building HVAC installations 10’, marine vessels 10” or factories 10”’ etc. In Figure 3, like parts to those of Figures 1 and 2 are given like reference numerals.

[0057] In this embodiment, a single local controller 10 is provided to communicate with all motor units 11 in each of the plurality of respective devices, such as electric road vehicles 10, building HVAC installations 10’, marine vessels 10” or factories 10’”. The local controller 20 is communicatively coupled to collect information from a single predictive analysis module 21 and energy management module 22 of the motor units 11 of each device.

[0058] The local controller 20 reads information, such as data obtained from devices for determining the Id / lq currents 23, voltage 24, motor winding resistance 30, motor winding insulation 31 , Ld / Lq inductances 28, torque 25, temperature 26, flux linkage 27, sensor data 29 and various other data from all connected motor unit 101 and consolidates it with the information received from the predictive analysis module 21 and energy management module 22. The information is dynamically communicated to the AIC 31 , where the AIC 31 reads the input values to continuously monitor the characteristics of the motor units 101.

[0059] In order to facilitate secure communication of information, each vehicle 10 or other device is equipped with an authentication system with a unique identifier. This identifier is required for sending and receiving information to and from the AIC 31. The authentication process involves validating the identifier against registered device information such as the chassis number. This number provides complete information about the hardware of the device, for example, details of the motor unit 11. The AIC 31 authenticates the device’s request to connect to the AIC and verifies the request’s validity.

[0060] The data collected from the various modules are analyzed by the AIC 31 . The AIC 31 then computes various energy-efficient scenarios based on the received parameters (real-time) such as speed, rpm, vibration, and resistance of motor windings and corresponds with historic data recorded in the database to determine the efficient configuration / mode of operating the vehicle 10 or another device. The instructions are read by the control module and proceed to adjust the parameters such as the motor winding inductances and speed etc. in real-time.

[0061] The data is used to optimize the energy usage of the vehicle or other device and maximize its efficiency. Overall, these systems work together to ensure that each device is performing optimally and efficiently while maintaining the health and life of the powertrain components.

[0062] The device data collected may be distributed to any clients such as manufacturers, management companies, marketing agencies or any other entity at a premium subscription.

[0063] In case of a malfunction / network / connectivity failure, the firmware in the motor unit 11 keeps the device operating without any interruption. When the communication is established, the transmission / reception of data automatically resumes.

[0064] The AIC 31 continuously learns and improves based on the parameters received. Over time, it can refine its suggestions and configurations with increased accuracy and effective vehicle management strategies. The Al algorithm can alter the reference values / parameters based on the learned information.

[0065] A control system in accordance with the present invention comprises a plurality of devices 23, 24, 30, 31 for determining operating values of each motor unit 11 and at least one local controller 20 for collecting data from the devices 23, 24, 30, 31 of at least one motor unit 11. The local controller 20 processes and dynamically communicates the collected data to a remote cloud-based computing management and control platform 31 which continuously analyses the characteristics of the motor units 11 utilizing artificial intelligence and transmits control data to the local controller 20, the local controller 20 being configured to adjust operating characteristics of the motor units 11 in response to control data in real time. The control system and associated control method of the present invention can thus independently or universally maximize the energy efficiency of a plurality of motor units 11 , such as the motor units of electric road vehicles 10, building HVAC installations 10’, marine vessels 10” or factories 10”’.

[0066] Although the invention has been described and illustrated with reference to specific embodiments thereof, it is not intended that the invention be limited to such illustrative embodiments. Those of skill in the art will recognise that many variations and modifications of such embodiments are possible without departing from the spirit of the invention. Accordingly, it is intended to be included within the invention all such variations and modifications as fall within the scope of the appended claims.

Claims

CLAIMS1 . A control system for motor units, the system comprising: a plurality of devices for determining operating values of each motor unit; a local controller for receiving data from said devices, the local controller having a transmitter for transmitting motor data and a receiver for receiving control data; a remote cloud-based computing management and control platform having a receiver for receiving said motor data transmitted by said local controller, a processor for analyzing said motor data and for generating said control data, and a transmitter for transmitting said control data to the local controller, the local controller being configured to adjust operating characteristics of at least one said motor unit in response to said control data in real-time.

2. A control system as claimed in claim 1 , in which the processor of the remote cloud-based computing management and control platform is an artificial intelligence controller adapted to determine the optimal mode of operation of each motor unit.

3. A control system as claimed in claim 1 , in which the remote cloud-based computing management and control platform comprises a database provided in a standalone or integrated cloud-based storage device, the database being adapted to store historic data relating to the motor units, the processor being arranged to generate the control data by comparing the received motor data with the historic data.

4. A control system as claimed in claim 1 , comprising a predictive analysis module adapted to analyze ageing parameters and / or health of the motor units and to update the local controller with analysis information.

5. A control system as claimed in claim 4, in which the local controller is communicatively coupled to collect said analysis information from the predictive analysis module and to transmit said analysis information to the remote cloudbased computing management and control platform.

6. A control system as claimed in claim 4, in which the predictive analysis module is arranged to transmit said analysis information to the remote cloud-based computing management and control platform.

7. A control system as claimed in claim 1 , in which the plurality of devices for determining operating values of each motor unit comprises devices selected from devices for determining Id / lq currents in the motor unit, devices for determining voltage in the motor unit, devices for determining the resistance of a winding in the motor unit, devices for determining the insulation of a winding in the motor unit, devices for determining the Ld / Lq inductances of a winding in the motor unit, devices for determining the temperature of a winding in the motor unit, devices for determining the magnetic flux linkage of a winding in the motor unit, devices for determining the torque generated by the motor unit and sensors.

8. A control system as claimed in claim 1 , in which the artificial intelligence controller utilizes machine learning algorithms to analyze the received motor data to detect health and anomalies in any motor unit, and to identify problems, such as component wear or impending failures.

9. A control system as claimed in claim 8, in which the artificial intelligence controller is adapted to generate the control data by determining the rate of change of operating values of each motor unit.

10. A control system as claimed in claim 1 , in which data received and generated by the remote cloud-based computing management and control platform is transmitted externally of the system for analysis by a third party.

11. A control system as claimed in claim 1 , comprising an energy management module adapted to collect energy-related information from various components and to update the local controller with energy information.

12. A control system as claimed in claim 11 , in which the local controller is communicatively coupled to collect said energy information from the energymanagement module and to transmit said energy information to the remote cloud-based computing management and control platform.

13. A control system as claimed in claim 11 , in which the energy management module is arranged to transmit said energy information to the remote cloudbased computing management and control platform.

14. A control system as claimed in claim 1 , comprising a plurality of local controllers for receiving data from devices of respective motor units.

15. A control system as claimed in claim 1 , comprising a single local controller for receiving data from devices of a plurality of motor units.

16. A method of controlling motor units, the method comprising: determining operating values of each motor unit; transmitting motor data from a local controller to a remote cloud-based computing management and control platform; analyzing said motor data at said remote cloud-based computing management and control platform and generating control data; transmitting said control data to the local controller; and adjusting operating characteristics of at least one said motor unit in response to said control data in real-time.

17. A method as claimed in claim 16, comprising storing historic data relating to the motor units at said remote cloud-based computing management and control platform, and generating the control data at said remote cloud-based computing management and control platform by comparing the received motor data with the historic data.

18. A method as claimed in claim 16, comprising utilizing a predictive analysis module to analyze ageing parameters and / or health of the motor units and to update the local controller with analysis information.

19. A method as claimed in claim 16, in which the determined operating values of each motor unit comprise values selected from Id / lq current values in the motorunit, voltage values in the motor unit, resistance values of a winding in the motor unit, insulation values of a winding in the motor unit, Ld / Lq inductance values of a winding in the motor unit, temperature values of a winding in the motor unit, magnetic flux linkage values of a winding in the motor unit, values of torque generated by the motor unit and sensors.

20. A method as claimed in claim 16, comprising utilizing machine learning algorithms to analyze the received motor data to detect health and anomalies in any motor unit, and to identify problems, such as component wear or impending failures.

21. A method as claimed in claim 16, comprising generating the control data by determining the rate of change of operating values of each motor unit.

22. A method as claimed in claim 16, comprising transmitting data received and generated by the remote cloud-based computing management and control platform to an external third party for analysis by a third party.

23. A method as claimed in claim 1 , comprising utilizing an energy management module adapted to energy-related information from various components and to update the local controller with energy information.

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