DESC:FORM 2

THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENT RULES, 2003

COMPLETE SPECIFICATION
(See Section 10 and Rule 13)

Title of invention:
HYBRID POWERTRAIN CONTROLLER AND A METHOD FOR OPTIMIZING FUEL EFFICIENCY

Applicant:
Tata Motors Limited
A company Incorporated in India under the Companies Act, 1956
Having address:
Bombay House, 24 Homi Mody Street,
Hutatma Chowk, Mumbai 400001,
Maharashtra, India

The following specification particularly describes the invention and the manner in which it is
to be performed.

CROSS-REFERENCE TO RELATED APPLICATIONS AND PRIORITY

[001] The present application claims priority from Indian provisional application no. 201921039543, filed on September 30, 2019. The entire contents of the aforementioned application are incorporated herein by reference.

TECHNICAL FIELD

[002] The present subject matter described herein, in general, relates to hybrid powertrain controller and a method for optimizing fuel efficiency.

BACKGROUND
[003] A Power-Train Control Module, (PCM) is an automotive component, a control unit, used on motor vehicles for controlling. The PCM is generally a combined control unit, consisting of an Engine Control Unit (ECU) and a transmission control unit (TCU). Further with the rise of hybrid technology, additional Hybrid control units (HCU) are being employed for managing the hybrid functions in the vehicle. The hybrid functions include to start a motor and to stop engine, to run both motor and engine, and to run only motor or the engine. Typically, PCM control strategies use many formulae and thousands of maps with extrapolation or interpolation. The PCM controllers are developed, based on handwritten code or by using model based PCM tools. Such hand-written or code based development of PCM tools is not just time and cost consuming, but is also error prone and inefficient. The power-train control module gets further complicated when hybrid functions are integrated in the PCM. Furthermore, individual personalization of driving preference on fly, such as continuous extreme to mild of Sports mode or Eco mode is impossible in conventional controller.

SUMMARY

[004] Before the present hybrid powertrain controller and a method for optimizing fuel efficiency is described, it is to be understood that this application is not limited to the particular systems, and methodologies described, as there can be multiple possible embodiments which are not expressly illustrated in the present disclosure. It is also to be understood that the terminology used in the description is for the purpose of describing the particular versions or embodiments only and is not intended to limit the scope of the present application. This summary is provided to introduce concepts related to hybrid powertrain controller and a method for optimizing fuel efficiency. This summary is not intended to identify essential features of the claimed subject matter nor is it intended for use in determining or limiting the scope of the claimed subject matter.
[005] In one implementation, Hybrid Powertrain Controller (HPC) for optimizing fuel efficiency is illustrated. The HPC may comprise a memory and a processor coupled in the memory. A controller is communicatively coupled with the processor, configured for peripheral control and conditioning. The processor may be configured to execute instructions stored in the memory to receive vehicle data from each of an internal source and an external source of a vehicle in real-time. Further the processor may be configured to compute one or more changes in parameters of the vehicle. The one or more changes in the parameters changes operation of the vehicle. Further the processor is configured to transmit one or more instructions associated with the one or more changes in parameters to the vehicle in order to optimize a fuel efficiency of the vehicle.
[006] In another implementation, a method for optimizing fuel efficiency is illustrated. The method may comprise, receiving, vehicle data, by a processor, from each of internal sources and external sources of a vehicle in form of internal data and external data. Further the method may comprise computing, by the processor, one or more changes in parameters of the vehicle. The one or more changes in the parameter changes operation of the vehicle. Furthermore, the method may comprise transmitting, by the processor one or more instructions associated with the one or more changes in parameters to the vehicle in order to optimize the fuel efficiency of the vehicle.

BRIEF DESCRIPTION OF THE DRAWINGS
[007] The foregoing detailed description of embodiments is better understood when read in conjunction with the appended drawings. For the purpose of illustrating of the present subject matter, an example of construction of the present subject matter is provided as figures; however, the invention is not limited to the specific method and system disclosed in the document and the figures.
[008] The present subject matter is described in detail with reference to the accompanying figures. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The same numbers are used throughout the drawings to refer various features of the present subject matter.
[009] Figure 1 illustrates an architecture diagram 100 of a Hybrid Power train Controller, in accordance with an embodiment of the present subject matter.
[010] Figure 2 illustrates a block level diagram of the HPC when implemented as the system 102, in accordance with an embodiment of the present subject matter.
[011] Figure 3 illustrates details of the HPC, in accordance with an embodiment of the present subject matter.
[012] Figure 4 illustrates a method 300 of optimizing fuel efficiency of a vehicle, in accordance with an embodiment of the present subject matter.
DETAILED DESCRIPTION

[013] Some embodiments of this disclosure, illustrating all its features, will now be discussed in detail. The words "comprising," "having," "containing," and "including," and other forms thereof, are intended to be open ended in that an item or items following any one of these words is not meant to be an exhaustive listing of such item or items, or meant to be limited to only the listed item or items. It must also be noted that as used herein and in the appended claims, the singular forms "a," "an," and "the" include plural references unless the context clearly dictates otherwise. Although any hybrid powertrain controller and a method for optimizing fuel efficiency and, similar or equivalent to those described herein may be used in the practice or testing of embodiments of the present disclosure, the exemplary, hybrid powertrain controller and a method for optimizing fuel efficiency are now described.
[014] Various modifications to the embodiment will be readily apparent to those skilled in the art and the generic principles herein may be applied to other embodiments. However, one of ordinary skill in the art will readily recognize that the present disclosure is not intended to be limited to the embodiments described but is to be accorded the widest scope consist in this regard, in a generic sense.
[015] As described above, in conventional PCM, the calibration is done manually and intermittently thus interpolation errors are prevalent. Furthermore, if the same calibration done continuously, huge memory is required. PCM control strategies use many formulae and thousands of maps with extrapolation or interpolation. The PCM controllers are developed, based on handwritten code or by using model based PCM tools. Such hand-written or code based development of PCM tools is not just time and cost consuming, but is also error prone and inefficient. The power-train control module gets further complicated when hybrid functions are integrated in the PCM. Furthermore, individual personalization of driving preference on fly, such as continuous extreme to mild of Sports mode or Eco mode is impossible in conventional controller.
[016] In one embodiment, Hybrid powertrain controller and a method for optimizing fuel efficiency are described in accordance with the present subject matter. In the present subject matter multiple control units e.g. Fuel, Air and Spark Artificial intelligence methodology, such as reinforced learning, for operation in non-linear environment. Reinforced learning is the HPC 102 learns from the intermediate data received from the internal and external sources of a vehicle. The artificial intelligence methodology does not assume the environment as a linear environment.
[017] Referring now to Figure 1, illustrates a network embodiment of the present subject matter. In one example, the system/HPC 102 (also as referred to as Hybrid powertrain controller/HPC) may be connected to multiple devices to take input from external sources such as may be Maps or surrounding data such as road profile and condition. The HPC 102 may 102 be connected with various parts of the vehicle such as engine 114, battery 110, motor112, and transmission 116.
[018] In one embodiment, the HPC 102 may receive vehicle data from each of an internal source and an external source of a vehicle in real-time. The external sources comprise Maps or surrounding data such as road profile and the road condition c. The internal data comprises data received from various parts of the vehicle such as engine 114, battery 110, motor112, and transmission gearbox 116.
[019] In one implementation, the communication network 105 may be a wireless network, a wired network, or a combination thereof. The communication network 106 can be implemented as one of the different types of networks, such as intranet, Local Area Network (LAN), Wireless Personal Area Network (WPAN), Wireless Local Area Network (WLAN), wide area network (WAN), the internet, and the like. The communication network 106 may either be a dedicated network or a shared network. The shared network represents an association of the different types of networks that use a variety of protocols, for example, MQ Telemetry Transport (MQTT), Extensible Messaging and Presence Protocol (XMPP), Hypertext Transfer Protocol (HTTP), Transmission Control Protocol/Internet Protocol (TCP/IP), Wireless Application Protocol (WAP), and the like, to communicate with one another. Further, the communication network 106 may include a variety of network devices, including routers, bridges, servers, computing devices, storage devices, and the like.
[020] Referring now to figure 2, a block diagram 200 of the HPC 102 is illustrated in accordance with an embodiment of the present subject matter. In one embodiment, the HPC 102 may include at least one processor 202, at least one controller 204, an input/output (I/O) interface 206, and a memory 208. The at least one processor 202 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the at least one processor 202 may be configured to fetch and execute computer-readable instructions stored in the memory 208. The processor 202 may be configured to handle the high computing load using artificial intelligence methodology. The at least one controller 204 may be communicatively coupled with the processor 202. The at least one controller 204 implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the at least one controller 204 may be configured for peripheral control and conditioning. The controller 204 may be a data-based Artificial Intelligence (AI) modelled controller. The controller 204 is configured for a periphery control of one or more brushless DC motors for a transmission control of a vehicle.
[021] The I/O interfaces 206 may include a variety of software and hardware interfaces, for example, a web interface, a graphical user interface, and the like. The I/O interfaces 206 may allow the HPC 102 to interact with a user directly. Further, the I/O interfaces 206 may enable the HPC 102 to communicate with other computing devices, such as web servers and external data servers (not shown). The I/O interfaces 206 can facilitate multiple communications within a wide variety of networks and protocol types, including wired networks, for example, LAN, cable, etc., and wireless networks, such as WLAN, cellular, or satellite.
[022] The memory 208, amongst other things, serves as a repository for storing data processed, received, and generated by one or more of modules 208. The memory 208 may include any computer-readable medium or computer program product known in the art including, for example, volatile memory, such as Static Random Access Memory (SRAM) and Dynamic Random Access Memory (DRAM), and/or non-volatile memory, such as Read Only Memory (ROM), Erasable Programmable ROM (EPROM), Electrically Erasable and Programmable ROM (EEPROM), flash memories, hard disks, optical disks, and magnetic tapes.
[023] The memory 208 may include data generated as a result of the execution of one or more of the modules 208. The memory 208 is connected to a plurality of modules 210. The HPC 102 comprises a receiving module 212, a computing module 214, a transmitting module 216.
[024] In one embodiment, during the HPC 102 may be developed using artificial intelligence methodology, such as reinforced learning, for operation in non-linear environment. Reinforced learning is the HPC learns from the intermediate data received from the internal and external sources of a vehicle. The artificial intelligence methodology does not assume the environment as a linear environment. The artificial intelligence model is suitable for higher order nonlinear curves. Further during the operation, the processor 202 may receive data from an external and an internal source of the vehicle. The external sources comprises Maps or surrounding data such as road profile and condition, traffic conditions, past visited places, home, office and the like. The internal sources comprise data from various parts of the vehicle such as engine 114, battery 110, motor112, and transmission gearbox 116.
[025] Further upon receiving the data, the processor 202 may compute one or more changes in a parameter of the vehicle parts. The one or more changes in the parameter’s refers to changes in operation of vehicle such as speed, acceleration. The one or more changes in the parameters are calculated through Artificial Intelligence (AI) algorithms. For example, peripheral or other controls are triggered by external conditions such as driver presses brake/ accelerator, the processor 202 executes the brake or accelerator according to the received data from the internal and external sources of the vehicle. That is the HPC 102 learns from the received data that is received previously and the brakes and accelerator is triggered according to the received data previously.
[026] Further the processor 202 is configured to transmit one or more instructions associated with the one or more changes to optimize the fuel efficiency of the vehicle. The fuel efficiency of the vehicle is optimized by 15 to 25% on actual on road condition.
[027] In an example of implementation of the present subject matter, construe that a vehicle comprising the HPC 102 is being driven in a city. During operation of the vehicle, the HPC 102 may obtain real time data from Google maps® and road conductions, such as presence of traffic congestion after 5 KM or a uphill drive of 1KM and a downhill drive of 1KM after 2 KM.
[028] Upon obtaining such data, HPC 102 may compute one or more changes in engine 114, battery 110, motor112, and transmission 116 to be executed for increasing fuel efficiency. For example, the engine behaves non-linearly between 3000 to 3500rpm (rotation per minute), the HPC 102 computes the change in the rotation per minutes to the accurate value anywhere between 3000 to 3500 rpm. The operation of the engine between 3000 to 3500 rpm is known as a transient operation. The HPC 102 eliminates the need of assuming the engine rpm as linear and interpolating an intermediate value to 3200 rpm for the engine. In example, where there is a traffic congestion, the HPC 102 may compute that the battery 110 has to be fully charged before reaching the traffic congestion and shifting to complete electric system (hybrid) for vehicle operation in traffic congestion for increasing fuel efficiency. In the example of uphill and downhill, the HPC 102 may compute that the battery of the vehicle has to be completely used up by the time uphill is completed to obtain maximum regeneration during downhill. Upon computing the one or more changes (alternative also to be understood as strategies), the HPC 102 may transmit one or more instructions associated with the change to the vehicle part to optimize fuel efficiency.
[029] In one embodiment The HPC 102 may determine the fuel-efficient path for the vehicle and optimize the efficiency of the vehicle. The HPC 102 from the data received everyday trains itself to identify the route at a specific time. For example, for a person travelling from home to office every day at 8 AM. Now, when the engine is turned on at 7.58 PM, the HPC through artificial intelligence-based learning identifies the destination. The HPC 102 may identify the route closures through the external sources such as Google Maps. If in case of a road closure the HPC 102, suggest an alternate path that is the shortest possible path for the vehicle to the destination.
[030] Referring to Figure 3, the HPC 102 for optimizing fuel efficiency is shown, in accordance with an embodiment of the present subject matter. Although the present subject matter is explained considering that the HPC 102 is implemented on a microprocessor and microcontroller it may be understood that the HPC 102 may also be implemented in a variety of computing systems including but not limited to, a smart phone, a tablet, a notepad, a personal digital assistant, a handheld device, a laptop computer, a notebook, a workstation, a mainframe computer, a server, and a network server. Further, in the Table 1 the pins of the HPC 102 are detailed.
Sr. No. Modules Pins Specification
1. Analog Inputs 7 analog inputs
2. Analog output 2 Analog output Signals Output Signal Range: 0 to 5V
3. PWM Inputs 1 PWM input for Neutral Gear Sensor Input Voltage : 0 to 5V
Frequency range: 0 to 10 KHz
Duty range: 0 to 100%
4. PWM Outputs 2 PWM outputs
5. VR type inputs 1 variable reluctance type differential signal
for input shaft RPM sensor
6. Digital Inputs 13 Active High digital inputs Voltage Range: 0 to 12V
7. Digital Outputs 9 Low-Side + 1 High-Side digital outputs for driving relays Voltage Range: 0 to 12V
8. Half bridge Drivers 9 Half-Bridge drivers for Gear and
Rank Motor Voltage Range: 0 to 12V
9. CAN bus interface 3 CAN Interfaces on the board Baud rate: max 1 mbps
10. LIN Bus interface LIN (Master node) Interface on the board Baud rate: max 20 kbps
11. Sensor Supply 2 sensor supplies on board 5V

[031] In the embodiment, Hybrid Powertrain Controller (HPC) 102, may be understood as a Hardware and Software based ECU, which does functions of three control units i.e. Hybrid Control Unit (HCU), Engine Management System (EMS), and Transmission Management System (TMS). The Hybrid Control Unit (HCU) is for controlling the hybrid functions. The hybrid functions include to start the motor 112 and to stop engine 110, to run both motor 112 and engine 110, and to run only motor112 or the engine 110. A regeneration energy during braking is also received and controlled by the HCU. The Engine Management system (EMS) controls the functions of the engine and the Transmission control system (TMS) controls the transmission of power through the transmission gearbox 116. In one other example, the HPC control model 102 is developed using Artificial Intelligent method. In one example, reinforced learning methodology is utilized for building the HPC control model. In one other example, the HPC model may be trained to optimized vehicle performance and thereby increase fuel efficiency. The data from the external sources such as surrounding data such as road profile and condition, traffic conditions, past visited places, home, office of the existing vehicles may be logged into the HPC 102. An artificial intelligence model is built using this received data, then reinforced learning techniques are applied on the HPC 102. The HPC learns from the received data through reinforced learning and optimizes the efficiency of the vehicle by controlling the internal sources.
[032] Referring to figure 4, now a method 400 for optimizing fuel efficiency is described, in accordance with an embodiment of the present subject matter. The method may be described in the general context of computer executable instructions. Generally, computer executable instructions can include routines, programs, objects, components, data structures, procedures, modules, functions, etc., that perform particular functions or implement particular abstract data types. The method may also be practiced in a distributed computing environment where functions are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, computer executable instructions may be located in both local and remote computer storage media, including memory storage devices.
[033] The order in which the method for optimizing fuel efficiency is described is not intended to be construed as a limitation, and any number of the described method blocks can be combined in any order to implement the method or alternate methods. Additionally, individual blocks may be deleted from the method without departing from the spirit and scope of the subject matter described herein. Furthermore, the method can be implemented in any suitable hardware, software, firmware, or combination thereof. However, for ease of explanation, in the embodiments described below, the method may be considered to be implemented in the above described Hybrid Powertrain Controller 102, as described above.
[034] At block 402, data from one or more the internal and the external sources is received by the receiving module 212. In one example, the data comprises the internal data and the external data. In one more example, the external sources comprises Maps or surrounding data such as road profile and condition, traffic conditions, past visited places, home, office and the like. The internal data comprises data from various parts of the vehicle such as engine 114, battery 110, motor112, and transmission 116. In other example, the external data may comprise data from GOOGLE MAPS®, road conditions, traffic conditions, past visited places, home, office and the like.
[035] At block 404, the computing module 214 computes one or more changes in parameters of the vehicle parts. The one or more changes in the parameter’s changes operation of the vehicle. The one or more changes in the parameters are calculated through Artificial Intelligence (AI) algorithms.
[036] At block 406, one or more instruction is associated with the one or more change are transmitted 216 by the transmitting module to the vehicle part for execution, thereby optimizing fuel efficiency. The efficiency of the vehicle is optimized by 15 to 25% by the HPC 102.
[037] Exemplary embodiments discussed above may provide certain advantages. Though not required to practice aspects of the disclosure, these advantages may include the following.
[038] Some embodiments may enable a system 102 and a method 400 to allow reduction in cost and time for calibration.
[039] Some embodiments may enable a system 102 and a method 400 to predict fuel efficiency.
[040] Some embodiments may enable a system 102 and a method 400 to be modular. The HPC may work as an independent controller. Therefore, it may be ported to any other vehicle and controller. Thereby making the HPC 102 modular.
[041] Some embodiments may enable a system 102 and a method 400 for non-linear behaviors and eliminate interpolation problems.
[042] Some embodiments may enable a system 102 and a method 400 for high speed computation.
[043] Some embodiments may enable a system 102 and a method 400 for transient operation.
[044] Some embodiments may enable a system 102 and a method 400 based on our everyday journey predict destination and suggest the driver most fuel-efficient path/ quickest path but may not fuel-efficient path.
[045] Although implementations for Hybrid powertrain controller and a method for optimizing fuel efficiency have been described in language specific to structural features and/or system, it is to be understood that the appended claims are not necessarily limited to the specific features or described. Rather, the specific features are disclosed as examples of implementations.
,CLAIMS:
1. A hybrid power train control system, comprising:
a processor, configured to handle the high computing load using artificial intelligence methodology;
a controller, communicatively coupled with the processor, configured for peripheral control and conditioning; and
a memory coupled to the processor, wherein the memory stores a set of instructions to be executed by the processor, wherein the processor is configured to:
receive vehicle data from each of an internal source and an external source of a vehicle in real-time;
compute one or more changes in parameters of the vehicle, wherein the one or more changes in the parameters changes operation of the vehicle; and
transmit one or more instructions associated with the one or more changes in parameters to the vehicle in order to optimize a fuel efficiency of the vehicle.
2. The system as claimed in claim 1, wherein the artificial intelligence methodology is reinforced learning, for operation in non-linear environment.
3. The system as claimed in claim 1, wherein internal data comprise data associated with one or more parts of the vehicle such as engine, battery, motor, power train, transmission.
4. The system as claimed in claim 1, wherein the external data comprise data from GOOGLE MAPS®, road conditions, traffic conditions, past visited places, home, office.
5. The system as claimed in claim 1, wherein the processor is configured to perform transient operation.
6. The system as claimed in claim 1, wherein the processor is configured to predict the destination and suggest fuel-efficient path.
7. A method for optimizing fuel efficiency of a vehicle, the method comprising:
receiving, vehicle data, by the processor, from each of an internal and an external source of a vehicle in real time;
computing, by the processor, one or more changes in parameters of the vehicle; wherein the one or more changes in the parameters changes operation of the vehicle and
transmitting, by the processor one or more instructions associated with the one or more changes in parameters to the vehicle in order to optimize the fuel efficiency of the vehicle.
8. The method as claimed in claim 5, wherein internal data comprise data associated with one or more parts of the vehicle such as engine, battery, motor, power train, transmission.
9. The method as claimed in claim 5, wherein the external data may comprise data from GOOGLE MAPS®, road conditions, traffic conditions, past visited places, home, office.
10. The method claimed in claim 5, wherein the method comprising: performing transient operation.
11. The method claimed in claim 5, wherein the method comprising:
predicting the destination and suggesting a fuel-efficient path.