Description:Complete Specification:
The following specification describes and ascertains the nature of this invention and the manner in which it is to be performed.

Field of the invention:
[0001] The present invention relates to a device for a Hydrogen Injection Pressure Regulator (HIPR) and method for the same.

Background of the invention:
[0002] According to a prior art CN116753089, an automatic pressure-regulating hydrogen supply system, engine and vehicle is disclosed. The prior art discloses an automatic pressure-regulating hydrogen supply system, an engine, and a vehicle. The hydrogen supply system comprises a hydrogen storage device, a pressure reducing valve, a stop valve, a first pressure sensor for collecting the upstream pressure of a proportional pressure regulating valve in a pipeline, the proportional pressure regulating valve, a second pressure sensor for collecting the downstream pressure of the proportional pressure regulating valve in the pipeline, a hydrogen rail and an ejector assembly which are sequentially connected in series through a pipeline in the hydrogen conveying direction. The engine rotating speed sensor is used for collecting the current rotating speed of the engine; the controller is used for obtaining the current working condition of the engine according to the current rotating speed, obtaining the target pressure according to the current working condition and obtaining the initial opening degree of the proportional pressure regulating valve according to the target pressure and the upstream pressure; and the opening degree of the proportional pressure regulating valve is dynamically adjusted according to the target pressure, the upstream pressure and the downstream pressure. Therefore, by means of the scheme, the current hydrogen supply pressure can be adjusted along with changes of the working conditions, the hydrogen consumption is reduced, and the requirements of an engine are met.

Brief description of the accompanying drawings:
[0003] An embodiment of the disclosure is described with reference to the following accompanying drawings,
[0004] Fig. 1 illustrates a block diagram of a device for a Hydrogen Injection Pressure Regulator (HIPR) in a hydrogen fuel injection system of a vehicle, according to an embodiment of the present invention, and
[0005] Fig. 2 illustrates a method for managing mass flow through the HIPR in the hydrogen fuel injection system of the vehicle, according to the present invention.

Detailed description of the embodiments:
[0006] Fig. 1 illustrates a block diagram of a device for a Hydrogen Injection Pressure Regulator (HIPR) in a hydrogen fuel injection system of a vehicle, according to an embodiment of the present invention. The fuel injection system 100 comprises a fuel flow path connecting a storage tank 102 to a fuel injector 108. The fuel injector 108 injects fuel which is combusted inside a combustion chamber of an engine. In a hydrogen based internal combustion engine (H2ICE), the hydrogen fuel is stored in high pressure (in form of gas) in storage tank 102 and passed to the combustion chamber of the engine by regulating the pressure using HIPR 104 and accumulating the gas in fuel rail 106 with reduced pressure (lesser than pressure in the storage tank 102) and injected into the combustion chamber through Hydrogen Gas Injector (HGI) or fuel injector 108, for accurate injection which helps for proper combustion. The dotted line connecting the storage tank 102 to the HIPR 104 indicates that there are many other components in between which are omitted here for the sake of simplicity such as pressure sensors, filters and the like, but the same must not be understood in limiting manner. The fuel rail 106 is installed with temperature sensor 116 and a pressure sensor 118. Further, the engine is also installed with engine speed sensor 122 and the engine torque is determined using engine torque determining means which is either derived using existing engine parameters or a measured using dedicated sensor.

[0007] The HIPR 104 is a unit comprising a System Isolation Valve (SIV), a Medium Pressure Sensor (PS) and a Proportional Valve (PV). The SIV is the inlet towards the storage tank 102, and the PV is the outlet of HIPR 104 connecting to the fuel rail 106. The PV is responsible for controlling the mass flow to maintain the required rail pressure in the fuel rail 106. Over the time and after regular usage, the PV undergoes wear and tear or ageing due to which the necessary rail pressure increase / decrease occurs with a delay affecting the engine performance.

[0008] According to the present invention, the device 120 for the HIPR 104 is disclosed. The device 120 is for estimating the mass flow of the HIPR 104 and calculation of mass flow deviation/drift through the HIPR 104. The device 120 comprises a controller configured to receive/read input parameters from respective sensors within the vehicle, process the received/read input parameters through an estimation module 114 stored in a memory element 112. The respective sensor corresponds to temperature sensor 116, pressure sensor 118, engine speed sensor 122, engine torque determining means and the like. The estimation module 114 is a trained Machine Learning (ML) model. The controller outputs, from the estimation module 114, an estimated mass flow through the HIPR 104. The controller further configured to calculate difference between a setpoint mass flow stored in the memory element 112 and the estimated mass flow, and determine mass flow deviation based on the difference.

[0009] According to an embodiment of the present invention, the controller configured to compare the mass flow deviation with a threshold deviation, and perform, based on said comparison, at least one selected from a group comprising, operation of the HIPR 104 in a manner to correct the determined mass flow deviation, and trigger an alert to replace/repair the HIPR 104. The alert is triggered to indicate the health status of the HIPR 104 so that necessary corrective steps is taken in time. An output 124 provided by the device comprises estimated mass flow, mass flow deviation, corrections and alert or health status.

[0010] According to an embodiment of the present invention, the input parameters are selected from a group comprising engine speed, fuel rail pressures, fuel rail temperature and engine torque. The fuel rail pressure comprises pressures measured between the energizations of the fuel injector 108, namely a first pressure measured after an injection event and a second pressure measured before a next consecutive/successive injection event. The injection event corresponds to opening the nozzle of the fuel injector 108 and injecting the fuel. The input parameters are measured by respective sensors as described above.

[0011] According to an embodiment of the present invention, the device 120 is at least one of an internal device and an external device. The internal device is an existing control unit of the vehicle, and the external device is at least one of a portable device 126 (or communication device) and a cloud based device 128 (or cloud or server). The internal device is at least one Electronic Control Unit (ECU) 110 selected from a group comprising is at least one of an Engine Management System (EMS) controller, a Telematics Control Unit (TCU) controller, or other existing control units and a combination thereof. The external device is at least one of the portable device 126 and the cloud based device 128. The external device is connected through a Telematic Control Unit (TCU) of the vehicle through at least one a wired and wireless means known in the art. The portable device 126 corresponds to electronic computing devices which enable a rider or driver or a user to communicate with others such as smartphone, wearable electronics such as smart watch, smart ring, etc. The cloud based device 128 corresponds to cloud computing architecture having network of servers, databases connected with each other and vehicle for processing of inputs and providing outputs.

[0012] According to an embodiment of the present invention, the device 120 is implementable in different manners or scenarios. In a first scenario, the device 120 is just the ECU 110 of the vehicle. Thus, the estimation of mass flow and determination of mass flow deviation is directly done by the ECU 110 of the vehicle. In a second scenario, the device 120 is the external device, i.e. at least one of the portable device 126 and the cloud based device 128. The input signals are transmitted to the portable device 126 and the cloud based device 128 through the TCU through wired or wireless means as known in the art. The input signals are signals for the input parameters. In a third scenario, the device 120 is combination of the internal device and external device, i.e. the device 120 is combination of the ECU 110 and the portable device 126, or combination of the ECU 110 and the cloud based device 128 or combination of the portable device 126 and the cloud based device 128 or the combination of the ECU 110, the portable device 126 and the cloud based device 128. The second scenario and the third scenario are explained later.

[0013] The device 120 which is at least one of the ECU 110 or controller, the portable device 126, and the cloud based device 128 and refers to computing devices/units comprising components such as memory element 112 such as Random Access Memory (RAM) and/or Read Only Memory (ROM), Analog-to-Digital Converter (ADC), Digital-to-Analog Convertor (DAC), clocks, timers and a processor (such as Central Processing Unit (CPU)) (capable of implementing machine learning) connected with the each other and to other components through communication bus channels. The components mentioned are just for understanding and may have more or less components as per requirement. The memory element 112 of the device 120 is prestored with map, table. Model, modules, logics, instructions, programs, applications, threshold deviation, or values, setpoint mass flow, which is accessed by the at least one processor as per the defined routines. The internal components of the controller are not explained for being state of the art, and the same must not be understood in a limiting manner. The device 120 is capable to communicate through wired and wireless means such as but not limited to Global System for Mobile Communications (GSM), 3G, 4G, 5G, Wi-Fi, Bluetooth, Ethernet, serial networks, Universal Serial Bus (USB) cable, micro-USB, and the like.

[0014] Further, the processor may be implemented as any or a combination of one or more microchips or integrated circuits interconnected using a parent board, hardwired logic, software stored in the memory element 108 and executed by a microprocessor, firmware, an application specific integrated circuit (ASIC), and/or a field programmable gate array (FPGA). The processor is configured to exchange and manage the processing of various Artificial Intelligence (AI) modules.

[0015] In accordance to an embodiment of the present invention and as per the second scenario, the device 120 is the external device, i.e. any one of the portable device 126 (or communication device) and the cloud based device 128. For ease of understanding, the device 120 is now explained as the cloud based device 128, but the same explanation is applicable when the external device is the portable device 126. When the device 120 is the cloud based device 128, the cloud based device 128 receives the input comprising the input parameters, directly from the ECU 110. The ECU 110 does not process the input signals and directly transmits the input signal to the cloud based device 128 through the TCU or through the portable device 126. The cloud based device 128 is configured to receive the input signals from the ECU 110, calculates the estimated mass flow followed by determination of mass flow deviation. The cloud based device 128 then either sends back correction data to the ECU 110 to modify operation of the HIPR 104 or triggers an alert to concerned user and indicates the health status of the HIPR 104.

[0016] Similarly, when the device 120 is the portable device 126, the portable device 126 is connected to the ECU 110 through suitable communication or networking means as described before, such as but not limited to Bluetooth™, Wi-Fi, Universal Serial Bus (USB) cables, etc. The application installed in the portable device 126 processes the input signals received from the ECU 110 and sends back the correction related data after determination or computation. The correction related data corrects the operation of the HIPR 104. Also, the application stores the result internally for later reference.

[0017] In accordance to an embodiment of the present invention and as per the third scenario, the device 120 is combination of internal device (the ECU 110) and the external device. The processing of the input signals is shared among the internal device(s) and the external device(s), and the output is finally performed in the vehicle. For example, consider the device 120 as combination of the ECU 110 and the cloud based device 128. The ECU 110 pre-processes the input parameters (estimates mass flow through the HIPR 104) and sends the estimated mass flow to the cloud based device 128 for calculation of mass flow deviation and preparation of correction data is required. Thus, the cloud based device 128 performs fewer steps and sends back the correction data to the vehicle. The ECU 110 then performs the correction for the HIPR 104 in the vehicle.

[0018] Further, the vehicle is any one selected from a group comprising a two-wheeler such as scooter, motorcycle, and a three-wheeler such as autorickshaw, four wheeler such as cars, multi-wheeler vehicles such as busses, trucks Off-Highway vehicles. Specifically, the vehicle is either internal combustion engine based or hybrid vehicle.

[0019] According to the present invention, a working of the device 120 is explained. The device 120 is usable either for determining mass flow through HIPR 104 or estimating the mass flow deviation through the HIPR 104 or both. Now consider the vehicle with H2ICE is running. During engine running, hydrogen fuel is continuously supplied from the PV of the HIPR 104 to the fuel rail 106, and from the fuel rail 106, the fuel is injected into the combustion chamber in regular intervals. As known, there is a gap between every injection event, i.e. duration between the energization of the fuel injector 108. During the gap, the fuel flows from the PV to the fuel rail 106. Since the outlet of the fuel rail 106 which is fuel injector 108 is closed, the rail pressure increases between the energizations of the fuel injector 108 for corresponding inflow from the PV. The controller is configured to capture or record the first pressure, after an injection event is completed, and a second pressure, before the next consecutive injection event is performed. The amount of rail pressure rise (Prise) that is difference of first pressure from the second pressure, is used to calculate the estimated fuel mass flow to the fuel rail 106 through the estimation module 114. The estimated mass flow is used to find the difference or delta (?) mass flow in comparison to the setpoint mass flow pre-stored in the memory element 112. The setpoint mass flow is configurable as per requirement.

[0020] According to the present invention, the delta mass flow between setpoint mass flow and the estimated mass flow is the drift / error of HIPR 104 which is usable to compare with the predefined threshold deviation to decide the health status of the HIPR 104. If the delta mass flow is within the permissible range, then the controller is configured to correct the tolerance and the HIPR 104 is continued to be used. If the delta mass flow is about to breach the permissible range, then no more correction is possible and the HIPR 104 is to be replaced, and the same is alerted. The information is used to send to user such as an Original Equipment Manufacturer (OEM) or end user, through Controller Area Network (CAN) and/or Cloud so that end user and service station are in same understanding for the component status. It is observed that during injections, the rail pressure drops and when no injections, pressure rise. The rail pressure rise, (Prise) and fall seems smoother without any oscillations because the PV is continuously open. If the demand increases, PV opening also increases slightly which is not creating any oscillations.

[0021] According to the present invention, details about the estimation module 114 is disclosed. Before the device 120 is ready, data is collected from the engine or a test bench comprising the fuel mass flow and injection scenario. Then the data is processed and used for training the ML model with the influencing input parameters. Once trained, the offline estimation module 114 (trained ML model) is then ported/flashed in the controller. The estimation module 114 is trained based on engine speed, rail pressure after an injection (first pressure), rail pressure rise between consecutive injections (Prise), rail temperature and engine torque. These parameters are interdependent to achieve the certain vehicle speed based on the driver demand.

[0022] According to an embodiment of the present invention, the device 120 for estimating the mass flow through the HIPR 104 is disclosed. The device 120 comprises the controller configured to receive/read input parameters from respective sensors within the vehicle, process the received/read input parameters through the estimation module 114 stored in the memory element 112. The estimation module 114 is the trained Machine Learning (ML) model. The controller outputs, from the estimation module 114, the estimated mass flow through the HIPR 104.

[0023] According to an embodiment of the present invention, the device 120 for calculating the mass flow deviation through the HIPR 104 is disclosed. Alternatively, the device 120 for calculating a drift in the mass flow of the HIPR 104 is disclosed. The device 120 comprises the controller configured to receive/read input parameters from respective sensors within the vehicle, process the received/read input parameters through the estimation module 114 stored in the memory element 112. The estimation module 114 is the trained Machine Learning (ML) model. The controller outputs, from the estimation module 114, the estimated mass flow through the HIPR 104. The controller further configured to calculate difference between the setpoint mass flow stored in the memory element 112 and the estimated mass flow, and determine mass flow deviation based on the difference. The mass flow deviation is followed by either correction in operation of the HIPR 104 or sending indication or alert regarding the health of the HIPR 104.

[0024] Fig. 2 illustrates a method for managing mass flow through the HIPR in the hydrogen fuel injection system of the vehicle, according to the present invention. The method is for estimating the mass flow of the HIPR 104 and calculating mass flow deviation/drift through the HIPR 104. The method comprises plurality of steps of which a step 202 comprises receiving/reading signal for input parameters from respective sensors within the vehicle. The respective sensor corresponds to temperature sensor 116, pressure sensor 118, engine speed sensor 122, engine torque determining means and the like. A step 204 comprises processing the received/read input parameters through the estimation module 114 stored in the memory element 112. The estimation module 114 is the trained Machine Learning (ML) model. A step 206 comprises outputting, from the estimation module 114, an estimated mass flow through the HIPR 104.

[0025] The method also comprises a step 208 which comprises calculating difference between the setpoint mass flow stored in the memory element 112 and the estimated mass flow. A step 210 comprises determining mass flow deviation based on the calculated difference. Furthermore, the method comprises a step 212 and a step 214. The step 212 comprises comparing the mass flow deviation with the threshold deviation. The step 214 comprises performing, based on the comparison, at least one selected from the group comprising, a first action 216 and a second action 218. The first action is operating the HIPR 104 in a manner to correct the determined mass flow deviation, and the second action 218 is triggering the alert to replace the HIPR 104. The alert is triggered to indicate the health status of the HIPR 104.

[0026] According to the method, the input parameters are selected from the group comprising engine speed, fuel rail pressures, fuel rail temperature and engine torque. The fuel rail pressure comprises pressures measured between the energizations of the fuel injector 108, namely the first pressure measured after the injection event and the second pressure measured before the next consecutive injection event.

[0027] According to the present invention, the method is performed by at least one of the internal device and the external device. The internal device is the existing control unit of the vehicle, and the external device is at least one of the portable device 126 and the cloud based device 128.

[0028] According to the method, when the vehicle is running on road, the estimation module 114 receives or reads the real-time values of the input parameter such as rail pressure change between injections (Prise) together with the other influencing input parameters for calculating the estimated mass flow to the fuel rail 106. The other influencing parameters such as density of hydrogen varies over temperature (even when pressure is constant), so the mass flow from HIPR 104 when its completely open for a certain duration varies for different temperature ranges. So all input parameters have influence on the fuel mass over the entire operating range.

[0029] The method first determines the estimated mass flow of the hydrogen fuel, followed by calculating delta mass flow from the difference of setpoint mass flow and the estimated mass flow. The delta mass flow is used as a correction factor for HIPR 104 mass flow calculation to improve the performance of HIPR 104. Alternatively, device 120 determines the health status of the HIPR 104 based on the delta mass flow and determines requirement of the replacement.

[0030] According to the present invention, the method for estimating the mass flow through the HIPR 104 is disclosed. The method comprises plurality of steps of which the step 202 comprises receiving/reading signal for input parameters from respective sensors within the vehicle. The step 204 comprises processing the received/read input parameters through the estimation module 114 stored in the memory element 112. The estimation module 114 is the trained Machine Learning (ML) model. The step 206 comprises outputting, from the estimation module 114, the estimated mass flow through the HIPR 104.

[0031] According to the present invention, the method for estimating the mass flow deviation through the HIPR 104 is disclosed. In other words, the method for calculating drift in mass flow through the HIPR 104 is disclosed. The method comprises plurality of steps of which the step 202 comprises receiving/reading signal for input parameters from respective sensors within the vehicle. The step 204 comprises processing the received/read input parameters through the estimation module 114 stored in the memory element 112. The estimation module 114 is the trained Machine Learning (ML) model. The step 206 comprises outputting, from the estimation module 114, the estimated mass flow through the HIPR 104. The method also comprises a step 208 which comprises calculating difference between the setpoint mass flow stored in the memory element 112 and the estimated mass flow. The step 210 comprises determining mass flow deviation based on the calculated difference. Further, based on mass flow deviation, the method comprises either correcting the operation of the HIPR 104 or indicating the health status of the HIPR 104 to the concerned user.

[0032] According to the present invention, the device 120 and method to calculate the drift in Hydrogen Injection Pressure Regulator (HIPR) 104 is disclosed. The present invention enables estimation of the exact amount of mass flow from HIPR 104. The difference between actual and setpoint mass is the drift / error /deviation which is usable for further actions such as corrective functions in the engine management functions/software that can reduce the delay in increase/decrease of rail pressure, or the drift/deviation is compared to worst case limits (threshold deviations) to decide the replacement of HIPR 104. A mass flow sensor is also possible to be avoided.

[0033] It should be understood that the embodiments explained in the description above are only illustrative and do not limit the scope of this invention. Many such embodiments and other modifications and changes in the embodiment explained in the description are envisaged. The scope of the invention is only limited by the scope of the claims.

, Claims:We claim:
1. A device (120) for a Hydrogen Injection Pressure Regulator (HIPR) (104) in a hydrogen fuel injection system (100) of a vehicle, said device (120) comprises a controller configured to,
receive input parameters from respective sensors within said vehicle;
process the received input parameters through an estimation module (114) stored in a memory element (112), said estimation module (114) is a trained Machine Learning (ML) model, and
output, from said estimation module (114), an estimated mass flow through said HIPR (104).

2. The device (120) as claimed in claim 1, wherein said controller configured to,
calculate difference between a setpoint mass flow stored in said memory element (112) and said estimated mass flow, and
determine mass flow deviation based on said difference.

3. The device (120) as claimed in claim 2, wherein said controller configured to
compare said mass flow deviation with a threshold deviation, and
perform, based on said comparison, at least one selected from a group comprising, operation of said HIPR (104) in a manner to correct said determined mass flow deviation, and trigger an alert to replace said HIPR (104).

4. The device (120) as claimed in claim 1, wherein said input parameters are selected from a group comprising engine speed, fuel rail pressures, fuel rail temperature and engine torque, wherein said fuel rail pressure comprises pressures measured between energizations of consecutive injections of a fuel injector (108), namely a first pressure measured after an injection event and a second pressure measured before a next consecutive injection event.

5. The device (120) as claimed in claim 1 is at least one of an internal device and an external device, wherein said internal device is an existing control unit of said vehicle and said external device is at least one of a portable device (126) and a cloud based device (128).

6. A method for managing mass flow through a Hydrogen Injection Pressure Regulator (HIPR) (104) in a hydrogen fuel injection system (100) of a vehicle, said method comprising the steps of,
receiving signal for input parameters from respective sensors within said vehicle;
processing the received input parameters through an estimation module (114) stored in a memory element (112), said estimation module (114) is a trained Machine Learning (ML) model, and
outputting, from said estimation module (114), an estimated mass flow through said HIPR (104).

7. The method as claimed in claim 6, wherein said method comprises,
calculating difference between a setpoint mass flow stored in said memory element (112) and said estimated mass flow, and
determining mass flow deviation based on said difference.

8. The method as claimed in claim 7, comprises,
comparing said mass flow deviation with a threshold deviation, and
performing, based on said comparison, at least one selected from a group comprising, operating said HIPR (104) in a manner to correct said determined mass flow deviation, and triggering an alert to replace said HIPR (104).

9. The method as claimed in claim 6, wherein said input parameters are selected from a group comprising engine speed, fuel rail pressures, fuel rail temperature and engine torque, wherein said fuel rail pressure comprises pressures measured between before and after consecutive energization of a fuel injector (108), namely a first pressure measured after an injection event and a second pressure measured before a next consecutive injection event.

10. The method as claimed in claim 6 is performed by at least one of an internal device and an external device, wherein said internal device is an existing control unit of said vehicle and said external device is at least one of a portable device (126) and a cloud based device (128).