Hybrid vehicle torque distribution and gear selection method, system and commercial vehicle

Abstract

The application provides a hybrid vehicle torque distribution and gear selection method, system and commercial vehicle, relates to the technical field of hybrid commercial vehicles, and utilizes an offline driving assistance map to acquire a current position and road information in front of the vehicle; long-range planning of the hybrid vehicle is carried out according to the long-distance road information in front of the vehicle, so that the power battery is maintained in a preset interval; the vehicle driving speed is estimated according to the long-range planning of the power battery and the road information of the preset distance in front, and the expected participation of the motor is solved by using a fuzzy rule; a preset gear is selected, predictive gear shifting and advance gear shifting are carried out; the torque distribution result of the minimum equivalent fuel consumption strategy is corrected according to the long-range and short-range planning of the SoC and the current gear of the gearbox, the final torque distribution method is obtained, and output is carried out. The application avoids that the vehicle is overcharged or cannot be fully recovered in the energy recovery process of long downhill, so that energy is wasted.

Description

Technical Field

[0001] This invention relates to the field of hybrid commercial vehicle technology, and in particular to a method, system and commercial vehicle for torque distribution and gear selection in hybrid vehicles. Background Technology

[0002] Hybrid electric vehicles (HEVs) are now widely used and represent an effective way to achieve sustainable energy development. HEVs combine the advantages of conventional vehicles—convenient and quick refueling and long driving range—with the low emissions and low energy consumption of pure electric vehicles. The energy management and shifting strategies of HEVs directly impact their fuel economy and driving performance.

[0003] Currently, energy management strategies for hybrid electric vehicles mainly fall into two categories: rule-based energy management strategies and optimization-based energy management strategies. Among them, optimization-based energy management strategies can obtain the final or suboptimal control scheme, but the computational load is too large and the calculation time is too long, making it unsuitable for the real-time output of automotive-grade controllers. Therefore, the energy management strategies currently installed in vehicles are generally rule-based.

[0004] While rule-based energy management strategies require less computation and can output results in real time, they struggle to find the optimal solution and require continuous optimization to approximate it. Existing shifting strategies typically select the target gear based on the vehicle's current position, speed, gradient, and throttle signal, lacking predictive shifting capabilities. Furthermore, due to the complex driving conditions and load characteristics of heavy commercial vehicles, the fuel efficiency of hybrid heavy commercial vehicles has remained unsatisfactory, and the power interruption caused by shifting gears during uphill driving under heavy loads poses a significant safety hazard. Summary of the Invention

[0005] To address the above problems, this invention provides a method for torque distribution and gear selection in hybrid vehicles. This method enables predictive energy management and gear control, effectively improving the economy and power of hybrid commercial vehicles and ensuring the driving safety of heavy vehicles to a certain extent.

[0006] The methods include:

[0007] S1: Obtain the vehicle's current location and road information ahead of the vehicle;

[0008] S2: Based on long-distance road information ahead of the vehicle, the hybrid vehicle SoC performs long-range planning to ensure that the power battery SoC remains within the preset range during and after the interval driving process.

[0009] S3: Based on the long-range planning of the power battery SoC and the road information at the preset distance ahead, the vehicle speed is estimated and the expected participation of the motor is solved using fuzzy rules;

[0010] S4: Selects a preset gear based on the vehicle's current position, slope, and road information at a preset distance ahead, performing predictive and advance gear shifting to effectively avoid dangerous situations such as power interruption caused by gear shifting on slopes;

[0011] S5: Based on the SoC's long and short-range planning and the current gear of the transmission, the torque distribution result of the equivalent fuel consumption minimization strategy is corrected to obtain the final torque distribution method, and then output.

[0012] It should be further noted that the method for obtaining road information in step S1 is as follows:

[0013] Road information is preloaded in the driver assistance map box, and the road information ahead of the vehicle is sent to the vehicle controller via bus based on the vehicle's current location obtained by GPS.

[0014] The road information sent includes: road gradient, traffic speed limit, and road curvature at various locations within a preset distance ahead.

[0015] It should be further explained that step S2 involves performing long-term SoC planning based on road information to maintain the power battery charge within a reasonable range. This specifically includes the following steps:

[0016] S21: Process the road gradient information for the next 2km, and divide it into 1km before and after to calculate the gradient and distance of the uphill and downhill sections respectively;

[0017] S22: Differentiate different road conditions based on statistical information; classify road conditions into: long-distance steep uphill, long-distance shallow uphill, long-distance flat road, long-distance steep downhill, long-distance shallow downhill, etc.

[0018] S23: Based on the identified road conditions and the current power battery charge, formulate a corresponding SoC long-term plan to maintain the battery charge within a preset range.

[0019] It should be further explained that the input of the fuzzy rule in step S3 is: power battery charge, first 300m slope, second 300km slope, and the output is the motor participation coefficient k, where -1≤k≤1. The closer the value of k is to 1, the greater the motor output torque. The closer the value of k is to -1, the greater the motor recovery torque.

[0020] It should be further noted that in step S4, predictive gear control is performed based on the current position and the road gradient 100m ahead.

[0021] Step S4 also includes: the transmission controller prioritizes selecting the current gear based on the vehicle's current position, slope, speed, and load information.

[0022] It should be further noted that the basic rules for predictive shifting in step S4 include:

[0023] If the vehicle is currently at the top of a hill, the transmission will shift up in advance to improve the energy recovery efficiency during downhill driving.

[0024] If the vehicle is currently at the bottom of a slope, the transmission will downshift in advance based on the magnitude and distance of the slope ahead.

[0025] If the vehicle is currently in the middle of a slope, gear shifting should be prohibited.

[0026] It should be further noted that step S5 also includes:

[0027] S51: Based on the motor efficiency map, engine fuel consumption map and battery charge and discharge efficiency curve information, solve the optimal motor and engine torque allocation table offline to obtain the requested torque of the motor and engine.

[0028] S52: Corrects the output of the equivalent fuel consumption minimization strategy based on SoC long-range planning and motor participation coefficient.

[0029] The present invention also provides a torque distribution and gear selection system for hybrid vehicles, the system comprising: a vehicle controller, a driving assistance map box, a power battery SoC control module, a gear prediction module, and a torque distribution module;

[0030] The driver assistance map box stores road information; the vehicle controller and the driver assistance map box are connected via CAN bus to obtain the vehicle's current position and road information ahead of the vehicle.

[0031] The power battery SoC control module is used to perform long-range planning for the hybrid vehicle SoC based on long-distance road information ahead of the vehicle, so that the power battery SoC remains within the preset range during and after the interval driving process.

[0032] The vehicle controller also estimates the vehicle speed based on the long-range planning of the power battery SoC and road information at the preset distance ahead, and uses fuzzy rules to solve the expected participation of the motor.

[0033] The gear prediction module is used to select a preset gear based on the vehicle's current position, slope, and road information at a preset distance ahead, and to perform predictive gear shifting and advance gear shifting.

[0034] The torque distribution module is used to modify the torque distribution result of the equivalent fuel consumption minimization strategy based on the SoC's long and short range planning and the current gear of the transmission, so as to obtain the final torque distribution method and output it.

[0035] The present invention also provides a commercial vehicle, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of a hybrid vehicle torque distribution and gear selection method.

[0036] As can be seen from the above technical solutions, the present invention has the following advantages:

[0037] The hybrid vehicle torque distribution and gear selection method provided by this invention introduces offline maps to obtain road information such as slope and curvature ahead of the vehicle. Based on an automotive-grade processor, predictive energy management and gear control of the vehicle are realized, avoiding energy waste caused by overcharging or insufficient energy recovery of the battery due to excessive SoC of the power battery during long downhill energy recovery.

[0038] This invention modifies the torque distribution result of the equivalent fuel consumption minimization strategy based on the SoC long-range and short-range planning and the current gear of the transmission, and obtains the final torque distribution method, which can avoid poor vehicle power performance when the vehicle is going uphill because the motor cannot output torque due to the low SoC of the power battery.

[0039] The method of this invention incorporates road information to more accurately control the power battery SoC, optimize the vehicle's fuel economy while ensuring the SoC remains stable within a suitable range, and effectively avoid SoC loss of control due to road conditions.

[0040] This invention selects a preset gear based on the vehicle's current position, slope, and road information at a preset distance ahead, performing predictive and advance gear shifting to effectively avoid dangerous situations such as power interruption caused by gear shifting during uphill climbing. Attached Figure Description

[0041] To more clearly illustrate the technical solution of the present invention, the accompanying drawings used in the description will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1 Flowchart of torque distribution and gear selection method for hybrid vehicles;

[0043] Figure 2 This is a schematic diagram of the energy management methodology process;

[0044] Figure 3 The system architecture is ADASIS v2.

[0045] Figure 4 Illustration of predictive energy management for hybrid power systems. Detailed Implementation

[0046] like Figure 1 and 2 The illustrations provided in the method for torque distribution and gear selection of hybrid vehicles provided by this invention are only schematic representations of the basic concept of this invention. Therefore, the illustrations only show the modules related to this invention and not the actual number and function of the modules in the actual implementation. In the actual implementation, the function, quantity and role of each module can be arbitrarily changed, and the function and purpose of the modules may also be more complex.

[0047] The torque distribution and gear selection method for hybrid vehicles can acquire and process related data based on artificial intelligence technology. Specifically, this invention utilizes a digital computer or a machine controlled by a digital computer to simulate, extend, and expand human intelligence, perceiving the environment, acquiring knowledge, and using that knowledge to obtain optimal results—a theory, method, technology, and application device.

[0048] The torque distribution and gear selection methods for hybrid vehicles involve both hardware and software technologies. The hardware can include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing, operating / interaction systems, and mechatronics. The software technologies primarily include computer vision techniques, speech processing, natural language processing, and machine learning / deep learning.

[0049] like Figure 1 and 2 The flowchart illustrates the torque distribution and gear selection method for hybrid vehicles provided by this invention. This method is applied to one or more commercial vehicles, including but not limited to microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), and embedded devices.

[0050] The networks in which commercial vehicles operate include, but are not limited to, the Internet, wide area networks, metropolitan area networks, local area networks, and virtual private networks (VPNs).

[0051] The following will combine Figures 1 to 4This invention will elaborate on the torque distribution and gear selection method for hybrid vehicles. The method of this invention can select the target gear based on the current vehicle speed, slope and throttle signal, and perform predictive gear shifting. By introducing the slope information of the road ahead, the method can shift gears in advance according to the road conditions, effectively avoiding power interruption caused by gear shifting during uphill driving. This has a positive effect on vehicle energy management and driving safety.

[0052] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0053] Please see Figures 1 to 4 The diagram shows a flowchart of a method for torque distribution and gear selection in a hybrid vehicle according to a specific embodiment. The method includes:

[0054] S1: Obtain the vehicle's current location and road information ahead. This invention can obtain the vehicle's current location and road information ahead by utilizing an offline driving assistance map.

[0055] Specifically, this invention pre-loads road information into the driving assistance map box, meaning it stores information about the roads the vehicle will travel on, such as road gradient, traffic speed limits, road curvature, etc. Users can update the road information stored in the driving assistance map box as needed, increasing the number of roads covered and meeting the requirements for torque distribution and gear selection when driving on multiple roads.

[0056] In this invention, the road information ahead of the vehicle is sent to the vehicle controller via a bus based on the vehicle's current location obtained by GPS. This information transmission follows the ADASSIS v2 Protocol.

[0057] The transmitted road information includes: road gradient, traffic speed limits, and road curvature at different locations within 2km ahead; the architecture of the driver assistance map system is as follows: Figure 3 As shown, the map provider pre-stores the collected road information in the driver assistance map box. When the car travels on the corresponding road, the driver assistance map box sends the road information to the vehicle controller via the CAN bus. The information transmission follows the ADASIS v2 protocol. The sent road information includes: road gradient at different locations within 2km ahead, traffic speed limits, road curvature, etc. After receiving the signal, the vehicle controller performs subsequent signal processing and application.

[0058] S2: Based on long-distance road information ahead of the vehicle, the hybrid vehicle SoC performs long-term planning to keep the power battery SoC within a reasonable range.

[0059] Step S2 specifically includes the following steps:

[0060] S21: Process the road gradient information for the next 2km, and divide it into 1km before and after to calculate the gradient and distance of the uphill and downhill sections respectively;

[0061] S22: Differentiate different road conditions based on statistical information: long-distance steep uphill, long-distance shallow uphill, long-distance flat road, long-distance steep downhill, long-distance shallow downhill, etc.

[0062] S23: Based on the identified road conditions and the current power battery level, formulate corresponding SoC long-term plans, such as reducing motor output, using the engine to charge the battery, and increasing motor output to maintain the battery level within an appropriate range.

[0063] S3: Perform short-range planning for the power battery SoC based on the vehicle's current location and short-distance road information ahead;

[0064] Using fuzzy rules to solve for the expected participation degree of the motor ensures vehicle power and economy. The basic principles of fuzzy rules are as follows: Figure 4 As shown, the battery is actively charged before going uphill to boost the battery SoC and ensure motor output during the uphill process.

[0065] At the top of the slope, the motor outputs low power or the engine outputs power alone, and the battery has sufficient charging space to prevent problems such as overcharging or insufficient recovery of braking energy.

[0066] When going downhill, make full use of the motor's regenerative braking function to charge the battery; on flat roads, the engine provides main output while the motor provides low power output or no output.

[0067] The inputs to the fuzzy rules of this invention are: power battery charge, first 300m slope, and second 300km slope. The output is the motor participation coefficient k, where -1≤k≤1. The closer k is to 1, the greater the motor output torque. The closer k is to -1, the greater the motor recovery torque.

[0068] S4: Select the appropriate gear based on the vehicle's current position, slope, and road information 100m ahead, making predictive and advance gear shifts to effectively avoid dangerous situations such as power interruption caused by shifting gears on slopes.

[0069] In S4, the transmission controller prioritizes selecting the current gear based on information such as the vehicle's current position, slope, speed, and load. The introduction of offline driving assistance maps can, to some extent, replace the slope sensor, resulting in higher accuracy and greater reliability.

[0070] Regarding the predictive shifting fundamental rules of this invention:

[0071] If the vehicle is currently at the top of a hill, the transmission will shift up in advance to improve the energy recovery efficiency during downhill driving.

[0072] If the vehicle is currently at the bottom of the slope, the transmission will downshift in advance based on the slope and distance ahead, ensuring that the vehicle completes the climb at a fixed gear and a relatively stable speed.

[0073] If the vehicle is currently in the middle of a slope, gear shifting should be prohibited.

[0074] S5: Based on the SoC's long and short-range planning and the current gear of the transmission, the torque distribution result of the equivalent fuel consumption minimization strategy is corrected to obtain the final torque distribution method and output it.

[0075] Step S5 of the present invention specifically includes the following steps:

[0076] S51: Based on information such as motor efficiency map, engine fuel consumption map and battery charge and discharge efficiency curve, solve the optimal motor and engine torque allocation table offline, and obtain the requested torque of motor and engine online by looking up the table. The table input parameters include the current state engine torque, current speed, throttle requested torque, and the equivalent fuel consumption minimization strategy result.

[0077] S52: Based on SoC long-range planning and motor participation coefficient, the output of the equivalent fuel consumption minimization strategy is corrected to avoid some unreasonable torque distribution states, such as: the motor outputting high power when the SoC is low, causing the battery to be over-discharged, or the engine charging the battery when there is a long downhill section ahead, causing the battery to be overcharged.

[0078] This invention addresses the problem that existing gear-shifting strategies typically select the target gear based on current vehicle speed, gradient, and throttle signal, failing to provide predictive shifting. By incorporating road gradient information, the invention allows for advance gear shifting based on road conditions, effectively preventing power interruption during uphill shifting. Furthermore, the method can leverage road information for vehicle energy management and the execution of corresponding gear-shifting strategies, resulting in more accurate vehicle control and ensuring safer and more scientific driving practices.

[0079] The following are embodiments of the hybrid vehicle torque distribution and gear selection system provided in this disclosure. This system and the hybrid vehicle torque distribution and gear selection methods in the above embodiments belong to the same inventive concept. For details not described in detail in the embodiments of the hybrid vehicle torque distribution and gear selection system, please refer to the embodiments of the hybrid vehicle torque distribution and gear selection methods described above.

[0080] The system includes: a vehicle controller, a driver assistance map box, a power battery SoC control module, a gear prediction module, and a torque distribution module;

[0081] The driver assistance map box stores road information; the vehicle controller and the driver assistance map box are connected via CAN bus to obtain the vehicle's current position and road information ahead of the vehicle.

[0082] The power battery SoC control module is used to perform long-range planning for the hybrid vehicle SoC based on long-distance road information ahead of the vehicle, so that the power battery SoC remains within the preset range during and after the interval driving process.

[0083] The vehicle controller also estimates the vehicle speed based on the long-range planning of the power battery SoC and road information at the preset distance ahead, and uses fuzzy rules to solve the expected participation of the motor.

[0084] The gear prediction module is used to select a preset gear based on the vehicle's current position, slope, and road information at a preset distance ahead, and to perform predictive gear shifting and advance gear shifting.

[0085] The torque distribution module is used to modify the torque distribution result of the equivalent fuel consumption minimization strategy based on the SoC's long and short range planning and the current gear of the transmission, so as to obtain the final torque distribution method and output it.

[0086] The units and algorithm steps of the various examples described in the embodiments of the hybrid vehicle torque distribution and gear selection method and system provided by this invention can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of each example have been generally described in terms of function in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0087] The flowcharts and block diagrams in the accompanying drawings of the method and system for torque distribution and gear selection in a hybrid vehicle illustrate the architecture, functionality, and operation of possible implementations of the apparatus, method, and computer program product according to various embodiments of this disclosure. In this regard, each block in the flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0088] In the torque distribution and gear selection method for hybrid vehicles, computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof. These programming languages ​​include, but are not limited to, object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or power server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (exemplarily using an Internet service provider for Internet connection).

[0089] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for torque distribution and gear selection in a hybrid vehicle, characterized in that the method... include: S1: Obtain the vehicle's current location and road information ahead of the vehicle; S2: Based on long-distance road information ahead of the vehicle, the hybrid vehicle SoC performs long-range planning to ensure that the power battery SoC remains within the preset range during and after the interval driving process. S3: Based on the long-range planning of the power battery SoC and the road information of the preset distance ahead, the vehicle speed is estimated and the expected participation of the motor is solved using fuzzy rules; S4: Select a preset gear based on the vehicle's current position, slope, and road information at a preset distance ahead, and perform predictive gear shifting and advance gear shifting; S5: Based on the SoC's long-range and short-range planning and the current gear of the transmission, the torque distribution result of the equivalent fuel consumption minimization strategy is corrected to obtain the final torque distribution method, and then output. Step S2 involves performing long-term SoC planning based on road information to maintain the power battery charge within a reasonable range. This includes the following steps: S21: Process the road gradient information for the next 2km, and divide it into 1km before and after to calculate the gradient and distance of the uphill and downhill sections respectively; S22: Differentiate different road conditions based on statistical information; classify road conditions into: long-distance steep uphill, long-distance shallow uphill, long-distance flat road, long-distance steep downhill, long-distance shallow downhill, etc. S23: Based on the identified road conditions and the current power battery level, formulate corresponding SoC long-term plans to maintain the battery level within a preset range; The inputs to the fuzzy rules in step S3 are: power battery charge, first 300m slope, and second 300km slope. The output is the motor participation coefficient k, where -1≤k≤1. The closer k is to 1, the greater the motor output torque. The closer k is to -1, the greater the motor recovery torque. In step S4, predictive gear control is performed based on the current position and the road gradient 100m ahead. Step S4 also includes: the transmission controller prioritizes selecting the current gear based on the vehicle's current position, slope, speed, and load information; In step S4 The basic rules of predictive shifting include: If the vehicle is currently at the top of a hill, the transmission will shift up in advance to improve the energy recovery efficiency during downhill driving. If the vehicle is currently at the bottom of a slope, the transmission will downshift in advance based on the magnitude and distance of the slope ahead. If the vehicle is currently in the middle of a slope, gear shifting should be prohibited. Step S5 also includes: S51: Based on the motor efficiency map, engine fuel consumption map and battery charge and discharge efficiency curve information, solve the optimal motor and engine torque allocation table offline to obtain the requested torque of the motor and engine. S52: Corrects the output of the equivalent fuel consumption minimization strategy based on SoC long-range planning and motor participation coefficient.

2. The method for torque distribution and gear selection in a hybrid vehicle according to claim 1, characterized in that, In step S1, the road information is obtained in the following way: Road information is preloaded in the driver assistance map box, and the road information ahead of the vehicle is sent to the vehicle controller via bus based on the vehicle's current location obtained by GPS.

3. The method for torque distribution and gear selection in a hybrid vehicle according to claim 2, characterized in that, The road information sent includes: road gradient, traffic speed limit, and road curvature at various locations within a preset distance ahead.

4. A torque distribution and gear selection system for a hybrid vehicle, characterized in that, The system employs the hybrid vehicle torque distribution and gear selection method as described in any one of claims 1 to 3; The system includes: a vehicle controller, a driver assistance map box, a power battery SoC control module, a gear prediction module, and a torque distribution module; The driver assistance map box stores road information; the vehicle controller and the driver assistance map box are connected via CAN bus to obtain the vehicle's current position and road information ahead of the vehicle. The power battery SoC control module is used to perform long-range planning for the hybrid vehicle SoC based on long-distance road information ahead of the vehicle, so that the power battery SoC remains within the preset range during and after the interval driving process. The vehicle controller also estimates the vehicle speed based on the long-range planning of the power battery SoC and road information at the preset distance ahead, and uses fuzzy rules to solve the expected participation of the motor. The gear prediction module is used to select a preset gear based on the vehicle's current position, slope, and road information at a preset distance ahead, and to perform predictive gear shifting and advance gear shifting. The torque distribution module is used to modify the torque distribution result of the equivalent fuel consumption minimization strategy based on the SoC's long and short range planning and the current gear of the transmission, so as to obtain the final torque distribution method and output it.

5. A commercial vehicle, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the hybrid vehicle torque distribution and gear selection method as described in any one of claims 1 to 3.

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