Hybrid vehicle energy mode management method and device

Abstract

The invention provides a hybrid vehicle energy mode management method and device. The method comprises the following steps: acquiring estimated mileage, estimated driving time, state of charge of a power battery, residual oil quantity and map information; according to the estimated mileage, the state of charge of the power battery, the remaining oil quantity and map information, determining the proportion of pure electricity, fuel oil and oil-electricity mixing through fuzzy control; pure electricity, fuel oil and a fuel-electricity mixing ratio are corrected according to road condition parameters of a planned route and the vehicle speed and power of the vehicle; pure electricity, fuel oil and a fuel-electricity mixing ratio are optimized through the driving habit data of the user; and distributing pre-working time of pure electricity, fuel oil and oil-electricity mixing under the planned route based on the estimated driving time, the pure electricity, the fuel oil and the oil-electricity mixing proportion. Through the technical scheme provided by the invention, the problem that the comprehensive performance and the driving mileage of the vehicle cannot be maximized is solved, the comprehensive performance and the driving mileage of the vehicle are improved, and the intelligent degree of the vehicle is improved.

Description

Technical Field

[0001] The present disclosure relates to the field of intelligent vehicles, and particularly to a method, device, electronic device, chip and medium for managing the energy mode of a hybrid vehicle. Background Art

[0002] With the increasing intelligent requirements for new energy hybrid vehicles, the vast majority of models on the market currently adopt the driver to manually select different energy mode switches, and only automatically switch the energy mode when the fuel level or the battery level is very low; or only switch the energy mode according to the vehicle speed, the state of charge (SOC) of the power battery and the driving power demand, resulting in the inability to maximize the comprehensive performance and driving range of the vehicle and the inability to meet the driver's demand for intelligent driving habits. Summary of the Invention

[0003] The present disclosure provides a method, device, electronic device, chip and medium for managing the energy mode of a hybrid vehicle to solve the problem that the comprehensive performance and driving range of the vehicle cannot be maximized. The proportions of pure electric, fuel, and hybrid fuel and electricity are determined by fuzzy control, and the proportions of pure electric, fuel, and hybrid fuel and electricity are corrected by using road condition information, and then the proportions of the three are further optimized by the user's driving habits. The comprehensive performance and driving range of the vehicle are improved, and the intelligent level of the vehicle is enhanced.

[0004] The first aspect embodiment of the present disclosure proposes a method for managing the energy mode of a hybrid vehicle, the method including:

[0005] Obtain the estimated mileage, estimated driving time, state of charge of the power battery, remaining fuel quantity, and map information of the planned route for the vehicle to reach the destination;

[0006] According to the estimated mileage, state of charge of the power battery, remaining fuel quantity, and map information, determine the first pure electric proportion, the first fuel proportion, and the first hybrid fuel and electricity proportion through fuzzy control, where the first pure electric proportion, the first fuel proportion, and the first hybrid fuel and electricity proportion respectively refer to the proportions of the energy modes in which the vehicle is powered only by the battery, only by the internal energy generated by engine fuel, and by both engine fuel and the battery in the three energy modes, and the sum of the first pure electric proportion, the first fuel proportion, and the first hybrid fuel and electricity proportion is 1;

[0007] According to the first pure electric proportion, the first fuel proportion, and the first hybrid fuel and electricity proportion, determine the second pure electric proportion, the second fuel proportion, and the second hybrid fuel and electricity proportion through the correction factors determined by the road condition parameters of the planned route, the vehicle speed, and the power;

[0008] Determine the third pure - electric ratio, the third fuel ratio, and the third hybrid ratio based on the second pure - electric ratio, the second fuel ratio, the second hybrid ratio, with the user driving habit data as an optimization factor.

[0009] Based on the estimated driving time, the third pure - electric ratio, the third fuel ratio, and the third hybrid ratio, allocate the pre - working time of the pure - electric mode, the fuel mode, and the hybrid mode under the planned route.

[0010] In an embodiment of the present disclosure, determining the first pure - electric ratio, the first fuel ratio, and the first hybrid ratio according to the estimated mileage, the state of charge of the power battery, the remaining fuel, and the map information through fuzzy control includes:

[0011] Determine the pure - electric driving range of the remaining power of the vehicle according to the state of charge of the power battery, and determine the fuel driving range according to the remaining fuel.

[0012] Determine the first pure - electric ratio according to the pure - electric driving range, the estimated mileage, the charging and energy - replenishing parameter, and the pure - electric priority mapping table, where the charging and energy - replenishing parameter is obtained from the charging intention and the distance between the vehicle's position and the target charging pile in the map information, the pure - electric priority mapping table is a mapping table between the first mileage difference, the charging and energy - replenishing parameter, and the pure - electric priority ratio, and the first mileage difference is the difference between the pure - electric driving range and the estimated mileage.

[0013] Determine the first fuel ratio according to the fuel driving range, the estimated mileage, the refueling and energy - replenishing parameter, and the fuel priority mapping table, where the refueling and energy - replenishing parameter is obtained from the refueling intention and the distance between the vehicle's position and the target gas station in the map information, the fuel priority mapping table is a mapping table between the second mileage difference, the refueling and energy - replenishing parameter, and the fuel priority ratio, and the second mileage difference is the difference between the fuel driving range and the estimated mileage.

[0014] Determine the first hybrid ratio according to the first pure - electric ratio and the first fuel ratio.

[0015] In an embodiment of the present disclosure, determining the first pure - electric ratio according to the pure - electric driving range, the estimated mileage, the charging and energy - replenishing parameter, and the pure - electric priority mapping table includes:

[0016] Fuzzify the first mileage difference into a pure - electric driving range fuzzy quantity.

[0017] Fuzzify the charging and energy - replenishing parameter into a charging and energy - replenishing fuzzy quantity.

[0018] Determine the first pure - electric ratio according to the pure - electric driving range fuzzy quantity, the charging and energy - replenishing fuzzy quantity, and the pure - electric priority mapping table.

[0019] In an embodiment of the present disclosure, determining the first fuel ratio according to the fuel driving range, the estimated mileage, the refueling and energy - replenishing parameter, and the fuel priority mapping table includes:

[0020] Fuzzify the second mileage difference into a fuel endurance fuzzy quantity;

[0021] According to the refueling and energy replenishment parameters, fuzzify them into refueling and energy replenishment fuzzy quantities;

[0022] Determine the first fuel ratio according to the fuel endurance fuzzy quantity, the refueling and energy replenishment fuzzy quantity, and the fuel priority mapping table.

[0023] In an embodiment of the present disclosure, according to the first pure - electric ratio, the first fuel ratio, and the first hybrid ratio, determine the second pure - electric ratio, the second fuel ratio, and the second hybrid ratio through a correction factor determined by the road condition parameters, vehicle speed, and power of the planned route, including:

[0024] In pure - electric mode, take the weighted sum of the road condition parameters, vehicle speed, and power as the pure - electric correction factor;

[0025] Correct the first pure - electric ratio through the pure - electric correction factor to obtain the second pure - electric ratio;

[0026] In fuel mode, take the weighted sum of the road condition parameters, vehicle speed, and power as the fuel correction factor;

[0027] Correct the first fuel ratio through the fuel correction factor to obtain the second fuel ratio;

[0028] Determine the second hybrid ratio according to the second pure - electric ratio and the second fuel ratio.

[0029] In an embodiment of the present disclosure, according to the second pure - electric ratio, the second fuel ratio, and the second hybrid ratio, determine the third pure - electric ratio, the third fuel ratio, and the third hybrid ratio through user driving habit data as an optimization factor, including:

[0030] Statistically calculate the pure - electric driving frequency, charging frequency, and refueling frequency of the vehicle within a preset time period in pure - electric mode, and take the weighted sum of the pure - electric driving frequency, charging frequency, and refueling frequency as the pure - electric optimization factor;

[0031] Optimize the second pure - electric ratio through the pure - electric optimization factor to obtain the third pure - electric ratio;

[0032] Statistically calculate the pure - electric driving frequency, charging frequency, and refueling frequency of the vehicle within a preset time period in fuel mode, and take the weighted sum of the pure - electric driving frequency, charging frequency, and refueling frequency as the fuel optimization factor;

[0033] Optimize the second fuel ratio through the fuel optimization factor to obtain the third fuel ratio;

[0034] Determine the third hybrid ratio based on the third pure - electric ratio and the third fuel ratio.

[0035] The second - aspect embodiment of the present disclosure proposes a hybrid - vehicle energy - mode management device, which includes:

[0036] An acquisition module, configured to acquire the estimated mileage, estimated driving time, state of charge of the power battery, remaining fuel quantity, and map information of the planned route for the vehicle to reach the destination;

[0037] A first energy - planning module, configured to determine the first pure - electric ratio, the first fuel ratio, and the first hybrid ratio according to the estimated mileage, state of charge of the power battery, remaining fuel quantity, and map information through fuzzy control, where the first pure - electric ratio, the first fuel ratio, and the first hybrid ratio respectively refer to the proportions of the energy - mode of the vehicle in which only the battery supplies energy, only the internal energy generated by engine fuel supplies energy, and both engine fuel and the battery supply energy in the three energy - modes, and the sum of the first pure - electric ratio, the first fuel ratio, and the first hybrid ratio is 1;

[0038] A second energy - planning module, configured to determine the second pure - electric ratio, the second fuel ratio, and the second hybrid ratio according to the first pure - electric ratio, the first fuel ratio, and the first hybrid ratio and a correction factor determined by the road - condition parameters of the planned route, the vehicle speed, and the power;

[0039] A third energy - planning module, configured to determine the third pure - electric ratio, the third fuel ratio, and the third hybrid ratio according to the second pure - electric ratio, the second fuel ratio, and the second hybrid ratio by using the user - driving - habit data as an optimization factor;

[0040] A working - time pre - allocation module, configured to allocate the pre - working times of the pure - electric mode, the fuel mode, and the hybrid mode under the planned route based on the estimated driving time, the third pure - electric ratio, the third fuel ratio, and the third hybrid ratio.

[0041] The third - aspect embodiment of the present disclosure proposes an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method of any item in the first - aspect embodiment of the present disclosure.

[0042] The fourth - aspect embodiment of the present disclosure proposes a non - transitory computer - readable storage medium storing computer instructions, characterized in that the computer instructions are used to cause a computer to execute the method in the first - aspect embodiment of the present disclosure.

[0043] An embodiment of the fifth aspect of the present disclosure provides a computer program product, which includes a computer program that, when executed by a processor, implements the method according to any one of the embodiments of the first aspect of the present disclosure.

[0044] An embodiment of the sixth aspect of the present disclosure provides a chip, which includes at least one processor and a communication interface; the communication interface is configured to receive signals input to the chip or signals output from the chip, and the processor communicates with the communication interface and implements the method according to any one of the embodiments of the first aspect of the present disclosure through logic circuits or by executing code instructions.

[0045] In summary, according to the hybrid vehicle energy mode management method proposed by the present disclosure, the estimated mileage, estimated driving time, state of charge of the power battery, remaining fuel quantity, and map information of the planned route for the vehicle to reach the destination are obtained, providing a data source for the hybrid vehicle energy mode management; according to the estimated mileage, state of charge of the power battery, remaining fuel quantity, and map information, through fuzzy control, the first pure electric ratio, the first fuel ratio, and the first hybrid fuel-electric ratio are determined, where the first pure electric ratio, the first fuel ratio, and the first hybrid fuel-electric ratio respectively refer to the proportions of the energy modes in which the vehicle is powered only by the battery, only by the internal energy generated by engine fuel, and by both engine fuel and the battery in the three energy modes, and the sum of the first pure electric ratio, the first fuel ratio, and the first hybrid fuel-electric ratio is 1, obtaining an energy mode allocation ratio with good robustness, high reliability, and good real-time performance; according to the first pure electric ratio, the first fuel ratio, and the first hybrid fuel-electric ratio, through the correction factors determined by the road condition parameters of the planned route, the vehicle speed, and the power, the second pure electric ratio, the second fuel ratio, and the second hybrid fuel-electric ratio are determined, and the energy mode allocation ratio is corrected using the road condition information and vehicle information; according to the second pure electric ratio, the second fuel ratio, and the second hybrid fuel-electric ratio, through the user driving habit data as the optimization factor, the third pure electric ratio, the third fuel ratio, and the third hybrid fuel-electric ratio are determined, planning an energy mode allocation that conforms to the user's driving habits; based on the estimated driving time, the third pure electric ratio, the third fuel ratio, and the third hybrid fuel-electric ratio, the pre-working times of the pure electric mode, fuel mode, and hybrid fuel-electric mode under the planned route are allocated, obtaining an energy mode allocation for maximizing the vehicle's cruising range. This improves the comprehensive performance and cruising range of the vehicle and enhances the intelligent level of the vehicle.

[0046] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The accompanying drawings here are incorporated into the specification and constitute a part of this specification, showing embodiments consistent with the present disclosure, and are used together with the specification to explain the principles of the present disclosure, and do not constitute an undue limitation on the present disclosure.

[0048] Figure 1 It is a flowchart of a method for managing the energy mode of a hybrid vehicle according to an embodiment of the present disclosure;

[0049] Figure 2 It is a flowchart of a method for determining a first pure - electric ratio, a first fuel ratio, and a first hybrid fuel - electric ratio according to the estimated mileage, the state of charge of the power battery, the remaining fuel quantity, and the map information through fuzzy control according to an embodiment of the present disclosure;

[0050] Figure 3 It is a flowchart of a method for determining the first pure - electric ratio according to the pure - electric cruising range, the estimated mileage, the charging and energy - replenishing parameters, and the pure - electric priority mapping table according to an embodiment of the present disclosure;

[0051] Figure 4 It is a flowchart of a method for determining the first fuel ratio according to the fuel - cruising range, the estimated mileage, the refueling and energy - replenishing parameters, and the fuel - priority mapping table according to an embodiment of the present disclosure;

[0052] Figure 5 It is a flowchart of a method for determining a second pure - electric ratio, a second fuel ratio, and a second hybrid fuel - electric ratio according to a correction factor determined by the road - condition parameters of the planned route, the vehicle speed, and the power according to the first pure - electric ratio, the first fuel ratio, and the first hybrid fuel - electric ratio according to an embodiment of the present disclosure;

[0053] Figure 6 It is a flowchart of a method for determining a third pure - electric ratio, a third fuel ratio, and a third hybrid fuel - electric ratio according to the second pure - electric ratio, the second fuel ratio, and the second hybrid fuel - electric ratio by using user driving - habit data as an optimization factor according to an embodiment of the present disclosure.

[0054] Figure 7 It is a flowchart of the energy - mode planning of a hybrid vehicle according to an embodiment of the present disclosure;

[0055] Figure 8 It is a flowchart of the energy - mode allocation of a fuzzy - controlled hybrid vehicle according to an embodiment of the present disclosure;

[0056] Figure 9 It is a schematic structural diagram of an energy - mode management device for a hybrid vehicle according to an embodiment of the present disclosure;

[0057] Figure 10 It is a block diagram of an electronic device for implementing the energy - mode management method of the hybrid vehicle of the present disclosure shown according to an exemplary embodiment;

[0058] Figure 11 It is a schematic structural diagram of the chip according to an embodiment of the present disclosure. Specific implementation manners

[0059] The embodiments of the present disclosure will be described in detail below. The examples of the embodiments are shown in the accompanying drawings, in which the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions from beginning to end. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to explain the present disclosure, and should not be construed as a limitation of the present disclosure.

[0060] In the field of hybrid electric vehicles, there are mainly two types: range-extended and plug-in. A range-extended hybrid electric vehicle mainly relies on an electric motor to drive the vehicle in pure electric mode. When the battery power runs out, the engine starts to provide electrical energy for the electric motor to drive the vehicle. A plug-in hybrid electric vehicle has two power sources, one is fuel and the other is electricity. When the battery is fully charged, it mainly uses electricity as the driving force; when the power is insufficient, it will switch to the fuel mode to provide power.

[0061] With the increasing requirements for the intelligence of new energy hybrid electric vehicles and the increasing requirements for the types and switching of energy modes, in order to further meet the requirements of high efficiency, comfort and extended cruising range of hybrid electric vehicles under different complex working conditions, it is necessary to develop an energy mode adaptive planning strategy that meets the requirements of multiple scenarios. At present, the vast majority of models on the market mainly use two schemes to switch energy modes. One is to manually select different energy mode switches, that is, the driver manually switches different energy modes. The second is that the vehicle automatically switches energy modes, that is, the vehicle automatically switches energy modes when the fuel level or the battery level is very low, or only switches energy modes according to the vehicle speed, the SOC of the power battery and the driving power demand. This results in the inability to maximize the comprehensive performance and cruising range of the vehicle and the inability to meet the driver's demand for intelligent driving habits.

[0062] The present disclosure aims to solve the problem that the comprehensive performance and cruising range of the vehicle cannot be maximized. The energy mode of the vehicle is initially planned through fuzzy control, the plan is corrected by using road condition information, and then the plan is optimized by the driver's driving habits to allocate the working ratios among the pure electric mode, the fuel mode and the hybrid mode. Ensure that the fuel consumption and power consumption of the vehicle reach the comprehensive optimum and the cruising range is maximized, and at the same time, it can better meet the demand of driving habits.

[0063] The method proposed in this disclosure is applied to the energy mode management scenario of hybrid vehicles. Its application has rich scenarios and can be used to improve the fuel economy of hybrid electric vehicles. By optimizing energy distribution and reducing energy consumption, the driving efficiency can be improved. Secondly, this method also helps to protect the healthy state of the vehicle system. Through the power management of key components such as the battery, over-discharge or over-charging can be avoided, and its service life can be extended. In addition, this energy mode planning method can also be used to reduce greenhouse gas emissions. By reasonably controlling the working state of the engine, exhaust emissions can be reduced. In the embodiments of this disclosure, the application scenarios are not limited.

[0064] The following will introduce in detail the hybrid vehicle energy mode management method provided in this disclosure with reference to the accompanying drawings.

[0065] Figure 1 It is a flowchart of a hybrid vehicle energy mode management method according to an embodiment of this disclosure. As Figure 1 shown in the embodiment, the hybrid vehicle energy mode management method includes:

[0066] Step 101, obtain the estimated mileage, estimated driving time, state of charge of the power battery, remaining fuel quantity, and map information of the planned route for the vehicle to reach the destination.

[0067] In this embodiment, the vehicle can be a plug-in hybrid vehicle or a range-extended hybrid vehicle, preferably a range-extended hybrid vehicle. The planned route is the driving route provided by the in-vehicle computer or other terminals through the navigation software or in-vehicle map according to the set destination. The estimated mileage of the planned route is the estimated driving mileage of the driving route. The estimated driving time is the duration required for the vehicle to complete the estimated mileage. The state of charge of the power battery is an important parameter reflecting the remaining capacity of the battery, indicating the ratio of the current remaining charge quantity to the rated charge quantity of the battery, that is, indicating the available state of the remaining charge in the battery, generally expressed as a percentage. The remaining fuel quantity is the current remaining gasoline quantity in the hybrid vehicle, which can be used for the vehicle to run on fuel or in a hybrid mode of fuel and electricity. The map information refers to the route information related to the planned route, the position information of the vehicle on the map, and the distribution information of gas stations or charging piles related to the planned route. Before starting to plan the energy mode, first obtain the planned route for the vehicle to reach the destination, the estimated distance and estimated driving time to complete the planned route, the state of charge of the power battery, the remaining fuel quantity, and the map information.

[0068] Step 102: Based on the estimated mileage, the state of charge of the power battery, the remaining fuel quantity, and the map information, determine the first pure-electric ratio, the first fuel ratio, and the first hybrid ratio through fuzzy control. Herein, the first pure-electric ratio, the first fuel ratio, and the first hybrid ratio respectively refer to the proportions of the energy modes of the vehicle in which the energy is supplied only by the battery, the energy is supplied only by the internal energy generated by engine fuel, and the energy is supplied by both engine fuel and the battery among the three energy modes. Moreover, the sum of the first pure-electric ratio, the first fuel ratio, and the first hybrid ratio is 1.

[0069] In this embodiment, fuzzy control is a non-linear control strategy based on fuzzy inference, which mimics the way of human thinking and implements control over objects for which it is difficult to establish an accurate mathematical model. It is the combined product of fuzzy mathematics and control theory and is also an important part of intelligent control. The core of fuzzy control is the fuzzy set theory, fuzzy language variables, and fuzzy logic inference. It fuzzifies the real-time signal according to the fuzzy rules compiled based on the experience of operators or experts, uses the fuzzified signal as the input of the fuzzy rules to complete fuzzy inference, and then adds the output quantity obtained after inference to the actuator. The energy modes of a hybrid vehicle include pure-electric mode, fuel mode, and hybrid mode. The first pure-electric ratio refers to the proportion of the energy mode in which the energy of the vehicle is supplied only by the battery among the three energy modes. The first fuel ratio refers to the proportion of the energy mode in which the energy is supplied only by the internal energy generated by engine fuel among the three energy modes. The first hybrid ratio refers to the proportion of the energy mode in which the energy is supplied by both engine fuel and the battery among the three energy modes. The sum of the first pure-electric ratio, the first fuel ratio, and the first hybrid ratio is 1.

[0070] Step 103: Based on the first pure-electric ratio, the first fuel ratio, and the first hybrid ratio, determine the second pure-electric ratio, the second fuel ratio, and the second hybrid ratio through the correction factors determined by the road condition parameters of the planned route, the vehicle speed, and the power of the vehicle.

[0071] In this embodiment, the road condition parameters of the planned route mainly refer to the road traffic conditions, including factors such as traffic flow, speed, congestion level, road surface condition, and weather condition. Common road conditions include smooth traffic, slow traffic, congestion, construction, bad weather, and accidents. For example, in the case of smooth traffic, there are fewer vehicles on the road, the vehicle speed is faster, and the traffic flow is smooth; while in the case of congestion, the traffic volume on the road is extremely large, the vehicle speed is very slow, and even in a standstill state. In addition, the road conditions also involve the technical conditions of the existing roadbed, road surface, structures, and ancillary facilities. Preferably, the road condition parameters of the planned route are characterized by the road surface condition. The vehicle speed refers to the normal driving speed of the vehicle. The power of the vehicle refers to the driving power, and the correction factor refers to the coefficient for correcting the first pure electric ratio, the first fuel ratio, and the first hybrid ratio in the above steps, which can be determined by the road condition parameters of the planned route, the vehicle speed, and the power. The second pure electric ratio refers to the proportion of the pure electric mode in the three energy modes re-determined after considering the vehicle information and road condition information on the basis of the first pure electric ratio. The second fuel ratio refers to the proportion of the fuel mode in the three energy modes re-determined after considering the vehicle information and road condition information on the basis of the first fuel ratio. The second hybrid ratio refers to the proportion of the hybrid mode in the three energy modes determined according to the second pure electric ratio and the second fuel ratio.

[0072] Step 104: According to the second pure electric ratio, the second fuel ratio, and the second hybrid ratio, and using the user driving habit data as an optimization factor, determine the third pure electric ratio, the third fuel ratio, and the third hybrid ratio.

[0073] In this embodiment, the user driving habit data includes the behavior patterns commonly used by the user during vehicle driving recorded by the vehicle. Preferably, it includes the pure electric driving frequency, the charging frequency, and the refueling frequency. The optimization factor refers to the coefficient for optimizing the second pure electric ratio, the second fuel ratio, and the second hybrid ratio in the above steps, which can be determined by the pure electric driving frequency, the charging frequency, and the refueling frequency. The third pure electric ratio refers to the proportion of the pure electric mode in the three energy modes re-determined after considering the user driving habit data such as the pure electric driving frequency, the charging frequency, and the refueling frequency on the basis of the second pure electric ratio. The third fuel ratio refers to the proportion of the fuel mode in the three energy modes re-determined after considering the user driving habit data such as the pure electric driving frequency, the charging frequency, and the refueling frequency on the basis of the second fuel ratio. The third hybrid ratio is the proportion of the hybrid mode in the three energy modes determined according to the third pure electric ratio and the third fuel ratio.

[0074] Step 105: Based on the estimated driving time, the third pure electric ratio, the third fuel ratio, and the third hybrid ratio, allocate the pre-working time of the pure electric mode, the fuel mode, and the hybrid mode under the planned route.

[0075] In this embodiment, according to the estimated driving time of the planned route, that is, the total driving duration, and the third pure - electric ratio, the third fuel ratio, and the third hybrid ratio, the corresponding pre - working times in the pure - electric mode, the fuel mode, and the hybrid mode are obtained by multiplying them respectively.

[0076] In summary, according to the hybrid vehicle energy mode management method proposed in the present disclosure, obtaining the estimated mileage, the estimated driving time, the state of charge of the power battery, the remaining fuel amount, and the map information of the planned route for the vehicle to reach the destination provides a data source for the hybrid vehicle energy mode management; according to the estimated mileage, the state of charge of the power battery, the remaining fuel amount, and the map information, through fuzzy control, the first pure - electric ratio, the first fuel ratio, and the first hybrid ratio are determined, and an energy mode allocation ratio with good robustness, high reliability, and good real - time performance is obtained; according to the first pure - electric ratio, the first fuel ratio, and the first hybrid ratio, through the correction factors determined by the road condition parameters of the planned route, the vehicle speed, and the power of the vehicle, the second pure - electric ratio, the second fuel ratio, and the second hybrid ratio are determined, and the energy mode allocation ratio is corrected using the road condition information and the vehicle information; according to the second pure - electric ratio, the second fuel ratio, and the second hybrid ratio, through the user driving habit data as the optimization factor, the third pure - electric ratio, the third fuel ratio, and the third hybrid ratio are determined, and the energy mode allocation that conforms to the user's driving habit is planned; based on the estimated driving time, the third pure - electric ratio, the third fuel ratio, and the third hybrid ratio, the pre - working times of the pure - electric mode, the fuel mode, and the hybrid mode under the planned route are allocated, and the energy mode allocation for maximizing the vehicle's cruising range is obtained. The comprehensive performance and cruising range of the vehicle are improved, and the intelligent level of the vehicle is enhanced.

[0077] Figure 2 It is a flowchart for determining the first pure - electric ratio, the first fuel ratio, and the first hybrid ratio according to the estimated mileage, the state of charge of the power battery, the remaining fuel amount, and the map information through fuzzy control in an embodiment of the present disclosure. Figure 2 It is a further explanation of Figure 1 Step 102, based on the Figure 2 embodiment shown, includes the following steps:

[0078] Step 201, according to the state of charge of the power battery, determine the pure - electric cruising range of the remaining power of the vehicle, and determine the fuel cruising range according to the remaining fuel amount.

[0079] In this embodiment, the pure - electric cruising range refers to the distance that the vehicle can travel only using the remaining power of the vehicle. The fuel cruising range refers to the distance that the vehicle can travel only using the remaining fuel amount. The pure - electric cruising range of the remaining power of the vehicle can be obtained according to the state of charge of the power battery; the fuel cruising range can be determined according to the remaining fuel amount of the vehicle.

[0080] Step 202: Determine the first pure-electric ratio according to the pure-electric cruising range, the estimated mileage, the charging and energy replenishment parameter, and the pure-electric priority mapping table. The charging and energy replenishment parameter is obtained from the charging intention and the distance between the vehicle's position in the map information and the target charging pile. The pure-electric priority mapping table is a mapping table between the first mileage difference, the charging and energy replenishment parameter, and the pure-electric priority ratio. The first mileage difference is the difference between the pure-electric cruising range and the estimated mileage.

[0081] In this embodiment, the charging and energy replenishment parameter refers to the necessary degree parameter for the vehicle to charge and replenish energy, which depends on the charging intention and the distance between the vehicle's position in the map information and the target charging pile. If the driver's charging intention is stronger, the charging and energy replenishment parameter is larger. If the vehicle is closer to the target charging pile, the charging and energy replenishment parameter is larger. The charging intention can be reflected by the interaction frequency related to charging in the driver's interaction with the vehicle-mounted computer. For example, in the voice interaction between the driver and the vehicle-mounted computer, the frequencies of words such as "pure-electric cruising range", "remaining battery power", and "charging pile" are counted. The higher the frequencies of the above keywords related to charging, the stronger the charging intention. The first mileage difference refers to the difference between the pure-electric cruising range and the estimated mileage. The pure-electric priority mapping table refers to a mapping table between the first mileage difference, the charging and energy replenishment parameter, and the pure-electric priority ratio. The first mileage difference and the charging and energy replenishment parameter determined according to the pure-electric cruising range and the estimated mileage can be used to index the pure-electric priority ratio in the pure-electric priority mapping table, that is, the first pure-electric ratio.

[0082] In an implementation manner of this embodiment, if the frequency of keywords related to charging in the voice interaction between the driver and the vehicle-mounted computer is a, and the larger the value of a is, that is, the stronger the driver's charging intention, and the distance between the current position of the vehicle in the map and the nearest charging pile is b, then the charging and energy replenishment parameter is c = k1*a + k2*b, where k1 is the preset weight value of the charging intention, and k2 is the preset weight value of the distance between the current position of the vehicle in the map and the nearest charging pile.

[0083] In an implementation manner of this embodiment, if the driver uses the vehicle-mounted computer system through touch or gesture interaction, and the frequency of query operations for the remaining battery power, pure-electric cruising range, and the number of times of querying the charging pile in the power theme is m, and the larger the value of m is, that is, the stronger the driver's charging intention. The distance between the current position of the vehicle in the map and the nearest charging pile is n, then the charging and energy replenishment parameter is f = w1*m + w2*n, where w1 is the preset weight value of the charging intention, and w2 is the preset weight value of the distance between the current position of the vehicle in the map and the nearest charging pile.

[0084] Step 203: Determine the first fuel ratio based on the fuel endurance mileage, the estimated mileage, the refueling and energy replenishment parameter, and the fuel priority mapping table. The refueling and energy replenishment parameter is obtained from the refueling intention and the distance between the vehicle's position in the map information and the target gas station. The fuel priority mapping table is a mapping table between the second mileage difference, the refueling and energy replenishment parameter, and the fuel priority ratio. The second mileage difference is the difference between the fuel endurance mileage and the estimated mileage.

[0085] In this embodiment, the refueling and energy replenishment parameter refers to the necessary degree parameter for the vehicle to refuel and replenish energy, which depends on the refueling intention and the distance between the vehicle's position in the map information and the target charging pile. If the driver's refueling intention is stronger, the refueling and energy replenishment parameter is larger. If the vehicle is closer to the target gas station, the refueling and energy replenishment parameter is larger. The refueling intention can be reflected by the interaction frequency related to refueling in the driver's interaction with the in-vehicle computer. For example, in the voice interaction between the driver and the in-vehicle computer, count the frequency of words such as "fuel endurance", "remaining fuel", and "gas station". The higher the frequency of the above keywords related to refueling, the stronger the refueling intention. Or in the in-vehicle computer system, convert the operation frequency of the remaining fuel, fuel endurance, and querying the gas station in the map under the fuel theme through touch or gesture interaction into the refueling intention. Similar to the implementation method of determining the charging and energy replenishment parameter in step 202, the refueling and energy replenishment parameter can be calculated by the weighted sum of the refueling intention and the distance between the vehicle's current position in the map information and the target gas station. The second mileage difference refers to the difference between the fuel endurance mileage and the estimated mileage. The fuel priority mapping table refers to the mapping table between the second mileage difference, the refueling and energy replenishment parameter, and the fuel priority ratio. According to the second mileage difference and the refueling and energy replenishment parameter, the fuel priority ratio, that is, the first fuel ratio, can be indexed in the fuel priority mapping table.

[0086] Step 204: Determine the first hybrid fuel and electricity ratio based on the first pure-electric ratio and the first fuel ratio.

[0087] In this embodiment, the sum of the first pure-electric ratio, the first fuel ratio, and the first hybrid fuel and electricity ratio is 1. Therefore, according to the first pure-electric ratio and the first fuel ratio determined in the above steps, the first hybrid fuel and electricity ratio can be determined.

[0088] In one implementation of this embodiment, when the estimated mileage minus the pure-electric endurance mileage is less than the first threshold, where the first threshold is a positive value, such as 200 km, it means that the estimated mileage for the vehicle to reach the destination is much less than the pure-electric endurance mileage of the vehicle, that is, the remaining power of the vehicle's battery is sufficient and can completely drive the estimated mileage. The pure-electric mode is preferentially planned.

[0089] In an implementation manner of this embodiment, when the estimated mileage is less than the second threshold of the pure - electric driving range and the fuel driving range is greater than the third threshold. For example, the second threshold is 300 km and the third threshold is 100 km. Evaluate whether the driver selects destination charging. If the driver selects destination charging, then evaluate the distance to the nearest charging pile. If the distance to the charging pile is relatively short, then preferentially plan the pure - electric mode; if the distance to the charging pile is relatively long, then plan the pure - electric mode and the hybrid - electric mode on the way of the planned route.

[0090] In an implementation manner of this embodiment, when the estimated mileage is less than the second threshold of the pure - electric driving range and the fuel driving range is less than the fourth threshold. For example, the second threshold is 300 km and the fourth threshold is 30 km. Evaluate whether the driver selects destination charging. If the driver selects destination charging and the destination is relatively close to the charging pile, then preferentially plan the pure - electric mode; if the destination is relatively far from the charging pile and there is a gas station near the destination, then plan the pure - electric mode and the hybrid - electric mode on the way of the planned route; if there is no gas station near the destination, then plan the pure - electric - priority mode.

[0091] In an implementation manner of this embodiment, when the estimated mileage minus the pure - electric driving range is greater than the fifth threshold. For example, the fifth threshold is 50 km. Then plan the energy mode according to the distribution of charging piles and gas stations on the map. If the fuel driving range is greater than the sixth threshold, for example, the sixth threshold is 400 km, then plan the fuel mode and the pure - electric mode. If the fuel driving range is less than the sixth threshold and greater than the seventh threshold, for example, the seventh threshold is 60 km, then plan the hybrid - electric mode and the pure - electric mode.

[0092] In an implementation manner of this embodiment, when the estimated mileage minus the pure - electric driving range is greater than the fifth threshold, preferentially plan the fuel mode and the hybrid - electric mode.

[0093] In this embodiment, according to the estimated mileage, the state of charge of the power battery, the remaining fuel, and the map information, through fuzzy control, determine the first pure - electric ratio, the first fuel ratio, and the first hybrid - electric ratio, and process the vehicle data into input variables of fuzzy control, which is convenient for fuzzy control to establish fuzzy rules for optimizing the planned energy mode, and obtain an energy - mode allocation ratio with good robustness, high reliability, and good real - time performance.

[0094] Figure 3 It is a flowchart for determining the first pure - electric ratio according to the pure - electric driving range, the estimated mileage, the charging and energy - replenishing parameters, and the pure - electric - priority mapping table in an embodiment of the present disclosure. Figure 3 It is a further description of Figure 2 Step 202 of, based on Figure 3 The embodiment shown, includes the following steps:

[0095] Step 301: Fuzzify the first mileage difference into a pure-electric range fuzzified quantity.

[0096] In this embodiment, the pure-electric range fuzzified quantity is a label of the first mileage difference under multiple different conditions or states, used to divide the values of different first mileage differences into multiple preset states. That is, fuzzify the first mileage difference into a pure-electric range fuzzified quantity.

[0097] In an implementation manner of this embodiment, take the first mileage difference as the first input X11, and fuzzify it into 11 fuzzified quantities: negative extremely large, negative large, negative moderately large, negative small, negative very small, zero, positive very small, positive small, positive moderately large, positive large, positive extremely large. The corresponding simplified variables are {NBBB, NBB, NB, NS, NSS, Z, PSS, PS, PB, PBB, PBBB}. The larger the negative value of X11, the farther the distance to the destination, and the less sufficient the pure-electric driving range to support the completion of the driving. The larger the positive value of X11, the closer the distance to the destination, and the more sufficient the pure-electric driving range to support the completion of the driving.

[0098] Step 302: Fuzzify the charging and energy supplement parameters into a charging and energy supplement fuzzified quantity.

[0099] In this embodiment, the charging and energy supplement fuzzified quantity is a label of the charging and energy supplement parameters under multiple different conditions or states, used to divide different charging and energy supplement parameters into multiple preset states. That is, fuzzify the charging and energy supplement parameters into a charging and energy supplement fuzzified quantity.

[0100] In an implementation manner of this embodiment, comprehensively consider whether the driver needs to charge when arriving at the destination and the distance from the destination to the charging pile as the charging and energy supplement parameter Y11 (charging cost function), and normalize its range to [0, 1]. Fuzzify it into a charging and energy supplement fuzzified quantity, that is, fuzzify it into zero, very small, small, medium, large, very large. The corresponding simplified variables are {Z, VS, S, M, B, BB, BBB}. The closer the value of Y11 is to 0, the stronger the charging intention and the closer the distance to the charging pile. The closer the value of Y11 is to 1, the weaker the charging intention and the farther the distance to the charging pile. The closer the value of Y11 is to 0.5, the weaker the charging intention and the farther the distance to the charging pile.

[0101] Step 303: Determine the first pure-electric ratio according to the pure-electric range fuzzified quantity, the charging and energy supplement fuzzified quantity, and the pure-electric priority mapping table.

[0102] In this embodiment, the pure-electric priority mapping table refers to the mapping table between the first mileage difference, the charging and energy replenishment parameters, and the pure-electric priority ratio. Among them, the first mileage difference is fuzzified into a pure-electric range fuzzy quantity, and the charging and energy replenishment parameters are fuzzified into a charging and energy replenishment fuzzy quantity. According to the pure-electric priority mapping table, the corresponding pure-electric ratio can be determined by using the pure-electric range fuzzy quantity and the charging and energy replenishment fuzzy quantity. Among them, the pure-electric priority mapping table is the proportion factor of the set pure-electric priority measured under different first mileage differences and different charging and energy replenishment parameters.

[0103] In one implementation manner of this embodiment, the pure-electric ratio Z11 corresponding to the pure-electric range fuzzy quantity and the charging and energy replenishment fuzzy quantity in the pure-electric priority mapping table ranges from [0, 1]. After being fuzzified, it is fuzzified into zero, very little, little, medium, much, very much, corresponding to the simplified variables {Z, VS, S, M, B, VB}.

[0104] The pure-electric priority mapping table 1 composed of the fuzzy quantity of the pure-electric ratio Z11 corresponding to the pure-electric range fuzzy quantity of X11 and the charging and energy replenishment fuzzy quantity of Y11 in this embodiment is shown as follows.

[0105] Table 1

[0106]

[0107] In this embodiment, according to the pure-electric range, the estimated mileage, and the charging and energy replenishment parameters, they are fuzzified into fuzzy quantities. According to the pure-electric priority mapping table, the first pure-electric ratio is determined, which provides a basis for the ratio allocation of the pure-electric mode in the energy mode management of hybrid vehicles.

[0108] Figure 4 It is a flowchart for determining the first fuel ratio according to the fuel range, the estimated mileage, the fuel replenishment parameters, and the fuel priority mapping table in an embodiment of the present disclosure. Figure 4 It is a Figure 2 specific description of step 203, based on Figure 4 the embodiment shown, including the following steps:

[0109] Step 401, fuzzify the second mileage difference into a fuel range fuzzy quantity.

[0110] In this embodiment, the fuel range fuzzy quantity is a mark of the second mileage difference under multiple different conditions or states, used to divide the values of different second mileage differences into multiple preset states. That is, fuzzify the second mileage difference into a fuel range fuzzy quantity.

[0111] In one implementation of this embodiment, the second mileage difference is taken as the first input X12, which is fuzzified into 11 fuzzy quantities: extremely large negative, large negative, moderately large negative, small negative, extremely small negative, zero, extremely small positive, small positive, moderately large positive, large positive, and extremely large positive. The corresponding simplified variables are {NBBB, NBB, NB, NS, NSS, Z, PSS, PS, PB, PBB, PBBB}. The larger the negative value of X12, the farther the distance to the destination and the less sufficient the fuel endurance mileage to support the completion of the journey. The larger the positive value of X12, the closer the distance to the destination and the more sufficient the fuel endurance mileage to support the completion of the journey.

[0112] Step 402: According to the refueling and energy replenishment parameters, perform fuzzification processing to obtain refueling and energy replenishment fuzzy quantities.

[0113] In this embodiment, the refueling and energy replenishment fuzzy quantity is a marker for the refueling and energy replenishment parameters under multiple different conditions or states, used to divide different refueling and energy replenishment parameters into multiple preset states. That is, the refueling and energy replenishment parameters are fuzzified into refueling and energy replenishment fuzzy quantities.

[0114] In one implementation of this embodiment, the refueling and energy replenishment parameter Y12 (refueling cost function) is comprehensively determined by whether the driver needs to refuel when arriving at the destination and the distance from the destination to the gas station, and its range is normalized to [0, 1]. It is fuzzified into refueling and energy replenishment fuzzy quantities, namely fuzzified into zero, very small, small, medium, large, and very large. The corresponding simplified variables are {Z, S, M, B, BB, BBB}. The closer the value of Y12 is to 0, the stronger the intention to refuel and the closer the distance to the gas station. The closer it is to 1, the weaker the intention to refuel and the farther the distance to the gas station. The closer it is to 0.5, the weaker the intention to refuel and the farther the distance to the gas station.

[0115] Step 403: Determine the first fuel ratio according to the fuel endurance fuzzy quantity, refueling and energy replenishment fuzzy quantity, and fuel priority mapping table.

[0116] In this embodiment, the fuel priority mapping table refers to the mapping table between the second mileage difference, refueling and energy replenishment parameters, and fuel priority ratio. Among them, the second mileage difference is fuzzified into a fuel endurance fuzzy quantity, and the refueling and energy replenishment parameters are fuzzified into refueling and energy replenishment fuzzy quantities. According to the fuel priority mapping table, the corresponding fuel ratio can be determined using the fuel endurance fuzzy quantity and refueling and energy replenishment fuzzy quantity. Among them, the fuel priority mapping table is the set fuel priority ratio factor through actual measurement under different second mileage differences and different refueling and energy replenishment parameters.

[0117] In one implementation of this embodiment, the fuel ratio Z12 corresponding to the fuel endurance fuzzy quantity and refueling and energy replenishment fuzzy quantity in the fuel priority mapping table has a range of [0, 1], and after fuzzification processing, it is fuzzified into zero, very few, few, medium, many, and very many. The corresponding simplified variables are {Z, VS, S, M, B, VB}.

[0118] In this embodiment, the fuel priority mapping table formed by the fuzzy quantity of the pure electric cruising range of X12 and the fuzzy quantity of the fuel filling and replenishment of Y12 corresponding to the fuel ratio Z12 is shown in Table 2 below.

[0119] Table 2

[0120]

[0121] In this embodiment, according to the fuel cruising range, the estimated mileage, the fuel filling and replenishment parameters, and the fuel priority mapping table, the first fuel ratio is determined, providing a basis for the ratio allocation of the fuel mode in the energy mode management of the hybrid vehicle.

[0122] Figure 5 It is a flowchart for determining the second pure electric ratio, the second fuel ratio, and the second hybrid ratio according to the correction factor determined by the road condition parameters, the vehicle speed, and the power of the planned route based on the first pure electric ratio, the first fuel ratio, and the first hybrid ratio in an embodiment of the present disclosure. Figure 5 It is a specific description of Figure 1 Step 103 of Figure 5 The shown embodiment includes the following steps:

[0123] Step 501, in the pure electric mode, use the weighted sum of the road condition parameters, the vehicle speed, and the power as the pure electric correction factor.

[0124] In this embodiment, the pure electric correction factor is a coefficient for correcting the first pure electric ratio using the road condition information and the vehicle information. When the vehicle is in the pure electric mode, the weighted sum of the road condition parameters, the vehicle speed, and the power is used as the pure electric correction factor y1.

[0125] Step 502, correct the first pure electric ratio through the pure electric correction factor to obtain the second pure electric ratio.

[0126] In this embodiment, use the product of the first pure electric ratio Z11 and the pure electric correction factor y1 as the second pure electric ratio Z21.

[0127] Step 503, in the fuel mode, use the weighted sum of the road condition parameters, the vehicle speed, and the power as the fuel correction factor.

[0128] In this embodiment, the fuel correction factor is a coefficient for correcting the first fuel ratio using the road condition information and the vehicle information. When the vehicle is in the fuel mode, the weighted sum of the road condition parameters, the vehicle speed, and the power is used as the fuel correction factor y2.

[0129] Step 504, correct the first fuel ratio through the fuel correction factor to obtain the second fuel ratio.

[0130] In this embodiment, the product of the first fuel ratio Z12 and the fuel correction factor y2 is used as the second fuel ratio Z22.

[0131] Step 505: Determine the second hybrid fuel - electricity ratio based on the second pure - electricity ratio and the second fuel ratio.

[0132] In this embodiment, since the sum of the second pure - electricity ratio, the second fuel ratio, and the second hybrid fuel - electricity ratio is 1, the second hybrid fuel - electricity ratio Z23 = 1 - Z21 - Z22 can be obtained according to the second pure - electricity ratio Z21 and the second fuel ratio Z22.

[0133] In this embodiment, according to the first pure - electricity ratio, the first fuel ratio, and the first hybrid fuel - electricity ratio, the second pure - electricity ratio, the second fuel ratio, and the second hybrid fuel - electricity ratio are determined through road - condition information and vehicle information. That is, the energy - mode distribution ratio is corrected using road - condition information and vehicle information, improving the utilization rate of the vehicle's comprehensive performance.

[0134] Figure 6 It is a flowchart of an embodiment of the present disclosure for determining the third pure - electricity ratio, the third fuel ratio, and the third hybrid fuel - electricity ratio by using user driving - habit data as an optimization factor according to the second pure - electricity ratio, the second fuel ratio, and the second hybrid fuel - electricity ratio. Figure 6 is a specific description of Figure 1 Step 104 of Figure 6 The shown embodiment includes the following steps:

[0135] Step 601: Statistically calculate the pure - electricity driving frequency, charging frequency, and refueling frequency of the vehicle in the pure - electricity mode within a preset time period, and use the weighted sum of the pure - electricity driving frequency, charging frequency, and refueling frequency as the pure - electricity optimization factor.

[0136] In this embodiment, the pure - electricity driving frequency is the number of times the vehicle is driven only by the battery within a preset time period. The charging frequency is the number of times the vehicle charges the battery within a preset time period. The refueling frequency is the number of times the vehicle refuels within a preset time period. The pure - electricity optimization factor is a coefficient for optimizing the second pure - electricity ratio using user driving habits. After recording the pure - electricity driving frequency, charging frequency, and refueling frequency in the pure - electricity mode in the vehicle or in the cloud, the weighted sum of the pure - electricity driving frequency, charging frequency, and refueling frequency is used as the pure - electricity optimization factor w1.

[0137] Step 602: Optimize the second pure - electricity ratio through the pure - electricity optimization factor to obtain the third pure - electricity ratio.

[0138] In this embodiment, the product of the second pure - electricity ratio Z12 and the pure - electricity optimization factor w1 is used as the third pure - electricity ratio Z31.

[0139] Step 603: Count the pure - electric driving frequency, charging frequency, and refueling frequency of the vehicle in the fuel mode within a preset time period, and use the weighted sum of the pure - electric driving frequency, charging frequency, and refueling frequency as the fuel optimization factor.

[0140] In this embodiment, the fuel optimization factor refers to the coefficient optimized according to the second fuel ratio using the user's driving habits. After recording the pure - electric driving frequency, charging frequency, and refueling frequency in the fuel mode in the vehicle or the cloud, the weighted sum of the pure - electric driving frequency, charging frequency, and refueling frequency is used as the fuel optimization factor w2.

[0141] Step 604: Optimize the second fuel ratio through the fuel optimization factor to obtain the third fuel ratio.

[0142] In this embodiment, the product of the second fuel ratio Z22 and the fuel optimization factor w2 is used as the third fuel ratio Z32.

[0143] Step 605: Determine the third hybrid fuel - electric ratio according to the third pure - electric ratio and the third fuel ratio.

[0144] In this embodiment, since the sum of the third pure - electric ratio, the third fuel ratio, and the third hybrid fuel - electric ratio is 1, according to the third pure - electric ratio Z31 and the third fuel ratio Z32, the third hybrid fuel - electric ratio Z33 = 1 - Z31 - Z32 can be obtained.

[0145] In this embodiment, according to the second pure - electric ratio, the second fuel ratio, and the second hybrid fuel - electric ratio, using the user's driving habit data as the optimization factor, the third pure - electric ratio, the third fuel ratio, and the third hybrid fuel - electric ratio are determined. Taking the user's driving habit as a variable in the energy - mode planning enables the energy - mode planning of the whole vehicle to conform to the driver's driving habit, ensuring the maximum cruising range on the basis of a comfortable driving experience and improving the intelligent level of the vehicle.

[0146] Figure 7 It is a flowchart of the energy - mode planning for a hybrid vehicle according to an embodiment of the present disclosure. In this embodiment, as Figure 7 shown, the following steps are included:

[0147] S701: Obtain the first energy - mode planning.

[0148] Before the energy - mode planning starts, the vehicle's on - board computer real - time obtains the battery SOC, remaining fuel quantity, driving distance, charging - pile, and gas - station information, and uses fuzzy control to complete the first energy - mode planning. Obtain the allocation ratios of the energy modes in the first energy - mode planning.

[0149] S702: Obtain the second energy - mode planning.

[0150] Based on information such as the reference vehicle model, driving power demand, and roadside status, perform the second energy mode planning. Obtain the allocation ratios of each energy mode for the second energy mode planning.

[0151] S703, obtain the third energy mode planning.

[0152] Based on the pure electric driving frequency, charging frequency, and refueling frequency, perform the third energy mode planning.

[0153] Obtain the allocation ratios of each energy mode for the third energy mode planning as the ratio values finally used for vehicle energy mode planning.

[0154] Figure 8 This is a flowchart of the energy mode allocation for a fuzzy control hybrid vehicle according to an embodiment of the present disclosure. In this embodiment, as Figure 8 shown, it includes the following steps:

[0155] S801, prepare the input of fuzzy control.

[0156] Take the difference between the pure electric driving range and the driving distance to the destination and the charging and refueling cost function as the input values of fuzzy control.

[0157] S802, perform fuzzy control.

[0158] Perform fuzzification, defuzzification, and fuzzy inference according to the established fuzzy rules in sequence, so as to determine the proportion of the pure electric priority and fuel priority mode planning.

[0159] S803, determine the energy mode allocation ratio.

[0160] According to the proportion of the pure electric priority mode planning and the proportion of the fuel priority mode planning, calculate the proportion of the hybrid priority mode, and take these three as the optimal energy mode allocation for the hybrid vehicle.

[0161] A hybrid vehicle energy mode management method according to an embodiment of the present disclosure obtains the estimated mileage, estimated driving time, state of charge of the power battery, remaining fuel quantity, and map information of the planned route for the vehicle to reach the destination, providing a data source for the hybrid vehicle energy mode management; according to the estimated mileage, state of charge of the power battery, remaining fuel quantity, and map information, through fuzzy control, a first pure electric ratio, a first fuel ratio, and a first hybrid fuel and electric ratio are determined, where the first pure electric ratio, the first fuel ratio, and the first hybrid fuel and electric ratio respectively refer to the proportions of the energy modes of the vehicle in which only the battery supplies energy, only the internal energy generated by engine fuel supplies energy, and both engine fuel and the battery supply energy in the three energy modes, and the sum of the first pure electric ratio, the first fuel ratio, and the first hybrid fuel and electric ratio is 1, obtaining an energy mode allocation ratio with good robustness, high reliability, and good real-time performance; according to the first pure electric ratio, the first fuel ratio, and the first hybrid fuel and electric ratio, through a correction factor determined by the road condition parameters of the planned route, the vehicle speed, and the power, a second pure electric ratio, a second fuel ratio, and a second hybrid fuel and electric ratio are determined, and the energy mode allocation ratio is corrected using the road condition information and the vehicle information; according to the second pure electric ratio, the second fuel ratio, and the second hybrid fuel and electric ratio, through the user driving habit data as an optimization factor, a third pure electric ratio, a third fuel ratio, and a third hybrid fuel and electric ratio are determined, planning an energy mode allocation that conforms to the user's driving habits; based on the estimated driving time, the third pure electric ratio, the third fuel ratio, and the third hybrid fuel and electric ratio, the pre-working times of the pure electric mode, the fuel mode, and the hybrid fuel and electric mode under the planned route are allocated, obtaining an energy mode allocation for maximizing the vehicle's cruising range. The comprehensive performance and cruising range of the vehicle are improved, and the intelligent level of the vehicle is enhanced. Corresponding to the methods provided in the above several embodiments, the present disclosure also provides a hybrid vehicle energy mode management device. Since the device provided in the embodiment of the present disclosure corresponds to the methods provided in the above several embodiments, the implementation manners of the methods are also applicable to the device provided in this embodiment and will not be described in detail in this embodiment.

[0162] Figure 9 It is a schematic structural diagram of a hybrid vehicle energy mode management device 900 according to an embodiment of the present disclosure. As Figure 9 shown, the hybrid vehicle energy mode management device includes:

[0163] An acquisition module 910, configured to acquire the estimated mileage, estimated driving time, state of charge of the power battery, remaining fuel quantity, and map information of the planned route for the vehicle to reach the destination;

[0164] The first energy planning module 920 is configured to determine a first pure - electric ratio, a first fuel ratio, and a first hybrid fuel - electric ratio based on the estimated mileage, the state of charge of the power battery, the remaining fuel quantity, and the map information through fuzzy control. Herein, the first pure - electric ratio, the first fuel ratio, and the first hybrid fuel - electric ratio respectively refer to the proportions of the energy modes of the vehicle in which the energy is supplied only by the battery, the energy is supplied only by the internal energy generated by the engine fuel, and the energy is supplied by both the engine fuel and the battery in the three energy modes, and the sum of the first pure - electric ratio, the first fuel ratio, and the first hybrid fuel - electric ratio is 1;

[0165] The second energy planning module 930 is configured to determine a second pure - electric ratio, a second fuel ratio, and a second hybrid fuel - electric ratio based on the first pure - electric ratio, the first fuel ratio, and the first hybrid fuel - electric ratio through a correction factor determined by the road condition parameters of the planned route, the vehicle speed, and the power of the vehicle;

[0166] The third energy planning module 940 is configured to determine a third pure - electric ratio, a third fuel ratio, and a third hybrid fuel - electric ratio based on the second pure - electric ratio, the second fuel ratio, and the second hybrid fuel - electric ratio by using the user driving habit data as an optimization factor;

[0167] The working time pre - allocation module 950 is configured to allocate the pre - working times of the pure - electric mode, the fuel mode, and the hybrid fuel - electric mode under the planned route based on the estimated driving time, the third pure - electric ratio, the third fuel ratio, and the third hybrid fuel - electric ratio.

[0168] In some embodiments, the first energy planning module 920 is configured to:

[0169] Determine the pure - electric driving range of the remaining power of the vehicle according to the state of charge of the power battery, and determine the fuel driving range according to the remaining fuel quantity;

[0170] Determine the first pure - electric ratio according to the pure - electric driving range, the estimated mileage, the charging and energy - replenishing parameters, and the pure - electric priority mapping table. Herein, the charging and energy - replenishing parameters are obtained from the charging intention and the distance between the vehicle's position and the target charging pile in the map information, the pure - electric priority mapping table is a mapping table between the first mileage difference, the charging and energy - replenishing parameters, and the pure - electric priority ratio, and the first mileage difference is the difference between the pure - electric driving range and the estimated mileage;

[0171] Determine the first fuel ratio according to the fuel driving range, the estimated mileage, the fuel - replenishing parameters, and the fuel priority mapping table. Herein, the fuel - replenishing parameters are obtained from the fuel - replenishing intention and the distance between the vehicle's position and the target gas station in the map information, the fuel priority mapping table is a mapping table between the second mileage difference, the fuel - replenishing parameters, and the fuel priority ratio, and the second mileage difference is the difference between the fuel driving range and the estimated mileage;

[0172] Determine the first hybrid ratio based on the first pure - electric ratio and the first fuel ratio.

[0173] In some embodiments, the first energy planning module 920 determines the first pure - electric ratio according to the pure - electric driving range, the estimated range, the charging and energy - replenishing parameters, and the pure - electric priority mapping table in the following manner, including:

[0174] Fuzzify the first mileage difference into a pure - electric driving range fuzzy quantity;

[0175] Fuzzify the charging and energy - replenishing parameters into a charging and energy - replenishing fuzzy quantity;

[0176] Determine the first pure - electric ratio according to the pure - electric driving range fuzzy quantity, the charging and energy - replenishing fuzzy quantity, and the pure - electric priority mapping table.

[0177] In some embodiments, the first energy planning module 920 determines the first fuel ratio according to the fuel driving range, the estimated range, the refueling and energy - replenishing parameters, and the fuel priority mapping table in the following manner, including:

[0178] Fuzzify the second mileage difference into a fuel driving range fuzzy quantity;

[0179] According to the refueling and energy - replenishing parameters, fuzzify them into a refueling and energy - replenishing fuzzy quantity;

[0180] Determine the first fuel ratio according to the fuel driving range fuzzy quantity, the refueling and energy - replenishing fuzzy quantity, and the fuel priority mapping table.

[0181] In some embodiments, the second energy planning module 930 is used for:

[0182] In pure - electric mode, take the weighted sum of the road condition parameters, vehicle speed, and power as the pure - electric correction factor;

[0183] Correct the first pure - electric ratio through the pure - electric correction factor to obtain the second pure - electric ratio;

[0184] In fuel mode, take the weighted sum of the road condition parameters, vehicle speed, and power as the fuel correction factor;

[0185] Correct the first fuel ratio through the fuel correction factor to obtain the second fuel ratio;

[0186] Determine the second hybrid ratio according to the second pure - electric ratio and the second fuel ratio.

[0187] In some embodiments, the third energy planning module 940 is used for:

[0188] Count the pure - electric driving frequency, charging frequency, and refueling frequency of the vehicle in a preset time period in pure - electric mode, and take the weighted sum of the pure - electric driving frequency, charging frequency, and refueling frequency as the pure - electric optimization factor;

[0189] Optimize the second pure electric proportion through the pure electric optimization factor to obtain the third pure electric proportion;

[0190] Statistically calculate the pure electric driving frequency, charging frequency, and refueling frequency of the vehicle in the fuel mode within a preset time period, and use the weighted sum of the pure electric driving frequency, charging frequency, and refueling frequency as the fuel optimization factor;

[0191] Optimize the second fuel proportion through the fuel optimization factor to obtain the third fuel proportion;

[0192] Determine the third hybrid fuel - electric proportion based on the third pure electric proportion and the third fuel proportion.

[0193] In summary, through the hybrid vehicle energy mode management device, obtain the estimated mileage, estimated driving time, state of charge of the power battery, remaining fuel quantity, and map information of the planned route for the vehicle to reach the destination; based on the estimated mileage, state of charge of the power battery, remaining fuel quantity, and map information, determine the first pure electric proportion, the first fuel proportion, and the first hybrid fuel - electric proportion through fuzzy control, where the first pure electric proportion, the first fuel proportion, and the first hybrid fuel - electric proportion respectively refer to the proportions of the energy modes in which the vehicle is powered only by the battery, only by the internal energy generated by engine fuel, and by both engine fuel and the battery in the three energy modes, and the sum of the first pure electric proportion, the first fuel proportion, and the first hybrid fuel - electric proportion is 1; based on the first pure electric proportion, the first fuel proportion, and the first hybrid fuel - electric proportion, determine the second pure electric proportion, the second fuel proportion, and the second hybrid fuel - electric proportion through the correction factor determined by the road condition parameters of the planned route, the vehicle speed, and power; based on the second pure electric proportion, the second fuel proportion, and the second hybrid fuel - electric proportion, determine the third pure electric proportion, the third fuel proportion, and the third hybrid fuel - electric proportion using the user driving habit data as the optimization factor; based on the estimated driving time, the third pure electric proportion, the third fuel proportion, and the third hybrid fuel - electric proportion, allocate the pre - working time of the pure electric mode, fuel mode, and hybrid fuel - electric mode under the planned route. This device solves the problem that the comprehensive performance and cruising range of the vehicle cannot be maximized, improves the comprehensive performance and cruising range of the vehicle, and enhances the intelligent level of the vehicle.

[0194] In the above - mentioned embodiments provided by the present disclosure, the methods and devices provided by the embodiments of the present disclosure are introduced. To implement each function in the methods provided by the above - mentioned embodiments of the present disclosure, the electronic device may include a hardware structure, software modules, and implement the above - mentioned functions in the form of a hardware structure, software modules, or a combination of a hardware structure and software modules. A certain function among the above - mentioned functions can be executed in the manner of a hardware structure, software module, or a combination of a hardware structure and software module.

[0195] Figure 10It is a block diagram of an electronic device 1000 for implementing the above hybrid vehicle energy mode management method shown according to an exemplary embodiment.

[0196] For example, the electronic device 1000 can be a mobile phone, a computer, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, etc.

[0197] Referring to Figure 10 , the electronic device 1000 may include one or more of the following components: a processing component 1002, a memory 1004, a power supply component 1006, a multimedia component 1008, an audio component 1010, an input / output (I / O) interface 1012, a sensor component 1014, and a communication component 1016.

[0198] The processing component 1002 generally controls the overall operation of the electronic device 1000, such as operations associated with display, telephone calls, data communication, camera operations, and recording operations. The processing component 1002 may include one or more processors 1020 to execute instructions to complete all or part of the steps of the above method. In addition, the processing component 1002 may include one or more modules to facilitate the interaction between the processing component 1002 and other components. For example, the processing component 1002 may include a multimedia module to facilitate the interaction between the multimedia component 1008 and the processing component 1002.

[0199] The memory 1004 is configured to store various types of data to support the operation of the electronic device 600. Examples of these data include instructions for any application or method operating on the electronic device 1000, contact data, phone book data, messages, pictures, videos, etc. The memory 1004 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, a magnetic disk, or an optical disk.

[0200] The power supply component 1006 provides power to various components of the electronic device 1000. The power supply component 1006 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the electronic device 1000.

[0201] The multimedia component 1008 includes a screen that provides an output interface between the electronic device 1000 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen can be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors can sense not only the boundaries of touch or swipe actions but also detect the duration and pressure associated with the touch or swipe operations. In some embodiments, the multimedia component 1008 includes a front camera and / or a rear camera. When the electronic device 1000 is in an operating mode, such as a shooting mode or a video mode, the front camera and / or the rear camera can receive external multimedia data. Each of the front camera and the rear camera can be a fixed optical lens system or have a focal length and optical zoom capabilities.

[0202] The audio component 1010 is configured to output and / or input audio signals. For example, the audio component 1010 includes a microphone (MIC) that is configured to receive external audio signals when the electronic device 1000 is in an operating mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals can be further stored in the memory 1004 or transmitted via the communication component 1016. In some embodiments, the audio component 1010 further includes a speaker for outputting audio signals.

[0203] The I / O interface 1012 provides an interface between the processing component 1002 and a peripheral interface module, and the peripheral interface module can be a keyboard, a click wheel, buttons, etc. These buttons can include but are not limited to: a home button, a volume button, a power button, and a lock button.

[0204] The sensor component 1014 includes one or more sensors for providing status assessments of various aspects of the electronic device 1000. For example, the sensor component 1014 can detect the on / off state of the electronic device 1000, the relative positioning of components, such as the display and the keypad of the electronic device 1000. The sensor component 1014 can also detect a change in the position of the electronic device 1000 or a component of the electronic device 1000, the presence or absence of user contact with the electronic device 1000, the orientation or acceleration / deceleration of the electronic device 1000, and the temperature change of the electronic device 1000. The sensor component 1014 can include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor component 1014 can also include a light sensor, such as a CMOS or a CCD image sensor, for use in imaging applications. In some embodiments, the sensor component 1014 can further include an acceleration sensor, a gyro sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0205] The communication component 1016 is configured to facilitate communication between the electronic device 1000 and other devices in a wired or wireless manner. The electronic device 1000 can access a communication standard-based wireless network, such as WiFi, 2G or 3G, 4G LTE, 5G NR (New Radio), or a combination thereof. In an exemplary embodiment, the communication component 1016 receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 1016 further includes a Near Field Communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on Radio Frequency Identification (RFID) technology, Infrared Data Association (IrDA) technology, Ultra Wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0206] In an exemplary embodiment, the electronic device 1000 can be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components for performing the above method.

[0207] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as the memory 1004 including instructions, and the above instructions can be executed by the processor 1020 of the electronic device 1000 to complete the above method. For example, the non-transitory computer-readable storage medium can be a ROM, Random Access Memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.

[0208] Embodiments of the present disclosure also propose a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause a computer to execute the hybrid vehicle energy mode management method described in the above embodiments of the present disclosure.

[0209] Embodiments of the present disclosure also propose a computer program product, including a computer program, and the computer program executes the hybrid vehicle energy mode management method described in the above embodiments of the present disclosure when being executed by a processor.

[0210] Figure 11 It is a schematic structural diagram of a chip 1100 for implementing the above hybrid vehicle energy mode management method shown according to an exemplary embodiment.

[0211] Referring to Figure 11, the chip 1100 includes at least one communication interface 1101 and a processor 1102; the communication interface 1101 is used to receive signals input to the chip 1100 or signals output from the chip 1100, and the processor 1102 communicates with the communication interface 1101 and implements the hybrid vehicle energy mode management method described in the above embodiments through logic circuits or by executing code instructions.

[0212] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above drawings are used to distinguish similar objects and do not necessarily need to be used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0213] In the description of this specification, the description with reference to terms such as "one embodiment", "some embodiments", "illustrative embodiments", "examples", "specific examples" or "some examples" means that the specific features, structures, materials or characteristics described in connection with the embodiments or examples are included in at least one embodiment or example of the present disclosure. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0214] Any process or method description shown in the flowchart or described in other ways herein can be understood as representing a module, segment or portion of code including one or more executable instructions for implementing a specific logical function or process, and the scope of the preferred embodiments of the present disclosure includes additional implementations, where the functions can be executed in a manner other than shown or discussed, including in a substantially simultaneous manner or in a reverse order according to the functions involved, which should be understood by those skilled in the technical field to which the embodiments of the present disclosure belong.

[0215] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a definitional sequence list of executable instructions for implementing logical functions, which can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processing module, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or used in conjunction with these instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device. More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection portion having one or more wirings (control method), a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, a computer-readable medium can even be paper or other suitable media on which a program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or otherwise processing as appropriate, and then storing it in a computer memory.

[0216] It should be understood that various parts of the embodiments of the present disclosure can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application-specific integrated circuits having suitable combinational logic gate circuits, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0217] Those of ordinary skill in the art of this technology can understand that all or part of the steps carried by the methods of the above embodiments can be completed by instructing relevant hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiments.

[0218] In addition, each functional unit in various embodiments of the present disclosure may be integrated into one processing module, or each unit may exist physically alone, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. When the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium. The above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disc, etc.

[0219] Although the embodiments of the present disclosure have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limitations on the present disclosure. Those of ordinary skill in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present disclosure.

Claims

1. A method for managing the energy mode of a hybrid vehicle, characterized in that, The method includes: Obtaining the estimated mileage, estimated driving time, state of charge of the power battery, remaining fuel quantity, and map information of the planned route for the vehicle to reach the destination; According to the estimated mileage, the state of charge of the power battery, the remaining fuel quantity, and the map information, determining the first pure-electric ratio, the first fuel ratio, and the first hybrid fuel-electric ratio through fuzzy control, where the first pure-electric ratio, the first fuel ratio, and the first hybrid fuel-electric ratio are respectively the ratios of the mode of only supplying energy by the battery, the mode of only supplying energy by the internal energy generated by engine fuel, and the mode of supplying energy by both engine fuel and the battery in the three energy modes of the vehicle's energy mode; According to the first pure-electric ratio, the first fuel ratio, and the first hybrid fuel-electric ratio, determining the second pure-electric ratio, the second fuel ratio, and the second hybrid fuel-electric ratio through the correction factors determined by the road condition parameters of the planned route, the vehicle speed, and the power; According to the second pure-electric ratio, the second fuel ratio, and the second hybrid fuel-electric ratio, determining the third pure-electric ratio, the third fuel ratio, and the third hybrid fuel-electric ratio by using the user driving habit data as the optimization factor; Based on the estimated driving time, the third pure-electric ratio, the third fuel ratio, and the third hybrid fuel-electric ratio, allocating the pre-working times of the pure-electric mode, fuel mode, and hybrid fuel-electric mode under the planned route.

2. The method according to claim 1, characterized in that, The determining the first pure-electric ratio, the first fuel ratio, and the first hybrid fuel-electric ratio through fuzzy control according to the estimated mileage, the state of charge of the power battery, the remaining fuel quantity, and the map information includes: Determining the pure-electric driving range of the remaining power of the vehicle according to the state of charge of the power battery, and determining the fuel driving range according to the remaining fuel quantity; Determining the first pure-electric ratio according to the pure-electric driving range, the estimated mileage, the charging and energy replenishment parameters, and the pure-electric priority mapping table, where the charging and energy replenishment parameters are obtained from the charging intention and the distance between the vehicle's position and the target charging pile in the map information, the pure-electric priority mapping table is the mapping table between the first mileage difference, the charging and energy replenishment parameters, and the pure-electric priority ratio, and the first mileage difference is the difference between the pure-electric driving range and the estimated mileage; Determining the first fuel ratio according to the fuel driving range, the estimated mileage, the refueling and energy replenishment parameters, and the fuel priority mapping table, where the refueling and energy replenishment parameters are obtained from the refueling intention and the distance between the vehicle's position and the target gas station in the map information, the fuel priority mapping table is the mapping table between the second mileage difference, the refueling and energy replenishment parameters, and the fuel priority ratio, and the second mileage difference is the difference between the fuel driving range and the estimated mileage; Determining the first hybrid fuel-electric ratio according to the first pure-electric ratio and the first fuel ratio.

3. The method according to claim 2, characterized in that, The determining the first pure-electric ratio according to the pure-electric driving range, the estimated mileage, the charging and energy replenishment parameters, and the pure-electric priority mapping table includes: Fuzzifying the first mileage difference into a pure-electric driving range fuzzy quantity; Fuzzify the charging and energy replenishment parameters into a charging and energy replenishment fuzzy quantity; Determine the first pure - electric ratio according to the pure - electric endurance fuzzy quantity, the charging and energy replenishment fuzzy quantity, and the pure - electric priority mapping table; 4. The method according to claim 2, wherein The determining the first fuel ratio according to the fuel endurance mileage, the estimated mileage, the refueling and energy replenishment parameters, and the fuel priority mapping table includes: Fuzzify the second mileage difference into a fuel endurance fuzzy quantity; Fuzzify the refueling and energy replenishment parameters into a refueling and energy replenishment fuzzy quantity according to the refueling and energy replenishment parameters; Determine the first fuel ratio according to the fuel endurance fuzzy quantity, the refueling and energy replenishment fuzzy quantity, and the fuel priority mapping table; 5. The method according to claim 1, characterized in that, The determining the second pure - electric ratio, the second fuel ratio, and the second hybrid - electric ratio according to the first pure - electric ratio, the first fuel ratio, the first hybrid - electric ratio, and the correction factor determined by the road condition parameters of the planned route, the vehicle speed, and the power includes: In the pure - electric mode, use the weighted sum of the road condition parameters, the vehicle speed, and the power as the pure - electric correction factor; Correct the first pure - electric ratio through the pure - electric correction factor to obtain the second pure - electric ratio; In the fuel mode, use the weighted sum of the road condition parameters, the vehicle speed, and the power as the fuel correction factor; Correct the first fuel ratio through the fuel correction factor to obtain the second fuel ratio; Determine the second hybrid - electric ratio according to the second pure - electric ratio and the second fuel ratio; 6. The method according to claim 1, characterized in that, The determining the third pure - electric ratio, the third fuel ratio, and the third hybrid - electric ratio according to the second pure - electric ratio, the second fuel ratio, the second hybrid - electric ratio, and using the user driving habit data as the optimization factor includes: Count the pure - electric driving frequency, charging frequency, and refueling frequency of the vehicle in the pure - electric mode within a preset time period, and use the weighted sum of the pure - electric driving frequency, the charging frequency, and the refueling frequency as the pure - electric optimization factor; Optimize the second pure - electric ratio through the pure - electric optimization factor to obtain the third pure - electric ratio; Count the pure - electric driving frequency, charging frequency, and refueling frequency of the vehicle in the fuel mode within a preset time period, and use the weighted sum of the pure - electric driving frequency, the charging frequency, and the refueling frequency as the fuel optimization factor; Optimize the second fuel ratio through the fuel optimization factor to obtain the third fuel ratio; Determine the third hybrid - electric ratio according to the third pure - electric ratio and the third fuel ratio; 7. A hybrid vehicle energy mode management device, characterized in that The device includes: An acquisition module, configured to acquire the estimated mileage, estimated driving time, state of charge of the power battery, remaining fuel quantity, and map information of the planned route for the vehicle to reach the destination; A first energy planning module, configured to determine a first pure - electric ratio, a first fuel ratio, and a first hybrid fuel - electric ratio according to the estimated mileage, the state of charge of the power battery, the remaining fuel volume, and the map information through fuzzy control, where the first pure - electric ratio, the first fuel ratio, and the first hybrid fuel - electric ratio respectively refer to the proportions of the energy modes of the vehicle in which the energy is supplied only by the battery, the energy is supplied only by the internal energy generated by engine fuel, and the energy is supplied by both engine fuel and the battery in the three energy modes, and the sum of the first pure - electric ratio, the first fuel ratio, and the first hybrid fuel - electric ratio is 1; A second energy planning module, configured to determine a second pure - electric ratio, a second fuel ratio, and a second hybrid fuel - electric ratio according to the first pure - electric ratio, the first fuel ratio, the first hybrid fuel - electric ratio, and a correction factor determined by the road condition parameters of the planned route, the vehicle speed, and the power of the vehicle; A third energy planning module, configured to determine a third pure - electric ratio, a third fuel ratio, and a third hybrid fuel - electric ratio according to the second pure - electric ratio, the second fuel ratio, the second hybrid fuel - electric ratio, with user driving habit data as an optimization factor; A working time pre - allocation module, configured to allocate the pre - working times of the pure - electric mode, the fuel mode, and the hybrid fuel - electric mode under the planned route based on the estimated driving time, the third pure - electric ratio, the third fuel ratio, and the third hybrid fuel - electric ratio.

8. An electronic device, characterized in that, Comprising: At least one processor; And A memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, and when the instructions are executed by the at least one processor, the at least one processor is enabled to execute the method according to any one of claims 1 - 6.

9. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to execute the method according to any one of claims 1 - 6.

10. A computer program product, characterized in that, Comprising a computer program which, when executed by a processor, implements the method according to any one of claims 1 - 6.

11. A chip, characterized in that, Comprising at least one processor and a communication interface; the communication interface is configured to receive signals input to the chip or signals output from the chip, and the processor communicates with the communication interface and implements the method according to any one of claims 1 - 6 through logic circuits or by executing code instructions.

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