CN114750743A - Intelligent energy management method and system for hybrid electric vehicle, vehicle and storage medium - Google Patents
Intelligent energy management method and system for hybrid electric vehicle, vehicle and storage medium Download PDFInfo
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- CN114750743A CN114750743A CN202210468804.9A CN202210468804A CN114750743A CN 114750743 A CN114750743 A CN 114750743A CN 202210468804 A CN202210468804 A CN 202210468804A CN 114750743 A CN114750743 A CN 114750743A
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W20/00—Control systems specially adapted for hybrid vehicles
- B60W20/10—Controlling the power contribution of each of the prime movers to meet required power demand
- B60W20/12—Controlling the power contribution of each of the prime movers to meet required power demand using control strategies taking into account route information
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W20/00—Control systems specially adapted for hybrid vehicles
- B60W20/10—Controlling the power contribution of each of the prime movers to meet required power demand
- B60W20/15—Control strategies specially adapted for achieving a particular effect
Abstract
The invention discloses an intelligent energy management method and system for a hybrid vehicle, the vehicle and a storage medium, wherein the intelligent energy management method comprises the following steps: s1, acquiring navigation path information, dividing the navigation path into N sections according to the congestion state, and recording the length, congestion state and traffic time of each section of road; s2, estimating the energy consumption and the corresponding SOC required by the vehicle in the pure electric mode through each congestion road section; s3, estimating the SOC of the charging increase required by the vehicle to run through each smooth road section; and S4, dynamically adjusting the target SOC of each road section according to the estimation results of the step S2 and the step S3. The invention can dynamically adjust the target SOC of the power battery based on the navigation information.
Description
Technical Field
The invention belongs to the technical field of hybrid vehicle control, and particularly relates to an intelligent energy management method and system for a hybrid vehicle, the vehicle and a storage medium.
Background
Intellectualization and networking become development trends of the automobile industry, and better experience is brought to people by automobiles with lower energy consumption and more intellectualization. The hybrid electric vehicle has two sets of power sources, combines the advantages of a fuel vehicle and a pure electric vehicle, has no mileage anxiety and has the characteristics of low pollution and low energy consumption. Hybrid vehicles have multiple operating modes, including pure electric drive, engine-only drive, engine and motor-driven drive, energy recovery, and the like. Each hybrid vehicle manufacturer optimizes the entire vehicle control strategy according to the objective of lowest energy consumption and good driving feeling.
For example, patent document CN109626967B discloses an energy management method, device and equipment for a hybrid electric vehicle, in which a driving mode of the hybrid electric vehicle and a target balance point of a power battery are adjusted by setting SOC related thresholds under different road conditions based on congestion information of a road ahead sent by a navigation module, so as to achieve the purpose of adaptively adjusting electric quantity. Also, as disclosed in CN109910866B, the method and system for hybrid electric vehicle energy management based on road condition prediction determine threshold values of key parameters related to energy management, such as maximum battery SOC, minimum battery SOC, and minimum engine torque coefficient, based on a road condition database and a navigation planned path, so as to optimize operating points of an engine and a motor, and achieve the effect of improving fuel economy of a hybrid electric vehicle. However, the above two methods have the disadvantages that the dynamic adaptability of the intelligent energy management strategy is not strong, the electric quantity cannot be adjusted according to the real-time operation condition, and the existing database and calibration are relied on. As in patent document CN109626967B, it is necessary to calibrate the SOC value, the congestion length, and the like at the time of the pure electric mode switching. In the patent document CN109910866B, the established road condition characteristic database is relied on, and if the actual road is different from the data in the road condition database, the effect is reduced.
Therefore, there is a need to develop an intelligent energy management method, system, vehicle and storage medium for a hybrid vehicle.
Disclosure of Invention
The invention aims to provide an intelligent energy management method, an intelligent energy management system, a vehicle and a storage medium for a hybrid vehicle, which can adaptively adjust a target SOC track according to real-time change of road traffic information.
In a first aspect, the invention provides an intelligent energy management method for a hybrid vehicle, comprising the following steps:
s1, acquiring navigation path information, dividing the navigation path into N sections according to the congestion state, and recording the length, congestion state and transit time of each section of road;
s2, estimating the energy consumption and the corresponding SOC required by the vehicle in the pure electric mode through each congestion road section;
s3, estimating the SOC of the charging increase required by the vehicle to run through each smooth road section;
and S4, dynamically adjusting the target SOC of each road section according to the estimation results of the step S2 and the step S3.
Optionally, the step S1 specifically includes:
acquiring navigation path information;
analyzing the navigation path information to obtain total navigation mileage, road congestion state and passing time;
and dividing the navigation path into N sections according to the congestion state, and recording the length, the congestion state and the passing time of each section of road.
Optionally, in the step S1, if the length of a certain road is less than the preset length, the certain road is merged into an adjacent previous road or next road.
Optionally, the step S2 specifically includes:
the method comprises the following steps that a vehicle is assumed to pass through a congested road section in a pure electric mode;
calculating the energy W consumed by the hybrid vehicle to pass through the congested road section in the pure electric mode:
W＝(Wdrive the+WAccessories+ΔW)·t；
WDrive the＝k·F·V；
Wherein, WDrive theConsumption for vehicle drive; k is a proportionality coefficient; f is the resistance of the hybrid vehicle; v is the vehicle speed of the vehicle; wAccessoriesAccessory consumption for the vehicle; Δ W is a calibrated value; t is the passing time of the congested road section;
calculating the SOC (state of charge) required by the hybrid vehicle to pass through the congested road section in a pure electric mode:
ΔSOC＝(Wdrive the+WAccessories+ΔW)·t/EBattery with a battery cell；
The delta SOC is the SOC required by the hybrid electric vehicle for passing the congested road section in a pure electric mode; eBattery with a battery cellThe total energy which can be discharged outwards when the power battery of the vehicle is fully charged.
Alternatively, the resistance F of the hybrid vehicle is calculated, in particular:
F＝A·V2+B·V+C；
wherein M is vehicle mass; a, B and C are all vehicle sliding resistance coefficients; v is the vehicle speed.
Optionally, in the step S3, if the SOC required for the congested road segment i is Δ SOC (i), the SOC required for charging increase on the clear road segment i-1 is calculated as:
SOC(i-1)＝min(ΔSOC(i),SOC(i-1)max)；
wherein, SOC (i-1) is the SOC which needs to be charged and increased for the smooth road section i-1, delta SOC (i) is the SOC which needs to be charged and increased for pure electric driving for the congested road section i, and SOC (i-1)maxThe maximum SOC value can be increased for the smooth road section i-1.
Optionally, when the sum of the SOC required by pure electric driving of each congested road section is greater than or equal to the sum of the maximum SOC values which can be increased by charging of the previous smooth road section corresponding to each congested road section, the SOC required by charging of each smooth road section is set according to the maximum SOC value which can be increased by charging of the smooth road;
when the sum of the SOC required by pure electric driving of each congested road section is less than the sum of the maximum SOC values which can be increased by charging of the previous smooth road section corresponding to each congested road section, and the SOC required by all the congested road sections is less than the maximum SOC value which can be increased by charging of the previous smooth road section corresponding to each congested road section, the SOC required by charging of the smooth road section is the SOC required by the corresponding next congested road section;
when the sum of the SOC required by pure electric driving of each congested road section is smaller than the sum of the maximum SOC required by charging and increasing of the previous smooth road section corresponding to each congested road section, and the SOC capable of being charged and increased of a certain smooth road section cannot meet the SOC required by pure electric driving of the corresponding next congested road section, distributing the insufficient SOC to the previous smooth road section, and so on.
Optionally, the maximum value of the SOC of the charging increase of the clear road section is obtained by calibrating the average vehicle speed and the length of the clear road section.
Optionally, the S4 specifically is: and dynamically adjusting a descending value of the target SOC of the congested road section based on the SOC required by the congested road section and the length of the congested road section, and dynamically adjusting an ascending value and an ascending position point of the target SOC of the unblocked road section based on the SOC required to be charged and increased by the unblocked road section and the length of the unblocked road section.
Optionally, setting an equivalence factor based on the target SOC and the actual SOC of the current battery, and dynamically distributing the torque of the engine and the motor by using an equivalent fuel consumption minimum strategy.
In a second aspect, the hybrid vehicle intelligent energy management system according to the invention comprises a controller and a memory, wherein the memory stores a computer readable program, and the computer readable program can execute the steps of the hybrid vehicle intelligent energy management method according to the invention when being called by the controller.
In a third aspect, the invention provides a vehicle, which adopts the intelligent energy management system of the hybrid vehicle.
In a fourth aspect, the present invention provides a storage medium having a computer readable program stored therein, the computer readable program when invoked causes the method to perform the steps of the hybrid vehicle intelligent energy management method of the present invention.
The invention has the following advantages:
(1) the target SOC track is adaptively adjusted according to the real-time change of the road traffic information, so that the control on the energy consumption is more refined;
(2) because the target electric quantity is dynamically and adaptively changed, compared with the existing method for setting a specific threshold value, the method has better actual effect;
(3) the specific SOC value of mode switching is not required to be calibrated, the self-adaptive switching between the pure electric mode and the hybrid mode of the engine is realized, and more vehicle use scenes are covered.
(4) The invention dynamically distributes the torques of the engine and the motor according to the minimum strategy of equivalent fuel consumption, can realize the optimal energy consumption in the whole process without changing the whole vehicle mode, has the advantage of stronger adaptability and greatly reduces the calibration work.
Drawings
FIG. 1 is a flowchart of the present embodiment;
fig. 2 is a schematic diagram of a navigation section in the present embodiment;
FIG. 3 is a schematic diagram illustrating an effect of setting an upper limit of electric quantity increase in an unobstructed road segment in the present embodiment;
fig. 4 is a diagram of a target SOC trace implemented in the present embodiment.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings.
As shown in fig. 1, in the present embodiment, a hybrid vehicle intelligent energy management method includes the following steps:
and S1, acquiring navigation path information, dividing the navigation path into N sections according to the congestion state, and recording the length, the congestion state and the passing time of each section of road.
In this embodiment, the step S1 specifically includes:
acquiring navigation path information;
analyzing the navigation path information to obtain total navigation mileage, road congestion state and passing time;
and dividing the navigation path into N sections according to the congestion state, recording the length, the congestion state and the passing time of each section of road, and sending the information of the N sections to the hybrid power control unit in real time.
And if the length of a certain road is less than the preset length, the road is combined into the adjacent previous road or next road.
In this embodiment, the controller includes a navigation module control unit, a hybrid control unit, an engine control unit, and a motor control unit.
The navigation module control unit is used for converting map information into information which is easy to calculate by the vehicle-end controller, sending the processed information to the hybrid power control unit, and acquiring path information when the navigation control unit detects that vehicle navigation is started or learns the position to which the vehicle is going to run through big data information. The navigation control unit analyzes the path information (including but not limited to total navigation mileage, road congestion state, co-traveling time and the like) according to a protocol defined by the navigation control unit and map software. The navigation control unit further processes the information, divides the navigation path into N sections according to the navigation state, and sends the information of the N sections to the hybrid power control unit in real time.
When the road information is processed, the length, the congestion state and the passing time of the N sections of roads are mainly analyzed. The forwarding of the navigation information can be further expanded to the communication between mobile phone navigation and vehicle navigation, and when a user uses the mobile phone navigation and associates the vehicle navigation with the mobile phone navigation through setting, the vehicle navigation control unit forwards the navigation information to the hybrid power control unit.
And S2, estimating the consumed energy and the corresponding required SOC required by the vehicle to pass through each congestion road section in the pure electric mode.
In this embodiment, step S2 specifically includes:
the method comprises the following steps that a vehicle is assumed to pass through a congested road section in a pure electric mode;
calculating the resistance F of the hybrid vehicle;
F＝A·V2+B·V+C；
wherein M is vehicle mass; a, B and C are vehicle sliding resistance coefficients and are related to the whole vehicle mass, rolling resistance, wind resistance and the like; v is the vehicle speed, which here is the average speed over the road segment.
Calculating the energy W consumed by the hybrid vehicle to pass through the congested road section in the pure electric mode:
W＝(Wdrive the+WAccessories+ΔW)·t；
WDrive the＝k·F·V；
Wherein k is a proportionality coefficient; wAccessoriesFor accessory consumption of vehicles, accessory consumption is generally referred to primarily as accessory consumptionThe consumption of the electric air conditioner can be set according to whether the air conditioner is turned on or not when the method is applied to an actual vehicle, and the power of an accessory is about 5kw generally; the delta W is a calibrated value and is set according to the actual road running condition to ensure that the electric quantity required by the vehicle can be enough in different scenes, and the value is mainly used for correcting the required energy increase caused by factors such as gradient and the like when the method is used for the actual vehicle; and t is the passing time of the congested road section.
Calculating the SOC (state of charge) required by the hybrid vehicle to pass through the congested road section in a pure electric mode:
ΔSOC＝(Wdrive the+WAccessories+ΔW)·t/EBattery with a battery cell；
The delta SOC is the SOC required by the hybrid electric vehicle for passing the congested road section in a pure electric mode; eBattery with a battery cellThe total energy which can be discharged outwards when the power battery of the vehicle is fully charged.
S3, estimating the SOC of the charging increase required by the vehicle to run through each smooth road section; the general principle of the step S3 is that the electric quantity required by the i-th congested road is provided by the i-1-th unblocked road segment, and so on, specifically:
if the SOC required by the congested road section i is delta SOC (i), calculating the SOC required by the charging increase of the smooth road section i-1 as follows:
SOC(i-1)＝min(ΔSOC(i),SOC(i-1)max)；
wherein, SOC (i-1) is the SOC which needs to be charged and increased for the smooth road section i-1, delta SOC (i) is the SOC which needs to be charged and increased for pure electric driving for the congested road section i, and SOC (i-1)maxThe maximum SOC value can be increased for the smooth road section i-1.
In the embodiment, the maximum value of the SOC which can be increased by charging on the smooth road section is obtained by calibrating the average vehicle speed and the length of the smooth road section, the increased SOC upper limit is limited, and the drivability problems such as NVH (noise, vibration and harshness) caused by large-torque charging of an engine on the smooth road section are prevented. It should be noted that, when calculating the SOC increase values of different smooth road sections, the difference adaptive optimization calculation is performed according to different road information scenes by combining the downstream demand calculation of the road section, specifically:
when the sum of the SOC required by pure electric driving of each congested road section is more than or equal to the sum of the maximum SOC values which can be increased by charging of the last smooth road section corresponding to each congested road section, the SOC required by charging of each smooth road section is set according to the maximum SOC value which can be increased by charging of the smooth road;
when the sum of the SOC required by pure electric driving of each congested road section is less than the sum of the maximum SOC values which can be increased by charging of the previous smooth road section corresponding to each congested road section, and the SOC required by all the congested road sections is less than the maximum SOC value which can be increased by charging of the previous smooth road section corresponding to each congested road section, the SOC required by charging of the smooth road section is the SOC required by the corresponding next congested road section;
when the sum of the SOC required by pure electric driving of each congested road section is smaller than the sum of the maximum SOC required by charging and increasing of the previous smooth road section corresponding to each congested road section, and the SOC capable of being charged and increased of a certain smooth road section cannot meet the SOC required by pure electric driving of the corresponding next congested road section, distributing the insufficient SOC to the previous smooth road section, and so on.
And S4, dynamically adjusting the target SOC of each road section according to the estimation results of the step S2 and the step S3. The general principle of this step S4 is that the clear road segments appropriately increase the target SOC, while the congested road segments appropriately decrease the target SOC. The principle of the method is that the method is applied to a torque distribution strategy based on an equivalent fuel consumption minimum algorithm, and the change of the target SOC influences the size of an equivalent factor and further influences the distribution of the torque of a motor and an engine. The calculation method is as follows:
J＝min(Pfuel(t)+s(t)·Pbatt(t))；
s(t)＝f(socTar-socAct)；
wherein J represents the equivalent power, Pfuel(t) power consumption of fuel, Pbatt(t) is the power consumed by the electric energy, s(t)Being an equivalent factor, socTarIs the target SOC, SOCActIs the actual SOC. When the algorithm is applied to an actual vehicle, the hybrid power controller can calculate equivalent powers corresponding to a plurality of engine working points and motor working points according to the formula, and the minimum value pair of the equivalent powers is takenThe corresponding working condition combination is used as the working point of the hybrid power system operation. The setting of the equivalent factor is related to the difference between the target SOC and the actual SOC, and is a calibration table.
In the embodiment, the descending value of the target SOC of the congested road section is dynamically adjusted based on the SOC required by the congested road section and the length of the congested road section, and the ascending value and the ascending position point of the target SOC of the unblocked road section are dynamically adjusted based on the SOC required to be charged and increased by the unblocked road section and the length of the unblocked road section.
In the embodiment, if the vehicle is in a congested road section, the target SOC is properly reduced, and the reduction value is increased along with the increase of the length of the road section; if the vehicle is in the unblocked road section, according to the SOC of the road needing to be charged and increased, the SOC (i-1) sets different distances from the next congested road section, and the target SOC is increased from the distance point. The method has the advantages that the target SOC track is adaptively adjusted according to the real-time change of the road traffic information, and the control on the energy consumption is more refined.
Through the processing of the road information and the resetting of target SOC of different road sections, the torques of the engine and the motor are dynamically and optimally distributed in the hybrid power control unit based on the algorithm with the minimum equivalent fuel consumption, and the purpose of reducing energy consumption is achieved.
In this embodiment, the engine control unit is configured to control execution of engine torque, and the motor control unit is configured to control execution of motor torque.
The method aims to increase the service time of the engine and properly increase the torque of the engine to charge the battery in the unblocked road section through strategy optimization, so that the vehicle can pass through the congested road section in a pure electric mode, and the purposes of reducing oil consumption and improving the driving experience of a user are achieved.
In the method, the actual effect of the method is better because the target electric quantity is set to be dynamically and adaptively changed instead of a specific threshold; in addition, the hybrid power control unit calculates the torques of the engine and the motor in real time according to an equivalent fuel consumption minimum algorithm, and the energy consumption optimization of the whole process can be realized without changing the whole vehicle mode.
The method introduces road information on the basis of an energy management algorithm with minimum equivalent fuel consumption, and predictively adjusts the electric quantity of the hybrid vehicle so as to realize optimal energy consumption based on different road conditions.
The method is described below by way of example, as shown in fig. 1, the navigation control unit sends the road information to the hybrid control unit in segments, and the hybrid control unit performs the road information processing of step S1, taking the road condition shown in fig. 2 as an example, n (i) represents the i-th road, the road length information is represented as L ═ L1, L2, L3, L4, and L5], L is a set of the lengths of the navigation path from the point a to the point B, the length from the starting point to the end point is L1+ L2+ L3+ L4+ L5, and the congestion state S is [ clear, congested, clear, slow-going, clear ]. In addition, the navigation control unit will give the time to pass through these 5 roads, T ═ T1, T2, T3, T4, T5], T is the set of segment lengths from point a to point B of the navigation path, and the time from the starting point to the end point is T1+ T2+ T3+ T4+ T5. Based on the information sent by the navigation control unit, the hybrid control unit calculates the average speed V (i) of the vehicle passing through each section respectively,
V(i)＝L(i)/t(i)。
in addition, this step performs merging processing on the partial navigation sections. When the length of a segment on the navigation path is too small (i.e., less than the preset length L0), the segment is considered to be merged into the next segment or the previous segment. Hereinafter, if L2 is smaller than L0, N2 and N3 are combined into one segment, and the combined length is added, and the state of each segment after combination is based on the state of the next segment.
Calculating the SOC (state of charge) required by the vehicle to pass through each congested road section in a pure electric mode, wherein the calculation result is as follows by adopting the calculation method in the embodiment:
socdmd＝[m1,m2,m3,m4,m5]；
wherein, socdmdFor the set of SOCs demanded by each congested segment, mi is the SOC demanded for the ith segment, where for a clear segment, this value is 0. The calculation formula is used for obtaining the electric quantity needed by the whole vehicle through the congested road section, and providing a basis for the charging strategy of the next smooth road section. At the same time, note that socdmdThe SOC is changed in real time according to the traffic road condition, and the calculated required SOC is also a value changed in real time.
Calculating the SOC (state of charge) required to be increased for each unblocked road section, wherein the calculation result is as follows by adopting the calculation method in the embodiment:
socchr＝[n1,n2,n3,n4,n5]；
wherein, socchrAnd ni is the SOC of the ith road section which needs to be charged by the engine with more torque, wherein the value is 0 for the smooth road section.
It should be noted that, for the ith road, if the section is a congested road section, the required electric quantity adopts a mi value; if the section is a smooth road section, the charge amount adopts a ni value.
In addition, an upper limit needs to be set for the SOC increase amount of the unblocked road section, the limit is calculated by calibration based on the vehicle speed and the road length, and the setting method is set as shown in fig. 3. That is, the SOC increase upper limit value that can be set is larger as the vehicle speed is higher and the clear road section is longer.
The calculation mode for refining the smooth road section ni specifically comprises the following steps:
scene one, as shown in FIG. 2, when Σ (m2+ m4] ≧ Σ (n1+ n 3), the SOC required to be charged for each clear segment is set according to the maximum value of the clear-road-chargeable increase SOC, in the example of FIG. 2, because there is no navigation information following segment 5, and segment 5 is a clear segment, the SOC increase of segment 5 is 0.
And in a second scenario, when sigma (m2+ m4] <sigma (n1+ n 3), m2 is less than n1, and m4 is less than n3, the SOC required to be charged in the clear road section is the SOC required in the corresponding next congested road section.
And in a third scenario, when sigma (m2+ m4] <sigma (n1+ n 3), and the SOC which can be charged and improved in the third section of the smooth road section cannot meet the SOC required by pure electric driving in the fourth section of the congested road section, distributing the insufficient part to the first section of the smooth road section, and so on.
Through the calculation, the SOC of each smooth road section needing the increase of the engine charging is calculated, and the next step is the track design of the target SOC. Assuming that the SOC of the 1 st segment and the 3 rd segment increased by charging are n1 and n3 respectively, the values of X1 and X2 are found according to a preset calibration table, wherein X1 and X2 represent the distance from the next congested road segment, and when the vehicle runs to the position points of X1 and X2, the target SOC is increased by n1 and n3 respectively, so that the hybrid control unit tends to allocate more torque to the engine for the purpose of charging. The calibration table is generally set according to the size and the mass of the battery of the vehicle and the electric quantity representing characteristics of the actual road. Similarly, for the 2 nd and 4 th paragraphs, the sizes s2 and s4 of the target SOC reduction value are queried according to a preset calibration, where s2 and s4 represent the SOC reduction values, and the longer the congested road segment is, the larger the reduction value is, as shown in fig. 4. Then, the size of the final attainable target SOC is:
socTar＝socref+soc(i)；
wherein, socrefThe target SOC value not applying the method of the invention, referred to herein as the reference target SOC value, is generally a constant value, SOC (i) is the target SOC increase or decrease value calculated according to the above algorithm, i.e., corresponding to segments N1-N5 as { N1, s2, N3, s4,0 }.
After the target SOC is determined, an equivalent factor can be set based on the target SOC and the actual SOC of the current battery, and the torque of the engine and the torque of the motor can be dynamically distributed by using an equivalent fuel consumption minimum strategy. Therefore, the vehicle is ensured to always run at a working point with high energy consumption, and the smooth road section is properly charged to meet the requirement of pure electric driving of the congested road section.
In this embodiment, a hybrid vehicle intelligent energy management system comprises a controller and a memory, wherein the memory stores a computer readable program, and the computer readable program when called by the controller can execute the steps of the hybrid vehicle intelligent energy management method described in the embodiment.
In the embodiment, a vehicle adopts the hybrid vehicle intelligent energy management system as described in the embodiment.
In the present embodiment, a storage medium has a computer readable program stored therein, which when invoked, can perform the steps of the hybrid vehicle intelligent energy management method as described in the present embodiment.
The above embodiments are preferred embodiments of the present invention, but the present invention is not limited to the above embodiments, and any other changes, modifications, substitutions, combinations, and simplifications which do not depart from the spirit and principle of the present invention should be construed as equivalents thereof, and all such changes, modifications, substitutions, combinations, and simplifications are intended to be included in the scope of the present invention.
Claims (13)
1. An intelligent energy management method for a hybrid vehicle, comprising the steps of:
s1, acquiring navigation path information, dividing the navigation path into N sections according to the congestion state, and recording the length, congestion state and traffic time of each section of road;
s2, estimating the energy consumption and the corresponding SOC required by the vehicle in the pure electric mode through each congestion road section;
s3, estimating the SOC of the charging increase required by the vehicle to run through each smooth road section;
and S4, dynamically adjusting the target SOC of each road section according to the estimation results of the step S2 and the step S3.
2. The intelligent energy management method for hybrid vehicles according to claim 1, wherein the step S1 is specifically as follows:
acquiring navigation path information;
analyzing the navigation path information to obtain total navigation mileage, road congestion state and passing time;
and dividing the navigation path into N sections according to the congestion state, and recording the length, the congestion state and the passing time of each section of road.
3. The hybrid vehicle intelligent energy management method of claim 2, characterized in that: in step S1, if the length of a certain road is less than the preset length, the certain road is merged into the adjacent previous road or next road.
4. The intelligent energy management method for a hybrid vehicle according to any one of claims 1 to 3, characterized in that: the step S2 specifically includes:
the method comprises the following steps that a vehicle is assumed to pass through a congested road section in a pure electric mode;
calculating the energy W consumed by the hybrid vehicle to pass through the congested road section in the pure electric mode:
W＝(Wdrive the+WAccessories+ΔW)·t；
WDrive the＝k·F·V；
Wherein, WDrive theConsumption for vehicle drive; k is a proportionality coefficient; f is the resistance of the hybrid vehicle; v is the vehicle speed of the vehicle; wAccessoriesAccessory consumption for the vehicle; Δ W is a calibrated value; t is the passing time of the congested road section;
calculating the SOC (state of charge) required by the hybrid vehicle to pass through the congested road section in a pure electric mode:
ΔSOC＝(Wdrive the+WAccessories+ΔW)·t/EBattery with a battery cell；
The delta SOC is the SOC required by the hybrid vehicle for passing the congested road section in a pure electric mode; eBattery with a battery cellThe total energy which can be discharged outwards when the power battery of the vehicle is fully charged.
5. The hybrid vehicle intelligent energy management method of claim 4, characterized in that: calculating the resistance F of the hybrid vehicle, specifically as follows:
F＝A·V2+B·V+C；
wherein M is vehicle mass; a, B and C are all vehicle sliding resistance coefficients; v is the vehicle speed.
6. The hybrid vehicle intelligent energy management method of claim 5, characterized in that: in step S3, if the SOC required for the congested road segment i is Δ SOC (i), the SOC required for charging and increasing for the smooth road segment i-1 is calculated as:
SOC(i-1)＝min(ΔSOC(i),SOC(i-1)max)；
whereinSOC (i-1) is the SOC required by charging and increasing for a smooth road section i-1, delta SOC (i) is the SOC required by pure electric driving for a congested road section i, and the SOC (i-1)maxAnd the maximum value of the SOC can be increased for the smooth road section i-1.
7. The hybrid vehicle intelligent energy management method of claim 6, characterized in that: when the sum of the SOC required by pure electric driving of each congested road section is more than or equal to the sum of the maximum SOC values which can be increased by charging of the last smooth road section corresponding to each congested road section, the SOC required by charging of each smooth road section is set according to the maximum SOC value which can be increased by charging of the smooth road;
when the sum of the SOC required by pure electric driving of each congested road section is smaller than the sum of the maximum SOC values which can be charged and increased in the last unblocked road section corresponding to each congested road section, and the SOC required by all the congested road sections is smaller than the maximum SOC value which can be charged and increased in the last unblocked road section corresponding to each congested road section, the SOC required to be charged in the unblocked road section is the SOC required by the corresponding next congested road section;
when the sum of the SOC required by pure electric driving of each congested road section is smaller than the sum of the maximum SOC value which can be increased by charging of the previous smooth road section corresponding to each congested road section, and the SOC which can be charged and improved by a certain smooth road section cannot meet the SOC required by pure electric driving of the next congested road section, distributing the insufficient SOC to the previous smooth road section, and so on.
8. The hybrid vehicle intelligent energy management method according to claim 6 or 7, characterized in that: the maximum value of the SOC of the smooth road section which can be charged and increased is obtained by calibrating the average speed and the length of the smooth road section.
9. The hybrid vehicle intelligent energy management method of claim 8, characterized in that: the S4 specifically includes: and dynamically adjusting a descending value of the target SOC of the congested road section based on the SOC required by the congested road section and the length of the congested road section, and dynamically adjusting an ascending value and an ascending position point of the target SOC of the unblocked road section based on the SOC required to be charged and increased by the unblocked road section and the length of the unblocked road section.
10. The hybrid vehicle intelligent energy management method of claim 9, characterized in that: and setting an equivalence factor based on the target SOC and the actual SOC of the current battery, and dynamically distributing the torques of the engine and the motor by using an equivalent fuel consumption minimum strategy.
11. A hybrid vehicle intelligent energy management system characterized in that: comprising a controller and a memory having a computer readable program stored therein, said computer readable program when invoked by the controller being capable of performing the steps of the hybrid vehicle intelligent energy management method of any of claims 1 to 10.
12. A vehicle, characterized in that: the hybrid vehicle intelligent energy management system of claim 10 is employed.
13. A storage medium, characterized by: stored therein is a computer readable program which when invoked is capable of performing the steps of the hybrid vehicle intelligent energy management method of any of claims 1 to 10.
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| CN117698689B (en) * | 2024-02-06 | 2024-04-05 | 北京航空航天大学 | Hybrid electric vehicle energy utilization track planning method based on time-varying scene |
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