CN112009267A - New energy passenger car self-adaptive optimization control method based on real-time working condition recognition - Google Patents
New energy passenger car self-adaptive optimization control method based on real-time working condition recognition Download PDFInfo
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Abstract
A new energy passenger car self-adaptive optimization control method based on real-time working condition recognition comprises the steps of carrying out real-time stable recognition on the driving working condition of a new energy passenger car, carrying out corresponding optimization control on each type of road working condition, calculating real-time ramp resistance under a climbing working condition, actively preventing a sliding slope and automatically switching power modes; reducing the driving torque change slope limit under the state of a small accelerator under a high-speed working condition; under the working condition of medium and low speed, the serial-parallel switching speed is improved, and the serial-parallel switching frequency is reduced; reducing the series power generation SOC threshold to the current SOC-delta SOC under the congestion condition, and limiting the lowest power generation SOC threshold SOC _ min; actively reducing the acceleration limit and the driving torque change slope limit under the working condition of rapid acceleration and rapid deceleration; and carrying out follow-up auxiliary control on the vehicle speed under the circulating working condition. The optimization control algorithm is reliable, the working condition adaptability is good, the energy is saved, the consumption is reduced, meanwhile, the tire wear is reduced, and the comprehensive performance of the vehicle is comprehensively optimized and improved.
Description
Technical Field
The invention relates to the technical field of new energy vehicles, in particular to a new energy bus self-adaptive optimization control method based on real-time working condition recognition.
Background
The new energy passenger car is in different actual operating conditions, including road slope, average speed, road jam condition, driver's driving habit etc. and the performance and the driving experience of same motorcycle type all probably all have certain difference. Therefore, adaptive control optimization is carried out on the new energy bus through working condition identification, vehicle performance can be improved, driver driving experience is improved, and energy conservation and consumption reduction can be achieved possibly.
The Chinese patent with application publication number CN 111038488A discloses an energy optimization control method for a hybrid electric vehicle, which comprises the following steps: (1) classifying road working conditions according to driving information in a driving area in front of a vehicle, and establishing a corresponding energy optimization model for each type of road working conditions, wherein each energy optimization model is established with the lowest energy consumption of the whole vehicle under the corresponding road working conditions; the driving information at least comprises two of average speed, maximum speed, parking time proportion and average acceleration; (2) solving each energy optimization model to correspondingly obtain energy optimal control parameters of each energy optimization model, wherein the energy optimal control parameters comprise the required power of the whole vehicle with the engine participating in the work, the speed of the vehicle with the engine participating in the work and the optimal power of the vehicle with the engine participating in the work; (3) the method comprises the steps of obtaining current driving information in a driving area in front of a vehicle, determining the type of a current road working condition according to the current driving information, controlling the vehicle according to energy optimization control parameters of an energy optimization model corresponding to the type of the current road working condition, and achieving energy optimization control of the vehicle. The road working condition is simply divided into urban congestion working condition, urban suburban working condition and expressway working condition, ramp and driver driving intensity difference are not considered, and working condition adaptability is not good enough; energy optimization is mainly only aimed at the required power, the vehicle speed, the optimal power and the SOC of an engine participating in work in hybrid power control, and how to minimize the energy consumption of the whole vehicle and the energy consumption optimization effect are not specifically disclosed. Therefore, a new energy passenger car self-adaptive optimization control method based on real-time working condition recognition is provided.
Disclosure of Invention
The invention provides a new energy passenger car adaptive optimization control method based on real-time working condition recognition, and aims to overcome the defects that a ramp is not considered in adaptive optimization control of the existing new energy passenger car, the driving intensity difference of a driver is not considered, the working condition adaptability is not good enough, the energy consumption optimization effect is uncertain and the like.
The invention adopts the following technical scheme:
a new energy passenger car self-adaptive optimization control method based on real-time working condition recognition comprises the following steps:
dividing the running working conditions of the new energy passenger car into a climbing working condition, a high-speed working condition, a medium-low speed working condition, a congestion working condition, a rapid acceleration and rapid deceleration working condition and a circulation working condition;
secondly, carrying out real-time stable identification on the running condition of the new energy bus;
thirdly, corresponding optimization control is carried out aiming at each type of road working condition, and a specific optimization control algorithm comprises the following steps: (1) if the current working condition is climbing, the slope recognition is combined
And vehicle weight estimation
Calculating the real-time ramp resistance
The active slope slipping prevention is realized, and the power mode is automatically switched; (2) if the current working condition is a high-speed working condition, when the accelerator is larger than a certain APS1 and continues for a certain time T6, the change slope of the normal driving torque is limited to be delta T1, and when the accelerator is smaller than a certain APS2 and continues for a certain time T4, the change slope of the driving torque is reduced to be delta T2, so that the driving torque is kept stable; (3) if the current working condition is the medium-low speed working condition, the serial-parallel switching speed Vk is increased, the serial-parallel switching frequency is reduced, and the energy consumption loss in the serial-parallel switching process is reduced; (4) if the current is a congestion working condition, reducing the SOC threshold value of the series power generation to the current SOC-delta SOC, and simultaneously limiting the lowest power generation SOC threshold value SOC _ min to be in a pure electric operation mode as far as possible; (5) if the current condition is a rapid acceleration and rapid deceleration working condition, actively reducing the threshold a _ lim of the driving acceleration limit, and reducing the driving torque change slope limit to be delta T3; (6) if the current is cycleUnder the condition of surrounding working conditions, the vehicle controller automatically performs follow-up auxiliary control on the vehicle speed under the working conditions, and the influence of driver operation difference on an energy consumption result is reduced by controlling torque output through a PI (proportional integral) controller.
Specifically, the stable identification of the climbing condition comprises static hill starting identification and dynamic hill climbing condition identification, wherein the static hill starting identification is to adopt a hill sensor to detect a static gradient value, and substitute the static gradient value into a vehicle dynamics equation in the vehicle starting process to estimate the vehicle weight:
wherein, in the step (A),
in order to drive the torque of the motor,
in order to change the speed ratio of the gear box,
as a main reduction gear ratio, the speed ratio,
for transmission efficiency, r is the tire radius,
the static gradient value is obtained, g is the gravity acceleration, a is the vehicle acceleration, f is the rolling resistance coefficient, f =0.0076+0.000056v is taken, and v is the vehicle speed; the dynamic climbing condition identification is that the accelerator is larger than a certain APS0 and lasts for a certain time t0, and meanwhile, the acceleration of the vehicle is smaller than a certain proportion K1 of the normal acceleration and lasts for a certain time t0, wherein the normal acceleration is estimated based on the vehicle weight and the current driving torque; the non-climbing working condition identification is that the acceleration of the vehicle is larger than a certain proportion K2 of the normal acceleration and lasts for a certain time t 0; the APS0 ranges from 80% to 95%, the t0 ranges from 3s to 5s, the K1 ranges from 50% to 60%, and the K2 ranges from 80% to 90%.
Preferably, the high-speed condition stable identification is that the average vehicle speed in a certain time t1 is above a certain vehicle speed V1, and the minimum vehicle speed in the certain time is not lower than a certain value V2; the non-high-speed working condition is identified in such a way that the average vehicle speed within a certain time t1 is less than a certain vehicle speed V1-delta V; the t1 value is 5-10 s, the V1 value is 50-60 km/h, the V2 value is 40-45 km/h, and the delta V value is 3-5 km/h.
Preferably, the medium-low speed working condition stable identification is that the average vehicle speed within a certain time t1 is V3-V4 within a certain vehicle speed range; meanwhile, the highest vehicle speed in the period t1 is not higher than a certain value V5; the non-medium and low speed working condition is identified in such a way that the average vehicle speed within a certain time t1 is greater than a certain vehicle speed V4 +. DELTA.V or less than V3-DELTA.V for a certain time t 0; wherein the t1 value is 5-10 s, the V3 value is 10-15 km/h, the V4 value is 25-30 km/h, the V5 value is 35-40 km/h, and the delta V value is 3-5 km/h.
Preferably, the congestion condition stable recognition is that the average vehicle speed in a certain time t1 is between V6 and V7, and the maximum vehicle speed in the certain time t1 is not higher than a certain value V8; identifying the average vehicle speed within a certain time t1 and lasting for a certain time t0 when the average vehicle speed is greater than a certain vehicle speed V7 plus delta V; wherein the t1 value is 5-10 s, the V6 value is 0-5 km/h, the V7 value is 10-15 km/h, the V8 value is 20-25 km/h, and the delta V value is 3-5 km/h.
Preferably, the rapid acceleration and rapid deceleration condition stable recognition is that the average acceleration within a certain time t5 is greater than a certain value a1, and the condition is determined as a rapid acceleration condition; the average deceleration absolute value in a fixed time t5 is larger than a fixed value a1, and the condition is judged as a rapid deceleration condition; wherein, t5 is in the range of 1-3 s, and a1 is in the range of 2-3 m/s2。
Preferably, the stable identification of the cycle condition is to write the cycle condition into the control model in advance, when the actual vehicle speed starts to be greater than a certain value V0 after the driving mode is entered, the condition comparison is started from the moment, and if the deviation between the actual vehicle speed and the theoretical vehicle speed is less than the set deviation Δ V1 for a certain time t2, a corresponding cycle condition identification result is output; the non-cyclic working condition algorithm identifies that the deviation between the actual vehicle speed and the theoretical working condition vehicle speed lasts for a certain time T3 and is larger than a set deviation delta V2, or the difference between the torque required by the driver and the torque required by the cyclic working condition is larger than a certain value delta T and lasts for a certain time T4; the V0 value range is 1-2 km/h, the delta V1 value range is 2-3 km/h, the delta V2 value range is 4-5 km/h, the T2 value range is 10-20 s, the T3 value range is 1-3 s, the T4 value range is 0.5-1 s, and the delta T value range is 200-400 Nm.
Furthermore, in the optimized control algorithm of the third step, under the climbing condition, if the vehicle has a parking brake function controlled by the electromagnetic valve, the electromagnetic valve is controlled to release the parking brake when the actual driving force is larger than the real-time ramp resistance.
Preferably, in the optimization control algorithm in the third step, the value ranges of Δ T1 and Δ T2 under the high-speed working condition are as follows: 500Nm/s <. DELTA.T 2 <. DELTA.T 1<2000Nm/s, the range of APS1 is 50-60%, the range of APS2 is 30-40%, and the range of T4 is 1-3 s.
Preferably, in the optimization control algorithm of the third step, the value of Vk under the medium-low speed working condition is greater than V4 +. DELTA.V, the value of V4 is 20-30 km/h, and the value of DELTA.V is 3-5 km/h.
Preferably, in the optimization control algorithm of the third step, the delta SOC value range under the congestion condition is 10% -30%, and the SOC _ min value range is 20% -30%.
Preferably, in the optimization control algorithm of the third step, the value range of a _ lim under the working condition of rapid acceleration and rapid deceleration is 1.6-2.0 m/s2The value range of delta T3 is 500-1000 Nm/s.
Furthermore, in the optimization control algorithm of the third step, under the circulation working condition, if the vehicle speed is greater than Vm and the brake pedal opening is greater than BPSm and continues for a certain time t5, the follow-up auxiliary control is quitted.
Preferably, the Vm value ranges from 5km/h to 10km/h, the BPSm value ranges from 80% to 90%, the t5 value ranges from 1s to 3s, the auxiliary control P value ranges from 300 s to 500 s, and the I value ranges from 5s to 10.
As can be seen from the above description of the present invention, compared with the prior art, the present invention has the following advantages:
the self-adaptive optimization control method of the new energy passenger car divides the driving working condition into a climbing working condition, a high-speed working condition, a medium and low speed working condition, a congestion working condition, a rapid acceleration and rapid deceleration working condition and a cycle working condition, fully considers the factors of a ramp, a vehicle speed, driver operation and the like, respectively and correspondingly implements an optimization control algorithm under each working condition, has reliable algorithm and good working condition adaptability, and the whole car optimization relates to the dynamic optimization of active slope prevention and climbing, reduces the tire wear while saving energy and reducing consumption, and comprehensively optimizes and promotes the comprehensive performance of the car.
Drawings
FIG. 1 is a flow chart of an optimization control algorithm of the present invention.
Detailed Description
The following describes embodiments of the present invention with reference to the drawings. Numerous details are set forth below in order to provide a thorough understanding of the present invention, but it will be apparent to those skilled in the art that the present invention may be practiced without these details. Well-known components, methods and processes are not described in detail below.
A new energy passenger car self-adaptive optimization control method based on real-time working condition recognition comprises the following steps:
the typical running working conditions of the new energy passenger car are divided into a climbing working condition, a high-speed working condition, a medium-low speed working condition, a congestion working condition, a rapid acceleration and rapid deceleration working condition and a circulation working condition.
Secondly, carrying out real-time stable identification on the typical running working condition of the new energy passenger car, wherein the stable identification algorithm of various running working conditions comprises the following steps:
(1) climbing condition stability identification algorithm: the method comprises static hill starting identification and dynamic climbing condition identification. The static hill starting identification is to adopt a hill sensor to detect a static slope value, substitute the static slope value into a vehicle dynamics equation in the vehicle starting process, and estimate the vehicle weight:
wherein, in the step (A),
in order to drive the torque of the motor,
in order to change the speed ratio of the gear box,
as a main reduction gear ratio, the speed ratio,
for transmission efficiency, r is the tire radius,
the static gradient value is obtained, g is the gravity acceleration, a is the vehicle acceleration, f is the rolling resistance coefficient, f =0.0076+0.000056v is taken, and v is the vehicle speed; the dynamic climbing condition identification is that the accelerator is larger than a certain APS0 and lasts for a certain time t0, and meanwhile, the acceleration of the vehicle is smaller than a certain proportion K1 of the normal acceleration and lasts for a certain time t0, wherein the normal acceleration is estimated based on the vehicle weight and the current driving torque; the non-climbing working condition identification is that the acceleration of the vehicle is larger than a certain proportion K2 of the normal acceleration and lasts for a certain time t 0; the APS0 ranges from 80% to 95%, the t0 ranges from 3s to 5s, the K1 ranges from 50% to 60%, and the K2 ranges from 80% to 90%.
(2) And (3) high-speed working condition stability identification algorithm: the average vehicle speed in a certain time t1 is more than a certain vehicle speed V1, and the minimum vehicle speed in the certain time is not less than a certain value V2; the non-high-speed working condition is identified in such a way that the average vehicle speed within a certain time t1 is less than a certain vehicle speed V1-delta V; the t1 value is 5-10 s, the V1 value is 50-60 km/h, the V2 value is 40-45 km/h, and the delta V value is 3-5 km/h.
(3) And (3) a medium-low speed working condition stability identification algorithm: the average vehicle speed within a certain time t1 is V3-V4 within a certain vehicle speed range; meanwhile, the highest vehicle speed in the period t1 is not higher than a certain value V5; the non-medium and low speed working condition is identified in such a way that the average vehicle speed within a certain time t1 is greater than a certain vehicle speed V4 +. DELTA.V or less than V3-DELTA.V for a certain time t 0; wherein the t1 value is 5-10 s, the V3 value is 10-15 km/h, the V4 value is 25-30 km/h, the V5 value is 35-40 km/h, and the delta V value is 3-5 km/h.
(4) The congestion condition stability identification algorithm comprises the following steps: the average speed in a certain time t1 is V6-V7, and the highest speed in the time t1 is not higher than a certain value V8; identifying the average vehicle speed within a certain time t1 and lasting for a certain time t0 when the average vehicle speed is greater than a certain vehicle speed V7 plus delta V; wherein the t1 value is 5-10 s, the V6 value is 0-5 km/h, the V7 value is 10-15 km/h, the V8 value is 20-25 km/h, and the delta V value is 3-5 km/h.
(5) The rapid acceleration and rapid deceleration working condition stability identification algorithm comprises the following steps: the average acceleration within a certain time t5 is larger than a certain value a1, and the condition is judged to be a rapid acceleration condition; the average deceleration absolute value in a fixed time t5 is larger than a fixed value a1, and the condition is judged as a rapid deceleration condition; wherein, t5 is in the range of 1-3 s, and a1 is in the range of 2-3 m/s2。
(6) And (3) a cyclic working condition stability identification algorithm: writing the circulation working condition into a control model in advance, comparing the working conditions from the moment when the actual vehicle speed is greater than a certain value V0 after the driving mode is started, and outputting a corresponding circulation working condition identification result if the deviation between the actual vehicle speed and the theoretical working condition vehicle speed is less than a set deviation delta V1 for a certain time t 2; the non-cyclic working condition algorithm identifies that the deviation between the actual vehicle speed and the theoretical working condition vehicle speed lasts for a certain time T3 and is larger than a set deviation delta V2, or the difference between the torque required by the driver and the torque required by the cyclic working condition is larger than a certain value delta T and lasts for a certain time T4; the V0 value range is 1-2 km/h, the delta V1 value range is 2-3 km/h, the delta V2 value range is 4-5 km/h, the T2 value range is 10-20 s, the T3 value range is 1-3 s, the T4 value range is 0.5-1 s, and the delta T value range is 200-400 Nm.
Thirdly, corresponding optimization control is carried out aiming at each type of road working condition, and referring to fig. 1, a specific optimization control algorithm comprises the following steps:
(1) if the current working condition is climbing, the slope recognition is combined
And vehicle weight estimation
Calculating the real-time ramp resistance
And the active slope slipping prevention is realized, and the power mode is automatically switched.
If the vehicle has a parking brake function controlled by an electromagnetic valve, the electromagnetic valve is controlled to release the parking brake when the actual driving force is greater than the real-time ramp resistance, so that active slope slipping prevention is realized, and normal ramp starting is completely prevented from slipping; and when the static hill starting or dynamic climbing working condition is identified, the whole vehicle controls the economic mode to automatically switch the power mode.
(2) If the current working condition is a high-speed working condition, when the accelerator is larger than a certain value APS1 and continues for a certain time T6, the normal driving torque change slope is limited to be delta T1, and when the accelerator is smaller than a certain value APS2 and continues for a certain time T4, the driving torque change slope is reduced and limited to be delta T2, so that the driving torque is kept stable. The value ranges of the delta T1 and the delta T2 are as follows: 500Nm/s <. DELTA.T 2 <. DELTA.T 1<2000Nm/s, the range of APS1 is 50-60%, the range of APS2 is 30-40%, and the range of T4 is 1-3 s.
(3) If the current working condition is the medium-low speed working condition, the serial-parallel switching speed Vk is increased, the serial-parallel switching frequency is reduced, and the energy consumption loss in the serial-parallel switching process is reduced. Wherein the Vk value is larger than V4 plus delta V, the V4 value is 20-30 km/h, and the delta V value is 3-5 km/h.
(4) If the current is a congestion working condition, the SOC threshold value of the series power generation is reduced to the current SOC-delta SOC, meanwhile, the lowest power generation SOC threshold value SOC _ min is limited and is in a pure electric operation mode as far as possible, the actual operation oil consumption/gas consumption is reduced, the value range of the delta SOC ranges from 10% to 30%, and the value range of the SOC _ min ranges from 20% to 30%.
(5) If the current condition is a rapid acceleration and rapid deceleration condition, the threshold a _ lim of the driving acceleration limit is actively reduced, and the driving torque change slope limit is reduced to delta T3. a _ lim ranges from 1.6 to 2.0m/s2The value range of delta T3 is 500-1000 Nm/s.
(6) If the current working condition is a circulating working condition, the vehicle controller automatically performs follow-up auxiliary control on the working condition vehicle speed, and the influence of driver operation difference on an energy consumption result is reduced by controlling torque output through PI. The follow assist control is exited when the vehicle speed is greater than Vm and the brake pedal opening is greater than BPSm for a certain time t 5. Wherein the Vm value range is 5-10 km/h, the BPSm value range is 80-90%, the t5 value range is 1-3 s, the auxiliary control P value range is 300-500, and the I value range is 5-10.
The above description is only an embodiment of the present invention, but the design concept of the present invention is not limited thereto, and any insubstantial modifications made by using the design concept should fall within the scope of infringing the present invention.
Claims (14)
1. A new energy passenger car self-adaptive optimization control method based on real-time working condition recognition is characterized by comprising the following steps:
dividing the running working conditions of the new energy passenger car into a climbing working condition, a high-speed working condition, a medium-low speed working condition, a congestion working condition, a rapid acceleration and rapid deceleration working condition and a circulation working condition;
secondly, carrying out real-time stable identification on the running condition of the new energy bus;
thirdly, corresponding optimization control is carried out aiming at each type of road working condition, and a specific optimization control algorithm comprises the following steps: (1) if the current working condition is climbing, the slope recognition is combined
And vehicle weight estimation
Calculating the real-time ramp resistance
The active slope slipping prevention is realized, and the power mode is automatically switched; (2) if the current working condition is a high-speed working condition, when the accelerator is larger than a certain APS1 and continues for a certain time T6, the change slope of the normal driving torque is limited to be delta T1, and when the accelerator is smaller than a certain APS2 and continues for a certain time T4, the change slope of the driving torque is reduced to be delta T2, so that the driving torque is kept stable; (3) if the current working condition is the medium-low speed working condition, the serial-parallel switching speed Vk is increased, the serial-parallel switching frequency is reduced, and the energy consumption loss in the serial-parallel switching process is reduced; (4) if the current is a congestion working condition, reducing the SOC threshold value of the series power generation to the current SOC-delta SOC, and simultaneously limiting the lowest power generation SOC threshold value SOC _ min to be in a pure electric operation mode as far as possible; (5) if the current condition is a rapid acceleration and rapid deceleration conditionActively reducing the threshold a _ lim of the driving acceleration limitation, and reducing the driving torque change slope limitation to be delta T3; (6) if the current working condition is a circulating working condition, the vehicle controller automatically performs follow-up auxiliary control on the working condition vehicle speed, and the influence of driver operation difference on an energy consumption result is reduced by controlling torque output through PI.
2. The adaptive optimization control method of the new energy passenger car based on real-time working condition recognition is characterized by comprising the following steps: the climbing condition stable identification comprises static hill starting identification and dynamic climbing condition identification, wherein the static hill starting identification is to adopt a hill sensor to detect a static slope value, and substitute the static slope value into a vehicle dynamics equation in the vehicle starting process to estimate the vehicle weight:
wherein, in the step (A),
in order to drive the torque of the motor,
in order to change the speed ratio of the gear box,
as a main reduction gear ratio, the speed ratio,
for transmission efficiency, r is the tire radius,
the static gradient value is obtained, g is the gravity acceleration, a is the vehicle acceleration, f is the rolling resistance coefficient, f =0.0076+0.000056v is taken, and v is the vehicle speed; the dynamic climbing condition identification is that the accelerator is larger than a certain APS0 and lasts for a certain time t0, meanwhile, the acceleration of the vehicle is smaller than a certain proportion K1 of the normal acceleration and lasts for a certain time t0, wherein the estimation of the normal acceleration is based onEstimating the vehicle weight and the current driving torque; the non-climbing working condition identification is that the acceleration of the vehicle is larger than a certain proportion K2 of the normal acceleration and lasts for a certain time t 0; the APS0 ranges from 80% to 95%, the t0 ranges from 3s to 5s, the K1 ranges from 50% to 60%, and the K2 ranges from 80% to 90%.
3. The adaptive optimization control method of the new energy passenger car based on real-time working condition recognition is characterized by comprising the following steps: the high-speed working condition stable identification is that the average vehicle speed in a certain time t1 is above a certain vehicle speed V1, and the minimum vehicle speed in the certain time is not lower than a certain value V2; the non-high-speed working condition is identified in such a way that the average vehicle speed within a certain time t1 is less than a certain vehicle speed V1-delta V; the t1 value is 5-10 s, the V1 value is 50-60 km/h, the V2 value is 40-45 km/h, and the delta V value is 3-5 km/h.
4. The adaptive optimization control method of the new energy passenger car based on real-time working condition recognition is characterized by comprising the following steps: the medium and low speed working condition stable identification is that the average speed within a certain time t1 is V3-V4 within a certain speed range; meanwhile, the highest vehicle speed in the period t1 is not higher than a certain value V5; the non-medium and low speed working condition is identified in such a way that the average vehicle speed within a certain time t1 is greater than a certain vehicle speed V4 +. DELTA.V or less than V3-DELTA.V for a certain time t 0; wherein the t1 value is 5-10 s, the V3 value is 10-15 km/h, the V4 value is 25-30 km/h, the V5 value is 35-40 km/h, and the delta V value is 3-5 km/h.
5. The adaptive optimization control method of the new energy passenger car based on real-time working condition recognition is characterized by comprising the following steps: the congestion working condition stable identification is that the average vehicle speed in a certain time t1 is V6-V7, and meanwhile, the highest vehicle speed in the certain time t1 is not higher than a certain value V8; identifying the average vehicle speed within a certain time t1 and lasting for a certain time t0 when the average vehicle speed is greater than a certain vehicle speed V7 plus delta V; wherein the t1 value is 5-10 s, the V6 value is 0-5 km/h, the V7 value is 10-15 km/h, the V8 value is 20-25 km/h, and the delta V value is 3-5 km/h.
6. The adaptive optimization control method of the new energy passenger car based on real-time working condition recognition is characterized by comprising the following steps: the steady recognition of the rapid acceleration and rapid deceleration working condition is that the average acceleration in a certain time t5 is greater than a certain value a1, and the working condition is judged to be the rapid acceleration working condition; the average deceleration absolute value in a fixed time t5 is larger than a fixed value a1, and the condition is judged as a rapid deceleration condition; wherein, t5 is in the range of 1-3 s, and a1 is in the range of 2-3 m/s2。
7. The adaptive optimization control method of the new energy passenger car based on real-time working condition recognition is characterized by comprising the following steps: the stable identification of the circulating working condition is to write the circulating working condition into a control model in advance, when the actual vehicle speed starts to be greater than a certain value V0 after the driving mode is started, working condition comparison is started from the moment, and if the deviation between the actual vehicle speed and the theoretical working condition vehicle speed is less than a set deviation delta V1 for a certain time t2, a corresponding circulating working condition identification result is output; the non-cyclic working condition algorithm identifies that the deviation between the actual vehicle speed and the theoretical working condition vehicle speed lasts for a certain time T3 and is larger than a set deviation delta V2, or the difference between the torque required by the driver and the torque required by the cyclic working condition is larger than a certain value delta T and lasts for a certain time T4; the V0 value range is 1-2 km/h, the delta V1 value range is 2-3 km/h, the delta V2 value range is 4-5 km/h, the T2 value range is 10-20 s, the T3 value range is 1-3 s, the T4 value range is 0.5-1 s, and the delta T value range is 200-400 Nm.
8. The adaptive optimization control method of the new energy passenger car based on real-time working condition recognition is characterized by comprising the following steps: in the optimization control algorithm of the third step, under the working condition of climbing, if the vehicle has a parking brake function controlled by the electromagnetic valve, the electromagnetic valve is controlled to release the parking brake when the actual driving force is greater than the real-time ramp resistance.
9. The adaptive optimization control method of the new energy passenger car based on real-time working condition recognition is characterized by comprising the following steps: in the optimization control algorithm of the third step, the value ranges of delta T1 and delta T2 under the high-speed working condition are as follows: 500Nm/s <. DELTA.T 2 <. DELTA.T 1<2000Nm/s, the range of APS1 is 50-60%, the range of APS2 is 30-40%, and the range of T4 is 1-3 s.
10. The adaptive optimization control method of the new energy passenger car based on real-time working condition recognition is characterized by comprising the following steps: in the optimization control algorithm of the third step, the value of Vk under the medium-low speed working condition is larger than V4 plus delta V, the value of V4 is 20-30 km/h, and the value of delta V is 3-5 km/h.
11. The adaptive optimization control method of the new energy passenger car based on real-time working condition recognition is characterized by comprising the following steps: in the optimization control algorithm of the third step, the delta SOC value range under the congestion working condition is 10% -30%, and the SOC _ min value range is 20% -30%.
12. The adaptive optimization control method of the new energy passenger car based on real-time working condition recognition is characterized by comprising the following steps: in the optimization control algorithm of the third step, the value range of a _ lim under the working condition of rapid acceleration and rapid deceleration is 1.6-2.0 m/s2The value range of delta T3 is 500-1000 Nm/s.
13. The adaptive optimization control method of the new energy passenger car based on real-time working condition recognition is characterized by comprising the following steps: in the optimization control algorithm of the third step, under the circulation working condition, if the vehicle speed is greater than Vm and the brake pedal opening is greater than BPSm and continues for a certain time t5, the follow-up auxiliary control is quitted.
14. The adaptive optimization control method of the new energy passenger car based on real-time working condition recognition is characterized by comprising the following steps: the Vm value range is 5-10 km/h, the BPSm value range is 80-90%, the t5 value range is 1-3 s, the auxiliary control P value range is 300-500, and the I value range is 5-10.
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