CN109774492B - Pure electric vehicle whole vehicle power distribution method based on future driving power demand - Google Patents
Pure electric vehicle whole vehicle power distribution method based on future driving power demand Download PDFInfo
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- CN109774492B CN109774492B CN201811638675.3A CN201811638675A CN109774492B CN 109774492 B CN109774492 B CN 109774492B CN 201811638675 A CN201811638675 A CN 201811638675A CN 109774492 B CN109774492 B CN 109774492B
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- 230000009194 climbing Effects 0.000 claims abstract description 16
- 238000005096 rolling process Methods 0.000 claims description 19
- 230000007704 transition Effects 0.000 claims description 11
- 239000011159 matrix material Substances 0.000 claims description 6
- 238000005457 optimization Methods 0.000 claims description 5
- 238000006243 chemical reaction Methods 0.000 claims description 3
- 230000005484 gravity Effects 0.000 claims description 3
- 238000010586 diagram Methods 0.000 description 8
- 238000005265 energy consumption Methods 0.000 description 6
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- 238000005516 engineering process Methods 0.000 description 1
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- 239000000295 fuel oil Substances 0.000 description 1
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/72—Electric energy management in electromobility
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02T90/10—Technologies relating to charging of electric vehicles
- Y02T90/16—Information or communication technologies improving the operation of electric vehicles
Abstract
The invention provides a pure electric vehicle power distribution method based on future driving power requirements, wherein a Markov chain predicts speed and acceleration information of the next state, a GPS navigation system calculates gradient information, and calculates future required power according to vehicle parameters; judging whether the next state is rapid acceleration or climbing, if so, judging whether the SOC is in a threshold range, and if not, preferentially meeting the power requirement of the current state; when the SOC is in the threshold range, the power requirements of the current state and the next state are simultaneously met; and when the SOC is not in the threshold range, if the current state is a rapid acceleration state or a climbing state, preferentially meeting the power requirement of the current state and carrying out proper deceleration, otherwise preferentially meeting the power requirement of the next state. The invention considers the required power of the future state under the condition of maintaining the SOC of the power battery in the threshold range as much as possible, and reduces the condition that the motor is damaged because the motor is in an underpower state when the future state is accelerated suddenly or climbs a slope.
Description
Technical Field
The invention relates to the technical field of new energy, in particular to a pure electric vehicle whole vehicle power distribution method based on future driving power requirements.
Background
The pure electric vehicle is a limited single energy source, and the power source of the pure electric vehicle is completely from a power battery. Compare in traditional fuel automobile, pure electric vehicles's dynamic property is better. However, the development of the pure electric vehicle is limited due to the incomplete development of the power battery technology, and the reasonable utilization of the limited energy of the pure electric vehicle becomes an important direction for the development of the pure electric vehicle. The power distribution of the current single-power-supply pure electric vehicle mainly performs power distribution on a driving motor and accessories on the principle of minimum energy consumption of the whole vehicle, and neglects the influence of a ramp on the power distribution. When the driving power provided by the power battery to the motor cannot meet the requirement, the motor is in an under-power state and damages the motor. Therefore, in order to reduce the condition that the motor is in an underpower state, the future power demand needs to be predicted in advance, and the power of the motor needs to be distributed reasonably.
Disclosure of Invention
The invention aims to provide a pure electric vehicle whole vehicle power distribution method based on a future driving power demand, which aims to obtain the driving power demand of a future state through prediction and reasonably distribute the limited energy of a power battery so as to reduce the state of motor under-power, and the technical scheme of the invention is as follows:
a pure electric vehicle whole power distribution method based on future driving power requirements comprises the following steps:
step 1): predicting the speed and acceleration information of the next state of the vehicle by a Markov prediction method, calculating gradient information by a GPS navigation system, and calculating the driving required power of the next state according to the parameters of the vehicle;
step 2): judging whether the next state is rapid acceleration or climbing, if so, executing the step 3); if not, the current power requirement is met preferentially;
step 3): judging whether the SOC of the power battery is in a threshold range, and if so, ensuring that the power battery simultaneously meets the power requirements of the current state and the next state; if not, executing step 4);
step 4): judging whether the current state is a rapid acceleration state or a climbing state, if so, preferentially meeting the power requirement of the current state, and meeting the power requirement of the next state through proper deceleration; if not, the power requirements of the next state are preferentially met.
Further, the markov prediction method adopts a rolling optimization mode: and carrying out state division on the historical data of the speed and the acceleration in the rolling historical time window T, and calculating a state transition probability matrix to predict the speed and the acceleration value of the next state.
Further, the value range of the rolling history time window is 100-200 s.
Further, the state transition probability matrix is
Further, the required power of the next state is:
wherein m is vehicle-mounted mass and g is gravity accelerationDegree, f is the rolling resistance coefficient, CDIs an air resistance coefficient, A is the windward area, alpha is the gradient value, delta is the rotating mass conversion coefficient, vt+1The speed of the next state is predicted for the markov chain prediction model,
the predicted next state acceleration for the Markov chain prediction model.
Further, the rapid acceleration is defined as an acceleration at an accelerator opening greater than 50%, when at+1When a is larger than a, the vehicle accelerates rapidly, and when i is larger than 0, the vehicle climbs.
The invention has the beneficial effects that:
1) when the power distribution of the pure electric vehicle is carried out, the influence of the future driving power requirement of the pure electric vehicle is considered, and the power distribution is carried out in a mode of combining an economic operation mode and a power operation mode. When the automobile is accelerated rapidly and climbs a slope in the future, the motor can be ensured to provide enough power to drive the automobile to run, and the motor is not damaged due to the fact that the motor is in an under-power state; and when the vehicle is not accelerated rapidly and climbs a slope, the power distribution is carried out on the principle that the energy consumption of the whole vehicle is minimum.
2) The invention adopts a rolling history time optimization method based on history data, so that the prediction result is closer to the reality, and gradient information is considered in the prediction of the future driving power demand, so that the calculation of the future driving power is more accurate.
Drawings
FIG. 1 is a schematic diagram of a scroll optimization according to an embodiment of the present invention;
wherein, the diagram (a) is a schematic diagram of the rolling time window at each moment, and the diagram (b) is a schematic diagram of the transition from the state i to the state j in the speed-acceleration two-dimensional state division;
FIG. 2 is a detailed flow chart of an embodiment of the present invention;
FIG. 3 is a hardware diagram according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the following drawings and detailed description, but the scope of the present invention is not limited thereto.
Fig. 1 is a schematic diagram of the rolling optimization according to the embodiment of the present invention, and the specific process includes: in the running process of the pure electric vehicle, new running data can be continuously generated along with the change of time, and old data can lose the effectiveness, so that the prediction of future working condition information needs to be optimized by adopting a method of rolling a historical time window. If the rolling history time window is obtained too small, enough data samples are lacked to cause the reduction of prediction accuracy, so that the result has no universality, and if the rolling history time window is obtained too large, the calculation amount is increased and excessive failure history data is contained, therefore, the value range of the rolling history time window is set to be 100-plus-200 s, the prediction result is compared with the actual value, the T with the minimum error is taken as the value of the final rolling history time window, the speed-acceleration history data in the rolling history time window T is divided into n states, and then the idea of frequency approximate probability is adopted to calculate the transition probability matrix between the states
According to
Determining the next state as k, where PijRepresenting the transition probability of transitioning from the i state to the j state; and outputting the average speed and the average acceleration in the next state as a prediction result.
Fig. 2 shows a specific flowchart of an embodiment of the present invention, which includes:
step 1): inputting altitude information acquired by a GPS (global positioning system) navigation system and speed and acceleration information acquired by an accelerator pedal acquired by a vehicle-mounted sensor into a vehicle control unit, dividing the speed and acceleration historical data in a rolling historical time window into states, calculating transition probability among the states, acquiring the product of a current state value and a transition probability matrix, acquiring the speed and acceleration information of the next state according to the maximum value of the transition probability, and calculating gradient information according to the acquired altitude difference; calculating the next state driving demand according to the predicted speed and acceleration information, the calculated gradient information and the parameters of the automobilePower of
Wherein m is vehicle-mounted mass, g is gravity acceleration, f is rolling resistance coefficient, CDIs an air resistance coefficient, A is the windward area, alpha is the gradient value, delta is the rotating mass conversion coefficient, vt+1The speed of the next state is predicted for the markov chain prediction model,
obtaining acceleration for a next state predicted by a Markov chain prediction model, wherein the Markov chain prediction model is a discrete prediction model in a Markov prediction method;
step 2): judging whether the next state is rapid acceleration or climbing, when at+1If the acceleration is more than a, the acceleration is regarded as rapid acceleration, wherein a is the acceleration when the opening degree of an accelerator pedal is more than 50 percent, the acceleration is regarded as climbing when alpha is more than 0, and if the acceleration is rapid acceleration or climbing, the step 3) is executed; if not, the output power of the power battery preferentially meets the current power requirement, and the vehicle controller performs power distribution with minimum vehicle energy consumption;
step 3): judging whether the SOC of the power battery is in a threshold range, if so, distributing power in a power running mode on the principle of ensuring rapid acceleration or climbing, and ensuring that the power battery can meet the driving power requirements of the current state and the next state simultaneously; if not, executing step 4);
step 4): judging whether the current state is a rapid acceleration state or a climbing state, if so, preferentially meeting the driving power requirement of the current state by the output power of the power battery, and reducing the power requirement of the next state by proper deceleration so as to ensure that the power battery of the next state can provide the required power for the motor; if not, the power distribution is carried out on the basis of minimum energy consumption of the whole vehicle (minimum energy consumption of the electric vehicle and minimum oil consumption of the fuel oil vehicle), and the next state is switched to a power running mode to meet the requirement of the sudden acceleration or climbing power in the next state.
Fig. 3 is a hardware diagram of an embodiment of the present invention, in which a thin solid line represents power transmission, a dotted line represents signal transmission, and a thick solid line represents mechanical transmission. The vehicle-mounted sensor detects the speed and acceleration signals of an accelerator pedal, the GPS navigation system is used for detecting the altitude information of a vehicle, the vehicle controller distributes the distributable power of the power battery by a method of combining an economic operation mode (power distribution is carried out on the principle of minimum energy consumption of the whole vehicle when rapid acceleration does not exist or climbing slopes) and a power operation mode (power distribution is carried out on the principle of ensuring the power performance of acceleration and climbing slopes when rapid acceleration exists or climbing slopes) according to the current driving state, the SOC of the power battery and the future driving power demand, finally the distributed power of the motor is determined, and an instruction is transmitted to the motor control device, so that the motor control device controls the operation of the motor to drive the transmission device.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the present invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive efforts by those skilled in the art based on the technical solution of the present invention.
Claims (6)
1. A pure electric vehicle finished vehicle power distribution method based on future driving power requirements is characterized by comprising the following steps:
step 1): predicting the speed and acceleration information of the next state of the vehicle by a Markov prediction method, calculating gradient information by a GPS navigation system, and calculating the driving required power of the next state according to the parameters of the vehicle;
step 2): judging whether the next state is rapid acceleration or climbing, if so, executing the step 3); if not, the current power requirement is met preferentially;
step 3): judging whether the SOC of the power battery is in a threshold range, and if so, ensuring that the power battery simultaneously meets the power requirements of the current state and the next state; if not, executing step 4);
step 4): judging whether the current state is a rapid acceleration state or a climbing state, if so, preferentially meeting the power requirement of the current state, and meeting the power requirement of the next state through proper deceleration; if not, the power requirements of the next state are preferentially met.
2. The electric vehicle power distribution method based on the future driving power demand of the pure electric vehicle as claimed in claim 1, wherein the Markov prediction method adopts a rolling optimization mode: and carrying out state division on the historical data of the speed and the acceleration in the rolling historical time window T, and calculating a state transition probability matrix to predict the speed and the acceleration value of the next state.
3. The electric vehicle power distribution method based on the future driving power demand of the pure electric vehicle as claimed in claim 2, wherein the rolling history time window has a value in a range of 100s-200 s.
4. The electric vehicle power distribution method based on future driving power demand of pure electric vehicles according to claim 2 or 3, characterized in that the state transition probability matrix is
pnnRepresenting transition probabilities between states.
5. The electric vehicle power distribution method based on the future driving power demand of the pure electric vehicle as claimed in claim 1, wherein the demanded power in the next state is:
wherein m is vehicle-mounted mass, g is gravity acceleration, f is rolling resistance coefficient, CDIs an air resistance coefficient, A is the windward area, alpha is the gradient value, delta is the rotating mass conversion coefficient, vt+1The speed of the next state is predicted for the markov chain prediction model,
the predicted next state acceleration for the Markov chain prediction model.
6. The electric vehicle power distribution method based on future driving power demand of pure electric vehicles according to claim 1, wherein the sudden acceleration is defined as the acceleration when the opening degree of an accelerator pedal is greater than 50%, and a ist+1A is rapid acceleration, when the gradient value alpha is larger than 0, the gradient is climbing, wherein, a is the acceleration when the opening degree of an accelerator pedal is larger than 50 percent, at+1Representing the predicted next state acceleration.
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| CN110435429B (en) * | 2019-08-14 | 2023-03-28 | 福州大学 | Dual-motor electric vehicle endurance mileage estimation method fusing energy consumption prediction |
| CN111267829B (en) * | 2020-03-11 | 2021-08-03 | 宁波工程学院 | Hybrid power vehicle energy management method and system |
| CN114056110B (en) * | 2020-08-04 | 2023-06-13 | 比亚迪股份有限公司 | Vehicle hill start control method and vehicle |
| CN112874520B (en) * | 2021-02-03 | 2023-03-24 | 宜宾丰川动力科技有限公司 | Vehicle control method, device, equipment and storage medium |
| CN112848971B (en) * | 2021-03-01 | 2023-04-28 | 上海电气集团股份有限公司 | Fuel cell power system and power control method thereof |
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Effective date of registration: 20240122 Address after: 230000 floor 1, building 2, phase I, e-commerce Park, Jinggang Road, Shushan Economic Development Zone, Hefei City, Anhui Province Patentee after: Dragon totem Technology (Hefei) Co.,Ltd. Country or region after: China Address before: Zhenjiang City, Jiangsu Province, 212013 Jingkou District Road No. 301 Patentee before: JIANGSU University Country or region before: China |