CN112124079B - Energy recovery self-learning method, equipment, storage medium and device - Google Patents
Energy recovery self-learning method, equipment, storage medium and device Download PDFInfo
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- CN112124079B CN112124079B CN202011070126.8A CN202011070126A CN112124079B CN 112124079 B CN112124079 B CN 112124079B CN 202011070126 A CN202011070126 A CN 202011070126A CN 112124079 B CN112124079 B CN 112124079B
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L7/00—Electrodynamic brake systems for vehicles in general
- B60L7/10—Dynamic electric regenerative braking
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
Abstract
The invention discloses an energy recovery self-learning method, equipment, a storage medium and a device, wherein the initial battery health degree is obtained, and the initial capacity of a battery is determined according to the initial battery health degree; acquiring an energy recovery condition and the current battery health attenuation degree; determining the current battery capacity according to the initial battery capacity and the current battery health attenuation degree; determining the maximum energy recovery power of the current battery according to the energy recovery condition and the current battery capacity; and recovering energy according to the maximum energy recovery power. Because the current battery capacity is determined according to the current battery health attenuation degree, and the maximum energy recovery power of the current battery is determined according to the energy recovery condition and the current battery capacity, compared with the prior art, the method can determine the energy recovery power corresponding to each stage of the battery without considering vehicle configuration, the energy recovery power is easy to adjust, and the development verification period is shortened.
Description
Technical Field
The invention relates to the technical field of automobiles, in particular to an energy recovery self-learning method, energy recovery self-learning equipment, an energy recovery self-learning storage medium and an energy recovery self-learning device.
Background
At present, with the development of the automobile industry, electric vehicles have been developed more rapidly under the support of national and local policies, in practical application, a driving device of the electric vehicle generally comprises a battery, a motor and other components, wherein a battery system is used as a power source of the electric vehicle, and in practical application, because the electric energy stored by the battery is limited, the improvement of the endurance mileage of the electric vehicle by utilizing the battery energy is more and more important, but the existing battery energy recovery needs to be adjusted according to the vehicle type, and the development and verification period and difficulty are increased through test and shaping.
The above is only for the purpose of assisting understanding of the technical aspects of the present invention, and does not represent an admission that the above is prior art.
Disclosure of Invention
The invention mainly aims to provide an energy recovery self-learning method, equipment, a storage medium and a device, and aims to solve the technical problems that in the prior art, the energy recovery power is difficult to adjust, and the development and verification period is long.
In order to achieve the above object, the present invention provides an energy recovery self-learning method, which comprises the following steps:
acquiring initial battery health degree, and determining the initial capacity of the battery according to the initial battery health degree;
acquiring an energy recovery condition and the current battery health attenuation degree;
determining the current battery capacity according to the initial battery capacity and the current battery health attenuation degree;
determining the maximum energy recovery power of the current battery according to the energy recovery condition and the current battery capacity;
and recovering energy according to the maximum energy recovery power.
Preferably, the step of obtaining the energy recovery condition and the current degree of battery health degradation includes:
acquiring the current vehicle running speed, the current driving state, the current brake temperature and the current battery health attenuation degree;
determining an energy recovery condition according to the current vehicle running speed, the current driving state and the current brake temperature;
preferably, the step of determining the maximum energy recovery power of the current battery according to the energy recovery condition and the current battery capacity includes:
when the current vehicle running speed reaches a preset speed, the current driving state is a deceleration control state and the current brake temperature is not higher than a preset temperature, controlling the current vehicle to enter a self-learning state;
and when the current vehicle is in a self-learning state, determining the maximum energy recovery power of the current battery according to the current battery capacity.
Preferably, the step of determining the maximum energy recovery power of the current battery according to the current battery capacity when the current vehicle is in the self-learning state includes:
when the current vehicle is in a self-learning state, acquiring a mapping relation between the opening degree of a brake pedal and the current vehicle speed;
and determining the maximum energy recovery power of the current battery according to the mapping relation and the current battery capacity.
Preferably, after the step of determining the maximum energy recovery power of the current battery according to the energy recovery condition and the current battery capacity, the method further includes:
determining the heat energy of the current brake according to the temperature of the current brake;
and adjusting the maximum energy recovery power of the current battery according to the heat energy of the current brake, and obtaining the adjusted maximum energy recovery power.
Preferably, the step of determining the current brake heat energy according to the current brake temperature includes:
judging whether the current brake temperature is greater than a preset temperature or not;
and when the current brake temperature is higher than the preset temperature, obtaining the heat energy of the current brake corresponding to the current brake temperature.
Preferably, the step of adjusting the maximum energy recovery power of the current battery according to the current brake heat energy and obtaining the adjusted maximum energy recovery power includes:
acquiring a difference value between the current brake heat energy and preset brake heat energy;
determining an energy recovery power interval to be increased according to the difference;
and adjusting the maximum energy recovery power according to the energy recovery power interval, and obtaining the adjusted maximum energy recovery power.
Furthermore, to achieve the above object, the present invention also proposes an energy recovery self-learning device, which comprises a memory, a processor and an energy recovery self-learning program stored on the memory and operable on the processor, the energy recovery self-learning program being configured to implement the steps of energy recovery self-learning as described above.
Furthermore, to achieve the above object, the present invention further proposes a storage medium having stored thereon an energy recovery self-learning program, which when executed by a processor implements the steps of the energy recovery self-learning method as described above.
In addition, to achieve the above object, the present invention further provides an energy recovery self-learning apparatus, including:
the data acquisition module is used for acquiring the initial battery health degree and determining the initial capacity of the battery according to the initial battery health degree;
the data acquisition module is also used for acquiring energy recovery conditions and the current battery health attenuation degree;
the capacity determining module is used for determining the current battery capacity according to the initial capacity of the battery and the current battery health attenuation degree;
the power determining module is used for determining the maximum energy recovery power of the current battery according to the energy recovery condition and the current battery capacity;
and the energy recovery module is used for recovering energy according to the energy recovery power.
According to the method, the initial battery health degree is obtained, and the initial capacity of the battery is determined according to the initial battery health degree; acquiring an energy recovery condition and the current battery health attenuation degree; determining the current battery capacity according to the initial battery capacity and the current battery health attenuation degree; determining the maximum energy recovery power of the current battery according to the energy recovery condition and the current battery capacity; and recovering energy according to the maximum energy recovery power. Compared with the prior art, the method can determine the energy recovery power corresponding to each stage of the battery without considering vehicle configuration, has simple energy recovery power adjustment and reduces the development and verification period.
Drawings
FIG. 1 is a schematic structural diagram of an energy recovery self-learning device of a hardware operating environment according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a first embodiment of the energy recovery self-learning method of the present invention;
FIG. 3 is a schematic flow chart of a second embodiment of the energy recovery self-learning method of the present invention;
FIG. 4 is a schematic flow chart of a third embodiment of the energy recovery self-learning method of the present invention;
FIG. 5 is a block diagram of the energy recovery self-learning apparatus according to the first embodiment of the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Referring to fig. 1, fig. 1 is a schematic structural diagram of an energy recovery self-learning device in a hardware operating environment according to an embodiment of the present invention.
As shown in fig. 1, the energy recovery self-learning device may include: a processor 1001, such as a Central Processing Unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Wherein a communication bus 1002 is used to enable connective communication between these components. The user interface 1003 may include a Display screen (Display), and the optional user interface 1003 may further include a standard wired interface and a wireless interface, and the wired interface for the user interface 1003 may be a USB interface in the present invention. The network interface 1004 may optionally include a standard wired interface, a WIreless interface (e.g., a WIreless-FIdelity (WI-FI) interface). The Memory 1005 may be a Random Access Memory (RAM) Memory or a Non-volatile Memory (NVM), such as a disk Memory. The memory 1005 may alternatively be a storage device separate from the processor 1001.
It will be appreciated by those skilled in the art that the configuration shown in fig. 1 does not constitute a limitation of the energy recovery self-learning device and may include more or fewer components than shown, or some components in combination, or a different arrangement of components.
As shown in FIG. 1, memory 1005, identified as one type of computer storage medium, may include an operating system, a network communication module, a user interface module, and an energy recovery self-learning program.
In the energy recovery self-learning apparatus shown in fig. 1, the network interface 1004 is mainly used for connecting to a background server and communicating data with the background server; the user interface 1003 is mainly used for connecting user equipment; the energy recovery self-learning device calls an energy recovery self-learning program stored in the memory 1005 through the processor 1001 and executes the energy recovery self-learning method provided by the embodiment of the invention.
Based on the hardware structure, the embodiment of the energy recovery self-learning method is provided.
Referring to fig. 2, fig. 2 is a schematic flow chart of a first embodiment of the energy recovery self-learning method of the present invention, and the first embodiment of the energy recovery self-learning method of the present invention is provided.
In a first embodiment, the energy recovery self-learning method comprises the steps of:
step S10: and acquiring initial battery health degree, and determining the initial capacity of the battery according to the initial battery health degree.
It should be noted that the execution main body of the embodiment may be a vehicle-mounted computer, and the vehicle-mounted computer is a special vehicle informatization product which is developed specially for the special operating environment of the vehicle and the characteristics of the electric circuit, has the functions of high temperature resistance, dust resistance and shock resistance, and can be fused with the electronic circuit of the vehicle. The vehicle-mounted computer in the embodiment can be equipment containing energy recovery self-learning software, and can also be equipment with an energy recovery self-learning function.
It should be understood that the initial battery health degree may be a battery health degree of the power battery at the time of factory shipment, or may be a battery health degree included in the history data.
It can be understood that the initial capacity of the battery may be the amount of stored electric quantity of the power battery, may be the battery capacity corresponding to the initial battery health degree when the power battery leaves the factory, or may be the battery capacity corresponding to the battery health degree included in the history data, and the battery capacity may represent the electric quantity discharged by the battery under a certain condition.
In specific implementation, the vehicle-mounted computer can acquire initial battery health degree from historical data according to an SOH algorithm and determine initial capacity of the battery according to the initial battery health degree.
Step S20: and acquiring an energy recovery condition and the current battery health attenuation degree.
It should be noted that the energy recovery condition may be a condition required for the vehicle to enter the energy recovery step, for example, when the vehicle enters the energy recovery step, the current vehicle speed needs to reach a preset threshold value.
It is understood that the current battery health degradation degree may be a degree of degradation of the current battery health degree compared to the initial battery health degree after the power battery is used for a period of time.
In specific implementation, the vehicle-mounted computer can obtain the energy recovery condition and the current battery health attenuation degree through the driving motor system and the power battery system, for example, the vehicle-mounted computer can determine the current motor rotating speed through the driving motor system so as to determine the current vehicle speed.
Step S30: and determining the current battery capacity according to the initial battery capacity and the current battery health attenuation degree.
It should be noted that the current battery capacity may be the actual maximum storable amount of electricity of the battery after the battery is used for a period of time.
In specific implementation, the vehicle-mounted computer can determine the actual maximum storable electric quantity of the current battery according to the initial capacity of the battery and the current health attenuation degree of the battery. For example, the initial capacity of the battery is 1000mAh, and the health of the battery decays by 2% after the battery is used for a period of time, i.e., the current battery capacity is 980 mAh. The maximum allowable energy recovery power of the battery is related to the battery capacity and the health degree of the battery, and the data can be obtained by analyzing test data stored in an on-board computer.
Step S40: and determining the maximum energy recovery power of the current battery according to the energy recovery condition and the current battery capacity.
It should be noted that, when the energy recovery condition is satisfied and the vehicle-mounted computer enters the self-learning state, the vehicle-mounted computer may determine the maximum energy recovery power of the current battery according to the current battery capacity.
It can be understood that the energy recovery can be a process of converting kinetic energy into electric energy to be stored and reused, and the condition of the braking energy recovery can be that the battery can fully receive regenerative braking energy, and the motor can fully participate in the regenerative braking process, and the generated current is fully utilized to charge the battery.
It should be understood that the maximum energy recovery power may be the maximum energy recovery power that the battery can accept, and the maximum energy recovery power may be reduced according to the degradation of the battery capacity and the battery health.
In the concrete implementation, the vehicle-mounted computer can determine the maximum energy recovery power of the current battery according to the current energy recovery condition and the current battery capacity, for example, when the vehicle-mounted computer can judge the current vehicle state' road state and the brake is in a cold state, the energy recovery function is in a normal state, the vehicle speed can be increased to 90km/h, the battery capacity and the battery health attenuation degree of the current battery are detected through energy recovery self-learning software carried by the vehicle-mounted computer, and the maximum energy recovery power of the current battery is determined.
Step S50: performing energy recovery according to the maximum energy recovery power
It should be noted that the vehicle-mounted computer performs energy recovery according to the maximum energy recovery power of the current battery.
In the specific implementation, in the braking energy recovery process, the vehicle-mounted computer determines the maximum energy recovery power of the current battery according to the energy recovery self-learning software to determine the energy required to be fed back by the driving motor, and converts the mechanical energy generated by the motor into electric energy to be fed back to the battery.
According to the embodiment, the initial battery health degree is obtained, and the initial capacity of the battery is determined according to the initial battery health degree; acquiring an energy recovery condition and the current battery health attenuation degree; determining the current battery capacity according to the initial battery capacity and the current battery health attenuation degree; determining the maximum energy recovery power of the current battery according to the energy recovery condition and the current battery capacity; and recovering energy according to the maximum energy recovery power. Because the current battery capacity is determined according to the current battery health attenuation degree, and the maximum energy recovery power of the current battery is determined according to the energy recovery condition and the current battery capacity, compared with the prior art, the embodiment can determine the energy recovery power corresponding to each stage of the battery without considering vehicle configuration, the energy recovery power is easy to adjust, and the development verification period is shortened.
Referring to fig. 3, fig. 3 is a schematic flow chart of a second embodiment of the energy recovery self-learning method of the present invention, and the second embodiment of the energy recovery self-learning method of the present invention is proposed based on the first embodiment shown in fig. 2.
In the second embodiment, the step S20 includes:
step S201: and acquiring the current vehicle running speed, the current driving state, the current brake temperature and the current battery health attenuation degree.
It should be noted that the current driving state may include a current road state, and whether energy recovery self-learning is appropriate or not may be determined according to the current road state, for example, the current road is in a clear state, and there are fewer vehicles on the road, and at this time, the current driving state is appropriate for controlling the vehicle to enter a self-learning state.
It is understood that the brake may be a device having a function of decelerating, stopping or maintaining a stopped state of a moving part, and is the most important main safety performance equipment of a vehicle, the stability of which is closely related to the driving safety, and heat energy is generated by friction between a friction material and the brake during the braking energy recovery process. The sensitivity of the brake temperature is therefore one of the main influencing factors of the braking stability.
It should be appreciated that the current brake temperature may be a temperature resulting from friction between the friction material and the brake during the braking energy recovery process.
In specific implementation, the vehicle-mounted computer can acquire the current vehicle running speed, the current driving state and the current brake temperature through the driving system.
And S202, determining an energy recovery condition according to the current vehicle running speed, the current driving state and the current brake temperature.
In the concrete implementation, the vehicle-mounted computer determines the energy recovery condition according to the current running speed, the current driving state and the current brake temperature.
Further, in order to determine that the vehicle accurately enters the energy recovery self-learning state, the step of determining the maximum energy recovery power of the current battery according to the energy recovery condition and the current battery capacity comprises the following steps: when the current vehicle running speed reaches a preset vehicle speed, the current driving state is a deceleration control state and the current brake temperature is not higher than a preset temperature, controlling the vehicle to enter a self-learning state; and when the current vehicle is in a self-learning state, determining the maximum energy recovery power of the current battery according to the current battery capacity.
It should be noted that the preset vehicle speed may be the lowest vehicle speed required for controlling the vehicle to perform the energy recovery self-learning. The preset temperature may be the highest temperature at which the brake normally enters the energy recovery self-learning state.
In the concrete implementation, when the current vehicle running speed reaches the preset vehicle speed, the vehicle-mounted computer can perform deceleration control on the vehicle and the current brake temperature can normally enter the temperature of the energy recovery self-learning state, the vehicle is controlled to enter the self-learning state, and the maximum energy recovery power of the current battery is determined according to the current battery capacity, for example: when the current vehicle speed is 40km/h, the vehicle is suitable for self-learning at present, namely the vehicle-mounted computer controls the driving motor to increase the vehicle speed to the vehicle speed suitable for energy recovery self-learning, such as: 90km/h, and determining the maximum energy recovery power of the current battery according to the current battery capacity.
Further, in order to maintain a stable self-learning state, the deceleration may be controlled by a brake pedal opening degree, and the step of determining the maximum energy recovery power of the current battery according to the current battery capacity while the current vehicle is in the self-learning state includes: when the current vehicle is in a self-learning state, acquiring a mapping relation between the opening degree of a brake pedal and the current vehicle speed; and determining the maximum energy recovery power of the current battery according to the mapping relation and the current battery capacity.
It should be noted that the brake pedal may be a pedal for limiting power, i.e. a pedal for a foot brake, and the brake pedal may be used for decelerating the vehicle. The operation of the automobile brake pedal comprises the following steps: slow braking (i.e., predictive braking), emergency braking, combined braking, and intermittent braking. In general, slow braking and emergency braking require the clutch pedal to be depressed to the bottom before the wheels lock up and come to a stop so that the engine does not stall and the vehicle speed can be changed again.
It will be appreciated that the brake pedal opening may be the angle of depression of the brake pedal, from which the current brake load condition may be determined.
It should be understood that the mapping relationship may be a correspondence relationship between the angle at which the brake pedal is depressed and the current vehicle speed in a mapping relationship table stored in an on-board computer database. For example: the brake pedal opening degree is stepped to 50% (or 25%, which represents the pedal opening degree requiring the maximum limit energy recovery power, and the energy recovery power will be lower than the maximum limit value at the current pedal opening degree higher or lower), and the vehicle is kept in straight-line deceleration running until complete stop according to the current pedal opening degree.
In the concrete implementation, when the current vehicle is in a self-learning state, the vehicle-mounted computer can obtain the corresponding relation between the opening degree of the brake pedal and the current vehicle speed through a mapping relation table in a database, and determine the maximum energy recovery power of the current battery according to the corresponding relation and the current battery capacity.
In the embodiment, the initial battery health degree is obtained, the initial capacity of the battery is determined according to the initial battery health degree, the current vehicle running speed, the current driving state, the current brake temperature and the current battery health attenuation degree are obtained, the energy recovery condition is determined according to the current vehicle running speed, the current driving state and the current brake temperature, the current battery capacity is determined according to the initial capacity of the battery and the current battery health attenuation degree, the maximum energy recovery power of the current battery is determined according to the energy recovery condition and the current battery capacity, and the energy recovery is performed according to the maximum energy recovery power. When the energy recovery condition meets the condition required by energy recovery self-learning, the maximum energy recovery power of the current battery is determined according to the current battery health attenuation degree, and the embodiment is more stable and accurate compared with the energy recovery self-learning process in the prior art.
Referring to fig. 4, fig. 4 is a schematic flow chart of a third embodiment of the energy recovery self-learning method of the present invention, and the third embodiment of the energy recovery self-learning method of the present invention is proposed based on the second embodiment shown in fig. 3.
In the third embodiment, after the step S40, the method further includes:
step S410, determining the heat energy of the current brake according to the temperature of the current brake;
it should be noted that the current brake thermal energy may be the energy released by the brake during friction.
In the concrete implementation, the vehicle-mounted computer can record the temperature change of the brake according to the temperature sensor of the brake, so that the heat energy change can be calculated according to the material property of the brake.
Step S420: and adjusting the maximum energy recovery power of the current battery according to the heat energy of the current brake, and obtaining the adjusted maximum energy recovery power.
It should be noted that the adjusted maximum energy recovery power may be the maximum energy recovery power obtained by adjusting the current maximum energy recovery power of the battery according to the heat energy generated by the brake in the energy recovery self-learning process.
In the implementation, during the process of recovering the braking energy, the relationship between the feedback electric energy and the braking heat energy is eliminated. The feedback electric energy is increased, and the braking heat energy is reduced; the feedback electric energy is reduced, and the braking heat energy is increased. And the vehicle-mounted computer adjusts the maximum energy recovery power of the current battery according to the increase or decrease of the feedback electric energy and obtains the adjusted maximum energy recovery power.
Further, in order to maintain good performance of the brake, temperature monitoring of the brake is required, and the step of determining the current brake heat energy according to the current brake temperature includes: judging whether the current brake temperature is greater than a preset temperature or not; and when the current brake temperature is higher than the preset temperature, obtaining the heat energy of the current brake corresponding to the current brake temperature.
It should be noted that the preset temperature may be a temperature corresponding to the heat dissipation generated by the brake during the energy recovery self-learning process. The current brake heat energy can be heat energy generated by friction of the current brake when the current brake performs energy recovery self-learning.
It can be understood that in the energy recovery self-learning process of controlling the vehicle, when the temperature of the current brake is higher than the temperature corresponding to the brake capable of dissipating heat by the brake, the energy recovery power of the current brake needs to be adjusted through the heat energy of the current brake.
In the concrete implementation, in the process of energy recovery self-learning of the vehicle controlled by the vehicle-mounted computer, in order to keep good performance of the brake, the temperature of the brake needs to be monitored, namely whether the current brake temperature is greater than the preset temperature is judged; and when the current brake temperature is higher than the preset temperature, obtaining the heat energy of the current brake corresponding to the current brake temperature, and determining the heat energy generated by the current brake according to the heat energy of the current brake.
Further, in order to avoid overheating of the brake and maintain stability of the brake, the step of adjusting the current maximum energy recovery power of the battery according to the current heat energy of the brake and obtaining the adjusted maximum energy recovery power includes: acquiring a difference value between the current brake heat energy and preset brake heat energy; determining an energy recovery power interval to be increased according to the difference; and adjusting the maximum energy recovery power according to the energy recovery power interval, and obtaining the adjusted maximum energy recovery power.
It should be noted that the preset braking heat energy may be braking heat energy dissipated by the brake through heat dissipation generated by the brake in the energy recovery self-learning process.
It will be appreciated that the energy recovery power interval may be the power required to increase or decrease the maximum energy recovery power of the current battery.
It should be understood that the adjusted maximum energy recovery power is compared with the maximum energy recovery power allowed in the current battery state, and the peak value of the adjusted maximum energy recovery power should be smaller than the maximum energy recovery power allowed in the current battery state.
In specific implementation, the vehicle-mounted computer acquires a difference value between the current brake heat energy and preset brake heat energy; determining an energy recovery power interval to be increased according to the difference; and adjusting the maximum energy recovery power according to the energy recovery power interval, and obtaining the adjusted maximum energy recovery power. For example: in the energy recovery self-learning process, the vehicle-mounted computer adjusts the maximum energy recovery power of the current battery, such as: and in the section where the heat energy of the current brake exceeds the preset brake heat energy, increasing the energy recovery power, wherein the heat energy of the current brake does not exceed the preset brake heat energy, and the peak power of the energy recovery does not exceed the maximum recovery power allowed by the battery in the current state.
In the embodiment, the initial battery health degree is obtained, the initial capacity of the battery is determined according to the initial battery health degree, the current vehicle running speed, the current driving state, the current brake temperature and the current battery health attenuation degree are obtained, the current brake heat energy is determined according to the current brake temperature, the maximum energy recovery power of the current battery is adjusted according to the current brake heat energy, the adjusted maximum energy recovery power is obtained, and the energy recovery is performed according to the maximum energy recovery power because the adjusted energy recovery power is compared with the maximum energy recovery power in the current state, and the maximum energy recovery in the current state is obtained. Compared with the prior art, the energy recovery power is adjusted to avoid overheating of the brake, so that the stability of the brake is kept to make a user experience more.
Furthermore, an embodiment of the present invention further provides a storage medium, on which an energy recovery self-learning program is stored, and the energy recovery self-learning program, when executed by a processor, implements the steps of the energy recovery self-learning method as described above.
Referring to fig. 5, fig. 5 is a block diagram illustrating the structure of the energy recovery self-learning apparatus according to the first embodiment of the present invention.
As shown in fig. 5, the energy recovery self-learning apparatus according to the embodiment of the present invention includes:
the data acquisition module 10 is configured to acquire an initial battery health degree and determine an initial capacity of the battery according to the initial battery health degree;
the data acquisition module 10 is further configured to acquire an energy recovery condition and a current battery health attenuation degree;
a capacity determining module 20, configured to determine a current battery capacity according to the initial battery capacity and the current battery health attenuation degree;
a power determining module 30, configured to determine a maximum energy recovery power of the current battery according to the energy recovery condition and the current battery capacity;
and the energy recovery module 40 is used for recovering energy according to the energy recovery power.
According to the embodiment, the initial battery health degree is obtained, and the initial capacity of the battery is determined according to the initial battery health degree; acquiring an energy recovery condition and the current battery health attenuation degree; determining the current battery capacity according to the initial battery capacity and the current battery health attenuation degree; determining the maximum energy recovery power of the current battery according to the energy recovery condition and the current battery capacity; and recovering energy according to the maximum energy recovery power. Because the current battery capacity is determined according to the current battery health attenuation degree, and the maximum energy recovery power of the current battery is determined according to the energy recovery condition and the current battery capacity, compared with the prior art, the embodiment can determine the energy recovery power corresponding to each stage of the battery without considering vehicle configuration, the energy recovery power is easy to adjust, and the development verification period is shortened.
Further, the data obtaining module 10 is further configured to obtain a current vehicle running speed, a current driving state, a current brake temperature, and a current battery health attenuation degree; and determining an energy recovery condition according to the current vehicle running speed, the current driving state and the current brake temperature.
Further, the power determining module 30 is further configured to control the current vehicle to enter a self-learning state when the current vehicle driving speed reaches a preset vehicle speed, the current driving state is a deceleration control state, and the current brake temperature is not higher than a preset temperature; and when the current vehicle is in a self-learning state, determining the maximum energy recovery power of the current battery according to the current battery capacity.
Further, the power determining module 30 is further configured to obtain a mapping relationship between the opening degree of the brake pedal and the current vehicle speed when the current vehicle is in a self-learning state; and determining the maximum energy recovery power of the current battery according to the mapping relation and the current battery capacity.
Further, the power determination module 30 is further configured to determine a current brake heat energy according to the current brake temperature; and adjusting the maximum energy recovery power of the current battery according to the heat energy of the current brake, and obtaining the adjusted maximum energy recovery power.
Further, the energy recovery self-learning device further comprises: the heat energy determining module is used for judging whether the current brake temperature is greater than a preset temperature or not; and when the current brake temperature is higher than the preset temperature, obtaining the heat energy of the current brake corresponding to the current brake temperature.
Further, the power determining module 30 is further configured to obtain a difference value between the current brake heat energy and a preset brake heat energy; determining an energy recovery power interval to be increased according to the difference; and adjusting the maximum energy recovery power according to the energy recovery power interval, and obtaining the adjusted maximum energy recovery power.
Furthermore, an embodiment of the present invention further provides a storage medium, on which an energy recovery self-learning program is stored, and the energy recovery self-learning program, when executed by a processor, implements the steps of the energy recovery self-learning method as described above.
It should be understood that the above is only an example, and the technical solution of the present invention is not limited in any way, and in a specific application, a person skilled in the art may set the technical solution as needed, and the present invention is not limited thereto.
It should be noted that the above-described work flows are only exemplary, and do not limit the scope of the present invention, and in practical applications, a person skilled in the art may select some or all of them to achieve the purpose of the solution of the embodiment according to actual needs, and the present invention is not limited herein.
In addition, the technical details that are not elaborated in the embodiment may refer to the energy recovery self-learning method provided by any embodiment of the present invention, and are not described herein again.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The use of the words first, second, third, etc. do not denote any order, but rather the words first, second, third, etc. are to be interpreted as names.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present invention or portions thereof that contribute to the prior art may be embodied in the form of a software product, where the computer software product is stored in a storage medium (e.g., a Read Only Memory (ROM)/Random Access Memory (RAM), a magnetic disk, an optical disk), and includes several instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. An energy recovery self-learning method, characterized in that the energy recovery self-learning method comprises the following steps:
acquiring initial battery health degree, and determining initial capacity of the battery according to the initial battery health degree;
acquiring an energy recovery condition and the current battery health attenuation degree;
determining the current battery capacity according to the initial battery capacity and the current battery health attenuation degree;
when the energy recovery condition meets the condition of entering a self-learning state, determining the maximum energy recovery power of the current battery according to the energy recovery condition and the current battery capacity;
and recovering energy according to the maximum energy recovery power.
2. The energy recovery self-learning method of claim 1 wherein the step of deriving energy recovery conditions and current battery health degradation comprises:
acquiring the current vehicle running speed, the current driving state, the current brake temperature and the current battery health attenuation degree;
and determining an energy recovery condition according to the current vehicle running speed, the current driving state and the current brake temperature.
3. The energy recovery self-learning method according to claim 2, wherein the step of determining the maximum energy recovery power of the current battery based on the energy recovery condition and the current battery capacity comprises:
when the current vehicle running speed reaches a preset speed, the current driving state is a deceleration control state and the current brake temperature is not higher than a preset temperature, controlling the current vehicle to enter a self-learning state;
and when the current vehicle is in a self-learning state, determining the maximum energy recovery power of the current battery according to the current battery capacity.
4. The energy recovery self-learning method of claim 3 wherein the step of determining the maximum energy recovery power of the current battery based on the current battery capacity while the current vehicle is in the self-learning state comprises:
when the current vehicle is in a self-learning state, acquiring a mapping relation between the opening degree of a brake pedal and the current vehicle speed;
and determining the maximum energy recovery power of the current battery according to the mapping relation and the current battery capacity.
5. The energy recovery self-learning method according to claim 2, wherein the step of determining the maximum energy recovery power of the current battery based on the energy recovery condition and the current battery capacity is followed by further comprising:
determining the heat energy of the current brake according to the temperature of the current brake;
and adjusting the maximum energy recovery power of the current battery according to the heat energy of the current brake, and obtaining the adjusted maximum energy recovery power.
6. The energy recovery self-learning method of claim 5, wherein the step of determining a current brake heat energy based on the current brake temperature comprises:
judging whether the current brake temperature is greater than a preset temperature or not;
and when the current brake temperature is higher than the preset temperature, obtaining the heat energy of the current brake corresponding to the current brake temperature.
7. The energy recovery self-learning method according to claim 6, wherein the step of adjusting the current maximum energy recovery power of the battery according to the current brake heat energy and obtaining the adjusted maximum energy recovery power comprises:
acquiring a difference value between the current brake heat energy and preset brake heat energy;
determining an energy recovery power interval to be increased according to the difference;
and adjusting the maximum energy recovery power according to the energy recovery power interval, and obtaining the adjusted maximum energy recovery power.
8. An energy recovery self-learning device, characterized in that the energy recovery self-learning device comprises: memory, a processor and an energy recovery self-learning program stored on the memory and executable on the processor, which when executed by the processor implements the steps of the energy recovery self-learning method according to any one of claims 1 to 7.
9. A storage medium having stored thereon an energy recovery self-learning program which, when executed by a processor, implements the steps of the energy recovery self-learning method according to any one of claims 1 to 7.
10. An energy recovery self-learning device, characterized in that the energy recovery self-learning device comprises:
the data acquisition module is used for acquiring the initial battery health degree and determining the initial capacity of the battery according to the initial battery health degree;
the data acquisition module is also used for acquiring energy recovery conditions and the current battery health attenuation degree;
the capacity determining module is used for determining the current battery capacity according to the initial capacity of the battery and the current battery health attenuation degree;
the power determining module is used for determining the maximum energy recovery power of the current battery according to the energy recovery condition and the current battery capacity when the energy recovery condition meets the condition of entering a self-learning state;
and the energy recovery module is used for recovering energy according to the maximum energy recovery power.
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