CN115257449A - Mixer truck charging control method and system - Google Patents
Mixer truck charging control method and system Download PDFInfo
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- CN115257449A CN115257449A CN202210946294.1A CN202210946294A CN115257449A CN 115257449 A CN115257449 A CN 115257449A CN 202210946294 A CN202210946294 A CN 202210946294A CN 115257449 A CN115257449 A CN 115257449A
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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
- B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
- B60L53/60—Monitoring or controlling charging stations
- B60L53/62—Monitoring or controlling charging stations in response to charging parameters, e.g. current, voltage or electrical charge
-
- 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
- B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
- B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
- B60L58/12—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries responding to state of charge [SoC]
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/0029—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries with safety or protection devices or circuits
- H02J7/00309—Overheat or overtemperature protection
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/0047—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries with monitoring or indicating devices or circuits
- H02J7/0048—Detection of remaining charge capacity or state of charge [SOC]
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/007—Regulation of charging or discharging current or voltage
- H02J7/00712—Regulation of charging or discharging current or voltage the cycle being controlled or terminated in response to electric parameters
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/007—Regulation of charging or discharging current or voltage
- H02J7/007188—Regulation of charging or discharging current or voltage the charge cycle being controlled or terminated in response to non-electric parameters
- H02J7/007192—Regulation of charging or discharging current or voltage the charge cycle being controlled or terminated in response to non-electric parameters in response to temperature
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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
- B60L2200/00—Type of vehicles
- B60L2200/40—Working vehicles
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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
- Y02T10/70—Energy storage systems for electromobility, e.g. batteries
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- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Transportation (AREA)
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- Life Sciences & Earth Sciences (AREA)
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- Sustainable Energy (AREA)
- Charge And Discharge Circuits For Batteries Or The Like (AREA)
Abstract
The application provides a mixer truck charging control method, which comprises the following steps: s1, determining charging capacity parameters of the electric mixer truck; s2, determining a charging trend parameter of the electric mixer truck through big data analysis based on the charging capability parameter and the working condition of the electric mixer truck; and S3, determining the charging control parameters of the electric mixer truck based on the charging trend parameters and the charging capacity parameters of the electric mixer truck. And S4, charging the electric mixer truck based on the charging control parameters. The application also provides a mixer truck charging control system for implementing the mixer truck charging control method. According to the method and the system for controlling the charging of the electric mixer truck, the electric quantity requirements of the electric mixer truck under various specific working conditions can be analyzed through big data, errors caused by manual calculation are avoided to the maximum extent, the electric data of the electric mixer truck is accurately analyzed, and the working efficiency is greatly improved.
Description
Technical Field
The invention relates to the field of engineering vehicles, in particular to a mixer truck charging control method and system.
Background
The agitating lorry is a kind of widely used engineering vehicle, and along with the development of electric motor car technique, more and more agitating lorries are changed into electronic from traditional internal-combustion engine drive, have promoted and have controlled the performance and cleaner environmental protection more nowadays.
The charging efficiency is an important factor influencing the working performance of the electric vehicle, and the electric mixer truck also needs to improve the charging efficiency as much as possible. However, the engineering field often needs to use a plurality of electric mixer trucks to work in coordination, and the electric mixer trucks are usually respectively under various different working conditions and need to be charged by correspondingly adopting different charging control parameters, so that an ideal charging effect can be obtained. Obviously, it is very heavy to want to analyze and determine the corresponding charge control parameter in time to the operating mode of each of a great number of electric mixer cars, and the electric mixer car sets up electric quantity consumption time under the estimated operating mode and actual electric quantity consumption time error usually, and efficiency and accuracy are not ideal if the manual analysis.
Disclosure of Invention
Based on the above problems in the prior art, an object of the present invention is to provide a mixer charging control method and a mixer charging control system for implementing the method, which can respectively perform accurate charging control according to the charging requirements of an electric mixer under various working conditions, implement automatic charging of the electric mixer without manual intervention under transportation working conditions, and improve working efficiency.
In a preferred embodiment of the present invention, a mixer truck charging control method is provided, including the steps of: s1, determining charging capacity parameters of the electric mixer truck; s2, determining a charging trend parameter of the electric mixer truck through big data analysis based on the charging capability parameter and the working condition of the electric mixer truck; s3, determining a charging control parameter of the electric mixer truck based on the charging trend parameter and the charging capability parameter of the electric mixer truck; and S4, charging the electric mixer truck based on the charging control parameters.
In some embodiments, the step S1 comprises: s11, acquiring the residual capacity and the temperature of a vehicle-mounted power supply of the electric mixer truck; and S12, calculating the maximum monomer voltage and the maximum battery temperature which can be reached by the vehicle-mounted power supply according to the residual capacity and the temperature of the vehicle-mounted power supply, and calculating to obtain the generator charging capacity suitable for charging the vehicle-mounted power supply in the current state as the charging capacity parameter of the electric mixer truck.
In some embodiments, the charging capability parameter is a current value of charging an onboard power source of the electric mixer vehicle.
In some embodiments, the step S2 comprises: s21, recording the electric quantity and the charging time required by the electric mixer truck under a specific working condition; and S22, inputting the electric quantity and the charging time required by the electric mixer truck under the specific working condition into a big data model, and counting the charging trend parameters of the electric mixer truck through a big data algorithm.
In some embodiments, the specific conditions include a feed and wait condition, a full load transport condition, a site discharge condition, and an empty return condition.
In some embodiments, the step S3 comprises: s31, comparing the electric quantity demand trend parameter (A) and the charging time trend parameter (B) corresponding to the current specific working condition of the electric mixer truck with the electric quantity demand trend parameter (A (t-1)) and the charging time trend parameter (B (t-1)) of the previous specific working condition of the electric mixer truck; and S32, determining a charging control parameter of the electric mixer truck according to the comparison result and the charging capability parameter of the electric mixer truck.
In some embodiments, the substep S32 comprises: if the comparison result is A > A (t-1) and B > B (t-1) at the same time, multiplying the charging capacity parameter by a preset first correction constant larger than 1 to serve as the charging control parameter; if the comparison result is A < A (t-1) and B < B (t-1), multiplying the charging capability parameter by a preset second correction constant smaller than 1 to serve as the charging control parameter; and if the comparison result is neither A > A (t-1) and B > B (t-1) at the same time, nor A < A (t-1) and B < B (t-1) at the same time, directly using the charging capability parameter as the charging control parameter.
In some embodiments, the step S4 comprises: s41, charging a vehicle-mounted power supply of the electric mixer truck, and controlling corresponding charging conditions based on the charging control parameters in the charging process; and S42, stopping charging when the voltage of the vehicle-mounted power supply of the electric mixer reaches a preset charging cut-off voltage.
In some embodiments, the step S4 further comprises: and when the voltage of the vehicle-mounted power supply of the electric mixer truck reaches a preset charge cut-off temperature, stopping charging.
An embodiment of another aspect of the present application provides a mixer truck charging control system, including: the charging capacity determining module is used for determining charging capacity parameters of the electric mixer truck; the charging trend determining module is used for determining the charging trend parameter of the electric mixer truck through big data analysis based on the charging capacity parameter and the working condition of the electric mixer truck; the charging parameter control module is used for determining charging control parameters of the electric mixer truck based on the charging trend parameters and the charging capacity parameters of the electric mixer truck; and the charging module is used for charging the electric mixer truck based on the charging control parameters.
Compared with the prior art, the mixer truck charging control method and system provided by the preferred embodiment of the invention can analyze the electric quantity requirements of the electric mixer truck under various specific working conditions through big data, avoid errors caused by manual calculation to the maximum extent, and can substitute manual work to reasonably use the electric quantity under various specific working conditions. Its power consumption data analysis to electric mixer is accurate, and can accomplish under electric mixer's the transport state and charge, has greatly improved work efficiency, is favorable to optimizing the configuration of electric mixer moreover, is favorable to promoting working property.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and those skilled in the art can also obtain other related drawings based on the drawings without inventive efforts.
Fig. 1 is a flowchart of a mixer truck charging control method according to a preferred embodiment of the present disclosure.
Fig. 2 is a flowchart of the detailed operation of step S1 in the mixer truck charging control method shown in fig. 1.
Fig. 3 is a flowchart of the detailed operation of step S2 in the mixer charging control method shown in fig. 1.
Fig. 4 is a flowchart illustrating a specific operation of step S3 in the mixer truck charging control method shown in fig. 1.
Fig. 5 is a flowchart of the specific operation of step S4 in the mixer charging control method shown in fig. 1.
Fig. 6 is a block diagram illustrating functional modules of a mixer truck charging control system according to a preferred embodiment of the present application.
Detailed Description
Specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be apparent that the described embodiments are only some of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the description of the invention without inventive step, are within the scope of protection of the invention.
A preferred embodiment of the present application provides a method for controlling mixer truck charging, which can be used to determine appropriate charging control parameters according to various specific operating conditions of an electric mixer truck to control the charging operation of the electric mixer truck through big data analysis, so as to ensure that the electric mixer truck obtains an ideal charging effect, and greatly improve efficiency and accuracy compared with manual operation.
Referring to fig. 1, the mixer truck charging control method includes the following steps:
s1, determining charging capability parameters of the electric mixer truck.
And S2, determining a charging trend parameter of the electric mixer truck through big data analysis based on the charging capability parameter and the working condition of the electric mixer truck.
And S3, determining a charging control parameter of the electric mixer truck based on the charging trend parameter and the charging capability parameter of the electric mixer truck.
And S4, charging the electric mixer truck based on the charging control parameters.
The steps S1 to S4 will be specifically described below with reference to fig. 2 to 5.
Referring to fig. 2, the step S1 may specifically include the following sub-steps:
s11, acquiring the residual capacity (SOC) and the temperature of the vehicle-mounted power supply of the electric mixer truck in real time. The SOC and the current temperature of the vehicle-mounted power supply can be acquired in real time by a Battery Management System (BMS) of the vehicle-mounted power supply, and a specific acquisition mode can be according to the prior art, which is not described herein.
S12, calculating the maximum cell voltage (MaxCell) and the maximum battery temperature (MaxCell) which can be reached by the vehicle-mounted power supply according to the SOC and the temperature of the vehicle-mounted power supply, calculating the charging capacity (ChrgI) of the generator suitable for charging the vehicle-mounted power supply in the current state, and taking the calculated charging capacity (ChrgI) of the generator as a charging capacity parameter of the electric mixer truck. In the present embodiment, the charging capability of the generator (ChrgI) is preferably characterized by the current value of the charging of the vehicle-mounted power supply by the generator, that is, the charging capability parameter of the electric mixer is actually the current value of the charging of the vehicle-mounted power supply by the generator. In other embodiments, other parameters may be adopted as the charging capability parameter of the electric mixer vehicle, such as a voltage value, a power value, and the like when the vehicle-mounted power supply of the electric mixer vehicle is charged by the generator. The generator charging capacity (ChrgI) may be an ideal value calculated further based on the maximum cell voltage MaxCellU) and the maximum battery temperature MaxCellU) or a value calculated by other means such as big data analysis to be closer to the actual situation.
In a specific implementation, the calculation operation of the substep S12 may be performed by the processor of the central control system of the electric mixer truck itself obtaining the SOC and the temperature data of the vehicle-mounted power supply from the BMS, or may be performed by the BMS remotely sending the SOC and the temperature data of the vehicle-mounted power supply to an external processing device, such as a vehicle dispatching server or a cloud.
Referring to fig. 3, the step S2 may specifically include the following sub-steps:
and S21, recording the required electric quantity (ChrgM) and the charging time (ChrgT (h)) of the electric mixer truck under each specific working condition.
In actual use, the electric mixer vehicle may be under a variety of specific conditions, such as a feed and wait condition, a full load transport condition, a worksite discharge condition, an empty return condition, and so forth. Because different specific working conditions respectively correspond to different stages in a complete transportation working cycle (called as a cycle working condition for short) of the mixer truck, the electric mixer trucks under different specific working conditions respectively need different electric quantities to complete at least one cycle working condition, and correspondingly need different charging time. In this embodiment, the electric mixer may be a preset reference value, an actual electric quantity and charging time value recorded in a previous working process of the electric mixer, or an average value calculated after recording the actual electric quantity and charging time value for a plurality of times, or a reference value generated through big data analysis after collecting sufficient samples of the electric quantity and charging time data corresponding to the specific working conditions of the electric mixer through a network. In addition, in some embodiments, the electric quantity (ChrgM) required by the electric mixer vehicle under each specific operating condition may be set by the above means, and then the charging time (ChrgT (h)) required by the electric mixer vehicle under each specific operating condition may be calculated according to the electric quantity (ChrgM) required by the electric mixer vehicle under each specific operating condition and the charging capability parameter (i.e. ChrgI) of the electric mixer vehicle determined in step S1.
S22, inputting the required electric quantity (ChrgM) and the required charging time (ChrgT (h)) of the electric mixer truck under each specific working condition into a big data model, and counting the charging trend parameters of the electric mixer truck through a big data algorithm.
In the embodiment, the charging trend parameters of the electric mixer truck comprise an electric quantity demand trend parameter (a) and a charging time trend parameter (B) corresponding to the electric mixer truck under each specific working condition. In some embodiments, the electric quantity demand trend parameter (a) and the charging time trend parameter (B) may be values of electric quantity and charging time required by the electric mixer vehicle under a specific working condition; in other embodiments, the trend parameter of demand for electric energy (a) and the trend parameter of charging time (B) may also be a rate of change of the electric quantity and the charging time required by the electric mixer vehicle under a specific condition or other data capable of representing a trend of the electric quantity and the charging time. The electric quantity demand trend parameter (A) and the charging time trend parameter (B) can be calculated and obtained by utilizing a big data model in a cloud background.
Referring to fig. 4, the step S3 may specifically include the following sub-steps:
s31, comparing the electric quantity demand trend parameter (A) and the charging time trend parameter (B) corresponding to the current specific working condition of the electric mixer truck with the electric quantity demand trend parameter (A (t-1)) and the charging time trend parameter (B (t-1)) of the previous specific working condition which is arranged one bit before the current specific working condition in the circulating working condition of the electric mixer truck. Wherein t represents the ordinal number of a particular condition in the cycle conditions of the electric mixer vehicle.
And S32, determining the charging control parameter of the electric mixer according to the comparison result and the charging capability parameter of the electric mixer.
In this embodiment, the substep S32 may include the following specific operations:
if the comparison result is A > A (t-1) and B > B (t-1), correspondingly determining that the electric quantity demand trend and the charging time trend of the electric mixer truck in the current specific working condition are both greater than the electric quantity demand trend and the charging time trend in the previous specific working condition, namely the electric quantity and the charging time required by the electric mixer truck in the current cycle working condition tend to increase. In this case, a correction is made to determine the charging control parameters of the electric mixer vehicle on the basis of the charging capability parameters (ChrgI) determined previously. The specific correction method may be to multiply the charging capability parameter (ChrgI) by a preset first correction constant a greater than 1 as the charging control parameter (ReqChrgI), i.e., reqChrgI = ChrgI a, where a preferably ranges from 1.1 to 1.5, and the specific value may be determined by a big data model analysis.
And if the comparison result is that A is less than A (t-1) and B is less than B (t-1), correspondingly determining that the electric quantity demand trend and the charging time trend of the electric mixer truck in the current specific working condition are both less than the electric quantity demand trend and the charging time trend in the previous specific working condition, namely that the electric quantity and the charging time required by the electric mixer truck in the current cycle working condition tend to be reduced. In this case, a correction is made to determine the charging control parameters of the electric mixer vehicle on the basis of the charging capability parameters (ChrgI) determined previously. The specific correction method may be to multiply the charging capability parameter (ChrgI) by a preset second correction constant b smaller than 1 to obtain a charging control parameter (ReqChrgI), i.e., reqChrgI = ChrgI b, where b preferably ranges from 0.6 to 0.9, and a specific value may be determined by a big data model analysis.
And if the comparison result is neither A > A (t-1) and B > B (t-1) at the same time, nor A < A (t-1) and B < B (t-1) at the same time, correspondingly determining that the electric quantity demand trend and the charging time trend of the electric mixer truck in the current specific working condition are not greater than or less than the electric quantity demand trend and the charging time trend in the previous specific working condition at the same time, and further determining that the electric quantity and the charging time required by the electric mixer truck in the current cycle working condition do not tend to increase or decrease at the same time. In this case, the previously determined charging capability parameter (ChrgI) is directly used as the charging control parameter (ReqChrgI), i.e. ReqChrgI = ChrgI.
It is to be understood that, since the charging capability parameter (ChrgI) of the electric mixer in the present embodiment is a current value at which the vehicle-mounted power source of the electric mixer is charged by the generator, the charging control parameter determined according to the above operation is actually an adjusted charging current value, in which the charging control parameter (ReqChrgI) determined according to ReqChrgI = ChrgI a is a current value larger than the charging capability parameter (ChrgI) and the charging control parameter (ReqChrgI) determined according to ReqChrgI = ChrgI b is a current value smaller than the charging capability parameter (ChrgI).
Referring to fig. 5, the step S4 may include the following sub-steps:
s41, charging a vehicle-mounted power supply of the electric mixer truck through a generator, and controlling corresponding charging conditions based on the charging control parameters (ReqChrgi) in the charging process. In this embodiment, since the charging control parameter (ReqChrgI) is the charging current, the specific operation of the sub-step S41 is to control the charging current to the charging control parameter (ReqChrgI). It is understood that in the case where the comparison result in the foregoing sub-step S22 is a > a (t-1) and B > B (t-1), controlling the charging current to the charging control parameter (ReqChrgI) is equivalent to increasing the charging current appropriately, so as to meet the requirement that the required electric quantity and the charging time of the electric mixer vehicle in the present cycle condition tend to increase. In the case where the comparison result in the aforementioned sub-step S22 is a < a (t-1) and at the same time B < B (t-1), controlling the charging current to the charging control parameter (ReqChrgI) is equivalent to appropriately decreasing the charging current, so as to satisfy the requirement that the electric quantity and the charging time required by the electric mixer vehicle in the present cycle condition tend to decrease. In the case that the comparison result in the aforementioned sub-step S22 is otherwise, the charging control parameter (ReqChrgI) is the charging capability parameter (ChrgI) determined before, which is equivalent to maintaining the supplied charging current of the generator in the initial state to maintain the stable and controllable charging state. Through the operation of controlling the charging parameters, the charging parameters can be automatically controlled according to the requirement of the current specific working condition of the electric mixer truck on the charging condition, and the charging efficiency is improved. And the charging parameters controlled by the corresponding operation under various conditions are of the same type (for example, the charging current in the embodiment), so that different charging operation modes can be avoided under different working conditions, inconsistent power supply application modes caused by different working conditions are also avoided, and the simplification and standardization of the charging work of the electric mixer truck are facilitated.
And S42, stopping charging when the voltage of the vehicle-mounted power supply of the electric mixer car reaches a preset charge cut-off voltage. The charge cutoff voltage may be the aforementioned maximum cell voltage (MaxCellU), or may be a specific voltage value set according to other standards.
In some other embodiments, the step S4 may further include the following sub-steps: and when the voltage of the vehicle-mounted power supply of the electric mixer truck reaches a preset charging cut-off temperature, stopping charging. The charge cutoff temperature may be the aforementioned maximum battery temperature (MatT), or may be a specific temperature set according to other standards.
The electric quantity demand that the trucd mixer that above-mentioned preferred embodiment of this application provided can be in under various specific operating mode through big data analysis electric trucd mixer, and the error that manual work caused is avoided furthest, can carry out rational use to the electric quantity of various specific operating modes in the agent manual work. Its power consumption data analysis to electric mixer is accurate, and can accomplish under electric mixer's the transport state and charge, has greatly improved work efficiency, is favorable to optimizing the configuration of electric mixer moreover, is favorable to promoting working property.
Referring to fig. 6, a mixer truck charging control system according to another aspect of the present disclosure is further provided, which can be used to implement the mixer truck charging control method provided in the foregoing embodiments. The mixer truck charging control system may include:
the charging capability determining module 10 is used for determining a charging capability parameter of the electric mixer truck.
And the charging trend determining module 20 is configured to determine a charging trend parameter of the electric mixer truck through big data analysis based on the charging capability parameter and the working condition of the electric mixer truck.
The charging parameter control module 30 is configured to determine a charging control parameter of the electric mixer truck based on the charging trend parameter and the charging capability parameter of the electric mixer truck.
And the charging module 40 is used for charging the electric mixer truck based on the charging control parameters.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are also within the scope of the present invention. Therefore, the protection scope of the present invention should be subject to the appended claims.
Claims (10)
1. The charging control method of the mixer truck is characterized by comprising the following steps of:
s1, determining charging capacity parameters of the electric mixer truck;
s2, determining a charging trend parameter of the electric mixer truck through big data analysis based on the charging capability parameter and the working condition of the electric mixer truck;
s3, determining a charging control parameter of the electric mixer truck based on the charging trend parameter and the charging capability parameter of the electric mixer truck;
and S4, charging the electric mixer truck based on the charging control parameters.
2. The method of claim 1, wherein the step S1 comprises:
s11, acquiring the residual capacity and the temperature of a vehicle-mounted power supply of the electric mixer truck;
and S12, calculating the maximum monomer voltage and the maximum battery temperature which can be reached by the vehicle-mounted power supply according to the residual capacity and the temperature of the vehicle-mounted power supply, and calculating to obtain the generator charging capacity suitable for charging the vehicle-mounted power supply in the current state as the charging capacity parameter of the electric mixer truck.
3. The method of claim 2, wherein the charging capability parameter is a current value to charge an onboard power source of the electric mixer vehicle.
4. The method of claim 1, wherein the step S2 comprises:
s21, recording the electric quantity and the charging time required by the electric mixer under a specific working condition;
and S22, inputting the electric quantity and the charging time required by the electric mixer under the specific working condition into a big data model, and counting the charging trend parameters of the electric mixer through a big data algorithm.
5. The method of claim 4, wherein the specific conditions include a feed and wait condition, a full load transport condition, a worksite discharge condition, and an empty return condition.
6. The method of claim 1, wherein the step S3 comprises:
s31, comparing the electric quantity demand trend parameter (A) and the charging time trend parameter (B) corresponding to the current specific working condition of the electric mixer truck with the electric quantity demand trend parameter (A (t-1)) and the charging time trend parameter (B (t-1)) of the previous specific working condition of the electric mixer truck;
and S32, determining a charging control parameter of the electric mixer truck according to the comparison result and the charging capability parameter of the electric mixer truck.
7. The method of claim 6, wherein the substep S32 comprises:
if the comparison result is A > A (t-1) and B > B (t-1), multiplying the charging capacity parameter by a preset first correction constant larger than 1 to serve as the charging control parameter;
if the comparison result is A < A (t-1) and B < B (t-1), multiplying the charging capability parameter by a preset second correction constant smaller than 1 to serve as the charging control parameter;
and if the comparison result is neither A > A (t-1) and B > B (t-1) at the same time, nor A < A (t-1) and B < B (t-1) at the same time, directly using the charging capability parameter as the charging control parameter.
8. The method of claim 1, wherein the step S4 comprises:
s41, charging a vehicle-mounted power supply of the electric mixer truck, and controlling corresponding charging conditions based on the charging control parameters in the charging process;
and S42, stopping charging when the voltage of the vehicle-mounted power supply of the electric mixer reaches a preset charging cut-off voltage.
9. The method of claim 8, wherein the step S4 further comprises: and when the voltage of the vehicle-mounted power supply of the electric mixer truck reaches a preset charging cut-off temperature, stopping charging.
10. The utility model provides a mixer truck charge control system which characterized in that includes:
the charging capacity determining module is used for determining charging capacity parameters of the electric mixer truck;
the charging trend determining module is used for determining the charging trend parameters of the electric mixer truck through big data analysis based on the charging capability parameters and the working conditions of the electric mixer truck;
the charging parameter control module is used for determining charging control parameters of the electric mixer truck based on the charging trend parameters and the charging capacity parameters of the electric mixer truck;
and the charging module is used for charging the electric mixer truck based on the charging control parameters.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107234982A (en) * | 2017-08-14 | 2017-10-10 | 成都雅骏新能源汽车科技股份有限公司 | A kind of power battery charging method counted based on big data |
| CN110866686A (en) * | 2019-11-07 | 2020-03-06 | 广州供电局有限公司 | Charging load evaluation method and device, computer equipment and storage medium |
| CN112319246A (en) * | 2020-11-10 | 2021-02-05 | 王文君 | Range-extending type whole vehicle fuel-saving optimization method |
| CN113200037A (en) * | 2021-06-18 | 2021-08-03 | 一汽解放青岛汽车有限公司 | Battery energy management method for hybrid power cement mixer |
| US20220123578A1 (en) * | 2020-04-23 | 2022-04-21 | Contemporary Amperex Technology Co., Limited | Battery charging control method and apparatus, battery management system, and medium |
-
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- 2022-08-08 CN CN202210946294.1A patent/CN115257449B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107234982A (en) * | 2017-08-14 | 2017-10-10 | 成都雅骏新能源汽车科技股份有限公司 | A kind of power battery charging method counted based on big data |
| CN110866686A (en) * | 2019-11-07 | 2020-03-06 | 广州供电局有限公司 | Charging load evaluation method and device, computer equipment and storage medium |
| US20220123578A1 (en) * | 2020-04-23 | 2022-04-21 | Contemporary Amperex Technology Co., Limited | Battery charging control method and apparatus, battery management system, and medium |
| CN112319246A (en) * | 2020-11-10 | 2021-02-05 | 王文君 | Range-extending type whole vehicle fuel-saving optimization method |
| CN113200037A (en) * | 2021-06-18 | 2021-08-03 | 一汽解放青岛汽车有限公司 | Battery energy management method for hybrid power cement mixer |
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