CN112285568B - Method for estimating residual discharge time based on energy state of power lithium battery - Google Patents
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/385—Arrangements for measuring battery or accumulator variables
- G01R31/387—Determining ampere-hour charge capacity or SoC
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/392—Determining battery ageing or deterioration, e.g. state of health
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Abstract
The invention relates to the technical field of battery state estimation algorithms, in particular to a method for estimating the residual discharge time based on the energy state of a power lithium battery. The method comprises the following steps: calculating a battery SOE through an AUPF algorithm; identifying the driving behavior of a driver through a BP neural network; calculating an estimated value of the RDT at the current moment by using the SOE result; optimizing the discharging strategy of the battery at the future moment according to the result of the steps; and sequentially calculating the SOE size and the RDT at the next moment until the working condition is finished. According to the invention, SOE is calculated through an AUPF algorithm, so that the accuracy of the algorithm can be improved, and RDT estimation errors caused by inaccuracy of state parameters are reduced; meanwhile, the change of the battery energy state is considered, so that the estimation accuracy can be further improved; in addition, the battery is discharged and optimized, so that the running state of the electric automobile can be improved, the over-discharge condition of the battery is prevented, the battery is well protected, and the service life of the battery is prolonged.
Description
Technical Field
The invention relates to the technical field of battery state estimation algorithms, in particular to a method for estimating the residual discharge time based on the energy state of a power lithium battery.
Background
At present, research on RDT estimation of electric vehicle power batteries is mostly focused on researching the relationship between the residual discharge time and the battery state, and the residual discharge time of the battery is obtained by using the state parameters of the battery. The traditional RDT estimation method still has the defects that firstly, the accuracy of a battery state estimation algorithm inevitably influences the estimation accuracy of the subsequent RDT, and the accuracy of the battery state estimation algorithm must be improved before the battery RDT is estimated; meanwhile, the most battery parameter used in the current estimation of the RDT is the SOC of the battery, the SOC reflects the charge state of the battery, and the voltage change of the battery is ignored, so that only the change of the SOC of the battery is considered in the estimation of the RDT, and a larger estimation error is often caused by the change of the ignored voltage; in addition, after estimating the RDT, few studies have been conducted to optimize the discharging strategy of the power battery at the future time according to the RDT size at the current time and other factors so as to improve the working state of the battery.
Disclosure of Invention
The invention aims to provide a method for estimating the residual discharge time based on the energy state of a power lithium battery, so as to solve the problems in the prior art.
In order to solve the above technical problems, one of the purposes of the present invention is to provide a method for estimating the residual discharge time based on the energy state of a power lithium battery, comprising the following steps:
s1, carrying out calculation of transfer probability on particles at each moment by using a battery SOE estimation algorithm based on UPF, and transferring particles with smaller weight to particles with better weight by using an AUPF algorithm;
s2, identifying the driving behavior of a driver through a BP neural network according to the driving condition and the speed of the automobile at the current moment;
s3, calculating an estimated value of the RDT at the current moment by using the SOE data result obtained in the S1;
s4, optimizing a discharging strategy of the battery at the future moment according to the driving behavior of the current driver, the RDT size at the current moment, the driving condition of the automobile and the SOE size;
s5, returning to the step S1, and calculating the SOE size and the RDT of the next moment until the working condition is ended.
The battery SOE is the energy state of the battery, UPF is unscented particle filter, AUPF algorithm is ant colony unscented particle filter algorithm, and RDT is the residual discharge time of the battery.
As a further improvement of the technical scheme, in the S1, the AUPF can utilize the characteristic that individuals in the ant colony algorithm gradually approach to the optimal solution, so that the particle diversity of UPF is enhanced on the premise of not increasing the particle number, and the estimation accuracy and the robustness are improved.
As a further improvement of the present technical solution, in S1, the method for estimating SOE by the AUPF algorithm includes the following steps:
s1.1, building an equivalent circuit model for estimating an SOE estimation algorithm, and identifying parameters of a battery model;
s1.2, initializing particles, and generating particles according to the initial probability density;
s1.3, measuring and updating UFK time to generate more accurate posterior probability distribution;
s1.4, resampling operation is carried out through an ant colony;
s1.5, ending SOE estimation at the current moment;
s1.6, judging whether the working condition is ended, if not, returning to S1.1, and performing operation of estimating the SOE at the next moment, and if so, ending the algorithm.
As a further improvement of the present technical solution, in S1, a calculation formula of SOE is:
wherein, the SOR of the battery represents the ratio of the residual energy of the battery to the bearing energy of the battery when the battery is full under the condition of constant current-constant voltage charging, and E is shown in the formula (1) res For remaining battery energy, E N For fully loading the battery, U oc (. Cndot.) is the open circuit voltage of the battery, is a function of SOC, C N Is the battery capacity; SOE is defined as the energy loss H caused by the SOR of the battery and the charge/discharge of the battery to the charge/discharge cut-off voltage under certain working conditions nt The difference is in the formula (2), E h The energy is lost to the battery, which depends on the battery impedance and operating conditions.
As a further improvement of the present technical solution, in S2, the method for identifying the driving behavior of the driver through the BP neural network includes the following steps:
s2.1, respectively identifying the traffic flow of the current driving road section, the highest speed allowed by the current road section, the gradient of the current road section and the driving track of the automobile by utilizing a wavelet neural network according to the travel working condition and the speed of the automobile at the current moment;
s2.2, inputting the identified result into the BP neural network, and identifying the driving behavior of the current driver;
and S2.3, feeding back driving behaviors of the driver to the battery management system.
As a further improvement of the present technical solution, in S3, the method for calculating the RDT estimation value by using SOE includes the following steps:
s3.1, calculating the discharge power of the battery at the moment by utilizing the relationship between the discharge power and SOE, the rated energy and voltage of the battery;
s3.2, calculating the RDT of the battery at the current moment according to the relation among the discharge power, the battery rated energy and the SOE;
s3.3, feeding back the calculation result to the battery management system;
s3.4, optimizing a discharging strategy of the battery at the future moment according to the driving behavior of the current driver, the RDT size at the current moment, the driving condition of the automobile and the SOE size;
s3.5, returning to the step S1, and calculating the SOE size and the RDT of the next moment until the working condition is ended.
As a further improvement of the present technical solution, in S3, the conventional estimation method of the residual discharge time RDT based on the SOC of the state of charge is improved, the SOC is replaced by SOE, the change of the battery voltage is considered, and the size of the battery SOE estimated by the AUPF algorithm is used as a parameter to be input into the improved method, so that the defect that the battery energy state is ignored in the conventional estimation method is overcome, and the accuracy of estimating RDT is improved.
As a further improvement of the present technical solution, in S4, the method for optimizing the discharge strategy of the battery at the future time includes the following steps:
s4.1, setting lower limit values for RDT and SOE before optimization;
s4.2, judging whether the sizes of the current SOE and RDT exceed a lower limit value;
s4.3, if one or more than one of the two battery packs exceeds the lower limit value, immediately sending a warning to the whole vehicle controller, and judging whether the battery pack needs to be immediately reduced in output at the next moment by a driver;
s4.4, if the two items do not exceed the lower limit value, judging whether the current automobile runs on a road surface which needs to climb a slope or is steeper according to the driving behavior of the current driver and the running condition of the automobile;
s4.5, if so, calculating the maximum output current and the maximum output voltage according to the current SOE and sending the maximum output current and the maximum output voltage to the whole vehicle controller;
and S4.6, if the vehicle is on a gentle road surface, calculating the output voltage and current which can prolong the RDT size at the future moment as far as possible according to the current SOE size, and sending a signal to the whole vehicle controller.
As a further improvement of the technical scheme, in the step S4, after estimating the size of the battery RDT at the current moment, the power battery discharging strategy may be optimized according to factors such as the driving behavior of the driver and the driving condition of the automobile, and meanwhile, the dynamic performance of the automobile may be increased or the RDT of the battery may be prolonged according to the actual situation, and the over-discharge of the battery may be prevented.
Another object of the present invention is to provide an apparatus for estimating a remaining discharge time based on an energy state of a power lithium battery, comprising a processor, a memory, and a computer program stored in the memory and running on the processor, wherein the processor is configured to implement any one of the above-mentioned methods for estimating a remaining discharge time based on an energy state of a power lithium battery when the computer program is executed.
A third object of the present invention is to provide a computer program stored in the computer readable storage medium, characterized in that: the computer program when executed by a processor implements the steps of any of the methods for estimating the remaining discharge time based on the energy state of a power lithium battery described above.
Compared with the prior art, the invention has the beneficial effects that: according to the method for estimating the residual discharge time based on the energy state of the power lithium battery, an AUPF algorithm developed by UPF is provided, and on the premise that the number of particles does not need to be increased, particles with lower weight are gradually transferred to particles with higher weight by utilizing the thought of an ant colony algorithm, so that the accuracy of the algorithm can be improved, and RDT estimation errors caused by inaccuracy of state parameters are reduced; meanwhile, the method for estimating the RDT based on the SOE considers the change of the battery energy state, so that the estimation accuracy can be further improved; in addition, after the RDT is estimated, the discharging optimization is carried out according to the driving behavior of a driver, the travel working condition of the automobile and the sizes of the RDT and the SOE at the current moment, so that the running state of the electric automobile can be improved, the over-discharging condition of the battery is prevented, the battery is well protected, and the service life of the battery is prolonged.
Drawings
FIG. 1 is a block diagram of the overall process of the present invention;
FIG. 2 is a flow chart of a method for estimating SOE by AUPF algorithm in the present invention;
FIG. 3 is a schematic diagram of an exemplary cycle for SOE estimation by the AUPF algorithm in accordance with the present invention;
FIG. 4 is a flow chart of a method for identifying driving behavior of a driver based on a BP neural network in the present invention;
FIG. 5 is a schematic diagram of an exemplary cycle for identifying driver driving behavior based on BP neural network in the present invention;
FIG. 6 is a flow chart of a SOE-based method for estimating RDT according to the present invention;
FIG. 7 is an exemplary cyclic schematic of SOE-based estimation RDT method of the present invention;
FIG. 8 is a flow chart of a method of SOE and RDT based discharge optimization strategy according to the present invention;
FIG. 9 is a schematic diagram of an exemplary cycle of SOE and RDT based discharge optimization strategy in accordance with the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Method embodiment
As shown in fig. 1-7, an object of the present embodiment is to provide a method for estimating a remaining discharge time based on an energy state of a power lithium battery, which includes the following steps:
s1, carrying out calculation of transfer probability on particles at each moment by using a battery SOE estimation algorithm based on UPF, and transferring particles with smaller weight to particles with better weight by using an AUPF algorithm;
s2, identifying the driving behavior of a driver through a BP neural network according to the driving condition and the speed of the automobile at the current moment;
s3, calculating an estimated value of the RDT at the current moment by using the SOE data result obtained in the S1;
s4, optimizing a discharging strategy of the battery at the future moment according to the driving behavior of the current driver, the RDT size at the current moment, the driving condition of the automobile and the SOE size;
s5, returning to the step S1, and calculating the SOE size and the RDT of the next moment until the working condition is ended.
The battery SOE is the energy state of the battery, UPF is unscented particle filter, AUPF algorithm is ant colony unscented particle filter algorithm, and RDT is the residual discharge time of the battery.
Specifically, in S1, the AUPF may utilize the characteristic that the individual in the ant colony algorithm gradually approaches to the optimal solution, so as to enhance particle diversity of the UPF without increasing the number of particles, and improve estimation accuracy and robustness.
In this embodiment, in S1, the method for estimating SOE by the AUPF algorithm includes the following steps:
s1.1, building an equivalent circuit model for estimating an SOE estimation algorithm, and identifying parameters of a battery model;
s1.2, initializing particles, and generating particles according to the initial probability density;
s1.3, measuring and updating UFK time to generate more accurate posterior probability distribution;
s1.4, resampling operation is carried out through an ant colony;
s1.5, ending SOE estimation at the current moment;
s1.6, judging whether the working condition is ended, if not, returning to S1.1, and performing operation of estimating the SOE at the next moment, and if so, ending the algorithm.
Further, in S1, the calculation formula of SOE is:
wherein, the SOR of the battery represents the ratio of the residual energy of the battery to the bearing energy of the battery when the battery is full under the condition of constant current-constant voltage charging, and E is shown in the formula (1) res For remaining battery energy, E N For fully loading the battery, U oc (. Cndot.) is the open circuit voltage of the battery, is a function of SOC, C N Is the battery capacity; SOE is defined as the energy loss H caused by the SOR of the battery and the charge/discharge of the battery to the charge/discharge cut-off voltage under certain working conditions nt The difference is in the formula (2), E h The energy is lost to the battery, which depends on the battery impedance and operating conditions.
In this embodiment, in S2, the method for identifying the driving behavior of the driver through the BP neural network includes the following steps:
s2.1, respectively identifying the traffic flow of the current driving road section, the highest speed allowed by the current road section, the gradient of the current road section and the driving track of the automobile by utilizing a wavelet neural network according to the travel working condition and the speed of the automobile at the current moment;
s2.2, inputting the identified result into the BP neural network, and identifying the driving behavior of the current driver;
and S2.3, feeding back driving behaviors of the driver to the battery management system.
In this embodiment, in S3, the method for calculating the RDT estimation value by using SOE includes the following steps:
s3.1, calculating the discharge power of the battery at the moment by utilizing the relationship between the discharge power and SOE, the rated energy and voltage of the battery;
s3.2, calculating the RDT of the battery at the current moment according to the relation among the discharge power, the battery rated energy and the SOE;
s3.3, feeding back the calculation result to the battery management system;
s3.4, optimizing a discharging strategy of the battery at the future moment according to the driving behavior of the current driver, the RDT size at the current moment, the driving condition of the automobile and the SOE size;
s3.5, returning to the step S1, and calculating the SOE size and the RDT of the next moment until the working condition is ended.
Specifically, in S3, the conventional estimation method of the residual discharge time RDT based on the state of charge SOC is improved, the SOC is replaced by SOE, the change of the battery voltage is considered, and the battery SOE estimated by the AUPF algorithm is used as a parameter to be input into the improved method, so that the defect that the battery energy state is ignored in the conventional estimation method is overcome, and the accuracy of estimating the RDT is improved.
In this embodiment, in S4, the method for optimizing the discharge strategy of the battery at the future time includes the following steps:
s4.1, setting lower limit values for RDT and SOE before optimization;
s4.2, judging whether the sizes of the current SOE and RDT exceed a lower limit value;
s4.3, if one or more than one of the two battery packs exceeds the lower limit value, immediately sending a warning to the whole vehicle controller, and judging whether the battery pack needs to be immediately reduced in output at the next moment by a driver;
s4.4, if the two items do not exceed the lower limit value, judging whether the current automobile runs on a road surface which needs to climb a slope or is steeper according to the driving behavior of the current driver and the running condition of the automobile;
s4.5, if so, calculating the maximum output current and the maximum output voltage according to the current SOE and sending the maximum output current and the maximum output voltage to the whole vehicle controller;
and S4.6, if the vehicle is on a gentle road surface, calculating the output voltage and current which can prolong the RDT size at the future moment as far as possible according to the current SOE size, and sending a signal to the whole vehicle controller.
Specifically, in S4, after estimating the size of the RDT of the battery at the current time, the power battery discharging strategy may be optimized according to factors such as the driving behavior of the driver and the driving condition of the vehicle, and meanwhile, the power performance of the vehicle may be increased or the RDT of the battery may be prolonged according to the actual situation, and the over-discharge of the battery may be prevented.
Electronic device embodiment
It is an object of this embodiment to provide an apparatus for estimating a remaining discharge time based on an energy state of a power lithium battery, the apparatus comprising a processor, a memory and a computer program stored in the memory and running on the processor.
The processor comprises one or more than one processing core, the processor is connected with the processor through a bus, the memory is used for storing program instructions, and the processor realizes the method for estimating the residual discharge time based on the energy state of the power lithium battery when executing the program instructions in the memory.
Alternatively, the memory may be implemented by any type or combination of volatile or nonvolatile memory devices such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disk.
In addition, the invention also provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program realizes the steps of the method for estimating the residual discharge time based on the energy state of the power lithium battery when being executed by a processor.
Optionally, the present invention also provides a computer program product containing instructions which, when run on a computer, cause the computer to perform the steps of the above aspects of a method for estimating the remaining discharge time based on the energy state of a power lithium battery.
It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by hardware related to a program for instructing a computer, where the program may be stored on a computer readable storage medium, and the above storage medium may be a read only memory, a magnetic disk or an optical disk, etc.
The foregoing has shown and described the basic principles, principal features and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the above-described embodiments, and that the above-described embodiments and descriptions are only preferred embodiments of the present invention, and are not intended to limit the invention, and that various changes and modifications may be made therein without departing from the spirit and scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (2)
1. The method for estimating the residual discharge time RDT based on the energy state of the power lithium battery is characterized by comprising the following steps of: the method comprises the following steps:
s1, carrying out calculation of transfer probability on particles at each moment by using a battery SOE estimation algorithm based on UPF, and transferring particles with smaller weight to particles with better weight by using an AUPF algorithm;
s2, identifying the driving behavior of a driver through a BP neural network according to the driving condition and the speed of the automobile at the current moment;
s3, calculating an estimated value of the RDT at the current moment by using the SOE data result obtained in the S1;
s4, optimizing a discharging strategy of the battery at the future moment according to the driving behavior of the current driver, the RDT size at the current moment, the driving condition of the automobile and the SOE size;
s5, returning to the S1, and calculating the SOE size and the RDT at the next moment until the working condition is finished;
in the step S1, the estimation algorithm of the battery SOE includes the following steps:
s1.1, building an equivalent circuit model for a battery SOE estimation algorithm, and identifying parameters of the battery model;
s1.2, initializing particles, and generating particles according to the initial probability density;
s1.3, measuring and updating UFK time to generate more accurate posterior probability distribution;
s1.4, resampling operation is carried out through an ant colony;
s1.5, ending SOE estimation at the current moment;
s1.6, judging whether the working condition is ended, if not, returning to S1.1, and performing operation of estimating SOE at the next moment, if so, ending the algorithm;
in S2, the method for identifying the driving behavior of the driver through the BP neural network includes the following steps:
s2.1, respectively identifying the traffic flow of the current driving road section, the highest speed allowed by the current road section, the gradient of the current road section and the driving track of the automobile by utilizing a wavelet neural network according to the travel working condition and the speed of the automobile at the current moment;
s2.2, inputting the identified result into the BP neural network, and identifying the driving behavior of the current driver;
s2.3, feeding back driving behaviors of a driver to the battery management system;
in S4, the method for optimizing the discharge strategy of the battery at the future time includes the following steps:
s4.1, setting lower limit values for RDT and SOE before optimization;
s4.2, judging whether the sizes of the current SOE and RDT exceed a lower limit value;
s4.3, if one or more than one of the two battery packs exceeds the lower limit value, immediately sending a warning to the whole vehicle controller, and judging whether the battery pack needs to be immediately reduced in output at the next moment by a driver;
s4.4, if the two items do not exceed the lower limit value, judging whether the current automobile runs on a road surface which needs to climb a slope or is steeper according to the driving behavior of the current driver and the running condition of the automobile;
s4.5, if so, calculating the maximum output current and the maximum output voltage according to the current SOE and sending the maximum output current and the maximum output voltage to the whole vehicle controller;
and S4.6, if the vehicle is on a gentle road surface, calculating the output voltage and the current of the RDT (remote data transfer) size capable of prolonging the future moment as much as possible according to the current SOE size, and sending signals to the whole vehicle controller.
2. The method for estimating a remaining discharge time based on an energy state of a power lithium battery according to claim 1, wherein: in the step S1, the calculation formula of SOE is as follows:
wherein, the SOR of the battery represents the ratio of the residual energy of the battery to the bearing energy of the battery when the battery is full under the condition of constant current-constant voltage charging, and E is shown in the formula (1) res For remaining battery energy, E N For battery full energy, uoc (·) is the battery open circuit voltage, is a function of SOC, C N Is the battery capacity; SOE is defined as the energy loss H caused by the SOR of the battery and the charge/discharge of the battery to the charge/discharge cut-off voltage under certain working conditions nt The difference is in the formula (2), E h The energy is lost to the battery, which depends on the battery impedance and operating conditions.
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| CN103954913A (en) * | 2014-05-05 | 2014-07-30 | 哈尔滨工业大学深圳研究生院 | Predication method of electric vehicle power battery service life |
| CN109738810A (en) * | 2019-01-21 | 2019-05-10 | 温州大学 | A kind of comprehensive estimate method of remaining battery service life |
| CN109917292A (en) * | 2019-03-28 | 2019-06-21 | 首都师范大学 | A kind of lithium ion battery life-span prediction method based on DAUPF |
| CN111639442A (en) * | 2020-06-09 | 2020-09-08 | 山东交通学院 | Method and system for screening influence factors of residual service life of power lithium battery |
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