CN112285568A - Estimation method of residual discharge time based on energy state of power lithium battery - Google Patents
Estimation method of residual discharge time based on energy state of power lithium battery Download PDFInfo
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- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
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Abstract
The invention relates to the technical field of battery state estimation algorithms, in particular to a method for estimating residual discharge time based on the energy state of a power lithium battery. The method comprises the following steps: calculating the SOE of the battery by an AUPF algorithm; identifying the driving behavior of a driver through a BP neural network; calculating the estimated value of the RDT at the current moment by using the SOE result; optimizing the discharge strategy of the battery at the future moment according to the results of the steps; and sequentially calculating the SOE size and the RDT at the next moment until the working condition is finished. The SOE is calculated through the AUPF algorithm, so that the accuracy of the algorithm can be improved, and the RDT estimation error caused by inaccurate state parameters is reduced; meanwhile, the change of the energy state of the battery is considered, so that the estimation precision can be further improved; in addition, the discharging optimization is carried out on the battery, 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.
Description
Technical Field
The invention relates to the technical field of battery state estimation algorithms, in particular to a method for estimating residual discharge time based on the energy state of a power lithium battery.
Background
Currently, much research on RDT estimation of electric vehicle power batteries focuses on studying the relationship between the remaining discharge time and the battery state, and the remaining discharge time of the battery is determined by using the state parameters of the battery. The traditional method for estimating the RDT still has the defects that firstly, the accuracy of the 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 for currently estimating the RDT is the SOC of the battery, the SOC reflects the state of charge of the battery, and the voltage change of the battery is ignored, so that when the RDT is estimated, only the change of the SOC of the battery is considered, and a large estimation error is often caused by ignoring the voltage change; in addition, after the RDT is estimated, few researches are carried out on optimizing the discharge strategy of the power battery at the future time according to the current time RDT size and other factors so as to improve the working state of the battery.
Disclosure of Invention
The present invention is directed to a method for estimating remaining discharge time based on energy status of a power lithium battery, so as to solve the problems mentioned in the background art.
In order to solve the above technical problem, an object of the present invention is to provide a method for estimating a remaining discharge time based on an energy state of a power lithium battery, including the steps of:
s1, based on UPF, the estimation algorithm of battery SOE calculates the transition probability of the particles at each moment, and the particles with smaller weight are transferred to the particles with better weight through AUPF algorithm;
s2, identifying the driving behavior of the driver through a BP neural network according to the driving condition and the speed of the automobile at the current moment;
s3, calculating the estimated value of the RDT at the current time by using the SOE data result obtained in S1;
s4, optimizing a discharging strategy of the battery at a future moment according to the driving behavior of the current driver, the RDT size of the current moment, the driving condition of the automobile and the SOE size;
and S5, returning to S1, and calculating the SOE size and the RDT at the next moment until the working condition is finished.
The battery SOE is the energy state of the battery, the UPF is the unscented particle filter, the AUPF algorithm is the ant colony unscented particle filter algorithm, and the RDT is the battery residual discharge time.
As a further improvement of the technical solution, in S1, the AUPF may utilize a characteristic that individuals gradually approach to an optimal solution in the ant colony algorithm, so as to enhance the particle diversity of the UPF without increasing the number of particles, and improve the estimation accuracy and the robustness.
As a further improvement of the present technical solution, in S1, the method for estimating the SOE by the AUPF algorithm includes the steps of:
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 the particles according to the initial probability density;
s1.3, measuring and updating UFK time to generate more accurate posterior probability distribution;
s1.4, performing resampling operation through an ant colony;
s1.5, finishing the SOE estimation at the current moment;
and S1.6, judging whether the working condition is finished or not, if not, returning to S1.1, and estimating the SOE at the next moment, and if so, finishing the algorithm.
As a further improvement of the present technical solution, in S1, the calculation formula of the SOE is:
wherein, the battery SOR represents the battery residual energy and the battery under the condition of constant current-constant voltage chargingRatio of energy carried at full charge, formula (1) EresFor remaining energy of the battery, ENFor full energy of the battery, Uoc(. is) the open circuit voltage of the battery, as a function of SOC, CNIs the battery capacity; SOE is defined as the energy loss H caused by the battery SOR and the battery charging/discharging to the charging/discharging cut-off voltage under a certain working conditionntThe difference of formula (2) is EhThe 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 running road section, the maximum speed allowed by the current road section, the gradient of the current road section and the running 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 a BP neural network, and identifying the driving behavior of the current driver;
and S2.3, feeding back the driving behavior 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 estimated value by using the SOE includes the following steps:
s3.1, calculating the discharge power of the battery at the moment by using the relation between the discharge power and the SOE, the rated energy and the 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 rated energy of the battery and the SOE;
s3.3, feeding back the calculation result to a battery management system;
s3.4, optimizing a discharging strategy of the battery at a 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;
and S3.5, returning to S1, and calculating the SOE value and the RDT at the next moment until the working condition is finished.
As a further improvement of the technical solution, in S3, the conventional RDT estimation method based on the SOC is improved, the SOC is replaced with the SOE, the change of the battery voltage is considered, and the SOE size of the battery estimated by the AUPF algorithm is input as a parameter into the improved method, so that the defect of neglecting the battery energy state in the conventional estimation method is overcome, and the accuracy of estimating the 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 a 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 the current RDT exceed a lower limit value or not;
s4.3, if one or more of the parameters exceed the lower limit value, immediately sending a warning to the whole vehicle controller, and judging whether the battery needs to be immediately reduced and 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 needing climbing or being steep according to the driving behavior of the current driver and the running condition of the automobile;
s4.5, if yes, 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 capable of prolonging the RDT at the future time as far as possible according to the current SOE, and sending a signal to the vehicle control unit.
As a further improvement of the present technical solution, in S4, after the battery RDT at the current time is estimated, the power battery discharge strategy may be optimized according to the driving behavior of the driver, the driving condition of the vehicle, and other factors, and meanwhile, the dynamic property of the vehicle may be increased or the battery RDT may be extended according to the actual situation, and the battery may be prevented from being over-discharged.
Another object of the present invention is to provide an apparatus for estimating remaining discharge time based on energy state of a lithium power 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 of the above-mentioned steps of the method for estimating remaining discharge time based on energy state of a lithium power battery when executing the computer program.
A third object of the present invention is to provide a computer-readable storage medium storing a computer program, characterized in that: the computer program is executed by a processor to realize the steps of any one of the above-mentioned estimation methods of the remaining discharge time based on the energy state of the power lithium battery.
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, the particles with lower weight are gradually transferred to the particles with higher weight by using the ant colony algorithm, so that the accuracy of the algorithm can be improved, and the RDT estimation error caused by inaccuracy of state parameters is reduced; meanwhile, the method for estimating the RDT based on the SOE considers the change of the energy state of the battery, and can further improve the estimation precision; in addition, after the RDT is estimated, discharge optimization is carried out according to the driving behavior of a driver, the travel working condition of the automobile, the RDT at the current moment and the SOE, 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.
Drawings
FIG. 1 is a block diagram of the overall process flow of the present invention;
FIG. 2 is a block diagram of the flow of the method for estimating SOE by AUPF algorithm according to the present invention;
FIG. 3 is a schematic diagram of an exemplary loop for estimating SOE by the AUPF algorithm in accordance with the present invention;
FIG. 4 is a flow chart of a method for identifying a driving behavior of a driver based on a BP neural network according to the present invention;
FIG. 5 is a schematic diagram of an exemplary cycle for identifying a driver's driving behavior based on a BP neural network according to the present invention;
FIG. 6 is a block diagram of a method flow of the SOE-based RDT estimation method of the present invention;
FIG. 7 is a schematic diagram of an exemplary cycle of the SOE-based estimated RDT method of the present invention;
FIG. 8 is a block diagram of a method flow of an SOE and RDT based discharge optimization strategy in accordance with the present invention;
FIG. 9 is a schematic diagram of an exemplary cycle of the SOE and RDT based discharge optimization strategy of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part 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 embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Method embodiment
As shown in fig. 1 to 7, the present embodiment aims to provide a method for estimating remaining discharge time based on energy state of a power lithium battery, comprising the following steps:
s1, based on UPF, the estimation algorithm of battery SOE calculates the transition probability of the particles at each moment, and the particles with smaller weight are transferred to the particles with better weight through AUPF algorithm;
s2, identifying the driving behavior of the driver through a BP neural network according to the driving condition and the speed of the automobile at the current moment;
s3, calculating the estimated value of the RDT at the current time by using the SOE data result obtained in S1;
s4, optimizing a discharging strategy of the battery at a future moment according to the driving behavior of the current driver, the RDT size of the current moment, the driving condition of the automobile and the SOE size;
and S5, returning to S1, and calculating the SOE size and the RDT at the next moment until the working condition is finished.
The battery SOE is the energy state of the battery, the UPF is the unscented particle filter, the AUPF algorithm is the ant colony unscented particle filter algorithm, and the RDT is the battery residual discharge time.
Specifically, in S1, the AUPF can utilize the characteristic that individuals gradually approach the optimal solution in the ant colony algorithm, enhance the particle diversity of the UPF without increasing the number of particles, and improve the estimation accuracy and robustness.
In this embodiment, in S1, the method for estimating the 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 the particles according to the initial probability density;
s1.3, measuring and updating UFK time to generate more accurate posterior probability distribution;
s1.4, performing resampling operation through an ant colony;
s1.5, finishing the SOE estimation at the current moment;
and S1.6, judging whether the working condition is finished or not, if not, returning to S1.1, and estimating the SOE at the next moment, and if so, finishing the algorithm.
Further, in S1, the calculation formula of SOE is:
wherein, the battery SOR represents the ratio of the residual energy of the battery to the bearing energy of the battery when the battery is fully charged under the condition of constant current-constant voltage charging, in the formula (1), EresFor remaining energy of the battery, ENFor full energy of the battery, Uoc(. is) the open circuit voltage of the battery, as a function of SOC, CNIs the battery capacity; SOE is defined as the energy loss H caused by the battery SOR and the battery charging/discharging to the charging/discharging cut-off voltage under a certain working conditionntThe difference of formula (2) is EhThe 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 running road section, the maximum speed allowed by the current road section, the gradient of the current road section and the running 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 a BP neural network, and identifying the driving behavior of the current driver;
and S2.3, feeding back the driving behavior of the driver to the battery management system.
In this embodiment, in S3, the method for calculating the RDT estimate using the SOE includes the following steps:
s3.1, calculating the discharge power of the battery at the moment by using the relation between the discharge power and the SOE, the rated energy and the 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 rated energy of the battery and the SOE;
s3.3, feeding back the calculation result to a battery management system;
s3.4, optimizing a discharging strategy of the battery at a 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;
and S3.5, returning to S1, and calculating the SOE value and the RDT at the next moment until the working condition is finished.
Specifically, in S3, the conventional method for estimating the remaining discharge time RDT based on the state of charge SOC is improved, the SOC is replaced with the SOE, the change in the battery voltage is considered, and the size of the battery SOE estimated by the AUPF algorithm is input as a parameter into the improved method, so that the disadvantage of ignoring the battery energy state 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 the current RDT exceed a lower limit value or not;
s4.3, if one or more of the parameters exceed the lower limit value, immediately sending a warning to the whole vehicle controller, and judging whether the battery needs to be immediately reduced and 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 needing climbing or being steep according to the driving behavior of the current driver and the running condition of the automobile;
s4.5, if yes, 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 capable of prolonging the RDT at the future time as far as possible according to the current SOE, and sending a signal to the vehicle control unit.
Specifically, in S4, after the size of the battery RDT at the current time is estimated, the power battery discharge strategy may be optimized according to the driving behavior of the driver and the driving condition of the vehicle, and the like, while increasing the dynamic performance of the vehicle or extending the RDT of the battery according to the actual situation, and preventing the battery from being over-discharged.
Electronic device embodiment
The present embodiment is directed to an apparatus for estimating remaining discharge time based on 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 executes the program instructions in the memory to realize the estimation method of the residual discharge time based on the energy state of the power lithium battery.
Alternatively, the memory may be implemented by any type or combination of volatile or non-volatile 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 disks.
In addition, the present invention further provides a computer readable storage medium, which stores a computer program, and the computer program, when executed by a processor, implements the steps of the estimation method for the remaining discharge time based on the energy state of the power lithium battery.
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-described aspects of the method for estimating remaining discharge time based on 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 instructions of a program, which may be stored in a computer-readable storage medium, such as a read-only memory, a magnetic or optical disk, and the like.
The foregoing shows and describes the general principles, essential 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 embodiments described above, and the preferred embodiments of the present invention are described in the above embodiments and the description, and are not intended to limit the present invention. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (9)
1. A method for estimating the residual discharge time based on the energy state of a power lithium battery is characterized in that: the method comprises the following steps:
s1, based on UPF, the estimation algorithm of battery SOE calculates the transition probability of the particles at each moment, and the particles with smaller weight are transferred to the particles with better weight through AUPF algorithm;
s2, identifying the driving behavior of the driver through a BP neural network according to the driving condition and the speed of the automobile at the current moment;
s3, calculating the estimated value of the RDT at the current time by using the SOE data result obtained in S1;
s4, optimizing a discharging strategy of the battery at a future moment according to the driving behavior of the current driver, the RDT size of the current moment, the driving condition of the automobile and the SOE size;
and S5, returning to S1, and calculating the SOE size and the RDT at the next moment until the working condition is finished.
2. The method for estimating remaining discharge time based on energy state of lithium-ion power battery as claimed in claim 1, wherein: in S1, the AUPF can utilize the feature that individuals gradually approach the optimal solution in the ant colony algorithm, thereby enhancing the particle diversity of the UPF without increasing the number of particles, and improving the estimation accuracy and robustness.
3. The method for estimating remaining discharge time based on energy state of lithium-ion power battery as claimed in claim 1, wherein: in S1, the method for estimating SOE by 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 the particles according to the initial probability density;
s1.3, measuring and updating UFK time to generate more accurate posterior probability distribution;
s1.4, performing resampling operation through an ant colony;
s1.5, finishing the SOE estimation at the current moment;
and S1.6, judging whether the working condition is finished or not, if not, returning to S1.1, and estimating the SOE at the next moment, and if so, finishing the algorithm.
4. The method for estimating remaining discharge time based on energy state of lithium-ion power battery as claimed in claim 1, wherein: in S1, the calculation formula of SOE is:
wherein the battery SOR represents the battery remainingThe ratio of the energy to the energy carried by the battery at full charge under constant current-constant voltage charging conditions, in formula (1), EresFor remaining energy of the battery, ENFor full energy of the battery, Uoc(. is) the open circuit voltage of the battery, as a function of SOC, CNIs the battery capacity; SOE is defined as the energy loss H caused by the battery SOR and the battery charging/discharging to the charging/discharging cut-off voltage under a certain working conditionntThe difference of formula (2) is EhThe energy is lost to the battery, which depends on the battery impedance and operating conditions.
5. The method for estimating remaining discharge time based on energy state of lithium-ion power battery as claimed in claim 1, wherein: 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 running road section, the maximum speed allowed by the current road section, the gradient of the current road section and the running 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 a BP neural network, and identifying the driving behavior of the current driver;
and S2.3, feeding back the driving behavior of the driver to the battery management system.
6. The method for estimating remaining discharge time based on energy state of lithium-ion power battery as claimed in claim 1, wherein: in S3, the method for calculating the RDT estimate using SOE includes the following steps:
s3.1, calculating the discharge power of the battery at the moment by using the relation between the discharge power and the SOE, the rated energy and the 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 rated energy of the battery and the SOE;
s3.3, feeding back the calculation result to a battery management system;
s3.4, optimizing a discharging strategy of the battery at a 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;
and S3.5, returning to S1, and calculating the SOE value and the RDT at the next moment until the working condition is finished.
7. The method for estimating remaining discharge time based on energy state of lithium-ion power battery as claimed in claim 1, wherein: in the step S3, the conventional RDT estimation method based on the SOC is improved, the SOC is replaced with the SOE, the change of the battery voltage is considered, and the SOE size of the battery estimated by the AUPF algorithm is input as a parameter into the improved method, so that the defect of neglecting the battery energy state in the conventional estimation method is overcome, and the accuracy of estimating the RDT is improved.
8. The method for estimating remaining discharge time based on energy state of lithium-ion power battery as claimed in claim 1, wherein: 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 the current RDT exceed a lower limit value or not;
s4.3, if one or more of the parameters exceed the lower limit value, immediately sending a warning to the whole vehicle controller, and judging whether the battery needs to be immediately reduced and 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 needing climbing or being steep according to the driving behavior of the current driver and the running condition of the automobile;
s4.5, if yes, 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 capable of prolonging the RDT at the future time as far as possible according to the current SOE, and sending a signal to the vehicle control unit.
9. The method for estimating remaining discharge time based on energy state of lithium-ion power battery as claimed in claim 1, wherein: in S4, after the current battery RDT is estimated, the power battery discharge strategy may be optimized according to the driving behavior of the driver, the driving condition of the vehicle, and other factors, and the power performance of the vehicle may be increased or the battery RDT may be extended according to the actual situation, and the battery may be prevented from being over-discharged.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2023082982A1 (en) * | 2021-11-12 | 2023-05-19 | 比亚迪股份有限公司 | Method and apparatus for estimating remaining charging time, and computer storage medium |
| CN117150334A (en) * | 2023-06-16 | 2023-12-01 | 合肥工业大学 | Lithium battery multi-condition prediction method and device based on optimized BiLSTM neural network |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| 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 |
| US20200081070A1 (en) * | 2017-11-20 | 2020-03-12 | The Trustees Of Columbia University In The City Of New York | Neural-network state-of-charge and state of health estimation |
| 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 |
-
2020
- 2020-10-21 CN CN202011130634.0A patent/CN112285568B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| 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 |
| US20200081070A1 (en) * | 2017-11-20 | 2020-03-12 | The Trustees Of Columbia University In The City Of New York | Neural-network state-of-charge and state of health estimation |
| 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 |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2023082982A1 (en) * | 2021-11-12 | 2023-05-19 | 比亚迪股份有限公司 | Method and apparatus for estimating remaining charging time, and computer storage medium |
| CN117150334A (en) * | 2023-06-16 | 2023-12-01 | 合肥工业大学 | Lithium battery multi-condition prediction method and device based on optimized BiLSTM neural network |
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