CN119986443A - A Lithium-ion Battery Health State Estimation Method Integrating Physical Information - Google Patents
A Lithium-ion Battery Health State Estimation Method Integrating Physical Information Download PDFInfo
- Publication number
- CN119986443A CN119986443A CN202510188963.7A CN202510188963A CN119986443A CN 119986443 A CN119986443 A CN 119986443A CN 202510188963 A CN202510188963 A CN 202510188963A CN 119986443 A CN119986443 A CN 119986443A
- Authority
- CN
- China
- Prior art keywords
- model
- feature
- calculate
- lithium
- segment
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Landscapes
- Secondary Cells (AREA)
Abstract
本发明涉及一种融合物理信息的锂离子电池健康状态估计方法，属于电池技术领域，包括以下步骤：S1：搜集同一车型的新能源汽车运行数据，包含充放电数据，建立新能源汽车运行数据库；S2：基于电池充电数据进行健康特征提取并计算容量标签；S3：基于前馈神经网络建立锂离子电池健康状态估计模型，定义融合物理信息的损失函数，利用梯度下降方法对模型进行训练；S4：基于训练的模型对新能源汽车锂离子电池健康状态进行估计。本发明解决了深度学习黑箱方法可解释性差、特征利用能力弱的缺点；同时结合了神经网络模型有效提取高维抽象特征的优点和模型能够正确理解物理信息特征对电池老化潜在影响的优点。
The present invention relates to a method for estimating the health state of a lithium-ion battery integrating physical information, which belongs to the field of battery technology and includes the following steps: S1: collecting operation data of new energy vehicles of the same model, including charging and discharging data, and establishing a new energy vehicle operation database; S2: extracting health features based on battery charging data and calculating capacity labels; S3: establishing a lithium-ion battery health state estimation model based on a feedforward neural network, defining a loss function integrating physical information, and training the model using a gradient descent method; S4: estimating the health state of lithium-ion batteries of new energy vehicles based on the trained model. The present invention solves the shortcomings of poor interpretability and weak feature utilization capabilities of deep learning black box methods; at the same time, it combines the advantages of the neural network model in effectively extracting high-dimensional abstract features and the model's ability to correctly understand the potential impact of physical information features on battery aging.
Description
Technical Field
The invention belongs to the technical field of batteries, and relates to a lithium ion battery health state estimation method integrating physical information.
Background
State of health (SOH) estimation methods of lithium ion batteries of new energy automobiles can be generally classified into an ampere-hour integration method, a model method and a data driving method. The model-based method depends on a high-precision battery model and reliable signal sampling, is difficult to apply in complex scenes, and the data-driven method builds a mapping model of the health features and the SOH by extracting the health features related to battery aging, so that SOH estimation is realized. However, the conventional machine learning model relied on by the data driving method is a black box model, the model has poor interpretability and weak feature utilization capability, and the healthy features containing high-value physical information cannot be correctly understood and utilized, so that the SOH estimation accuracy of the model in a complex scene is reduced. At present, an effective battery SOH estimation method fused with physical information is not proposed yet for accurately estimating the SOH of the lithium ion battery of the new energy automobile.
Disclosure of Invention
Therefore, the invention aims to provide the method for estimating the health state of the lithium ion battery of the new energy automobile by fusing the physical information, which can realize the accurate estimation of the health state of the lithium ion battery of the new energy automobile by considering the physical information.
In order to achieve the above purpose, the present invention provides the following technical solutions:
a lithium ion battery health state estimation method integrating physical information comprises the following steps:
S1, collecting new energy automobile operation data of the same automobile type, including charge and discharge data, and establishing a new energy automobile operation database;
s2, extracting health characteristics based on battery charging data and calculating capacity labels;
S3, establishing a lithium ion battery health state estimation model based on a feedforward neural network, defining a loss function fused with physical information, and training the model by using a gradient descent method;
and S4, estimating the health state of the lithium ion battery of the new energy automobile based on the trained model.
Further, the step S1 specifically includes the following steps:
s11, collecting operation data of a certain electric automobile, including charge and discharge current, battery cell voltage, battery cell temperature, time, mileage and state of charge (SOC);
S12, according to the collected vehicle operation data, an operation database of a certain automobile is built.
Further, the step S2 specifically includes:
S21, analyzing charging data of the electric automobile, and screening charging fragments which meet the condition that the charging start SOC is smaller than the first percentage and the charging end SOC is the second percentage as capacity calculation fragments;
s22, calculating the charging capacity of the capacity calculation segment by an ampere-hour integration method, and dividing the charging capacity by the SOC variation of the capacity calculation segment to obtain a capacity label;
S23, dividing the capacity label by the rated capacity of the battery to obtain a health state SOH label;
S24, calculating an average single voltage sequence, a highest single voltage sequence and a lowest single voltage sequence of the capacity calculation segment according to the single voltage of the battery;
s25, selecting a characteristic starting voltage and a characteristic cut-off voltage, and intercepting a characteristic extraction segment from the calculation segment according to the average single voltage sequence, wherein the starting point of the characteristic extraction segment is the moment when the average single voltage sequence reaches the characteristic starting voltage, and the end point of the characteristic extraction segment is the moment when the average single voltage sequence reaches the characteristic cut-off voltage;
s26, calculating accumulated charge quantity of the feature extraction segment by an ampere-hour integration method to be used as a health feature 1, calculating standard deviation of an electric quantity increment sequence of the feature extraction segment by the ampere-hour integration method to be used as a health feature 2, calculating highest monomer voltage of a start point of the feature extraction segment to be used as a feature 3, calculating lowest monomer voltage of a start point of the feature extraction segment to be used as a feature 4, calculating highest monomer voltage of an end point of the feature extraction segment to be used as a feature 5, calculating lowest monomer voltage of the end point of the feature extraction segment to be used as a feature 6, calculating the extreme difference of the monomer voltage of the start point of the feature extraction segment to be used as a feature 7, calculating the extreme difference of the monomer voltage of the end point of the feature extraction segment to be used as a feature 8, calculating average monomer temperature of the feature extraction segment to be used as a feature 9, and extracting accumulated running mileage of a vehicle to be used as a feature 10.
Further, the step S3 specifically includes:
S31, establishing a lithium ion battery SOH estimation model based on a multi-layer full-connection layer neural network, wherein the input of the model is characterized by 1-10, and the output of the model is an SOH label;
s32, calculating partial differential dT of the model output value to the characteristic 9, and defining the physical loss 1L1 of the model as the larger of 0 and-dT;
s33, calculating partial differential dM of the model output value to the feature 10, and defining the physical loss 2L2 of the model as the larger of 0 and dM;
S34, calculating an average absolute percentage error MAPE of the model output value and the SOH label, and defining the MAPE as an estimated loss L3 of the model;
S35, defining the total loss L of the model as weighted summation of L1, L2 and L3;
and S36, carrying out iterative training on the neural network model according to the L and the gradient descent algorithm until the L descends to a preset value or no descending trend exists.
Further, the step S4 specifically includes:
s41, extracting characteristics 1-10 from charging data of a vehicle to be detected;
And S42, inputting the characteristics 1-10 of the vehicle to be detected into a model, and outputting an estimated value of SOH by the model.
The invention has the beneficial effects that:
1) The health characteristics capable of effectively reflecting the aging state of the battery are extracted from the charging data of the lithium ion battery of the new energy automobile;
2) The neural network model is built, and physical information is integrated into the model from the aspect of model training, so that the defects of poor interpretability and weak characteristic utilization capability of a deep learning black box method are overcome;
3) The proposal combines the advantages of effectively extracting the high-dimensional abstract features of the neural network model and the advantages of correctly understanding the potential influence of the physical information features on the battery aging by the model.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objects and other advantages of the invention may be realized and obtained by means of the instrumentalities and combinations particularly pointed out in the specification.
Drawings
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in the following preferred detail with reference to the accompanying drawings, in which:
FIG. 1 is a flow chart of the overall method of the present invention;
fig. 2 is a general frame diagram of an embodiment method.
Detailed Description
Other advantages and effects of the present invention will become apparent to those skilled in the art from the following disclosure, which describes the embodiments of the present invention with reference to specific examples. The invention may be practiced or carried out in other embodiments that depart from the specific details, and the details of the present description may be modified or varied from the spirit and scope of the present invention. It should be noted that the illustrations provided in the following embodiments merely illustrate the basic idea of the present invention by way of illustration, and the following embodiments and features in the embodiments may be combined with each other without conflict.
It should be noted that the illustrations provided in the following embodiments merely illustrate the basic concept of the present invention by way of illustration, and only the components related to the present invention are shown in the drawings and are not drawn according to the number, shape and size of the components in actual implementation, and the form, number and proportion of the components in actual implementation may be arbitrarily changed, and the layout of the components may be more complicated.
In the following description, numerous details are set forth in order to provide a more thorough explanation of embodiments of the present invention, it will be apparent, however, to one skilled in the art that embodiments of the present invention may be practiced without these specific details, in other embodiments, well-known structures and devices are shown in block diagram form, rather than in detail, in order to avoid obscuring the embodiments of the present invention.
A new energy automobile lithium ion battery health state estimation method integrating physical information.
Referring to fig. 1, a method for estimating the health state of a lithium ion battery of a new energy vehicle with integrated physical information can be divided into the following steps:
Step S1, collecting new energy automobile operation data of the same automobile type, including charge and discharge data, and establishing a new energy automobile operation database;
S2, extracting health characteristics based on battery charging data and calculating capacity labels;
Step S3, a lithium ion battery health state estimation model is established based on a feedforward neural network, a loss function fused with physical information is defined, and the model is trained by using a gradient descent method;
S4, estimating the health state of the lithium ion battery of the new energy automobile based on the trained model;
as an alternative embodiment, a complete technical roadmap of the present solution is shown in fig. 2.
As an alternative embodiment, the step S1 specifically includes steps S11 to S12:
Collecting operation data of a certain electric automobile, including charge and discharge current, battery cell voltage, battery cell temperature, time, mileage and state of charge (SOC);
And step S12, establishing an operation database of a certain automobile according to the collected vehicle operation data.
As an alternative embodiment, the step S2 specifically includes steps S21 to S26:
s21, analyzing charging data of the electric automobile, and screening charging fragments meeting that the charging start SOC is less than 20% and the charging end SOC is 100% as capacity calculation fragments;
Step S22, calculating the charging capacity of the capacity calculation segment by an ampere-hour integration method, and dividing the charging capacity by the SOC variation of the capacity calculation segment to obtain a capacity label Ca, wherein the specific formula is as follows:
Wherein I is charging current, SOC e is capacity calculation segment end SOC, and SOC s is capacity calculation segment start SOC, and step S23 is to divide the capacity label by the rated capacity of the battery to obtain a health state SOH, specifically shown in the following formula:
Wherein Ca rated is the rated capacity of the battery;
step S24, calculating an average single voltage sequence, a highest single voltage sequence and a lowest single voltage sequence of the capacity calculation segment according to the single voltage of the battery;
And S25, selecting a characteristic starting voltage and a characteristic cut-off voltage, and cutting off a characteristic extraction segment from the calculation segment according to the average single voltage sequence, wherein the starting point of the characteristic extraction segment is the moment when the average single voltage sequence reaches the characteristic starting voltage, and the ending point of the characteristic extraction segment is the moment when the average single voltage sequence reaches the characteristic cut-off voltage.
As an optional embodiment, the selecting of the characteristic starting voltage and the characteristic cut-off voltage based on the charging data statistics and the user behavior analysis specifically includes:
Counting the charging initial average monomer voltages of a large number of charging fragments of the same vehicle type, and calculating the average value of the charging initial average monomer voltages to be 3380mV, wherein the initial average monomer voltage of more than 80% of charging behaviors is lower than 3400mV;
counting the average monomer voltage of the charging end of a large number of charging fragments of the same model, calculating the average value of the average monomer voltage of the charging end to be 3600mV, and the initial average monomer voltage of the charging behavior exceeding 80% is higher than 3580mV;
selecting a characteristic starting voltage of 3400mV and a characteristic cut-off voltage of 3580mV, wherein most users have charging behaviors which span the interval, meet the condition of health characteristic extraction, and perform health characteristic extraction from the interval, so that the universality of the method is ensured;
Step S26, calculating the accumulated charge quantity of the feature extraction segment by an ampere-hour integration method to be used as a health feature 1, calculating the standard deviation of an electric quantity increment sequence of the feature extraction segment by the ampere-hour integration method to be used as a health feature 2, calculating the highest monomer voltage of the starting point of the feature extraction segment to be used as a feature 3, calculating the lowest monomer voltage of the starting point of the feature extraction segment to be used as a feature 4, calculating the highest monomer voltage of the end point of the feature extraction segment to be used as a feature 5, calculating the lowest monomer voltage of the end point of the feature extraction segment to be used as a feature 6, calculating the extreme difference of the monomer voltage of the starting point of the feature extraction segment to be used as a feature 7, calculating the extreme difference of the monomer voltage of the end point of the feature extraction segment to be used as a feature 8, calculating the average monomer temperature of the feature extraction segment to be used as a feature 9, and extracting the accumulated running mileage of the vehicle to be used as a feature 10.
As an alternative embodiment, the step S3 specifically includes steps S31 to S36:
Step S31, a lithium ion battery SOH estimation model is built based on a multi-layer full-connection layer neural network, the input of the model is characterized by 1-10, and the output of the model is an SOH label;
Step S32, calculating partial differentiation (dT) of the model output value to the feature 9, and defining the larger of the physical loss 1 (L1) of the model to be 0 and-dT, wherein the calculation mode is as follows:
L1=max(0,-dT)
In the formula, For the SOH estimate output by the model, T is feature 9 (average monomer temperature of the feature extraction segment), which physical loss causes the SOH estimate to rise as the temperature rises;
Step S33, calculating partial differentiation (dM) of the model output value to the feature 10, defining the larger of the model physical loss 2 (L2) of 0 and dM, wherein the calculation mode is as follows:
L2=max(0,dM)
Where M is feature 10 (vehicle cumulative mileage), which physical loss causes the SOH estimate to decrease as the vehicle mileage increases;
Step S34, calculating the average absolute percentage error (MAPE) of the model output value and the SOH label, defining the MAPE as the estimated loss (L3) of the model, and also adopting other error calculation modes, such as Root Mean Square Error (RMSE) and the like;
Step S35, defining the total loss L of the model as weighted summation of L1, L2 and L3, wherein the weighted summation is shown in the following formula:
L=α*L1+β*L2+γ*L3
wherein alpha, beta and gamma are weight items which can be adjusted according to actual needs;
and step S36, carrying out iterative training on the neural network model according to the L and the gradient descent algorithm until the L descends to a preset value or no descending trend exists.
As an alternative embodiment, the step S4 specifically includes steps S41 to S42:
s41, extracting characteristics 1-10 from charging data of a vehicle to be detected;
And S42, inputting the characteristics 1-10 of the vehicle to be detected into a model, and outputting an estimated value of SOH by the model.
In the foregoing embodiments, references in the specification to "this embodiment" indicate that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least some, but not necessarily all, embodiments. Multiple occurrences of "this embodiment" do not necessarily all refer to the same embodiment.
In the above embodiments, while the present invention has been described in conjunction with specific embodiments thereof, many alternatives, modifications and variations of these embodiments will be apparent to those skilled in the art in light of the foregoing description. For example, other memory structures (e.g., dynamic RAM (DRAM)) may use the embodiments discussed. The embodiments of the invention are intended to embrace all such alternatives, modifications and variances which fall within the broad scope of the appended claims.
The present embodiment also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements any of the methods of the present embodiments.
The embodiment also provides an electronic terminal, which comprises a processor and a memory;
The memory is configured to store a computer program, and the processor is configured to execute the computer program stored in the memory, so that the terminal executes any one of the methods in the present embodiment.
The computer readable storage medium of the present embodiment, those of ordinary skill in the art will appreciate that all or part of the steps of implementing the above-described method embodiments may be implemented by computer program related hardware. The aforementioned computer program may be stored in a computer readable storage medium. The program, when executed, performs the steps comprising the method embodiments described above, and the storage medium described above includes various media capable of storing program code, such as ROM, RAM, magnetic or optical disk.
The electronic terminal provided in this embodiment includes a processor, a memory, a transceiver, and a communication interface, where the memory and the communication interface are connected to the processor and the transceiver and complete communication with each other, the memory is used to store a computer program, the communication interface is used to perform communication, and the processor and the transceiver are used to run the computer program, so that the electronic terminal performs each step of the above method.
In this embodiment, the memory may include a random access memory (Random Access Memory, abbreviated as RAM), and may further include a non-volatile memory (non-volatile memory), such as at least one disk memory.
The processor may be a general-purpose processor, including a central Processing unit (Central Processing Unit, CPU), a network processor (Network Processor, NP), a digital signal processor (DIGITAL SIGNAL Processing, DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable gate array (FPGA) or other Programmable logic device, discrete gate or transistor logic device, or discrete hardware components.
The invention is operational with numerous general purpose or special purpose computing system environments or configurations. Such as a personal computer, a server computer, a hand-held or portable device, a tablet device, a multiprocessor system, a microprocessor-based system, a set top box, a programmable consumer electronics, a network PC, a minicomputer, a mainframe computer, a distributed computing environment that includes any of the above systems or devices, and the like.
The invention may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The invention may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
Finally, it is noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made thereto without departing from the spirit and scope of the present invention, which is intended to be covered by the claims of the present invention.
Claims (5)
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202510188963.7A CN119986443A (en) | 2025-02-20 | 2025-02-20 | A Lithium-ion Battery Health State Estimation Method Integrating Physical Information |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202510188963.7A CN119986443A (en) | 2025-02-20 | 2025-02-20 | A Lithium-ion Battery Health State Estimation Method Integrating Physical Information |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN119986443A true CN119986443A (en) | 2025-05-13 |
Family
ID=95627453
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202510188963.7A Pending CN119986443A (en) | 2025-02-20 | 2025-02-20 | A Lithium-ion Battery Health State Estimation Method Integrating Physical Information |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN119986443A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN120334756A (en) * | 2025-06-20 | 2025-07-18 | 麦田能源股份有限公司 | A method for estimating SOH of a battery pack |
| CN120831581A (en) * | 2025-09-18 | 2025-10-24 | 中国汽车工程研究院股份有限公司 | A rapid detection method for the health status of retired lithium-ion batteries |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116430245A (en) * | 2023-06-14 | 2023-07-14 | 威海谱跃光电科技有限公司 | Battery Thermal Runaway Prediction Method Based on Gradient Optimized Multi-physics Information Neural Network |
| CN117269805A (en) * | 2023-11-23 | 2023-12-22 | 中国人民解放军国防科技大学 | Vehicle lithium battery pack health status assessment model training, prediction method and device |
| CN117723998A (en) * | 2023-12-19 | 2024-03-19 | 河海大学 | Joint estimation method of health status and remaining life of lithium-ion battery based on PINN |
| CN119272000A (en) * | 2024-09-19 | 2025-01-07 | 重庆大学 | Lithium-ion battery health status estimation method based on TSLANet network |
-
2025
- 2025-02-20 CN CN202510188963.7A patent/CN119986443A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116430245A (en) * | 2023-06-14 | 2023-07-14 | 威海谱跃光电科技有限公司 | Battery Thermal Runaway Prediction Method Based on Gradient Optimized Multi-physics Information Neural Network |
| CN117269805A (en) * | 2023-11-23 | 2023-12-22 | 中国人民解放军国防科技大学 | Vehicle lithium battery pack health status assessment model training, prediction method and device |
| CN117723998A (en) * | 2023-12-19 | 2024-03-19 | 河海大学 | Joint estimation method of health status and remaining life of lithium-ion battery based on PINN |
| CN119272000A (en) * | 2024-09-19 | 2025-01-07 | 重庆大学 | Lithium-ion battery health status estimation method based on TSLANet network |
Non-Patent Citations (1)
| Title |
|---|
| 张颛利 等: "基于物理信息神经网络的金属多轴疲劳寿命预测进展", 机械强度, vol. 47, no. 2, 31 December 2024 (2024-12-31), pages 44 - 52 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN120334756A (en) * | 2025-06-20 | 2025-07-18 | 麦田能源股份有限公司 | A method for estimating SOH of a battery pack |
| CN120831581A (en) * | 2025-09-18 | 2025-10-24 | 中国汽车工程研究院股份有限公司 | A rapid detection method for the health status of retired lithium-ion batteries |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113219357B (en) | Battery pack health state calculation method and system and electronic equipment | |
| EP3465241B1 (en) | Method and system for estimating state of charge of a battery using gaussian process regression | |
| CN119986443A (en) | A Lithium-ion Battery Health State Estimation Method Integrating Physical Information | |
| CN111323719A (en) | A method and system for online determination of health status of electric vehicle power battery pack | |
| CN111812515A (en) | Estimation of state of charge of lithium-ion battery based on XGboost model | |
| CN113189495B (en) | A method, device and electronic device for predicting battery health status | |
| CN110109028A (en) | A kind of power battery remaining life indirect predictions method | |
| Sangeetha et al. | Validation of EKF based SoC estimation using vehicle dynamic modelling for range prediction | |
| CN113093027B (en) | Battery SOC calibration method, device, system, medium and program product | |
| CN116298927B (en) | Battery capacity estimation method and device, computer equipment and storage medium | |
| CN111816936B (en) | Battery echelon utilization grouping method and system, terminal equipment and storage medium | |
| CN114415041B (en) | Battery remaining capacity estimation method, device and terminal equipment | |
| CN113219358B (en) | Battery pack health state calculation method and system and electronic equipment | |
| CN115994441A (en) | Online battery life prediction method for big data cloud platform based on mechanism information | |
| CN116482540A (en) | Analysis and prediction method, device and system for battery voltage inconsistency | |
| CN115700717A (en) | A power distribution analysis method based on electric vehicle power demand | |
| CN104678303A (en) | Method and system for battery pack state of health estimation | |
| CN116106761A (en) | Real-time estimation method of lithium-ion battery capacity based on canonical correlation analysis | |
| CN107064808A (en) | The SOC methods of estimation and device of battery | |
| CN112782601A (en) | Method, system and equipment for acquiring health degree of battery and readable storage medium | |
| CN120722208A (en) | A method for estimating battery health status, storage medium and electronic device | |
| CN115079008A (en) | Soft package lithium ion battery SOC estimation method and system considering stress influence | |
| CN118944238B (en) | A new energy storage lithium battery control system and control method | |
| CN118962504A (en) | Battery life prediction method, device, computer equipment, readable storage medium and program product | |
| CN208350965U (en) | A kind of pure electric automobile battery state-of-health measures device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |