Under the dual drive of policy support and terminal market demand growth, with the development of new energy vehicles and energy storage batteries in full swing, the lithium battery industry is extremely hot. However, behind the strong rise, risks are accelerating, problems such as overcapacity and reduced profits are gradually spreading, and the industry has begun a new round of big reshuffle: High-end production capacity is insufficient, low-end production capacity is seriously excess, and it is gradually eliminated Raw material costs continue to rise, profit margins are compressed, the urgent need to reduce costs and increase efficiency The industry pattern is further concentrated, and some battery factories may be forced to delist ......
Based on this, compete lithium industry manufacturing upgrade, get rid of low-end competition, Through digital research and development technology to help enterprises reduce costs and increase efficiency, to build research and development innovation barriers, has become a new direction of competitionNingde, BYD, Zhongchuang Airlines, Xinwang Da and other lithium battery giants have taken the lead in opening.
The author's team has been deeply engaged in the field of battery, material R & D and testing full-chain digital operation services for many years, in the process of in-depth cooperation and communication with a number of industry leaders, fromThree different levelsDig deep into battery research and development6 big pain pointsAnd "the right medicine" one by one, to create a setA customized digital intelligence platform integrating battery research and development, testing and verification, big data acquisition and processing, simulation calculation and analysis, material parameter library and other application services, For battery material development, battery research and development enterprises to solve problems!
Pain point 01.
The complexity and difficulty of laboratory management are increasing day by day
With the increasing number of laboratory test instruments and test samples, superimposing the need for laboratory's own certification and accreditation, The traditional laboratory management model based on manual and paper or the lims system which can only meet the basic process is no longer suitable，Many problems have been exposed: Traditional lims can only meet the basic business process, but it has poor scalability and low performance efficiency. It is difficult to track the whole processing process of the sample, and it is difficult to trace the error when it is found, which wastes manpower and material resources and slows down the progress. Data is easy to lose, difficult to accumulate, difficult to reuse, and manual transmission can not guarantee the authenticity of data, timeliness; The experimental data are scattered in different systems/files, so it is difficult to make use of the data analysis value.
Solution: Strengthen business management and break through data barriers
一体化平台中dlims系统通过数字化以及智能化手段，完成了全要素考虑（人机料法环），全场景覆盖（下单、审批、领样、检测等），全成员参与（研发人员、检测人员、管理者）的深度闭环。通过线上快速提单，自动流转审核等，大幅降低线下纸质审核工作成本，提升业务流转效率；基于CMA/CNAS认证体系要求并融合实际业务场景，实现全流程可监控/可追溯，强化实验测试结果的可靠性和权威性；针对过去的数据孤岛和信息处理孤岛等数据分析困境，通过自动采集，聚合各环节资源数据，实现实验室资源的实时追踪、快速查阅，快速调用，降低资源管理难度，提升管理者的决策效率。
Pain point 02.
The testing equipment is many and complex, which requires a lot of manpower operation
In the current laboratory testing business, it is usually encountered a large number of different manufacturers and different types of testing equipment, thus facing Device interfaces are inconsistent, data protocols are incompatible, and data formats are incompatible Inconsistency and other problems lead to high cost of data acquisition, long cycle, difficult integration, and the difficulty and cost of data transmission between systems are doubled.
Solution: Build a complete, shared and unified data environment
The dconnet (Data Acquisition System) in the integrated platform integrates a large number of commonly used data media communication protocols, which can be efficiently connected with various test manufacturers Multi-source heterogeneous data media collection, real-time/historical data collection, multi-category data base and storage platform, and other functions, the average daily data processing capacity exceeds 1T ，In order to realize the real-time collection, processing and upload of field equipment information, it provides a guarantee for the real, effective and real-time availability of data.
Pain point 03.
Data processing is time-consuming, laborious and error-prone
The data obtained from the test equipment need to go through manual calculation, display, comparison, screening, fitting and other operations to find the difference, analyze the results to determine whether the chart is meaningful, and distribute and report. Engineers process between 200 and 500GB of data per day from different sources and types，It is not only labor-intensive, the error rate is high, and the data is not deeply mined, and the utilization rate is low.
Solution: a set of platforms to complete data acquisition, viewing, analysis
dCore (Data Storage and Processing Platform) was built56 data scripts (equipment data standardization, data warehouse processing, data quality exploration) and 3 data processing models (pre-processing model, temperature channel model, work step number model), Process the large amount of data generated daily in a T+1 fashion， Distributed computing solves data cleaning and analysis statistics, and the final small amount of data enters the high-performance database Clickhouse to meet the flexible display needs of the business. Multi-dimensional presentation of visual information, monitoring test data in time to find anomalies, to avoid long-term waste of resources!
Pain point 04.
The format and form of the report are different, and the issuing efficiency is low
Battery testing needs to go through a large number of complex and repetitive verification work, during which the amount of data generated is far beyond our imagination, and engineers often need to collect and calculate a large amount of information and data through excel, and then use word to make various analysis reports, such as physical and chemical test reports, safety test reports, electrical performance test reports, etc. In the face of huge workload, problems such as software stutter, format distortion, data asynchronism, and manual operation are often a headache for engineers. If we continue to use the traditional reporting method, it will not only waste a lot of time and labor costs, but also the generated report data icon is not intuitive enough, and it is difficult to realize data sharing and management.
Solution: One-click report generation
dreport (Intelligent Report) includes200+ common templates, automatically generate test reports, issue a single report can be reduced from the original 8 hours to 2 hours ，Greatly improve work efficiency. PlatformSupport a variety of input methods for test data， Including manual input, device data import, data warehouse access, data input and then automatically generate reports through templates. Researchers can flexibly set templates according to their own needs, test requirements, and report formats to meet specific application scenarios and requirements. In addition, it can be presented in its own way, such as tables, charts, images, etc., which can greatly improve the readability and practicality of the report, and help researchers better understand and use the test results.
Pain point 05.
Materials are difficult to find and accuracy cannot be guaranteed
In the research and development of lithium batteries，Material selection and optimization is one of the key factors to improve work efficiency， Researchers or engineers looking for detailed data on materials need to look at multiple websites，The efficiency is very low and the accuracy is difficult to guarantee， The selection between different materials requires viewing a large amount of data for comparison, which is very tedious.
Solution: Knowledge base precipitation, query and comparative analysis at any time
dMaterial (Battery Material Library) is provided Detailed and accurate information on various materials, including their physical and chemical properties, performance parameters, and related research and application data, such as positive electrode material library, negative electrode material library, electrolyte material library, conductive material library, PACK material library, diaphragm material library, bonded material library, and failure material library， In-depth mining of material data, longitudinal and horizontal analysis of the difference between materials indicators, help battery researchers, engineers and manufacturers to compare materials, test samples, supplier samples, and quickly screen hundreds of materials to find the best candidate materials. Achieve the effect of twice the result with half the effort, reduce the frequency of R & D testing, and effectively reduce costs and increase efficiency for enterprises.
Pain point 06.
Massive data sinks, unable to dig deep
In the data-intensive battery industry, the inability to utilize test and analysis data is increasingly becoming a bottleneck， However, the material innovation, structural innovation and system integration innovation of batteries are inseparable from the use of information technology, and it is increasingly necessary to integrate and drive the entire research and development system through calculation and data.
Solution: Precipitation, reuse and efficient application of data
dAlgo (Computational Analysis Platform) can help the R&D team form a knowledge base accumulation through data precipitation, which can be used in various analysis scenarios such as battery life, charge-discharge performance, failure, gram volume ratio, energy efficiency ratio, etc. At the same time, it has the ability to efficiently process and calculate a large amount of raw data generated in the process of trial production and testing of materials and finished products. Such as: Cell volume change analysis，Integrated solution based on device + data Material test prediction，Quickly predict battery performance by measuring and analyzing resistance such as powder and pole plate The test cycle is reduced by mechanism model, aging attenuation database and parameter identification algorithm Intelligent analysis of test data anomalies，Access performance test equipment data → Create anomaly monitoring project scope and indicator model → Create anomaly monitoring automatic operation program → call monitoring program → Output abnormal data result Life prediction analysis， Based on the first-principles aging analysis process, the battery structure database and material property database are established, the aging model is trained by machine learning method, and the model is constantly improved by calculation and actual test data to form the predictive analysis ability of existing or similar formulas Digital twin technology，The laboratory is restored one-to-one, combining data acquisition and numerical control technology, so that remote operation WYSIWYG
It can be seen that a set of efficient battery test and analysis integrated platform will bring three competitive advantages to enterprises and win strategic initiative for enterprises: Reduce personnel redundancy and strengthen business management Improve efficiency and ensure data accuracy Efficient use of R&D data to improve product yield When the industrial environment changes, the trend of technological evolution changes, and the core competitive factors change, Only by building an efficient R & D system around new core capabilities with The Times can enterprises achieve new product research and development at a faster speed than their competitors, so as to take the lead in a new round of competition！
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