电芯智能研发
为电芯研发工程师提供电芯快速设计工具、管理系统和材料数据库，实现电芯研发从基于经验的设计到基于材料数据库和人工智能的智能辅助设计，打通产品需求信息、设计信息、试制信息及测试信息的数据壁垒，在一个平台上实现从原材料到产品数据的全生命周期管理。
价值收益
VALUE RETURN
研发效率提升
通过快速设计工具、数据驱动决策、智能推荐等，大幅度提升电芯研发的工作效率。
2-3倍
数据管理与追溯能力
100%
全面的数据管理功能确保所有研发数据得到妥善保存、分析与追溯，有利于知识积累、问题排查和决策支持。
研发成本降低
50%
从设计初期即进行成本估算，并在后续过程中持续监控与优化，有助于企业在保证产品质量的前提下，有效控制研发成本，实现经济效益最大化。
开发周期缩短
20%
通过智能选材、智能设计、自动分析等工具，工程师能更高效地生成和优化电芯方案、减少传统的试错性实验的次数，大幅缩短研发周期。
背景分析
BACKGROUND ANALYSIS
行业痛点
INDUSTRY PAIN POINTS
电芯研发通常会经过0阶段-产品导入、A阶段-原型设计阶段、B阶段-样件及DV验证阶段、C-小批量及PV验证阶段、D-生产准备阶段、SOP阶段-量产阶段，即使一切顺利，完成周期仍长达一年。
试产周期长
01
跨部门协作困境
1、电芯研发涉及设计、仿真、测试等多个独立团队，沟通协作效率低。
2、“单向传递”工作模式忽视深度双向沟通，不利于高效集成与资源整合。
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研发管理水平低下
研发过程不透明，成本状况不明，导致资源浪费严重，改善无从下手。
03
成本把控弱
1、管理粗放，仅关注总账，缺乏对物料、人工、实验等成本的精细化分项管理。
2、成本数据分散，与业务脱节，导致统计困难，影响成本优化决策。
02
电化学核心业务缺乏数字化支撑
1、检验实验、配方数据管理等核心业务缺乏数字化手段支撑，效率低下。
2、专利、配方等重要数据存在泄漏风险，原材料、配方、化学体系平台、电芯产品等缺乏企业级资源库和知识库。
05
业务挑战
BUSINESS CHALLENGES
设计参数繁多，普通Excel设计表比较凌乱
参数之间的关联性比较复杂，难梳理
逐项填写设计参数时较费时，且易出错
化学体系中各材料间的逻辑关系及相互影响性极其复杂，行业一直在探索和攻坚，必须要借用Al工具
设计表版本变更历史记录不清晰，无法浏览整个项目中设计的变更及优化过程
设计参数管理难
验证分析效率低
设计表预计能达到的性能表现无法模拟，依赖耗时、昂贵的实验测
测试数据繁多，原始数据量巨大，分析难度大
测试数据结果与设计参数之间的关联性不能直观展示
设计表参数繁多、数值散乱，失效分析时，逻辑关系难梳理
失效分析时无理论数据的制程，异常问题难处理
难将制程数据与设计表中重要设计参数进行关联和对应
材料选择困扰
新型号材料开发周期长，无材料数据库沉淀导致设计时不能及时被调用
材料数据存储方式、路径较散乱，需要时要从重不同路径查找，较耗时间，准确获取困难
材料数据时效性差，以往材料评测数据不能被很好的保存和共享
成本把控弱
原材料成本、物料用量、制程损耗以及能耗设备折旧等陈本数据获取比较困难
成本结果数据化、图表化展示效果不佳
成本计算过程比较耗时间
电芯设计的核心挑战在于平衡能量密度、使用寿命、充放电速率及成本等多维指标，这一过程涉及因素众多、相互作用复杂，极具挑战性。
现行研发模式往往依赖大量试错来摸索理想方案，高度依赖设计者个人的经验、专业洞察及创新思维，导致迭代周期长且效率受限。因此，打破传统依赖经验积累和逐步试错的“经验驱动”模式，转向融合实验技术、理论分析、先进计算及大数据的“理性设计”新范式，成为当前亟待推进的战略转变。
应用场景
APPLICATION SCENARIOS
核心功能及应用场景
FEATURES AND APPLICATION SCENARIOS
方案技术架构
TECHNICAL ARCHITECTURE OF THE PLAN
