
以三电测试验证、BMS/VCU/MCU HIL、电池包在环、整车 EOL 为入口,将新能源研发、产线、检测与运维数据转化为可复用的 Physical AI 数据资产。 Using powertrain testing, BMS/VCU/MCU HIL, battery pack-in-loop and vehicle EOL as entry points to transform new energy R&D, production, inspection and maintenance data into reusable Physical AI data assets.
清研精准的新能源测试验证业务不只是"卖设备"——每一套测试系统都是一个持续产生真实物理数据的入口,在提供工程服务的同时,沉淀 物理AI 的核心数据资产。 Tsing Standard's new energy testing business isn't just "selling equipment" — every testing system is an entry point that continuously generates real physical data, providing engineering services while accumulating core Physical AI data assets.
新能源系统具备高复杂度、高安全要求和强数据闭环特征。电池、电驱、电控、整车通信、充放电、热管理和售后诊断,都是复杂物理系统——涉及电压、电流、温度、SOC、SOH、通信状态、故障码、工况变化和检测结果的动态交互。 New energy systems feature high complexity, high safety requirements and strong data loop characteristics. Battery, powertrain, control, communication, charging, thermal management and diagnostics are complex physical systems — involving dynamic interactions of voltage, current, temperature, SOC, SOH, communication status, fault codes, condition changes and test results.
电池安全、整车可靠性和售后诊断直接影响车企质量与用户安全,对数据准确性和系统稳定性要求极高。Battery safety, vehicle reliability and diagnostics directly impact OEM quality and user safety, requiring extremely high data accuracy and system stability.
涉及电、热、机械、通信、软件控制等多物理过程耦合,数据维度丰富,是 Physical AI 的理想训练场景。Involves coupling of electrical, thermal, mechanical, communication and software control processes with rich data dimensions — ideal training scenarios for Physical AI.
测试结果、检测标准、异常样本和质量判定天然可量化,模型效果可客观评测。Test results, standards, anomaly samples and quality judgments are naturally quantifiable, enabling objective model evaluation.
从研发、生产到售后均可持续产生真实场景数据,形成自增强的数据飞轮。From R&D to production to after-sales, continuously generating real-world data to form a self-reinforcing data flywheel.
从电芯到 PACK,从充放电到热管理,构建电池安全与寿命数据闭环。电压、电流、温度、SOC/SOH 轨迹是世界模型的核心训练数据。From cells to packs, from charging to thermal management, building battery safety and lifecycle data loops. Voltage, current, temperature, SOC/SOH trajectories are core training data for the world model.
BMS、VCU、MCU 及三电联调,采集不同工况下的状态—动作—结果轨迹。多域在环仿真测试可复现边界工况和异常样本,大幅降低长尾数据采集成本。BMS, VCU, MCU and powertrain integration, capturing state-action-result trajectories across conditions. Multi-domain in-the-loop simulation reproduces edge conditions and anomaly samples, greatly reducing long-tail data collection costs.
EOL、IQC、零部件测试,将产线质量验证转化为质量判定数据。每辆车的下线检测数据都是异常识别模型的训练样本。EOL, IQC and component testing, transforming production quality verification into quality judgment data. Every vehicle's end-of-line data is a training sample for anomaly detection models.
CAN/LIN/以太网通信状态感知,捕捉车云与车内通信链路数据。通信异常样本是故障预测模型的关键训练数据。CAN/LIN/Ethernet communication state awareness, capturing vehicle-cloud and in-vehicle communication data. Communication anomaly samples are key training data for fault prediction models.
售后故障、诊断路径、维修结果和异常样本的真实数据入口。真实故障样本是最难采集、最有价值的 Physical AI 训练数据。Real-world data entry for after-sales faults, diagnostic paths, repair results and anomaly samples. Real fault samples are the hardest to collect and most valuable Physical AI training data.
规模化终端检测网络,获取大规模车辆健康状态与法规性在用检测数据。在用年检数据覆盖不同品牌、不同年限、不同工况的真实车辆状态分布。Scaled terminal inspection network, acquiring large-scale vehicle health and regulatory in-use inspection data. In-use inspection data covers real vehicle state distributions across different brands, ages and conditions.
清研精准不是简单地"卖测试设备",而是将每一套测试验证系统转化为持续产生数据资产的入口。 Tsing Standard doesn't simply "sell testing equipment" — every testing system is transformed into an entry point that continuously generates data assets.
电池制造 / 三电测试 / 整车下线 / 售后诊断 / 法规性在用年检Battery manufacturing / Powertrain testing / Vehicle EOL / Diagnostics / Regulatory Inspection
多域在环仿真测试 / OTA / 通信测试 / 充放电测试 / 诊断仪 / 法规性在用年检Multi-domain in-the-loop simulation / OTA / Communication / Charging / Diagnostics / Regulatory inspection
电压 / 电流 / 温度 / 振动 / 声音 / 通信 / 故障码 / 工艺参数 / 检测结果Voltage / Current / Temperature / Vibration / Sound / Communication / Fault codes / Process parameters / Test results
异常样本库 / 工况库 / 测试用例库 / 评测集 / 设备状态轨迹Anomaly library / Condition library / Test case library / Evaluation sets / Device state trajectories
状态理解 / 异常预测 / 质量判定 / 策略优化 / 经验迁移State understanding / Anomaly prediction / Quality judgment / Strategy optimization / Experience transfer
研发提效 / 质量提升 / 风险前置 / 售后降本 / 智能运维 → 结果回流,飞轮加速R&D efficiency / Quality improvement / Risk prevention / After-sales cost reduction / Intelligent O&M → Results feed back, flywheel accelerates
无论你现在处于哪个阶段,清研精准都有对应的升级路径,帮助你在不改变现有业务的前提下,逐步构建 Physical AI 数据基础设施。 Regardless of your current stage, Tsing Standard has a corresponding upgrade path to help you gradually build Physical AI data infrastructure without changing existing business.
部署清研精准测试验证系统,提供工程服务的同时开始采集真实物理数据。测试系统即数据入口。Deploy Tsing Standard testing systems to provide engineering services while beginning to collect real physical data. Testing systems are data entry points.
将测试数据结构化为异常样本库、工况库、评测集。引入 5D 数据管线和 Cross-Embodiment 平台,让数据可复用、可评测、可迁移。Structuring test data into anomaly libraries, condition libraries and evaluation sets. Introducing 5D data pipeline and Cross-Embodiment platform to make data reusable, measurable and transferable.
基于沉淀的数据资产,训练新能源世界模型,提供状态预测、异常识别、质量判定、策略优化等 AI 服务。Training new energy world models based on accumulated data assets, providing state prediction, anomaly identification, quality judgment and strategy optimization AI services.
基于清研精准在新能源测试验证领域的工程积累,以下现有产品与测试验证平台已在多家头部车企、Tier1 和检测机构中部署应用。每一套系统既是成熟商业产品,也是持续产生高价值工业数据的入口。 Based on Tsing Standard's engineering expertise in new energy testing and validation, these current products and testing platforms have been deployed across leading OEMs, Tier1s and testing institutions. Each system is both a mature commercial product and an entry point that continuously generates high-value industrial data assets.
实现整车模型的实时运行,模拟三电系统各被测控制器的输入信号,实现各控制器对实时模型的闭环控制。Real-time vehicle model operation, simulating input signals for powertrain controllers for closed-loop control.
采用实时运行整车模型,通过接口板卡连接 BMS 控制器,实现 BMS 控制算法验证和故障诊断测试。Real-time vehicle model with interface cards connecting BMS controllers for algorithm verification and fault diagnosis.
通过模拟车辆被控对象,验证被测 VCU 的一系列功能,实现整车测试。Simulating vehicle controlled objects to verify VCU functions for complete vehicle testing.
结合电池与 HIL 技术,将电池与 BMS、控制系统、充放电设备、温箱设备实时集成。Combining battery with HIL technology, integrating battery with BMS, control systems and charging equipment in real-time.
用于整车生产下线阶段的高压安全、车辆故障与通用功能测试,保障整车交付质量。Vehicle production end-of-line testing for high-voltage safety, vehicle faults and general functions.
适用于 VCU/MCU/BMS 下线测试与来料入库测试,覆盖功能、性能与通信接口验证。VCU/MCU/BMS end-of-line and incoming material testing covering function, performance and communication.
面向新能源汽车动力蓄电池、驱动电机、电控系统及电气安全等关键部件,开展法规性在用检测与健康状态评估。Regulatory in-use inspection and health-state evaluation for key components including power batteries, drive motors, motor controllers and electrical safety systems.
面向动力电池售后检测与维护场景,涵盖电池包综合测试、充放电测试、密封性检测、故障诊断与健康评估等能力。Designed for aftermarket battery testing and maintenance, covering comprehensive battery-pack testing, charge/discharge testing, sealing inspection, fault diagnosis and health evaluation.
清研精准的新能源测试验证系统已在多类头部客户中稳定应用,持续沉淀真实工业现场的数据采集、系统验证与闭环优化能力。 Tsing Standard's new energy testing and validation systems have been stably deployed among leading clients, continuously accumulating capabilities in industrial data capture, system validation and closed-loop optimization.
围绕新能源测试验证体系开展长期合作,覆盖测试系统、验证平台与现场交付能力,支持车企在研发验证、整车测试与数据沉淀中的持续升级。Long-term cooperation around new energy testing and validation, covering test systems, validation platforms and on-site delivery capabilities to support continuous upgrading in R&D validation, vehicle testing and data accumulation.
为中车株洲所提供测试系统与电池模型相关能力,支撑复杂能源系统验证场景,帮助客户提升系统级测试效率与多工况仿真能力。Providing testing systems and battery-model capabilities for CRRC Zhuzhou Institute, supporting complex energy-system validation scenarios and improving system-level test efficiency and multi-condition simulation capability.
在法规性在用年检、终端检测网络和后市场智能诊断等方向展开合作,帮助检测机构形成规模化数据入口与真实车辆健康状态数据库。Cooperating in regulatory in-use inspection, terminal inspection networks and aftermarket intelligent diagnostics, helping institutions build large-scale data entry points and real vehicle health databases.
无论是车企、Tier1、电池厂、储能企业、检测机构还是后市场服务商,清研精准都可以基于既有测试验证系统,帮助客户构建可采集、可验证、可复用、可迭代的新能源物理数据闭环。Whether you're an OEM, Tier1, battery manufacturer, energy storage company, testing institution or aftermarket service provider, Tsing Standard can help build a capturable, verifiable, reusable and iterable new energy physical data loop.