新能源公交电池在线监控数据集

新能源公交车辆在使用过程中通过OBD口采集电池数据，通过4G网络传输到云服务平台，后经过数据校验、数据清洗、数据转换、数据标准化、评价指标计算等得出电池的状态评价数据；经过数据清洗和校验，能够剔除掉噪声和无用的干扰数据，后经过算法处理得到用于评价电池状态的数据集。该数据集应用于电池健康状态分析，实现对新能源公交车辆运行过程中的安全监控和电池性能的快速监控和评价。利用该数据集进行新能源公交车电池监控能够有效降低检测成本和提升检测效率。基于该数据集可实现实时数据可视化，展示车辆状态、告警等信息；在该数据集的基础上，基于规则或机器学习模型，分析数据异常，提前预测潜在故障（如电池压差过大）并发出预警，实现故障诊断与预警。在该数据集的基础上，生成驾驶行为报告，为UBI（基于驾驶行为的保险）或车队安全管理提供数据支持，实现驾驶行为分析。基于该数据集可以计算电池健康度（SOH）、电池健康度衰减趋势等指标，定期生成健康报告；利用该数据集还可以对全量车辆数据进行大数据聚合分析，继而进行产品设计优化和用户习惯分析等。该数据集用于车辆运营企业监控车辆运行状态、识别故障。New energy public transit buses collect battery data via the OBD interface during operational use, transmit the data to the cloud service platform over a 4G network, and subsequently generate battery status evaluation data through processes including data verification, data cleaning, data transformation, data standardization, and evaluation index calculation. After data cleaning and verification, noise and useless interfering data are eliminated, and the dataset for battery status evaluation is obtained via algorithmic processing. This dataset is applied to battery health status analysis, enabling real-time safety monitoring of new energy public transit buses during operation and rapid monitoring and evaluation of battery performance. Using this dataset for battery monitoring of new energy buses can effectively reduce detection costs and improve detection efficiency. Real-time data visualization can be implemented based on this dataset, displaying vehicle status, alarms and other relevant information; furthermore, by leveraging rules or machine learning models, data anomalies can be analyzed, potential faults (such as excessive battery voltage difference) can be predicted in advance and early warnings issued, thereby realizing fault diagnosis and early warning. Additionally, driving behavior reports can be generated based on this dataset, providing data support for UBI (Usage-Based Insurance, i.e., insurance based on driving behavior) or fleet safety management, and facilitating driving behavior analysis. Using this dataset, indicators such as State of Health (SOH) and the attenuation trend of battery health can be calculated, and regular health reports can be generated; in addition, big data aggregation analysis can be conducted on full-vehicle data, followed by product design optimization and user habit analysis, among other applications. This dataset is utilized by vehicle operation enterprises to monitor vehicle operating status and identify faults.