发明人：林倪，王震坡，温爽，张照生

申请日期：2023.06.13

摘要：本发明提供了一种适用于V2G电动汽车用锂电池的健康状态评估方法，以新能源实车大数据为基础设置用于表征锂电池SOH的表征参数并计算其参考值，同时提取出与表征参数高度相关的多种运行特征参数共同构建训练样本集，还创新性地引入了NGBoost模型建立SOH估计框架，使训练好的模型对SOH估计精确性和稳健性均 ...

发明人：胡晓松，王震坡，刘鹏，贺劲松，雷小诗，王艺苏

申请日期：2023.05.26

摘要：本发明提供了一种基于乘用车充电意愿的公共快慢充电量分布预测方法，包括S1、基于新能源汽车国家监测与管理平台中纯电动乘用车的充电数据，按照年份将所述充电数据进行分组；S2、判断所述纯电动乘用车的公共快慢充类型；S3、判断所述纯电动乘用车有无慢充配套设置；S4、根据所述公共快慢充类型和慢充配套设置构建所 ...

发明人：王震坡，王舒慧，程夕明，张章，张照生

申请日期：2022.11.07

摘要：本发明提供了一种基于概率分布的锂电池组内异常单体检测方法，通过对单体的充电电压数据进行统一的统计特征提取，并对相应统计值执行后续聚类分析，避免了对单体电压逐帧采集的现有处理方式，显著降低了计算成本。方法中所采用的高斯混合模型属于无监督聚类，相比现有技术中所采用的K‑means聚类方法，能够更加平滑地 ...

作者：Wang Yan¹;，Cui Mingguang²;，Wang Zhenpo¹; （¹Beijing Institute of Technology；²Beijing Bitnei Co.,Ltd.）

出处：2022中国汽车工程学会年会暨展览会 2022

关键词：intelligent and connected vehicles；privacy computing；data circulation；data fusion；internet of vehicles

摘要：Cross-industry and cross-domain data fusion has driven the development of the intelligent and connected vehicles（ICV）ecology,but also faces the challe ...

作者：张雷^1,2;，徐同良^1,2;，李嗣阳^1,2;，程树辉^1,2;，丁晓林^1,2;，王震坡^1,2;，孙逢春^1,2; （¹北京理工大学电动车辆国家工程研究中心；²北京理工大学北京电动车辆协同创新中心）

出处：机械工程学报 2023

关键词：全线控底盘；分布式驱动；电动汽车；底盘协同控制；安全性；舒适性

摘要：全线控分布式驱动电动汽车底盘协同控制能够有效提高车辆的安全性与乘坐舒适性。以整车安全性与乘坐舒适性为目标,围绕车辆操纵稳定性控制、底盘容错控制、车身姿态控制与车辆平顺性控制四个方面,全面综述全线控分布式驱动电动汽车底盘协同控制关键技术与研究进展。针对操纵稳定性,介绍了驱动防滑与制动防抱死等车辆纵向稳 ...

作者：Tongxin Shan;Xiaoqing Zhu;Zhenpo Wang;Hsin Wang;Lei Li （[a]National Engineering Research Center of Electric Vehicles, Beijing Institute of Technology, Beijing 100081, ChinaNational Engineering Research Center of Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China[b]Materials Science and Technology Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USAMaterials Science and Technology Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA[c]Department of Materials Science and Engineering, University of Tennessee, Knoxville, TN 37996, USADepartment of Materials Science and Engineering, University of Tennessee, Knoxville, TN 37996, USA）

出处：Applied Thermal Engineering 2023

摘要：Explosion is the most extreme case of thermal runaway of lithium-ion (Li-ion) batteries. In this study, explosion dynamics of large-format Li-ion cell ...

作者：Wang, Cong ¹ ;Wang, Zhenpo ¹ ;Zhang, Lei ¹ ;Yu, Huilong ² ;Cao, Dongpu ³ ; （¹  Beijing Institute of Technology, National Engineering Research Center for Electric Vehicles, Beijing, China (GRID:grid.43555.32) (ISNI:0000 0000 8841 6246); Beijing Institute of Technology, School of Mechanical Engineering, Beijing, China (GRID:grid.43555.32) (ISNI:0000 0000 8841 6246)  ²  Beijing Institute of Technology, School of Mechanical Engineering, Beijing, China (GRID:grid.43555.32) (ISNI:0000 0000 8841 6246)  ³  Tsinghua University, School of Vehicle and Mobility, Beijing, China (GRID:grid.12527.33) (ISNI:0000 0001 0662 3178) ）

出处：Chinese Journal of Mechanical Engineering = Ji xie gong cheng xue bao, English ed. 2022

摘要：There is an increasing awareness of the need to reduce traffic accidents and fatality due to vehicle collision. Post-impact hazards can be more seriou ...

发明人：王震坡，潘金泉，程夕明，张照生，王舒慧

申请日期：2023.08.22

摘要：本发明提供了一种电动汽车锂离子电池组热失控风险识别方法，首先筛选出电池组所有单体电池充电周期的热失控风险强关联数据特征，再利用高斯混合模型计算每个单体电池样本相对于参考样本的风险特征距离和累积风险特征距离，基于累积风险特征距离阈值即可作为热失控风险有效识别的量化指标。还可对风险特征距离及累积执行标准 ...

发明人：王震坡，刘鹏，贾子润，张泽坤，张照生，林倪，李磊

申请日期：2023.08.22

摘要：本发明提供了一种基于LSTM‑GAN的车用动力电池系统故障诊断方法，利用车辆历史运行数据并基于LSTM‑GAN的模型对各电池单体电压与各表征参数间关系进行无监督学习训练，并通过对使用相同电池系统车辆由LSTM‑GAN模型得到的偏差系数进行统计分析，来实现单体电压异常的阈值检测。该方法有效解决了动力电 ...

作者：Qiushi Wang^1,2,3,4;Zhenpo Wang^1,2;CA1;Peng Liu^1,2;Lei Zhang^1,2;Dirk Uwe Sauer^3,4,5,6;Weihan Li^3,4,5,7;CA2 （¹National Engineering Research Center of Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China;²Beijing Co-innovation Center for Electric Vehicles, Beijing 100081, China;³Chair for Electrochemical Energy Conversion and Storage Systems, Institute for Power Electronics and Electrical Drives (ISEA), RWTH Aachen University, Campus-Boulevard 89, 52074 Aachen, Germany;⁴Center for Ageing, Reliability and Lifetime Prediction of Electrochemical and Power Electronics Systems (CARL), RWTH Aachen University, Campus-Boulevard 89, 52074 Aachen, Germany;⁵Jülich Aachen Research Alliance, JARA-Energy, Templergraben 55, 52056 Aachen, Germany;⁶Helmholtz Institute Münster (HI MS), IEK 12, Forschungszentrum Jülich, 52425 Jülich, Germany;⁷Lead contact）

出处：Cell Reports Physical Science 2023

关键词：field data;lithium-ion batteries;aging prediction;machine learning;statistical features

摘要：Accurately predicting battery aging is critical for mitigating performance degradation during battery usage. While the automotive industry recognizes ...