Automotive Innovation最新文献_第9页

Internal Short Circuit Detection for Parallel-Connected Battery Cells Using Convolutional Neural Network 基于卷积神经网络的并联电池内部短路检测

IF 6.1 1区工程技术

Automotive Innovation Pub Date : 2022-04-20 DOI: 10.1007/s42154-022-00180-6

Niankai Yang, Ziyou Song, Mohammad Reza Amini, Heath Hofmann

{"title":"Internal Short Circuit Detection for Parallel-Connected Battery Cells Using Convolutional Neural Network","authors":"Niankai Yang, Ziyou Song, Mohammad Reza Amini, Heath Hofmann","doi":"10.1007/s42154-022-00180-6","DOIUrl":"10.1007/s42154-022-00180-6","url":null,"abstract":"<div><p>Reliable and timely detection of an internal short circuit (ISC) in lithium-ion batteries is important to ensure safe and efficient operation. This paper investigates ISC detection of parallel-connected battery cells by considering cell non-uniformity and sensor limitation (i.e., no independent current sensors for individual cells in a parallel string). To characterize ISC-related signatures in battery string responses, an electro-thermal model of parallel-connected battery cells is first established that explicitly captures ISC. By analyzing the data generated from the electro-thermal model, the distribution of surface temperature among individual cells within the battery string is identified as an indicator for ISC detection under the constraints of sensor limitations. A convolutional neural network (CNN) is then designed to estimate the ISC resistance by using the cell surface temperature and the total capacity of the string as inputs. Based on the estimated ISC resistance from CNN, the strings are classified as faulty or non-faulty to guide the examination or replacement of the battery. The algorithm is evaluated in the presence of signal noises in terms of accuracy, false alarm rate, and missed detection rate, verifying the effectiveness and robustness of the proposed approach.</p></div>","PeriodicalId":36310,"journal":{"name":"Automotive Innovation","volume":"5 2","pages":"107 - 120"},"PeriodicalIF":6.1,"publicationDate":"2022-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s42154-022-00180-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50038608","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Non-invasive Characteristic Curve Analysis of Lithium-ion Batteries Enabling Degradation Analysis and Data-Driven Model Construction: A Review 锂离子电池无创特性曲线分析及退化分析与数据驱动模型构建综述

IF 6.1 1区工程技术

Automotive Innovation Pub Date : 2022-04-15 DOI: 10.1007/s42154-022-00181-5

Rui Cao, Hanchao Cheng, Xuefeng Jia, Xinlei Gao, Zhengjie Zhang, Mingyue Wang, Shen Li, Cheng Zhang, Bin Ma, Xinhua Liu, Shichun Yang

{"title":"Non-invasive Characteristic Curve Analysis of Lithium-ion Batteries Enabling Degradation Analysis and Data-Driven Model Construction: A Review","authors":"Rui Cao, Hanchao Cheng, Xuefeng Jia, Xinlei Gao, Zhengjie Zhang, Mingyue Wang, Shen Li, Cheng Zhang, Bin Ma, Xinhua Liu, Shichun Yang","doi":"10.1007/s42154-022-00181-5","DOIUrl":"10.1007/s42154-022-00181-5","url":null,"abstract":"<div><p>Power battery technology is essential to ensuring the overall performance and safety of electric vehicles. Non-invasive characteristic curve analysis (CCA) for lithium-ion batteries is of particular importance. CCA can provide characteristic data for further applications such as state estimation and thermal runaway warning without disassembling the batteries. This paper summarizes the characteristic curves consisting of incremental curve analysis, differential voltage analysis, and differential thermal voltammetry from the perspectives of exploring the aging mechanism of batteries and constructing the data-driven model. The process of quantitative analysis of battery aging mechanism is presented and the steps of constructing data-driven models are induced. Moreover, the recent progress and application of the main features and methodologies are discussed. Finally, the applicability of battery CCA is discussed by converting non-quantifiable battery information into transportable data covering macrostate and micro-reaction information. Combined with the cloud-based battery management platform, the above-mentioned battery characteristic curves could be used as a valuable dataset to upgrade the next-generation battery management system design.</p></div>","PeriodicalId":36310,"journal":{"name":"Automotive Innovation","volume":"5 2","pages":"146 - 163"},"PeriodicalIF":6.1,"publicationDate":"2022-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50026646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

Energy Security Planning for Hydrogen Fuel Cell Vehicles in Large-Scale Events: A Case Study of Beijing 2022 Winter Olympics 大型赛事中氢燃料电池汽车能源安全规划——以北京2022年冬奥会为例

IF 6.1 1区工程技术

Automotive Innovation Pub Date : 2022-04-12 DOI: 10.1007/s42154-022-00183-3

Pinxi Wang, Qing Xue, Jun Yang, Hao Ma, Yilun Li, Xu Zhao

{"title":"Energy Security Planning for Hydrogen Fuel Cell Vehicles in Large-Scale Events: A Case Study of Beijing 2022 Winter Olympics","authors":"Pinxi Wang, Qing Xue, Jun Yang, Hao Ma, Yilun Li, Xu Zhao","doi":"10.1007/s42154-022-00183-3","DOIUrl":"10.1007/s42154-022-00183-3","url":null,"abstract":"<div><p>Energy security planning is fundamental to safeguarding the traffic operation in large-scale events. To guarantee the promotion of green, zero-carbon, and environmental-friendly hydrogen fuel cell vehicles (HFCVs) in large-scale events, a five-stage planning method is proposed considering the demand and supply potential of hydrogen energy. Specifically, to meet the requirements of the large-scale events’ demand, a new calculation approach is proposed to calculate the hydrogen amount and the distribution of hydrogen stations. In addition, energy supply is guaranteed from four aspects, namely hydrogen production, hydrogen storage, hydrogen delivery, and hydrogen refueling. The emergency plan is established based on the overall support plan, which can realize multi-dimensional energy security. Furthermore, the planning method is demonstrative as it powers the Beijing 2022 Winter Olympics as the first “green” Olympic, providing both theoretical and practical evidence for the energy security planning of large-scale events. This study provides suggestions about ensuring the energy demand after the race, broadening the application scenarios, and accelerating the application of HFCVs.</p></div>","PeriodicalId":36310,"journal":{"name":"Automotive Innovation","volume":"5 2","pages":"209 - 220"},"PeriodicalIF":6.1,"publicationDate":"2022-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50043505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Autonomous Overtaking for Intelligent Vehicles Considering Social Preference Based on Hierarchical Reinforcement Learning 基于层次强化学习的考虑社会偏好的智能车辆自动超车

IF 6.1 1区工程技术

Automotive Innovation Pub Date : 2022-04-07 DOI: 10.1007/s42154-022-00177-1

Hongliang Lu, Chao Lu, Yang Yu, Guangming Xiong, Jianwei Gong

{"title":"Autonomous Overtaking for Intelligent Vehicles Considering Social Preference Based on Hierarchical Reinforcement Learning","authors":"Hongliang Lu, Chao Lu, Yang Yu, Guangming Xiong, Jianwei Gong","doi":"10.1007/s42154-022-00177-1","DOIUrl":"10.1007/s42154-022-00177-1","url":null,"abstract":"<div><p>As intelligent vehicles usually have complex overtaking process, a safe and efficient automated overtaking system (AOS) is vital to avoid accidents caused by wrong operation of drivers. Existing AOSs rarely consider longitudinal reactions of the overtaken vehicle (OV) during overtaking. This paper proposed a novel AOS based on hierarchical reinforcement learning, where the longitudinal reaction is given by a data-driven social preference estimation. This AOS incorporates two modules that can function in different overtaking phases. The first module based on semi-Markov decision process and motion primitives is built for motion planning and control. The second module based on Markov decision process is designed to enable vehicles to make proper decisions according to the social preference of OV. Based on realistic overtaking data, the proposed AOS and its modules are verified experimentally. The results of the tests show that the proposed AOS can realize safe and effective overtaking in scenes built by realistic data, and has the ability to flexibly adjust lateral driving behavior and lane changing position when the OVs have different social preferences.</p></div>","PeriodicalId":36310,"journal":{"name":"Automotive Innovation","volume":"5 2","pages":"195 - 208"},"PeriodicalIF":6.1,"publicationDate":"2022-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s42154-022-00177-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50012410","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

An Enhanced Data-Driven Model for Lithium-Ion Battery State-of-Health Estimation with Optimized Features and Prior Knowledge 基于优化特征和先验知识的锂离子电池健康状态估计数据驱动模型

IF 6.1 1区工程技术

Automotive Innovation Pub Date : 2022-04-04 DOI: 10.1007/s42154-022-00175-3

Huanyang Huang, Jinhao Meng, Yuhong Wang, Lei Cai, Jichang Peng, Ji Wu, Qian Xiao, Tianqi Liu, Remus Teodorescu

{"title":"An Enhanced Data-Driven Model for Lithium-Ion Battery State-of-Health Estimation with Optimized Features and Prior Knowledge","authors":"Huanyang Huang, Jinhao Meng, Yuhong Wang, Lei Cai, Jichang Peng, Ji Wu, Qian Xiao, Tianqi Liu, Remus Teodorescu","doi":"10.1007/s42154-022-00175-3","DOIUrl":"10.1007/s42154-022-00175-3","url":null,"abstract":"<div><p>In the long-term prediction of battery degradation, the data-driven method has great potential with historical data recorded by the battery management system. This paper proposes an enhanced data-driven model for Lithium-ion (Li-ion) battery state of health (SOH) estimation with a superior modeling procedure and optimized features. The Gaussian process regression (GPR) method is adopted to establish the data-driven estimator, which enables Li-ion battery SOH estimation with the uncertainty level. A novel kernel function, with the prior knowledge of Li-ion battery degradation, is then introduced to improve the modeling capability of the GPR. As for the features, a two-stage processing structure is proposed to find a suitable partial charging voltage profile with high efficiency. In the first stage, an optimal partial charging voltage is selected by the grid search; while in the second stage, the principal component analysis is conducted to increase both estimation accuracy and computing efficiency. Advantages of the proposed method are validated on two datasets from different Li-ion batteries: Compared with other methods, the proposed method can achieve the same accuracy level in the Oxford dataset; while in Maryland dataset, the mean absolute error, the root-mean-squared error, and the maximum error are at least improved by 16.36%, 32.43%, and 45.46%, respectively.</p></div>","PeriodicalId":36310,"journal":{"name":"Automotive Innovation","volume":"5 2","pages":"134 - 145"},"PeriodicalIF":6.1,"publicationDate":"2022-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s42154-022-00175-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50007408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 11

Impact, Challenges and Prospect of Software-Defined Vehicles 软件定义汽车的影响、挑战和前景

IF 6.1 1区工程技术

Automotive Innovation Pub Date : 2022-03-25 DOI: 10.1007/s42154-022-00179-z

Zongwei Liu, Wang Zhang, Fuquan Zhao

引用次数: 6

Structural Topology and Dynamic Response Analysis of an Electric Torque Vectoring Drive-Axle for Electric Vehicles 电动汽车电动扭矩矢量驱动桥结构拓扑与动态响应分析

IF 6.1 1区工程技术

Automotive Innovation Pub Date : 2022-03-25 DOI: 10.1007/s42154-022-00178-0

Junnian Wang, Shoulin Gao, Yue Qiang, Meng Xu, Changyang Guan, Zidong Zhou

{"title":"Structural Topology and Dynamic Response Analysis of an Electric Torque Vectoring Drive-Axle for Electric Vehicles","authors":"Junnian Wang, Shoulin Gao, Yue Qiang, Meng Xu, Changyang Guan, Zidong Zhou","doi":"10.1007/s42154-022-00178-0","DOIUrl":"10.1007/s42154-022-00178-0","url":null,"abstract":"<div><p>In-wheel motor-drive electric vehicles have the advantage of independently controllable wheel torque and the disadvantages of unsprung mass rise and power restriction. To address the disadvantages, a centralized layout electric torque vectoring drive-axle system (E-TVDS) with dual motors is proposed, which can realize arbitrary distribution of driving torque between the left and right wheels. First, the speed and torque distribution principle of E-TVDS based on velocity diagram are analyzed, and a virtual prototype of the whole vehicle with basic gear ratio relation model of the E-TVDS is built for simulation to verify the theoretical results and the basic effect of E-TVDS on the steering performance of the vehicle. Second, the characteristics of 36 types of the novel E-TVDS topology structure are compared and analyzed, and the optimal structure scheme is selected. Third, the accurate multiple degrees of freedom dynamic model for the optimal structure is established by using the bond graph method, and its dynamic response characteristics are analyzed. The results show that the vehicle equipped with the proposed E-TVDS can distribute the driving torque with the almost identical amount but opposite sign between the left and right wheels in any direction, and varying amount according to different chassis dynamics control requirements, and the torque response performance is great with little delay and overshoot. The function and dynamic response of the proposed E-TVDS show that it has potential application value for various performance improvements of electric vehicles.</p></div>","PeriodicalId":36310,"journal":{"name":"Automotive Innovation","volume":"5 2","pages":"164 - 179"},"PeriodicalIF":6.1,"publicationDate":"2022-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50047663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Review of Testing Object-Based Environment Perception for Safe Automated Driving 安全自动驾驶中基于对象的环境感知测试综述

IF 6.1 1区工程技术

Automotive Innovation Pub Date : 2022-02-24 DOI: 10.1007/s42154-021-00172-y

Michael Hoss, Maike Scholtes, Lutz Eckstein

引用次数: 27

Cutting-Edge Technologies on Automotive and Mobility Applied in Beijing 2022 Winter Olympics 北京2022年冬奥会汽车与移动领域的前沿技术

IF 6.1 1区工程技术

Automotive Innovation Pub Date : 2022-01-31 DOI: 10.1007/s42154-022-00174-4

Cheng Lin, Diange Yang

引用次数: 1

Crowdsourced Road Semantics Mapping Based on Pixel-Wise Confidence Level 基于像素级置信度的众包道路语义映射

IF 6.1 1区工程技术

Automotive Innovation Pub Date : 2022-01-29 DOI: 10.1007/s42154-021-00173-x

Benny Wijaya, Kun Jiang, Mengmeng Yang, Tuopu Wen, Xuewei Tang, Diange Yang

{"title":"Crowdsourced Road Semantics Mapping Based on Pixel-Wise Confidence Level","authors":"Benny Wijaya, Kun Jiang, Mengmeng Yang, Tuopu Wen, Xuewei Tang, Diange Yang","doi":"10.1007/s42154-021-00173-x","DOIUrl":"10.1007/s42154-021-00173-x","url":null,"abstract":"<div><p>High-definition map has become a vital cornerstone in the navigation of autonomous vehicles in complex traffic scenarios. Thus, the construction of high-definition maps has become crucial. Traditional methods relying on expensive mapping vehicles equipped with high-end sensor equipment are not suitable for mass map construction because of the limitation imposed by its high cost. Hence, this paper proposes a new method to create a high-definition road semantics map using multi-vehicle sensor data. The proposed method implements crowdsourced point-based visual SLAM to align and combine the local maps derived by multiple vehicles. This allows users to modify the extraction process by using a more sophisticated neural network, thus achieving a more accurate detection result when compared with traditional binarization method. The resulting map consists of road marking points suitable for autonomous vehicle navigation and path-planning tasks. Finally, the method is evaluated on the real-world KAIST urban dataset and Shougang dataset to demonstrate the level of detail and accuracy of the proposed map with 0.369 m in mapping errors in ideal condition.</p></div>","PeriodicalId":36310,"journal":{"name":"Automotive Innovation","volume":"5 1","pages":"43 - 56"},"PeriodicalIF":6.1,"publicationDate":"2022-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50052717","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1