Automotive Innovation is dedicated to the publication of innovative findings in the automotive field as well as other related disciplines, covering the principles, methodologies, theoretical studies, experimental studies, product engineering and engineering application. The main topics include but are not limited to: energy-saving, electrification, intelligent and connected, new energy vehicle, safety and lightweight technologies. The journal presents the latest trend and advances of automotive technology.

Automotive Innovation is dedicated to publishing innovative research findings in the automotive field and related disciplines, covering principles, methodologies, theoretical studies, experimental studies, product engineering, and engineering applications. The main topics include, but are not limited to: energy-saving, electrification, intelligent and connected vehicles, new energy vehicles, safety, and lightweight technologies. The journal presents the latest trends and advancements in automotive technology.

《Automotive Innovation》致力于发布汽车领域及相关学科的创新研究成果，涵盖原理、方法论、理论研究、实验研究、产品工程和工程应用等方面。主要主题包括但不限于：节能、电气化、智能与互联、新能源汽车、安全和轻量化技术。期刊展示了汽车技术的最新趋势和进展。

Automotive Innovation

Abstract 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.

Niankai Yang

Ziyou Song

Mohammad Reza Amini

Heath Hofmann

Internal Short Circuit Detection for Parallel-Connected Battery Cells Using Convolutional Neural Network

Jian Hu

Zhongbao Wei

Hongwen He

An Online Adaptive Internal Short Circuit Detection Method of Lithium-Ion Battery

Abstract 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.

Hongliang Lu

Chao Lu

Yang Yu

Guangming Xiong

Jianwei Gong

Autonomous Overtaking for Intelligent Vehicles Considering Social Preference Based on Hierarchical Reinforcement Learning

基于分层强化学习的智能车辆自主超车考虑社会偏好的研究

Abstract The design with intent (DwI) toolkit assists designers in creating novel designs and interfaces. DwI, however, is not constrained to any degree, making it impossible to know whether the produced designs adequately account for users’ needs. In contrast, cognitive work analysis (CWA) is a human factors research tool that seeks to map a system and account for users’ needs, yet does not provide clear guidelines for progressing such analysis into workable designs with which users can interact. This paper seeks to present a proof-of-concept investigation to demonstrate that DwI can be suitably constrained and validated by insights gained from CWA. CWA, in turn, benefits by having a suitable toolkit for progressing insights. Two teams of individuals without design backgrounds were able to develop mock-up in-vehicle interfaces aimed at reducing fuel use. The teams were able to use DwI toolkit to articulate the genesis of their ideas, which in turn could be directly linked to system needs identified within CWA.

Craig K. Allison

Neville A. Stanton

Constraining Design: Applying the Insights of Cognitive Work Analysis to the Design of Novel In-Car Interfaces to Support Eco-Driving

约束设计：将认知工作分析的见解应用于新型车内界面设计以支持生态驾驶

Seog-Chan Oh

Vehicle-as-a-Conveyor and Its Control During Vehicle Production

车辆作为输送机及其在车辆生产中的控制

Tao Xu

Wei Fan

Zhitao Chen

Xuanhao Cao

Robust Speed and Spacing Control Framework for Autonomous Vehicles via µ-synthesis with Descriptor Form Representation

基于描述符形式表示的μ综合的自主车辆鲁棒速度与间距控制框架

Yuande Jiang

Dezong Zhao

Bing Zhu

Zhanwen Liu

Xiangmo Zhao

Driving Segment Embedding and Patterns Dictionary Generation from Real-World Data Using Self-Supervised Learning

利用自监督学习从真实世界数据中驱动分段嵌入与模式字典生成

Tao Deng

Shengyu Wu

Qibin Chen

Ping Liu

Multi-objective Energy Management Strategy for PHEVs Based on Working Condition Information Prediction and Time-Varying Equivalence Factor ECMS

基于工况信息预测和时变等效因子ECMS的插电式混合动力汽车多目标能量管理策略

Haiyang Hu

Duanfeng Chu

Jianhua Yin

Liping Lu

Double Deep Q-Networks Based Game-Theoretic Equilibrium Control of Automated Vehicles at Autonomous Intersection

基于双深度Q网络的自动驾驶车辆在自主交叉口博弈均衡控制

Abbreviation	Automot. Innov.
Journal Impact	4.80
Quartiles(Global)	TRANSPORTATION SCIENCE & TECHNOLOGY(Q1)

ISSN	2096-4250, 2522-8765
h-index	24

Publication Information	Publisher: Springer Science + Business Media，Publishing cycle: 4 issues per year，Journal Type: journal，Open Access Journals: No
Basic data	Year of publication: 2018，Proportion of original research papers: 91.18%，Self Citation Rate:8.30%， Gold OA Rate: 40.00%

Automotive Innovation

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Internal Short Circuit Detection for Parallel-Connected Battery Cells Using Convolutional Neural Network

An Online Adaptive Internal Short Circuit Detection Method of Lithium-Ion Battery

Autonomous Overtaking for Intelligent Vehicles Considering Social Preference Based on Hierarchical Reinforcement Learning

Constraining Design: Applying the Insights of Cognitive Work Analysis to the Design of Novel In-Car Interfaces to Support Eco-Driving

Vehicle-as-a-Conveyor and Its Control During Vehicle Production

Robust Speed and Spacing Control Framework for Autonomous Vehicles via µ-synthesis with Descriptor Form Representation

Vehicle-as-a-Conveyor and Its Control During Vehicle Production

Driving Segment Embedding and Patterns Dictionary Generation from Real-World Data Using Self-Supervised Learning

Multi-objective Energy Management Strategy for PHEVs Based on Working Condition Information Prediction and Time-Varying Equivalence Factor ECMS

Double Deep Q-Networks Based Game-Theoretic Equilibrium Control of Automated Vehicles at Autonomous Intersection