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The journal Automotive Innovation is sponsored by China SAE, published through Springer, distributed around the world, and reflects the top-level research and technical advance of automotive engineering. Automotive Innovation newsletter in January includes the following contents： 1. Article Recommendation——Four papers on Electric Vehicles 2. China SAE News: · ISC 2023 Call for Papers |
  
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Comparative Study on Traction Battery Charging Strategies from the Perspective of Material Structure Mengyang Gao, Liduo Chen, Tianyi Ma, Weijian Hao, Zhipeng Sun, Yuhan Sun, Shiqiang Liu |
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| The service life of an electric vehicle is, to some extent, determined by the life of the traction battery. A good charging strategy has an important impact on improving the cycle life of the lithium-ion battery. Here, this paper presents a comparative study on the cycle life and material structure stability of lithium-ion batteries, based on typical charging strategies currently applied in the market, such as constant current charging, constant current and constant voltage charging, multi-stage constant current charging, variable current intermittent charging, and pulse charging. Compared with the reference charging strategy, the charging capacity of multi-stage constant current charging reaches 88%. Moreover, the charging time is reduced by 69%, and the capacity retention rate after 500 cycles is 93.3%. Through CT, XRD, SEM, and Raman spectroscopy analysis, it is confirmed that the smaller the damage caused by this charging strategy to the overall structure of the battery and the layered structure and particle size of the positive electrode material, the higher the capacity retention rate is. This work facilitates the development of a better charging strategy for a lithium-ion battery from the perspective of material structure. | |||
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Keywords: Traction battery · Charging strategy · Cycle life · Material evolution Gao, M., Chen, L., Ma, T. et al.: Comparative study on traction battery charging strategies from the perspective of material structure. Automot. Innov. 5(4), 427-437 (2022) |
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Evaluation of Transmission Losses of Various Battery Electric Vehicles Johannes Hengst, Matthias Werra, Ferit Küçükay |
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| Transmission losses in battery electric vehicles have compared to internal combustion engine powertrains a larger share in the total energy consumption and play therefore a major role. Furthermore, the power flows not only during propulsion through the transmissions, but also during recuperation, whereby efficiency improvements have a double effect. The investigation of transmission losses of electric vehicles thus plays a major role. In this paper, three simulation models of the Institute of Automotive Engineering (the lossmap-based simulation model, the modular simulation model, and the 3D simulation model) are presented. The lossmap-based simulation model calculates transmission losses for electric and hybrid transmissions, where three spur gear transmission concepts for battery electric vehicles are investigated. The transmission concepts include a single-speed transmission as a reference and two two-speed transmissions. Then, the transmission lossmaps are integrated into the modular simulation model (backward simulation) and in the 3D simulation model (forward simulation), which improves the simulation results. The modular simulation model calculates the optimal operation of the transmission concepts and the 3D simulation model represents the more realistic behavior of the transmission concepts. The different transmission concepts are investigated in Worldwide Harmonized Light Vehicle Test Cycle and evaluated in terms of transmission losses as well as the total energy demand. The map-based simulation model allows the transmission losses to be broken down into the individual component losses, thus allowing transmission concepts to be examined and evaluated in terms of their efficiency in the early development stage to develop optimum powertrains for electric axle drives. By considering transmission losses in detail with a high degree of accuracy, less efficient concepts can be eliminated at an early development stage. As a result, only relevant concepts are built as prototypes, which reduces development costs. | |||
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Keywords: Battery electric vehicle (BEV) ·Transmission losses ·Efficiency analysis · WLTC · Backward simulation · Forward simulation · Energy consumption
Hengst, J., Werra, M., Küçükay, F.: Evaluation of transmission losses of various battery electric vehicles. Automot. Innov. 5(4), 388-399 (2022) |
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Multi-scale Battery Modeling Method for Fault Diagnosis Shichun Yang, Hanchao Cheng, Mingyue Wang, Meng Lyu, Xinlei Gao, Zhengjie Zhang, Rui Cao, Shen Li, Jiayuan Lin, Yang Hua, Xiaoyu Yan, Xinhua Liu |
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| Fault diagnosis is key to enhancing the performance and safety of battery storage systems. However, it is challenging to realize efficient fault diagnosis for lithium-ion batteries because the accuracy diagnostic algorithm is limited and the features of the different faults are similar. The model-based method has been widely used for degradation mechanism analysis, state estimation, and life prediction of lithium-ion battery systems due to the fast speed and high development efficiency. This paper reviews the mainstream modeling approaches used for battery diagnosis. First, a review of the battery’s degradation mechanisms and the external factors affecting the aging rate is presented. Second, the different modeling approaches are summarized, from microscopic to macroscopic scales, including density functional theory, molecular dynamics, X-ray computed tomography technology, electrochemical model, equivalent circuit model, distributed model and neural network algorithm. Subsequently, the advantages and disadvantages of these model approaches are discussed for fault detection and diagnosis of batteries in different application scenarios. Finally, the remaining challenges of model-based battery diagnosis and the future perspective of using cloud control and battery intelligent networking to enhance diagnostic performance are discussed. | |||
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Keywords: Lithium-ion battery · Simulation model · Fault diagnosis · Electrochemical performance · State of health estimation Yang, S., Cheng, H., Wang, M. et al.: Multi-scale battery modeling method for fault diagnosis. Automot. Innov. 5(4), 400-414 (2022) |
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Adaptive Fitting Capacity Prediction Method for Lithium-Ion Batteries Xiao Chu, Fangyu Xue, Tao Liu, Junya Shao, Junfu Li |
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| Lithium-ion batteries have become the mainstream power source for electric vehicles because of their excellent performance. However, lithium-ion batteries still experience aging and capacity attenuation during usage. It is therefore critical to accurately predict battery remaining capacity for increasing battery safety and prolonging battery life. This paper first adopts the metabolism grey algorithm and a simplified electrochemical model to predict battery capacity under different operating conditions. To improve the prediction performance where the capacity changes nonlinearly, a decoupling analysis of battery capacity loss is then conducted based on the simplified electrochemical model. Finally, an adaptive fitting method is developed for capacity prediction, aiming at improving the prediction accuracy at the inflection point of battery capacity diving. The prediction results indicate that the developed adaptive fitting method can achieve high prediction accuracy under battery capacity attenuation at different discharge stages with errors lower than 2.2%. And the battery capacity decay shows linear variation, and the proposed method effectively forecast the inflection point of battery capacity diving. | |||
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Keywords: Lithium-ion battery · Capacity prediction · Capacity diving · Adaptive fitting capacity prediction Chu, X., Xue, F., Liu, T. et al.: Adaptive fitting capacity prediction method for lithium-ion batteries. Automot. Innov. 5(4), 359-375 (2022) |
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| ISC 2023 Call for Papers | |||
 China SAE and FISITA are delighted to confirm that the fifth FISITA Intelligent Safety Conference (ISC 2023) will take place summer 2023, in Chongqing, China, with physical and online participation available to registered participants. ISC 2023 will again see an international speaker line up considering some of the most important topics within the safety of future mobility arena, including SOTIF, Cybersecurity, Human Factors, AI, and Intelligent Safety Protection. The ISC Committee invites you to submit a technical paper for ISC 2023. Paper submission deadline: March 30, 2023 Submission website www.fisita.com/isc . |
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| IANMC 2023 to be held on 30-31 March 2023 in Wuhu, China | |||
 China SAE and the Automotive Lightweight Technology Innovation Strategic Alliance will jointly hold the 2023 International Automotive New Materials Conference (IANMC2023) in Wuhu, China on March 30-31, 2023. The conference aims to build an international platform for the exchange of technology of energy-saving and new energy vehicle new materials. According to the theme of the conference, there will be two main venues and 5-6 sub-venues, with nearly 70 technical reports. It is expected that more than 200 institutions including major material companies, automotive companies, universities and research institutes will participate, with 500-600 people attending. For information of the event and how to become a partner or exhibitor click here . |
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| China SAE launched “Top 10 China Automotive Technology Trends in 2023” | |||
 To seize the major opportunities presented by the new round of technological revolution and industrial transformation, China SAE has organized the forecast of automotive technology and led the sustainable high-quality development of China's automotive industry. The "Top 10 China Automotive Technology Trends" mainly focus on the roadmap of energy-saving and new energy vehicle technology in the "nine areas", focusing on major breakthroughs in 2023, significant increases in new production and application scale, and other "three types" of technical trends. It conducts surveys and research for enterprise CTOs, experts and scholars, technical backbone, etc., and forms the annual technical trend research results that have reached industry consensus. For specific content, please click here to view. |
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Automotive Innovation Sponsored by China SAE and published globally via Springer Nature, Automotive Innovation aims to be a world-class journal that provides abundant sources of innovative findings for automotive engineers and scientists. The journal is published quarterly, ensuring high-quality papers satisfying international standards. With the editorial board consisting of world-renowned experts, it has attracted readers from 72 countries and regions. The highest download of a single article wins more than 29,000. The journal is indexed in Ei Compendex, ESCI, and Scopus (CiteScore=5.0). The journal provides a forum for the research of principles, methodologies, designs, theoretical background, and cutting-edge technologies in connection with the development of vehicle and mobility. The main topics cover: energy-saving, electrification, intelligent and connected, safety, and emerging vehicle technologies. Editors-in-Chief Jun Li, Academician of CAE, President of China SAE, Professor of Tsinghua University Frank Zhao, Honorary Lifetime President of FISITA, Director of Tsinghua Automotive Strategy Research Institute, Professor of Tsinghua University Honorary and Founding Executive Editor-in-Chief Prof. Fangwu (Mike) Ma Executive Associate Editor-in-Chief Prof. Xinjie Zhang, Professor of Jilin University |
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Paper submission and browse www.ChinaSAEJournal.com.cn www.springer.com/42154 Contacts: Ms. Lily Lu Tel: +86-10-50950101 Email: jai@sae-china.org 
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