Revolutionizing Battery Management
-AI-Powered Digital Twin for Predictive Maintenance and Enhanced Performance-

Nikolaus Keuth･Gerhard Schagerl (AVL List)

This presentation introduces an AI-powered Digital Twin technology designed to enhance battery safety and reduce warranty costs. Leveraging data from development, telematics, and in-vehicle usage, it offers accurate state-of-health monitoring, anomaly detection, and range optimization. Key benefits include a 97% identification rate of battery issues a month before occurrence, a 92% reduction in recall volume, and thrice more accurate range predictions. The AI-powered Digital Twin uses machine learning for continuous BMS improvement and a scalable analytics backend, enhancing battery performance and safety significantly.

Study on a Slip Rate Estimation Method Based on Motor Torque Control of Four In-Wheel Motors

Wataru Hatsuse (Hitachi)･Toshiyuki Ajima･Masaru Yamasaki (Astemo)

In order to control the four-wheel in-wheel motors and improve vehicle characteristics, a slip rate estimation method using motor torque has been developed. In this presentation, we report a basic study of slip rate estimation method based on motor torque control of each wheel during driving, and the effectiveness of the method is verified on a test vehicle.

Development of Next-Generation e:HEV Control System for Synchronization with Driver Emotions

Ryosuke Narimoto･Sadaharu Maeda･Yohei Ukai･Shinobu Kurachi･Masatoshi Saito･Naoya Murata･Akari Nagakura･Hiroki Gunji･Kazuki Shiki (Honda Motor)

Due to global trends towards environmental protection, the electrification of automobiles is accelerating. However, pursuing emotional value in electric vehicles is also important in automobile development. In this study, a new control system has been developed for the e: HEV system that synchronizes with the driver's emotions to enhance the emotional value of electric vehicles, and confirmed performance that sets a new benchmark for electric vehicles.

Changes in dQ/dV Depending on Battery Abnormalities

Hyunjun Jo･Sijoong Kim･Hyunjun Jang･Taekyu Kang･Woosung Kim (Hyundai Motor)

As the adoption of electric vehicle(EV) expands, the need for diagnostic technology for the safety of battery is gaining attention. A variety of abnormal conditions may occur in battery, each leading to distinct impacts. The Abnormalities are dependent on each other and affect each other, which makes difficult to understand the exact occurrence mechanism of the event. This research suggests the relationship with the change in dQ/dV depending on diverse battery abnormalities. It shows that dQ/dV changes occur under abnormal conditions, enabling the diagnosis of abnormal cells and also the classificaiton of the abnormalities. We expect the abnormal cells can be identified through monitoring the changes in dQ/dV.