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.

4輪インホイールモータのトルク制御に基づくスリップ率推定手法の開発

安島　俊幸・初瀬　渉・山崎　勝（日立製作所）

4輪インホイールモータを採用したEV車両の駆動力を制御して車両特性を向上させるため，モータトルクを用いたスリップ率の推定手法の開発に取り組んだ．本講演では，走行中の各輪のモータトルク制御に基づいたスリップ率推定手法について基礎検討し，手法の有効性を試験車両で検証した結果について報告する．

High fidelity IPMSM model used for sensor-less algorithm comparative study

Marek Lazor･Petr Kuchar･Xiang Wang (Ricardo PLC)

Mathematical models of Interior Permanent Magnet Synchronous Motors (IPMSM) are well known in industry, however they are normally low fidelity or computationally intensive. Recent interest has focused on higher fidelity models, known as Digital Twins. These models complement the development of advanced control techniques, such as sensor-less algorithms, where sensitivity of position estimation accuracy to minor parameters like cross-coupling terms can be understood. These algorithms enable reduced cost and increased robustness in automotive applications. This paper presents a high-fidelity digital twin model, a comparative study of published sensor-less algorithms, and an innovative sensor-less algorithm allowing low-speed operation without high-frequency injection.

ドライバーの感性とシンクロする次世代e: HEV制御技術の開発

成元　椋祐・前田　定治・鵜飼　洋平・齋藤　雅利・倉地　忍・村田　直也・郡司　宙輝・志岐　一輝・長倉　朱里（本田技研工業）

環境保護に対する世界的な動向により自動車の電動化が加速している一方，電動車においても感性価値を追及することは自動車を開発するうえで非常に重要である．今回，電動車の感性価値向上の為にドライバーの感性とシンクロする新たなe: HEVシステムの制御技術を開発し，電動車の新たな指標となる性能を確認した．

Changes in dQ/dV depending on battery abnormalities

Hyunjun Jo･Hyunjun Jang･Sijoong Kim･Taekyu Kang (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.