摘要： 目前先進的電動汽車開發(fā)和應用已成為實現(xiàn)“脫碳”的關鍵技術。準確的電池健康狀態(tài)(State of health,SOH)預估可有效地表征動力電池性能,對電動汽車動力電池維護和壽命管理具有重要意義。近年來,以深度學習、強化學習和大數據技術等為代表的新一代人工智能技術在電動汽車電池狀態(tài)預估的應用已成為研究熱點。首先簡要介紹人工智能技術、SOH的含義以及影響SOH主要因素,然后分別從電池單體與電池系統(tǒng)的角度對幾種人工智能模型在SOH預估中的研究進行總結與討論,最后結合大數據、云計算、區(qū)域鏈等新興技術,對電池健康狀態(tài)預估問題進行展望,為提升當前動力電池全生命周期管理能力提供一些思路。

關鍵詞: 人工智能, 健康狀態(tài), 電池系統(tǒng), 現(xiàn)狀與趨勢

Abstract: The development and application of advanced electric vehicles has become the key technology to achieve “decarbonization”. Accurate state of health(SOH) prediction of battery can effectively characterize its operation performance. It is of great significance to the maintenance and life management of battery in electric vehicle. In recent years, a new generation of artificial intelligence technology represented by deep learning, reinforcement learning and big data technology has become a research hotspot in the application of battery state prediction. The basic theory of artificial intelligence technology and SOH and SOH influence factors is briefly introduced. Several main artificial intelligence algorithms in SOH prediction are summarized and discussed from the perspective of battery cell and battery system respectively. Finally, combined with emerging technologies such as big data, cloud computing and regional chain, some battery SOH prediction problems are discussed, which provides some ideas for breaking through the bottleneck of current power battery full life cycle management technology.

Key words: artificial intelligence, state of health, battery system, status and trend

中圖分類號: 

TM912

相關知識

學院在動力電池健康狀態(tài)估計研究領域取得新進展
動態(tài)工況下鋰離子動力電池荷電狀態(tài)估計和健康狀態(tài)預測
電動汽車鋰離子動力電池健康狀態(tài)估計方法研究.pdf
車用鋰電池健康狀態(tài)下快充方法研究綜述
鋰離子電池安全狀態(tài)評估研究進展
智能電池健康度評估
基于動態(tài)工況模型誤差特征的動力電池健康狀態(tài)估計方法
基于數據驅動的電動汽車電池健康狀態(tài)估計研究
電動汽車動力電池健康狀態(tài)評估方法與流程
電動汽車鋰離子電池健康狀態(tài)估計及容量衰退預測

網址: 人工智能在動力電池健康狀態(tài)預估中的研究綜述 http://m.gysdgmq.cn/newsview1294317.html