[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2214":3},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":8,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":22,"tags":24,"view_count":30,"doi":8,"paper":31,"created_at":40},2214,"中国农业大学信电学院余强教授团队:面向田间工况的电动拖拉机混合储能系统分层自适应能量管理策略","https:\u002F\u002Fciee.cau.edu.cn\u002Fart\u002F2026\u002F9\u002F8\u002Fart_50389_1135985.html","中国农业大学为该论文唯一完成单位,余强教授为论文通讯作者,2022级博士研究生何雄林为第一作者。论文提出运行信息融合(OIF)方法,通过主成分分析、K-means聚类和概率神经网络实现田间作业工况的在线识别,并构建分层自适应能量管理策略。运行模式识别在线准确率达97.2%,速度和牵引阻力预测精度分别提高7.2%-12.9%;与传统模型预测控制相比,所提策略使动力电池最终荷电状态提高12.0%,系统总运行成本降低17.2%,电池退化成本降低16.7%。",null,"中国农业大学信息与电气工程学院 2026年9月8日","2026-09-08T00:00:00Z","论文",10,false,79,{"impact":16,"substance":17,"depth":16,"authority":18,"freshness":19,"relevant":20,"comment":21},18,23,14,6,1,"中国农大团队提出面向田间工况的电动拖拉机分层自适应能量管理策略，方法新颖、数据详实，对智能农机电动化具有参考价值。",[23],{"name":9,"url":6},[25,26,27,28,29],"智慧农业","农业人工智能","智能农机","电动拖拉机","能量管理",0,{"doi":8,"openalex_id":8,"authors":32,"venue":8,"cited_by_count":30,"oa_url":8,"card":33,"direction":37,"ingested_from":39},[],{"tldr":34,"method":35,"finding":36,"direction":37,"opportunity":38},"提出面向田间工况的电动拖拉机混合储能分层自适应能量管理策略。","主成分分析、K-means聚类与概率神经网络在线识别工况，结合分层自适应策略。","工况识别准确率97.2%，电池荷电状态提高12.0%，总运行成本降低17.2%。","农业绿色发展与碳","可探索多机协同与真实农田复杂工况下的能量管理泛化能力及碳减排量化。","agent","2026-09-12T00:06:40.429924Z"]