锂电池荷电状态(state of charge,SOC)估计技术是保证电力储能和电动汽车合理应用的核心技术,也是锂电池系统控制运营、监测维护的基础。在锂电池实际应用中,其表现出非线性、时变性、影响因素复杂性和不确定性的问题,造成了荷电状态估...锂电池荷电状态(state of charge,SOC)估计技术是保证电力储能和电动汽车合理应用的核心技术,也是锂电池系统控制运营、监测维护的基础。在锂电池实际应用中,其表现出非线性、时变性、影响因素复杂性和不确定性的问题,造成了荷电状态估计难度大、精度不高和适应能力不足。为此,众多锂电池荷电状态估计算法及改进策略应运而生。与此同时,部分研究人员针对不同估计方法和改进策略的实现方式和优缺点开展了分析与对比,但相关综述对估计方法的技术特点和适用性方面的论述不足且缺乏系统性总结。本文首先分析了锂电池荷电状态估计的影响因素和测试标准;然后从基于实验计算的传统方法、基于电池模型的滤波类算法、基于数据驱动的机器学习技术以及数模混合估计方法四个方面开展对比分析,归纳总结各类方法的技术特点、实现过程、适用条件、难题痛点以及应用优势,系统全面地论述了现有锂电池荷电状态估计技术的研究重点和应用现状;最后,展望了锂电池荷电状态估计算法的未来研究方向。展开更多
Supposing that the overall situation is dug out from the distributed monitoring nodes, there should be two critical obstacles, heterogenous schema and instance, to integrating heterogeneous data from different monitor...Supposing that the overall situation is dug out from the distributed monitoring nodes, there should be two critical obstacles, heterogenous schema and instance, to integrating heterogeneous data from different monitoring sensors. To tackle the challenge of heterogenous schema, an instance-based approach for schema mapping, named instance-based machine-learning （IML） approach was described. And to solve the problem of heterogenous instance, a novel approach, called statistic-based clustering （SBC） approach, which utilized clustering and statistics technologies to match large scale sources holistically, was also proposed. These two algorithms utilized the machine-leaning and clustering technology to improve the accuracy. Experimental analysis shows that the IML approach is more precise than SBC approach, reaching at least precision of 81% and recall rate of 82%. Simulation studies further show that SBC can tackle large scale sources holisticalty with 85% recall rate when there are 38 data sources.展开更多
When detecting deletions in complex human genomes,split-read approaches using short reads generated with next-generation sequencing still face the challenge that either false discovery rate is high,or sensitivity is l...When detecting deletions in complex human genomes,split-read approaches using short reads generated with next-generation sequencing still face the challenge that either false discovery rate is high,or sensitivity is low.To address the problem,an integrated strategy is proposed.It organically combines the fundamental theories of the three mainstream methods(read-pair approaches,split-read technologies and read-depth analysis) with modern machine learning algorithms,using the recipe of feature extraction as a bridge.Compared with the state-of-art split-read methods for deletion detection in both low and high sequence coverage,the machine-learning-aided strategy shows great ability in intelligently balancing sensitivity and false discovery rate and getting a both more sensitive and more precise call set at single-base-pair resolution.Thus,users do not need to rely on former experience to make an unnecessary trade-off beforehand and adjust parameters over and over again any more.It should be noted that modern machine learning models can play an important role in the field of structural variation prediction.展开更多
文摘锂电池荷电状态(state of charge,SOC)估计技术是保证电力储能和电动汽车合理应用的核心技术,也是锂电池系统控制运营、监测维护的基础。在锂电池实际应用中,其表现出非线性、时变性、影响因素复杂性和不确定性的问题,造成了荷电状态估计难度大、精度不高和适应能力不足。为此,众多锂电池荷电状态估计算法及改进策略应运而生。与此同时,部分研究人员针对不同估计方法和改进策略的实现方式和优缺点开展了分析与对比,但相关综述对估计方法的技术特点和适用性方面的论述不足且缺乏系统性总结。本文首先分析了锂电池荷电状态估计的影响因素和测试标准;然后从基于实验计算的传统方法、基于电池模型的滤波类算法、基于数据驱动的机器学习技术以及数模混合估计方法四个方面开展对比分析,归纳总结各类方法的技术特点、实现过程、适用条件、难题痛点以及应用优势,系统全面地论述了现有锂电池荷电状态估计技术的研究重点和应用现状;最后,展望了锂电池荷电状态估计算法的未来研究方向。
基金Projects(2007AA01Z126, 2007AA01Z474) supported by the National High-Tech Research and Development Program of ChinaProject(NCET-06-0928) supported by the Program for New Century Excellent Talents in University
文摘Supposing that the overall situation is dug out from the distributed monitoring nodes, there should be two critical obstacles, heterogenous schema and instance, to integrating heterogeneous data from different monitoring sensors. To tackle the challenge of heterogenous schema, an instance-based approach for schema mapping, named instance-based machine-learning （IML） approach was described. And to solve the problem of heterogenous instance, a novel approach, called statistic-based clustering （SBC） approach, which utilized clustering and statistics technologies to match large scale sources holistically, was also proposed. These two algorithms utilized the machine-leaning and clustering technology to improve the accuracy. Experimental analysis shows that the IML approach is more precise than SBC approach, reaching at least precision of 81% and recall rate of 82%. Simulation studies further show that SBC can tackle large scale sources holisticalty with 85% recall rate when there are 38 data sources.
基金Project(61472026)supported by the National Natural Science Foundation of ChinaProject(2014J410081)supported by Guangzhou Scientific Research Program,China
文摘When detecting deletions in complex human genomes,split-read approaches using short reads generated with next-generation sequencing still face the challenge that either false discovery rate is high,or sensitivity is low.To address the problem,an integrated strategy is proposed.It organically combines the fundamental theories of the three mainstream methods(read-pair approaches,split-read technologies and read-depth analysis) with modern machine learning algorithms,using the recipe of feature extraction as a bridge.Compared with the state-of-art split-read methods for deletion detection in both low and high sequence coverage,the machine-learning-aided strategy shows great ability in intelligently balancing sensitivity and false discovery rate and getting a both more sensitive and more precise call set at single-base-pair resolution.Thus,users do not need to rely on former experience to make an unnecessary trade-off beforehand and adjust parameters over and over again any more.It should be noted that modern machine learning models can play an important role in the field of structural variation prediction.
