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A Pragmatic SOH and SOC Co-Estimator for Lithium-ion Batteries in Smart Grid Applications

机译:智能电网应用中锂离子电池的实用SOH和SOC协同估计器

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Reliable online co-estimation of state of health (SOH) and state of charge (SOC) of Li-ion batteries were of paramount importance for the realistic battery management system (BMS). This work aimed at bridging laboratory test and real-life battery operation data with a comprehensive analysis to provide a coherent and non-invasive approach based on probability density function (PDF), for building a pragmatic model to co-evaluate SOH and SOC of Li-ion batteries for smart grid applications. PDF results based on practical applications revealed that there was a prominent regularity of the voltage probabilities with regards to the SOH, which were exploited for setting up an online SOH evaluation scale gauge. Utilization of the load current of realistic smart grid further improve the practical generality of the proposed algorithm. The battery online SOC was determined afterwards based on the online OCV variation and the extracted SOH values. Commercial Li-ion batteries at arbitrary SOH and SOC level were tested to validate the effectiveness and robustness of the proposed algorithm, and the test results showed high accuracy and reliability of the proposed algorithm for co-evaluating SOH and SOC.
机译:对于实际的电池管理系统(BMS),可靠的锂离子电池健康状态(SOH)和充电状态(SOC)的在线共同估计至关重要。这项工作旨在通过综合分析桥接实验室测试和实际电池运行数据,以提供基于概率密度函数(PDF)的一致且非侵入​​性的方法,以建立实用的模型来共同评估锂的SOH和SOC用于智能电网应用的离子电池。基于实际应用的PDF结果显示,关于SOH的电压概率具有明显的规律性,这些电压概率被用于建立在线SOH评估标尺。利用现实智能电网的负载电流进一步提高了该算法的实用性。之后,基于在线OCV变化和提取的SOH值确定电池在线SOC。对商用锂离子电池在任意SOH和SOC级别上进行了测试,以验证该算法的有效性和鲁棒性,测试结果表明,该算法可对SOH和SOC进行共同评估,具有较高的准确性和可靠性。

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