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首页> 外文期刊>Journal of Energy Storage >Hardware-in-the-loop Implementation of ANFIS based Adaptive SoC Estimation of Lithium-ion Battery for Hybrid Vehicle Applications
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Hardware-in-the-loop Implementation of ANFIS based Adaptive SoC Estimation of Lithium-ion Battery for Hybrid Vehicle Applications

机译：基于ANFIS的混合动力汽车应用锂离子电池自适应SoC估计的硬件在环实现

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摘要

This paper presents an effective method to estimate the state of charge (SoC) of a Lithium-ion battery. This parameter is very crucial as it indicates the performance and health of the battery. The battery SoC estimation equivalent circuit provided in MATLAB has been modified by adding the 3-RC pairs in series with its internal resistance. The values of the RC pairs have been calculated mathematically by solving the circuit model, based on charging and discharging dynamics of the battery. The values of these parameters have also been optimized using a "lsqnonlin" function. The SoC of the battery is estimated using the combination of coulomb counting and open-circuit voltage methods to minimize the error in estimation. The obtained SoC is further corrected for errors using ANFIS based algorithms. The effect of temperature has also been accounted for modelling the battery and in SoC estimation. These obtained SoCs for 3 cases, i.e. without RC/with RC pairs and then tuned with ANFIS based optimization are compared for the same load. The parameter calculation method adopted here results in an efficient and accurate model that keeps track of correct battery SoC. The complete system is validated in real-time using hardware-in-the-loop laboratory setup.
机译：本文提出了一种有效的方法来估计锂离子电池的充电状态（SoC）。此参数非常重要，因为它指示电池的性能和运行状况。 MATLAB中提供的电池SoC估算等效电路已通过将3-RC对与其内部电阻串联而进行了修改。 RC对的值已基于电池的充电和放电动力学，通过求解电路模型以数学方式进行了计算。这些参数的值也已使用“ lsqnonlin”函数进行了优化。电池的SoC使用库仑计数和开路电压方法相结合来估计，以最大程度地减少估计误差。使用基于ANFIS的算法进一步校正所获得的SoC的错误。温度的影响也已用于电池建模和SoC估算中。针对3种情况（即不使用RC /使用RC对）然后使用基于ANFIS的优化进行了优化，针对相同的负载对这些获得的SoC进行了比较。此处采用的参数计算方法可产生有效且准确的模型，该模型可跟踪正确的电池SoC。完整的系统使用硬件在环实验室设置进行实时验证。

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