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首页> 外文期刊>Power Electronics, IET >Neuro-fuzzy controller for battery equalisation in serially connected lithium battery pack
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Neuro-fuzzy controller for battery equalisation in serially connected lithium battery pack

机译：神经模糊控制器，用于串联锂电池组中的电池均衡

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摘要

This study presents a non-linear, dynamic control method for equalising battery cell voltages in a serially connected lithium-ion battery system based on an adaptive neuro-fuzzy inference system. By using a combination of neuron networks and fuzzy logic, the optimal control method is obtained by self-learning capability to equalise the current between battery cells. The duty cycle used to control the metal-oxide-semiconductor field-effect transistors in individual battery cell equalisers are changed based on the dynamic equalising and system status. While energy is transferred from higher voltage cells to lower voltage cells, online measurement is utilised to collect data for tracking. Therefore the duty cycle control has an optimal response in this battery system. The state of the optimal control output is presented in simulation results. To demonstrate the effectiveness of the proposed control scheme and robustness of the acquired neuron-fuzzy controller, the controller was implemented in a serially connected lithium battery system model using a microprocessor. The proposed system achieved a learning accuracy error of 1.8 × 10, and the equalising time was approximately 3000 s for a 0.25-V voltage gap.
机译：这项研究提出了一种非线性动态控制方法，用于基于自适应神经模糊推理系统的串联锂离子电池系统中的电池单元电压均衡。通过结合神经元网络和模糊逻辑，通过自学习能力获得了最佳的控制方法，以均衡电池单元之间的电流。根据动态均衡和系统状态来更改用于控制单个电池单元均衡器中的金属氧化物半导体场效应晶体管的占空比。当能量从高压电池传递到低压电池时，在线测量被用来收集数据以进行跟踪。因此，占空比控制在该电池系统中具有最佳响应。仿真结果显示了最佳控制输出的状态。为了证明所提出的控制方案的有效性以及所获取的神经元模糊控制器的鲁棒性，使用微处理器在串行连接的锂电池系统模型中实现了该控制器。拟议的系统实现了1.8×10的学习精度误差，对于0.25V的电压间隙，均衡时间约为3000 s。

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