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Li-ion battery SOC estimation method using a Neural Network trained with data generated by a P2D model

机译：利用P2D模型产生的神经网络培训的锂离子电池SOC估计方法

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摘要

The State Of Charge (SOC) estimation of a Li-ion battery is still an open problem. The most classical method, Coulomb Counting (CC) is vulnerable to current measurement bias. Measuring the Open-Circuit Voltage (OCV) allows to correct the error accumulated by the CC method, but only after the battery has been unsolicited long enough. Regarding these deficiencies, advanced SOC estimation methods try to combine current and voltage information, and are either based on an Extended Kalman Filter (EKF), which represents a certain algorithmic complexity and is hard to calibrate, or on black-box methods. In particular, methods using a Neural Network (NN) have been investigated in the literature, but take usually into account only instantaneous information, failing to represent the dynamic of ion diffusion in the electrodes. By considering also a close-past current integral as an input, this paper proposes a NN model able to correct initial SOC estimation errors and handle current measurement bias, and achieving a better estimation performance than a classical NN model taking only instantaneous information as an input.
机译：锂离子电池的充电状态（SOC）估计仍然是一个开放的问题。最古典的方法，库仑计数（CC）容易受到电流测量偏差的影响。测量开路电压（OCV）允许校正CC方法累计累计的误差，但仅在电池不旋转后足够长。关于这些缺陷，高级SOC估计方法尝试组合电流和电压信息，并且基于扩展卡尔曼滤波器（EKF），该滤波器（EKF）表示某种算法复杂性，并且很难校准，或者在黑盒方法上。特别地，已经在文献中研究了使用神经网络（NN）的方法，但通常考虑仅考虑瞬时信息，未能表示电极中的离子扩散的动态。通过考虑作为输入的近似电流积分，提出了一种能够校正初始SOC估计误差并处理电流测量偏差的NN模型，并且实现了比仅瞬时信息作为输入的瞬时信息的更好的估计性能。

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