利用回归树和改进的自适应无气味粒子滤波增强了锂离子电池充电状态和能量状态的联合估计

IF 8.9 2区 工程技术 Q1 ENERGY & FUELS
Junhao Xu, Li Zhang, Lijun Xu, Qing Huang, Jianming Zhu, Wenyi Yuan
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引用次数: 0

摘要

准确估计电池的荷电状态(SOC)和能量状态(SOE)对于提高电动汽车电池管理系统的性能至关重要。本文提出了一种基于回归树模型和改进的自适应无气味粒子滤波算法的SOC和SOE高精度联合估计方法。首先,采用一阶电阻-电容等效电路模型,利用变遗忘因子递归最小二乘法在宽温度范围内在线识别模型参数,解决了传统离线参数识别方法性能不足的问题;其次,提出了一种基于rt的开路电压(OCV)-SOC/SOE映射方法,与传统的多项式拟合相比,显著降低了误差。最后,为了克服标准粒子滤波器的粒子退化限制,提出了一种改进的自适应无气味粒子滤波算法,大大提高了估计精度和稳定性。在0°C、25°C和45°C的动态应力测试和联邦城市驾驶计划剖面下进行的实验验证表明,该方法的SOC估计结果的均方根误差(RMSE)、平均绝对误差(MAE)和平均绝对百分比误差(MAPE)分别小于0.96%、0.77%和1.61%,而SOE估计对应的指标分别小于0.96%、0.84%和1.59%。结果表明,所开发的连接估计框架具有较高的精度和足够的鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Enhanced joint estimation of lithium-ion battery state of charge and state of energy using regression tree and improved adaptive unscented particle filtering
Precise estimation of battery state of charge (SOC) and state of energy (SOE) is critical for enhancing the performance of electric vehicle battery management systems. This work presents a high-precision joint estimation method for SOC and SOE based on a regression tree (RT) model and an improved adaptive unscented particle filtering algorithm. Firstly, a first-order resistance-capacitance (RC) equivalent circuit model is employed, with model parameters identified online across a wide temperature range using variable forgetting factor recursive least squares method, addressing the insufficient performance of conventional offline parameter identification. Secondly, an RT-based open-circuit voltage (OCV)-SOC/SOE mapping approach is proposed, significantly reducing errors compared to traditional polynomial fitting. Finally, to overcome the particle degeneracy limitation of standard particle filters, an improved adaptive unscented particle filtering algorithm is introduced, substantially improving estimation accuracy and stability. Experimental validation under dynamic stress test and federal urban driving schedule profiles at 0 °C, 25 °C, and 45 °C demonstrates that the proposed method achieves root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) values of SOC estimation results below 0.96 %, 0.77 %, 1.61 % respectively, while those metrics corresponding to SOE estimation are less than 0.96 %, 0.84 % and 1.59 % respectively. Those results showcase the developed join estimation framework's high precision and enough robustness ability across wide-temperature range usage.
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来源期刊
Journal of energy storage
Journal of energy storage Energy-Renewable Energy, Sustainability and the Environment
CiteScore
11.80
自引率
24.50%
发文量
2262
审稿时长
69 days
期刊介绍: Journal of energy storage focusses on all aspects of energy storage, in particular systems integration, electric grid integration, modelling and analysis, novel energy storage technologies, sizing and management strategies, business models for operation of storage systems and energy storage developments worldwide.
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