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通过基于梯度的优化提高电池健康状态估计的准确性:电动汽车应用中的案例研究

Mouncef El Marghichi¹, Soufiane Dangoury², Younes Zahrou³

¹Faculty of Sciences and Technology, Hassan First University, Settat, Morocco.

|November 2, 2023

概括

本研究引入了一种新型的渐变型优化器 (GBO),以提高电动汽车 (EV) 和混合动力电动汽车 (HEV) 的离子电池健康状况 (SOH) 估计的准确性. 与现有的算法相比,GBO方法显著减少了错误,并提高了预测性能.

科学领域:

电池技术电池技术
电气工程电气工程
优化算法优化算法

背景情况:

离子电池对于电动汽车 (EV) 开发至关重要,需要准确的健康状况 (SOH) 预测,以确保安全性和性能.
目前的SOH估计方法面临不确定性,影响电动汽车和混合动力电动汽车 (HEV) 性能指标的可靠性.
电池技术的进步推动了电动汽车的采用,增加了对精确电池管理系统的需求.

研究的目的:

为了提高离子电池健康状况 (SOH) 估计的准确性.
为了减少电荷状态 (SOC) 估计和测量的不确定性,以改善SOH预测.
引入和评估用于SOH评估的新型梯度基础优化器 (GBO).

主要方法:

提出了一个新的渐变型优化器 (GBO) 来评估离子电池SOH.
在 GBO 中使用记忆色遗忘因子来选择最佳的 SOH 更新.
与粒子优化-最小方位支向量回归 (PSO-LSSV),BCRLS-多重加权双扩展卡尔曼过 (BCRLS-MWDEKF),总最小方位 (TLS) 和近似加权总最小方位 (AWTLS) 的GBO性能进行比较.

主要成果:

在EV和HEV应用中,GBO方法始终超过了替代算法.
GBO实现了最低的最大误差,在EV (0.65%-1.57%) 和HEV (0.81%-3.21%) 场景中指出了特定的范围.
具有较低的平均平方误差 (MSE),根平均平方误差 (RMSE) 和平均绝对百分比误差 (MAPE) 的优异预测性能.

结论:

梯度基础优化器 (GBO) 在离子电池 SOH 估计的准确性和可靠性方面提供了显著的改进.
拟议的方法通过提供更精确的SOH预测来增强EV和HEV电池管理系统.
在减少估计错误和改进预测指标方面,GBO的有效性使其成为监测电池健康状况的宝贵工具.

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