电动汽车动力电池研究展望：智能电池、智能管理与智慧能源

doi:10.19562/j.chinasae.qcgc.2022.04.017

摘要/Abstract

摘要：

本文针对电动汽车动力电池系统现存关键问题和主要需求，从材料科学与系统科学等多个层面对动力电池及其相关技术展开评述，聚焦于智能电池、智能管理和智慧能源三大方向阐述动力电池系统从感知、监测、管理直至能源互动的研究现状及发展趋势，为电动汽车动力系统在安全性、动力性、耐久性等多维度的综合管理和在能源与交通的智慧互联研究提供参考。

关键词: 动力电池系统, 智能电池, 电池缺陷的安全监测, 电池衰退的智能管理, 智慧能源

Abstract:

Aiming at the existing key issues and main requirements of traction battery in electric vehicle， the traction battery and its related technologies are reviewed from multi-layers of material science and system science in this paper. The research status quo and development trends of traction battery system in terms of perception， monitoring， management and energy interaction are expounded with focus on intelligent battery， wise management and smart energy three directions， providing references for the multi-dimension comprehensive management of safety， power performance and durability of electric vehicle powertrain and the research on the intelligent interconnection in energy and traffic.

Key words: traction battery system, intelligent battery, safety monitoring of battery defects, intelligent management of battery decay, smart energy

王亚楠,韩雪冰,卢兰光,冯旭宁,李建秋,欧阳明高. 电动汽车动力电池研究展望：智能电池、智能管理与智慧能源[J]. 汽车工程, 2022, 44(4): 617-637.

Yanan Wang,Xuebing Han,Languang Lu,Xuning Feng,Jianqiu Li,Minggao Ouyang. Prospects of Research on Traction Batteries for Electric Vehicles: Intelligent Battery, Wise Management, and Smart Energy[J]. Automotive Engineering, 2022, 44(4): 617-637.

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模型	典型分类		辨识方法
电化学模型	P2D		实验测量方法（直接拆解）；激励测量（有过拟合问题）：最小二乘、遗传算法、粒子群算法
	单粒子模型（SPM）
	eSPM
	多粒子模型
ECM	1RC		遗传算法、粒子群算法、最小二乘、卡尔曼滤波、粒子滤波
	2RC
	物理ECM
数据驱动	SVM/LR /KNR/EL		优化算法
	DL	（1）RNN： LSTM， GRU， NARXNN；	优化算法，如随机梯度下降方法、Adagrad、NAdam
	DL	（2）CNN	优化算法，如随机梯度下降方法、Adagrad、NAdam

类型	建模方法	建模对象	优势	不足
经验模型	经验公式	容量，内阻	简单，计算量小	基于大量实验
半机理模型	经验公式+ LLI，LAM，ORI	正负极LLI，LAM，ORI	部分反映寿命衰减机理，计算量小	基于大量实验
机理模型	P2D模型，并建立寿命衰减副反应方程（SEI膜增厚、内阻增加等）	活性物质/锂离子消耗速率，SEI膜增厚速率	基于第一性原理，全面描述寿命衰减情况	模型复杂，部分参数标定难度大