基于魯棒H∞濾波的鋰離子電池SOC估計

潘鳳文; 弓棟梁; 高瑩; 寇亞林

doi:10.13374/j.issn2095-9389.2020.09.21.002

基于魯棒H_∞濾波的鋰離子電池SOC估計

doi: 10.13374/j.issn2095-9389.2020.09.21.002

潘鳳文^{1, 2},
弓棟梁^{1, 2},
高瑩^{1, 2, ,},
寇亞林^{1, 2}

1.
吉林大學汽車仿真與控制國家重點實驗室，長春 130025
2.
吉林大學汽車工程學院，長春 130025

基金項目: 國家重點研發計劃資助項目（2016YFB0100300）

詳細信息

通訊作者:
E-mail：gaoying@jlu.edu.cn

中圖分類號: TM911.3
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出版歷程
- 收稿日期: 2020-09-21
- 刊出日期: 2021-05-25

Lithium-ion battery state of charge estimation based on a robust H_∞ filter

PAN Feng-wen^{1, 2},
GONG Dong-liang^{1, 2},
GAO ying^{1, 2
, ,},
KOU Ya-lin^{1, 2}

1.
State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130025, China
2.
College of Automotive Engineering, Jilin University, Changchun 130025, China

More Information

Corresponding author: E-mail: gaoying@jlu.edu.cn

摘要

摘要: 荷電狀態（State of charge, SOC）估計是電池管理系統的核心功能之一，它在電動汽車的生命周期中起著重要作用。針對鋰離子電池溫度影響模型參數，進而導致SOC估計不準確的問題，本文提出了基于魯棒H_∞濾波的SOC估計方法。首先，以二階Thevenin等效電路模型做為鋰離子電池基礎模型，并將溫度對電池模型參數的影響建模為標稱電阻值和電池總容量的加性變量，視溫度變化為系統的外部擾動。其次，采用滑動線性法對電池模型進行線性化，并在此基礎上運用線性矩陣不等式技術設計了對SOC進行估計的魯棒H_∞濾波器。最后，分別采用四種不同類型的動態電流激勵進行仿真實驗驗證，并將SOC的估計結果與kalman濾波對SOC的估計結果進行對比。結果表明所設計的魯棒H_∞濾波器能夠實現對SOC更為準確的跟蹤，同時對外部擾動具有較好的魯棒性。
- 鋰離子電池 /
- SOC估計 /
- 模型參數攝動 /
- 模型線性化 /
- H_∞濾波器
Abstract: The state of charge (SOC) estimation is one of the core functions of the battery management system; it can play a significant role in the life cycle of electric vehicles. The SOC estimation method has attracted considerable research attention in recent years, particularly about improving estimation accuracy. However, most studies are limited by only focusing on known or fixed battery model parameters and not considering their temperature dependence. This indicates a need to explore how the lithium-ion battery temperature affects the model parameters, which leads to inaccurate SOC estimation. The principal objective of this study is to investigate the robust H_∞ filter-based method for the problem that temperature affects battery model parameters and thus leads to inaccurate SOC estimation. First, the second-order Thevenin equivalent circuit model with two parallel resistor–capacitor pairs is taken as the basic model of the lithium-ion battery. The influence of temperature on battery model parameters is modeled as an additive variable of the nominal resistance value and the total battery capacity, and the temperature change is considered an external disturbance of the system. Afterward, the sliding linear method is used to linearize this battery model; on this basis, a robust H_∞ filter for SOC estimation is designed using linear matrix inequality technology. Finally, the effectiveness of the proposed approach is verified using four different types of dynamic current load profiles (the BJDST-Beijing Dynamic Stress Test, FUDS-Federal Urban Driving Schedule, US06-US06 Highway Driving Schedule and BJDST-FUDS-US06 joint dynamic test) compared with the Kalman filter-based SOC estimation method. The simulation analysis results indicate that the proposed SOC estimation approach can realize a higher SOC estimation accuracy even if the model parameters vary with temperature, and it has good robustness to external disturbances.
- lithium-ion battery /
- SOC estimation /
- model parameter perturbation /
- model linearization /
- H_∞ filter

HTML全文

圖 1 等效電路模型

Figure 1. Equivalent circuit model

下載: 全尺寸圖片幻燈片

圖 2 BJDST，FUDS，US06動態測試時間歷程

Figure 2. BJDST, FUDS, US06 dynamic test time history

下載: 全尺寸圖片幻燈片

圖 3 BJDST?FUDS?US06聯合動態測試時間歷程

Figure 3. BJDST?FUDS?US06 joint dynamic test time history

下載: 全尺寸圖片幻燈片

圖 4 BJDST激勵的SOC估計

Figure 4. SOC estimation excited by BJDST

下載: 全尺寸圖片幻燈片

圖 5 BJDST激勵的SOC估計誤差

Figure 5. SOC estimation error excited by BJDST

下載: 全尺寸圖片幻燈片

圖 6 FUDS激勵的SOC估計

Figure 6. SOC estimation excited by FUDS

下載: 全尺寸圖片幻燈片

圖 7 FUDS激勵的SOC估計誤差

Figure 7. SOC estimation error excited by FUDS

下載: 全尺寸圖片幻燈片

圖 8 US06激勵的SOC估計

Figure 8. SOC estimation excited by US06

下載: 全尺寸圖片幻燈片

圖 9 US06激勵的SOC估計誤差

Figure 9. SOC estimation error excited by US06

下載: 全尺寸圖片幻燈片

圖 10 BJDST?FUDS?US06激勵的SOC估計

Figure 10. SOC estimation excited by BJDST?FUDS?US06

下載: 全尺寸圖片幻燈片

圖 11 BJDST?FUDS?US06 US06激勵的SOC估計誤差

Figure 11. SOC estimation error excited by BJDST?FUDS?US06

下載: 全尺寸圖片幻燈片

表 1 電池模型參數

Table 1. Battery model parameters

R₀ / Ω	R₁ / Ω	R₂ / Ω	C₁ / F	C₂ / F	Q /(A·h)	a	b
0.00867	0.0124	0.0123	2239	41831	2.3	1/3	3.05

下載: 導出CSV

表 2 SOC估計及其估計誤差的均方根值

Table 2. RMS value of SOC estimation and estimation error

Dynamic test	Model output (RMS)	H_∞ filter (RMS)	Kalman filter (RMS)
BJDST	0.4874	0.4879	0.4861
BJDST estimation error		0.0019	0.0041
FUDS	0.4703	0.4704	0.4694
FUDS estimation error		0.0018	0.0036
US06	0.4991	0.4992	0.4986
US06 estimation error		0.0019	0.0043
BJDST?FUDS?US06	0.3186	0.3186	0.3175
BJDST?FUDS?US06 estimation error		0.0022	0.0033

下載: 導出CSV

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