Lithium-ion battery parameter identification based on adaptive multilayer RLS

doi:10.19799/j.cnki.2095-4239.2023.0605

Abstract

Abstract:

Accurate identification of battery parameters is the foundation for achieving high-precision state estimation in electric vehicle battery management systems. To address the issue of insufficient accuracy when identifying changing battery parameters using the forgetting factor recursive least square (FFRLS) method, this study proposes an adaptive multilayer recursive least squares (AMLRLS) online battery parameter identification method, which updates parameters hierarchically. The AMLRLS algorithm uses the voltage error of the identified parameters of the L-1 layer as the target value for the L-th layer. It recursively separates the parameter quantities from the voltage error and aggregates them from all layers to form the identification result for a single data point, creating a MLRLS structure. To address the problem of computing up to the maximum set layer in each identification step, a layer selector is designed. It takes the voltage error from the FFRLS identification result of the first layer as input and adaptively selects the number of layers based on the magnitude of the voltage error, reducing computational load. A battery model is constructed, and simulations are conducted to verify the parameter tracking capability of AMLRLS. Results demonstrate that AMLRLS reduces parameter errors by up to 69% compared to RLS and 46.5% compared to adaptive FFRLS. In experimental validation, AMLRLS considerably reduces the root mean square error and average absolute error of voltage by 43.9% and 32.1%, respectively, under dynamic stress test (DST) conditions compared to other algorithms. Results across different currents, temperatures, and the initial state of charge conditions validate the strong applicability of AMLRLS. Finally, the computation time of various algorithms is compared. AMLRLS reduces computation time by 37.4% under DST conditions and by 28.6% under federal urban driving schedule conditions compared to scenarios without a layer selector, thus alleviating the computational burden of the battery management system.

Key words: lithium-ion battery, parameter identification, least squares, equivalent circuit model

CLC Number:

TM 912

Shuangming DUAN, Shengli ZHANG. Lithium-ion battery parameter identification based on adaptive multilayer RLS[J]. Energy Storage Science and Technology, 2024, 13(2): 712-720.

Figures/Tables 12

Fig. 1

Table 1

The parameters identification steps of AMLRLS"

初始化 $θ ̂ L 1$ 至 $θ ̂ L m a x$ 、 $P$ 、 $e b a s e$ 、 $L m i n$ 、 $L m a x$ 、 $h$ 、 $λ$
步骤一：以式(11)更新 $e k$ ，以式(24)更新 $L k$
步骤二：以式(10)更新 $K (k)$ ，以式(13)更新 $P (k)$
步骤三：以式(12)更新 $θ ̂ L 1 (k)$ ，若 $L k = 1$ ，则进入步骤六
步骤四：以式(20)更新 $y L m (k)$ ，以式(21)更新 $e L m (k)$
步骤五：以式(22)更新 $θ ̂ L m (k)$ ，并判断是否计算到第 $L (k)$ 层。若是，则进入步骤六；若否，则以步骤四步骤五计算下一层参数
步骤六：以式(23)计算所有层的参数和 $θ ̂ (k)$

Table 1

Fig. 2

Table 2

Fig. 3

Fig. 4

Fig. 5

Table 3

Fig. 6

Fig. 7

Table 4

Table 5

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项目	AMLRLS	AFFRLS	RLS
图2(a)MAE/F	8.1929	14.7935	21.8266
图2(b)MAE/F	11.4883	21.4742	37.0916

电流	方法	RMSE×10^-4/V	MAE×10^-4/V
DST	AMLRLS	8.7832	3.3571
	AFFRLS	14.1702	4.4647
	RLS	15.6808	4.9464

FUDS	AMLRLS	17.7456	6.0669
	AFFRLS	22.3721	7.2088
	RLS	24.5875	7.8741

电流文件	温度/℃	初始SOC/%	算法	RMSE×10^-4/V	MAE×10^-4/V
DST	0	50	AMLRLS	26.4872	9.8814
			AFFRLS	41.1485	14.9455
			RLS	44.8108	17.2250
		80	AMLRLS	21.7088	7.3372
			AFFRLS	33.9587	11.0849
			RLS	36.4855	12.4792
	45	50	AMLRLS	8.6529	3.1361
			AFFRLS	13.2339	4.8080
			RLS	14.4473	4.8633
		80	AMLRLS	4.9829	2.5069
			AFFRLS	6.7103	3.2715
			RLS	8.2974	3.4952
FUDS	0	50	AMLRLS	89.1604	46.0336
			AFFRLS	104.4175	54.9780
			RLS	112.4889	59.5618
		80	AMLRLS	50.2489	22.7675
			AFFRLS	59.5605	27.1466
			RLS	64.0396	29.1888
	45	50	AMLRLS	10.3818	4.6798
			AFFRLS	13.5460	5.8574
			RLS	16.4590	6.8622
		80	AMLRLS	7.0688	4.1482
			AFFRLS	8.6230	4.8412
			RLS	10.4544	5.2304

项目	DST工况辨识时间/s	FUDS工况辨识时间/s
AMLRLS	0.0839	0.0958
AMLRLS(未设置层数选择器)	0.1340	0.1341
AFFRLS	0.0521	0.0532
RLS	0.0507	0.0494

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