State of charge estimation for lithium-ion batteries under multiple temperatures based on the MIAEK algorithm

doi:10.19799/j.cnki.2095-4239.2023.0533

Abstract

Abstract:

With the widespread application of lithium-ion batteries in electric vehicles, accurate estimation of the state of charge (SOC) of batteries is crucial for ensuring battery safety and optimal performance. However, traditional Kalman estimation methods face challenges maintaining accuracy under variable temperature conditions. To address this issue, this study proposes a SOC estimation method based on multi-innovation adaptive extended Kalman filtering (MIAEKF). This study initially performs parameter identification using battery data from experiments conducted at various temperatures. The forgetting factor recursive least squares method is employed for this purpose, providing battery parameters at different SOC stages under multiple temperature conditions. Second, a function fitting approach is used to establish a model with SOC and temperature as independent variables and battery parameters as dependent variables, describing the dynamic behavior of battery parameters. Finally, the MIAEKF algorithm is introduced, incorporating adaptivity and multi-innovation concepts from the Kalman filtering algorithm. The sliding window is introduced to replace the traditional adaptive factor for adjusting the process noise and measurement noise covariance adaptively. The mean of multi-innovations within the window is used to augment the error innovation for the posterior estimate. By selecting an appropriate window length and innovation mean coefficient, the accuracy of SOC estimation is effectively improved. Verification using experimental data shows that the SOC estimation method based on MIAEKF outperforms traditional extended Kalman filtering and adaptive extended Kalman filtering in terms of estimation accuracy and stability under the same conditions. Under multiple temperature conditions, MIAEKF can adaptively estimate SOC at different temperatures, with estimation errors within ±1%. In summary, this study effectively addresses the complexity of SOC estimation for lithium-ion batteries under variable temperature conditions by proposing the SOC estimation method based on MIAEKF.

Key words: MIAEKF, lithium batteries, SOC, multiple temperatures, function fitting

CLC Number:

TM 912

Zhaokai YUAN, Qiuhua FAN, Dongqing WANG, Tianmin SUN. State of charge estimation for lithium-ion batteries under multiple temperatures based on the MIAEK algorithm[J]. Energy Storage Science and Technology, 2024, 13(2): 680-690.

Figures/Tables 13

Fig. 1

Fig. 2

Table 1

Parameter identification results for HPPC at SOC=100%"

温度/℃	$R 0$ /Ω	$R 1$ /Ω	$C 1$ /F	$R 2$ /Ω	$C 2$ /F
-10	0.0209	0.00055	19848.738	0.0005	200000
0	0.0104	0.00049	20218.943	0.0005	201614
10	0.0067	0.00050	19871.568	0.0005	200136
25	0.0064	0.00050	20001.977	0.00049	199951
40	0.0017	0.00052	19575.562	0.00052	199164

Table 1

Fig. 3

Fig. 4

Fig. 5

Table 2

Estimation accuracy of AEKF for different window lengths"

a	15	25	50	100	200
$R M S E$ /%	0.0601	0.0583	0.0609	0.0650	0.0993
$M a x s o c e r r o r$ /%	0.1049	0.1018	0.1059	0.1127	0.1672

Table 2

Fig. 6

Table 3

The maximum error of MIAEKF SOC estimation for different λ?under a=25"

$λ$	0.5	1	2	3	4	5
$R M S E$ /%	0.0560	0.0562	0.0448	0.0465	0.0470	0.0485
$M a x s o c e r r o r$ /%	0.0980	0.0984	0.0838	0.0902	0.0878	0.0897

Table 3

Fig. 7

Table 4

Comparison of data for different state of charge (SOC) estimation algorithms"

算法	$M a x s o c e r r o r$ /%	$R M S E$ /%
EKF	3.8833	2.1205
AEKF	0.1018	0.0583
MIAEKF	0.0838	0.0448

Table 4

Fig. 8

Fig. 9

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