SOH estimation of real-world power batteries based on Soft-DTW algorithm and multisource reature fusion

doi:10.19799/j.cnki.2095-4239.2025.0046

Abstract

Abstract:

The State of Health (SOH) of electric vehicle (EV) power batteries is a critical factor in ensuring efficient operation and extending service life. However, accurately assessing SOH is challenging owing to the complex and variable charging patterns in real-world EV usage, large sampling intervals in individual charge-discharge cycles, and missing feature data. To address these challenges, this study proposes a multisource feature fusion method for SOH estimation method using real-world vehicle operation data. The proposed method utilizes the soft dynamic time warping (Soft-DTW) algorithm to dynamically fuse parameters from weekly charging segment incremental capacity (IC) curves, generating an overall weekly IC fusion feature. By integrating these fused IC curve features with statistical features, a multisource feature set is constructed. Furthermore, a real-world SOH estimation model based on the Bidirectional Gated Recurrent Unit-eXtreme Gradient Boosting (BiGRU-XGBoost) is proposed. The model was tested using a real-world dataset comprising 20 EVs. K-fold cross-validation results demonstrates that the proposed SOH estimation method achieves a root mean square error (RMSE) within 1.21% and a mean absolute error (MAE) below 0.9%. Comparative experiments with GRU-XGBoost and long short-term memory (LSTM) models further validate the superiority of the BiGRU-XGBoost model, showing 36.1% and 47.6% reductions in RMSE. These findings highlight the enhanced robustness and generalization capabilities of the BiGRU-XGBoost model.

Key words: electric vehicle, state of health estimation, incremental capacity

CLC Number:

TM 911

Ping DING, Taotao LI, Linfeng ZHENG, Weixiong WU. SOH estimation of real-world power batteries based on Soft-DTW algorithm and multisource reature fusion[J]. Energy Storage Science and Technology, 2025, 14(5): 2081-2097.

Figures/Tables 19

Table 1

Fig. 1

Fig. 2

Fig. 3

Table 2

Fig. 4

Fig. 5

Fig. 6

Fig. 7

Fig. 8

Fig. 9

Table 3

Parameter and feature correlation"

参数值	峰值高度与SOH的相关系数	峰值电压与SOH的相关系数
$γ$ =0.25	0.613	-0.633
$γ$ =0.5	0.646	-0.596
$γ$ =0.75	0.724	-0.679
$γ$ =1	0.767	-0.742
$γ$ =1.25	0.654	-0.590
$γ$ =1.5	0.658	-0.587
$γ$ =1.75	0.617	-0.554
$γ$ =2	0.579	-0.532

Table 3

Fig. 10

Fig. 11

Fig. 12

Table 4

Fig. 13

Fig. 14

Fig. 15

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记录时间	电池组SOC	电池组电压/V	电池组电流/A	单体电池最高电压/V	单体电池最低电压/V	单体电池最高温度/℃	单体电池最高温度/℃	能量/kW	可用容量/Ah
2019-07-26 20:02:35	27.2	328.2	-52.2	3.656	3.64	41	38	12.4	37.28
2019-07-26 20:02:43	27.6	328.5	-52.2	3.663	3.645	41	38	12.44	37.39
2019-07-26 20:02:51	27.6	328.6	-52.2	3.665	3.647	41	38	12.47	37.51
2019-07-26 20:02:59	27.6	328.6	-52.2	3.666	3.649	41	38	12.52	37.64
2019-07-26 20:03:07	27.6	328.8	-52.2	3.666	3.649	41	38	12.56	37.76
…	…	…	…	…	…	…	…	…	…
2021-11-15 16:46:00	94.8	371.9	-41.7	4.146	4.112	34	33	41.14	123.69
2021-11-15 16:46:08	94.8	371.9	-41.9	4.147	4.114	34	33	41.17	123.78
2021-11-15 16:46:16	94.8	372.1	-41.8	4.147	4.114	34	33	41.2	123.89
2021-11-15 16:46:24	94.8	372.1	-41.8	4.148	4.114	34	33	41.24	124

筛选条件	EV1	EV2	EV3	EV4	EV5	EV6	EV7	EV8	EV9	EV10
原始	1613	1637	1535	1607	1603	1564	1577	1564	1503	1376
S1	1560	1575	1491	1527	1518	1495	1508	1495	1447	1314
[S1，S2]	1485	1491	1422	1449	1437	1403	1429	1413	1378	1249
保留度/%	92.06	91.08	92.63	90.17	89.64	89.70	90.62	94.01	91.68	90.77
筛选条件	EV11	EV12	EV13	EV14	EV15	EV16	EV17	EV18	EV19	EV20
原始	1544	1570	1526	1537	1309	1594	1453	1632	1530	1554
S1	1483	1504	1433	1467	1253	1550	1393	1540	1458	1489
[S1，S2]	1403	1420	1342	1383	1195	1481	1334	1460	1372	1425
保留度/%	90.86	90.45	87.94	89.98	91.29	92.91	81.74	89.46	89.67	91.70

不同K值	数据集	训练集		测试集
不同K值	数据集	MAE/%	RMSE/%	MAE/%	RMSE/%
K=2	max	0.63	0.75	1.37	1.78
	min	0.61	0.78	1.16	1.54
	average	0.62	0.77	1.27	1.67

K=4	max	0.47	0.6	1.18	1.56
	min	0.46	0.59	1.00	1.27
	average	0.47	0.6	1.12	1.50

K=6	max	0.45	0.57	1.09	1.37
	min	0.43	0.58	0.88	1.26
	average	0.44	0.57	1.01	1.35

K=8	max	0.44	0.56	1.12	1.45
	min	0.41	0.55	0.85	1.08
	average	0.42	0.56	0.95	1.21

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