Adaptive prediction of charging duration for different modes of electric vehicles based on a deep feature fusion model

doi:10.19799/j.cnki.2095-4239.2025.0490

Abstract

Abstract:

Accurately predicting the charging time of lithium batteries can improve charging efficiency and optimize resource allocation-an important factor for developing electric vehicles. This study proposes an adaptive prediction method for electric vehicle charging duration under different modes based on a deep feature fusion model. First, vehicle operation data collected by a new energy vehicle monitoring platform are cleaned and segmented, and charging modes are classified based on voltage, current, and average power to form fast- and slow-charging datasets. Next, based on the charging dataset, principal component analysis is applied to extract model input features. Then, a multilayer perceptron (MLP) model is constructed by integrating the attention mechanism to obtain intermediate features through a nonlinear mapping of input features. As features directly extracted from raw data cannot fully capture the complex relationship with charging duration, a random forest (RF) model is introduced to construct leaf-node rule features based on the internal splitting principle of RF, exploring implicit feature information. A "rule layer" is subsequently established in the MLP to fuse intermediate and rule features, achieving structural fusion of the two models. Finally, the prediction results of the attention MLP-RF fusion model are validated, demonstrating an average absolute error of 4.25 and 6.68 minutes for fast-and slow-charging modes, respectively, with an average absolute percentage error of 4.33% and 3.86%, indicating accurate prediction of different electric vehicle charging durations. Moreover, this method maintains high accuracy in predicting charging duration under battery aging and short-term charging conditions, with an average prediction error of less than 2 min. Overall, the fusion model demonstrates strong predictive performance and generalization capabilities.

Key words: lithium-ion battery, data driven, charge mode, charging duration, feature fusion

CLC Number:

TM 912

Jieyang WEI, Jiangwei SHEN, Zheng CHEN, Fuxing WEI, Xuelei XIA, Yonggang LIU. Adaptive prediction of charging duration for different modes of electric vehicles based on a deep feature fusion model[J]. Energy Storage Science and Technology, 2025, 14(11): 4330-4345.

Figures/Tables 20

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Table 1

Fig. 6

Table 2

Fig. 7

Table 3

Table 4

Fig. 8

Fig. 9

Fig. 10

Fig. 11

Table 5

Table 6

Fig. 12

Fig. 13

Fig. 14

References 34

[1]	GIL-GARCÍA I C, GARCÍA-CASCALES M S, DAGHER H, et al. Electric vehicle and renewable energy sources: Motor fusion in the energy transition from a multi-indicator perspective[J]. Sustainability, 2021, 13(6): 3430. DOI: 10.3390/su13063430.
[2]	SHEN J W, ZHANG Z, SHEN S Q, et al. Accurate state of temperature estimation for lithium-Ion batteries based on square root cubature Kalman filter[J]. Applied Thermal Engineering, 2024, 242: 122452. DOI: 10.1016/j.applthermaleng.2024.122452.
[3]	TAO Y C, QIU J, LAI S Y, et al. Adaptive integrated planning of electricity networks and fast charging stations under electric vehicle diffusion[J]. IEEE Transactions on Power Systems, 2023, 38(1): 499-513. DOI: 10.1109/TPWRS.2022.3167666.
[4]	EDDINE M D, SHEN Y M. A deep learning based approach for predicting the demand of electric vehicle charge[J]. The Journal of Supercomputing, 2022, 78(12): 14072-14095. DOI: 10.1007/s11227-022-04428-0.
[5]	YAGHOUBI E, YAGHOUBI E, KHAMEES A, et al. A systematic review and meta-analysis of machine learning, deep learning, and ensemble learning approaches in predicting EV charging behavior[J]. Engineering Applications of Artificial Intelligence, 2024, 135: 108789. DOI: 10.1016/j.engappai.2024.108789.
[6]	申江卫, 刘伟强, 高承志, 等. 宽温度环境下基于迁移模型的锂电池组SOC估计[J]. 中国公路学报, 2024, 37(5): 383-396. DOI: 10. 19721/j.cnki.1001-7372.2024.05.025.
	SHEN J W, LIU W Q, GAO C Z, et al. State of charge estimation of lithium battery packs in wide temperature environments based on migration model[J]. China Journal of Highway and Transport, 2024, 37(5): 383-396. DOI: 10.19721/j.cnki.1001-7372.2024.05.025.
[7]	SUBASHINI M, SUMATHI V. Smart algorithms for power prediction in smart EV charging stations[J]. Journal of Engineering Research, 2024, 12(2): 124-134. DOI: 10.1016/j.jer.2023.11.028.
[8]	ADAIKKAPPAN M, SATHIYAMOORTHY N. Modeling, state of charge estimation, and charging of lithium-ion battery in electric vehicle: A review[J]. International Journal of Energy Research, 2022, 46(3): 2141-2165. DOI: 10.1002/er.7339.
[9]	RAGONE M, YURKIV V, RAMASUBRAMANIAN A, et al. Data driven estimation of electric vehicle battery state-of-charge informed by automotive simulations and multi-physics modeling[J]. Journal of Power Sources, 2021, 483: 229108. DOI: 10.1016/j.jpowsour.2020.229108.
[10]	SHI J Z, TIAN M, HAN S, et al. Electric vehicle battery remaining charging time estimation considering charging accuracy and charging profile prediction[J]. Journal of Energy Storage, 2022, 49: 104132. DOI: 10.1016/j.est.2022.104132.
[11]	ZHOU B R, FAN G D, WANG Y S, et al. Life-extending optimal charging for lithium-ion batteries based on a multi-physics model and model predictive control[J]. Applied Energy, 2024, 361: 122918. DOI: 10.1016/j.apenergy.2024.122918.
[12]	OEHLER F F, NÜRNBERGER K, STURM J, et al. Embedded real-time state observer implementation for lithium-ion cells using an electrochemical model and extended Kalman filter[J]. Journal of Power Sources, 2022, 525: 231018. DOI: 10.1016/j.jpowsour. 2022.231018.
[13]	POWELL S, VIANNA CEZAR G, APOSTOLAKI-IOSIFIDOU E, et al. Large-scale scenarios of electric vehicle charging with a data-driven model of control[J]. Energy, 2022, 248: 123592. DOI: 10.1016/j.energy.2022.123592.
[14]	ZHAO Y, WANG Z P, SHEN Z M, et al. Data-driven framework for large-scale prediction of charging energy in electric vehicles[J]. Applied Energy, 2021, 282: 116175. DOI: 10.1016/j.apenergy. 2020.116175.
[15]	ALMAGHREBI A, ALJUHESHI F, RAFAIE M, et al. Data-driven charging demand prediction at public charging stations using supervised machine learning regression methods[J]. Energies, 2020, 13(16): 4231. DOI: 10.3390/en13164231.
[16]	ULLAH I, LIU K, YAMAMOTO T, et al. Grey wolf optimizer-based machine learning algorithm to predict electric vehicle charging duration time[J]. Transportation Letters, 2023, 15(8): 889-906. DOI: 10.1080/19427867.2022.2111902.
[17]	SHAHRIAR S, AL-ALI A R, OSMAN A H, et al. Prediction of EV charging behavior using machine learning[J]. IEEE Access, 2021, 9: 111576-111586.
[18]	YI Z Y, LIU X C, WEI R, et al. Electric vehicle charging demand forecasting using deep learning model[J]. Journal of Intelligent Transportation Systems, 2022, 26(6): 690-703. DOI: 10.1080/15472450.2021.1966627.
[19]	王毅, 谷亿, 丁壮, 等. 基于模糊熵和集成学习的电动汽车充电需求预测[J]. 电力系统自动化, 2020, 44(3): 114-121.
	WANG Y, GU Y, DING Z, et al. Charging demand forecasting of electric vehicle based on empirical mode decomposition-fuzzy entropy and ensemble learning[J]. Automation of Electric Power Systems, 2020, 44(3): 114-121.
[20]	ULLAH I, LIU K, YAMAMOTO T, et al. Prediction of electric vehicle charging duration time using ensemble machine learning algorithm and Shapley additive explanations[J]. International Journal of Energy Research, 2022, 46(11): 15211-15230. DOI: 10.1002/er.8219.
[21]	胡杰, 陈琳, 王志红, 等. 基于Transformer的纯电动汽车充电时间预测[J]. 汽车工程, 2024, 46(11): 2059-2067. DOI: 10.19562/j.chinasae. qcgc.2024.11.012.
	HU J, CHEN L, WANG Z H, et al. Transformer-based prediction of charging time for pure electric vehicles[J]. Automotive Engineering, 2024, 46(11): 2059-2067. DOI: 10.19562/j.chinasae.qcgc.2024. 11.012.
[22]	陈媛, 章思源, 蔡宇晶, 等. 融合多项式特征扩展与CNN-Transformer模型的锂电池SOH估计[J]. 储能科学与技术, 2024, 13(9): 2995-3005. DOI: 10.19799/j.cnki.2095-4239.2024.0465.
	CHEN Y, ZHANG S Y, CAI Y J, et al. State-of-health estimation of lithium batteries based on polynomial feature extension of the CNN-transformer model[J]. Energy Storage Science and Technology, 2024, 13(9): 2995-3005. DOI: 10.19799/j.cnki.2095-4239.2024.0465.
[23]	PIAS T S, EISENBERG D, FRESNEDA FERNANDEZ J. Accuracy improvement of vehicle recognition by using smart device sensors[J]. Sensors, 2022, 22(12): 4397. DOI: 10.3390/s22124397.
[24]	时珊珊, 王凯, 张宇, 等. 城市轨道交通混合储能系统无源性协同控制方法[J]. 储能科学与技术, 2024, 13(11): 4040-4052. DOI: 10. 19799/j.cnki.2095-4239.2024.0516.
	SHI S S, WANG K, ZHANG Y, et al. Collaborative passivity-based control method for hybrid energy storage systems in urban rail transit[J]. Energy Storage Science and Technology, 2024, 13(11): 4040-4052. DOI: 10.19799/j.cnki.2095-4239.2024.0516.
[25]	宾涛. 电动汽车混合充换电站选址定容建模及求解[D]. 重庆: 重庆交通大学, 2024. DOI: 10.27671/d.cnki.gcjtc.2024.000200.
	BIN T. Modeling and solution of site selection and constant volume of hybrid charging and replacing power station for electric vehicle[D]. Chongqing: Chongqing Jiaotong University, 2024. DOI: 10.27671/d.cnki.gcjtc.2024.000200.
[26]	李兴建. 电动汽车充电桩运行管理系统设计与实现[D]. 成都: 电子科技大学, 2020. DOI: 10.27005/d.cnki.gdzku.2020.004222.
	LI X J. Design and implementation of electric vehicle charging station operation management system[D]. Chengdu: University of Electronic Science and Technology of China, 2020. DOI: 10. 27005/d.cnki.gdzku.2020.004222.
[27]	王明深, 袁晓冬, 曾飞, 等. 电动汽车充电设施规划运行关键技术研究综述[J]. 电力自动化设备, 2025, 45(5): 65-76. DOI: 10.16081/j.epae.202502002.
	WANG M S, YUAN X D, ZENG F, et al. Review of key technologies research for electric vehicle charging facilities planning and scheduling[J]. Electric Power Automation Equipment, 2025, 45(5): 65-76. DOI: 10.16081/j.epae.202502002.
[28]	NASKATH J, SIVAKAMASUNDARI G, BEGUM A A S. A study on different deep learning algorithms used in deep neural nets: MLP SOM and DBN[J]. Wireless Personal Communications, 2023, 128(4): 2913-2936. DOI: 10.1007/s11277-022-10079-4.
[29]	AZAD M A K, MALLICK J, ISLAM A R M T, et al. Estimation of solar radiation in data-scarce subtropical region using ensemble learning models based on a novel CART-based feature selection[J]. Theoretical and Applied Climatology, 2024, 155(1): 349-369. DOI: 10.1007/s00704-023-04638-3.
[30]	李旭东, 张向文. 基于主成分分析与WOA-Elman的锂离子电池SOH估计[J]. 储能科学与技术, 2022, 11(12): 4010-4021. DOI: 10. 19799/j.cnki.2095-4239.2022.0384.
	LI X D, ZHANG X W. State of health estimation method for lithium-ion batteries based on principal component analysis and whale optimization algorithm-Elman model[J]. Energy Storage Science and Technology, 2022, 11(12): 4010-4021. DOI: 10.19799/j.cnki.2095-4239.2022.0384.
[31]	ZHANG Y, SHI X H, ZHANG H X, et al. Review on deep learning applications in frequency analysis and control of modern power system[J]. International Journal of Electrical Power & Energy Systems, 2022, 136: 107744. DOI: 10.1016/j.ijepes.2021.107744.
[32]	刘素贞, 袁路航, 张闯, 等. 基于超声时域特征及随机森林的磷酸铁锂电池荷电状态估计[J]. 电工技术学报, 2022, 37(22): 5872-5885. DOI: 10.19595/j.cnki.1000-6753.tces.211585.
	LIU S Z, YUAN L H, ZHANG C, et al. State of charge estimation of LiFeO₄ batteries based on time domain features of ultrasonic waves and random forest[J]. Transactions of China Electrotechnical Society, 2022, 37(22): 5872-5885. DOI: 10.19595/j.cnki.1000-6753.tces. 211585.
[33]	THAJEEL I K, SAMSUDIN K, HASHIM S J, et al. Machine and deep learning-based XSS detection approaches: A systematic literature review[J]. Journal of King Saud University - Computer and Information Sciences, 2023, 35(7): 101628. DOI: 10.1016/j.jksuci.2023.101628.
[34]	韩毅, 侯震梅. 基于RF-RFECV特征选择的BO-CNN-BiLSTM-attention中国资源型城市碳排放影响因素分析与达峰情景模拟[J/OL]. 环境科学, 2025: 1-26. (2025-05-14). https://link.cnki.net/doi/10.13227/j.hjkx.202501278.
	HAN Y, HOU Z M. BO-CNN-BiLSTM-attention based on RF-RFECV feature selection analysis and peak simulation of carbon emissions in resource-based cities of China[J/OL]. Environmental Science, 2025: 1-26. (2025-05-14). https://link.cnki.net/doi/10. 13227/j.hjkx.202501278.

初始SOC	结束SOC	初始总电压/V	初始最低温度/℃	单体最高电压/V	单体最低电压/V	充电时长/s
59	100	391.1	0	3.77	3.74	14894
70	90	400	6	3.85	3.83	6820
39	100	377.5	3	3.63	3.62	20940
23	99	370.4	4	3.57	3.55	26285
14	97	364.6	4	3.51	3.49	31240
20	100	363.4	5	3.62	3.60	28152

“规则层”使用激活函数	MAE/min	MAPE/%
ReLU	4.25	4.33
LeakyReLU	6.73	6.98
Swish	7.18	7.24

注意力头数	MLP每层神经元数	树最大深度	MAE/min	MAPE/%
4	32	10	5.82	6.03
8	32	10	5.37	5.48
12	32	10	7.18	7.24
8	64	10	4.25	4.33
8	128	10	8.74	8.92
8	64	5	6.51	6.83
8	64	15	10.06	9.55

模型结构	是否含“规则层”	是否含Attention	MAE/min	MAPE/%
Attention MLP-RF	是	是	4.25	4.33
Attention MLP-RF	否	是	6.62	6.87
MLP-RF	是	否	9.13	9.51
MLP	否	否	13.52	14.02
RF	否	否	12.14	12.67

模型名称	MAE/min	RMSE/min	MAPE/%
Attention MLP-RF	4.25	5.16	4.33
BiLSTM-RF	7.51	8.81	6.52
RF	9.01	10.68	8.71
Transformer	4.92	6.07	5.14
XGBoost	6.08	7.25	5.85