Available online at www.sciencedirect.com
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www.elsevier.com/locate/procedia
Applied Energy Symposium and Forum, Renewable Energy Integration with Mini/Microgrids,
REM 2018, 29–30 September 2018, Rhodes, Greece
Assessing the feasibility of using the heat demand-outdoor temperature function for a long-term district heat demand forecast
China
I. Andri ć a,b,c *, A. Pina a , P. Ferrão a , J. Fournier b ., B. Lacarrière c , O. Le Corre c a IN+ Center for Innovation, Technology and Policy Research - Instituto Superior Técnico, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal b Veolia Recherche & Innovation, 291 Avenue Dreyfous Daniel, 78520 Limay, France
Battery is the bottleneck technology of electric vehicles. The complex chemical reactions inside the battery are difficult to monitor directly. The establishment of a precise mathematical model for the battery is of great significance in ensuring the secure and stable operation of the battery management system. First of all, a data cleaning method based on machine learning is put forward, which is applicable to the characteristics of big data from batteries in electric vehicles. Secondly, this paper establishes a lithium-ion battery model based on deep learning algorithm and the error of model based on different algorithms is compared. The data of
District heating networks are commonly addressed in the literature as one of the most effective solutions for decreasing the modeling method in this paper is able to simulate the battery characteristics accurately, and the mean absolute percentage error of the terminal voltage estimation is within 2.5%.
© 2019 The Authors. Published by Elsevier Ltd.
This is an open access article under the CC-BY-NC-ND license (
Renewable Energy Integration with Mini/Microgrids, REM 2018.
The battery and its management system are the most significant components of the electric vehicles (EVs), with great impact on the efficiency and safety of the EVs. The research of the battery and its management system mainly improve the accuracy of heat demand estimations.
https://creativecommons.org/licenses/by-nc-nd/4.0/
Selection and peer-review under responsibility of the scientific committee of the Applied Energy Symposium and Forum,
Renewable Energy Integration with Mini/Microgrids, REM 2018.
) compared with results from a dynamic heat demand model, previously developed and validated by the authors.
The results showed that when only weather change is considered, the margin of error could be acceptable for some applications
(the error in annual demand was lower than 20% for all weather scenarios considered). However, after introducing renovation scenarios, the error value increased up to 59.5% (depending on the weather and renovation scenarios combination considered).
The value of slope coefficient increased on average within the range of 3.8% up to 8% per decade, that corresponds to the decrease in the number of heating hours of 22-139h during the heating season (depending on the combination of weather and renovation scenarios considered). On the other hand, function intercept increased for 7.8-12.7% per decade (depending on the coupled scenarios). The values suggested could be used to modify the function parameters for the scenarios considered, and focus on the parameter identification, State of Charge (SoC) estimation and fault detection based on the equivalent
© 2017 The Authors. Published by Elsevier Ltd.
Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and
Cooling.
E-mail address: hwhebit@bit.edu.cn
1876-6102 Copyright © 2018 Elsevier Ltd. All rights reserved.
Selection and peer-review under responsibility of the scientific committee of the Applied Energy Symposium and Forum, Renewable Energy
Integration with Mini/Microgrids, REM 2018.
1876-6102 © 2019 The Authors. Published by Elsevier Ltd.
This is an open access article under the CC-BY-NC-ND license ( https://creativecommons.org/licenses/by-nc-nd/4.0/ )
Selection and peer-review under responsibility of the scientific committee of the Applied Energy Symposium and Forum, Renewable Energy
Integration with Mini/Microgrids, REM 2018.
10.1016/j.egypro.2018.12.046
10.1016/j.egypro.2018.12.046
1876-6102
2 Shuangqi Li / Energy Procedia 00 (2018) 000–000 circuit model and electrochemical model [1]. However, these models cannot adapt to changes of battery life and environment, which have effect on the normal operation of the battery management system [2]. Therefore, in order to improve the safety and stability of the battery management system, it is extremely important to put forward a battery model that is able to work stably in whole life cycle and multi-variable environment.
In previous work, the neural network algorithm has been widely used in battery modeling. A black box model for the battery based on neural network algorithm is established by Kang L W et al. [3], who designed a model that can accurately represent the highly nonlinear mapping between the input and output of the battery, but a tremendous amount of data is needed for improving the accuracy and adaptability of the model. However, with the increase of data volume, the training process of neural network becomes complicated, and there is a serious overfitting problem in the case of poor data quality.
For improving the accuracy of the battery model, Haq I N et al [4] used the Support Vector Regression (SVR) algorithm to model the battery, and achieved effective results in experimental stage. However, as the amount of training data increasing, the training time of the model increases explosively, making it difficult to adapt to the big data environment.
Based on the aforementioned discussion, the contributions of this paper are in 3-fold: (1) A data cleaning method based on machine learning algorithm is proposed to improve the quality of the data; (2) A lithium-ion battery model based on deep learning algorithm is established ; (3) The error of model based on different algorithms is compared.
2. Methodology
2.1. Short description of SVR
The SVR is an algorithm for solving the regression problem which adheres to the principle of minimizing structural risks [5]. SVR makes use of a kernel function to map data into high-dimensional space, where a line can be used to fit the data points. The loss function of SVR allows the model to have an acceptable error for the training data, meanwhile,
SVR uses slack variables in the training process, which will prevent the model from being affected by the dead pixel data. Since SVR algorithm can effectively prevent the model from over-fitting, it obtain a better effect than Back
Propagation neural network when processing the data which is polluted by noise or bad points [6].
2.2. Short description of SDAE
In contrast to neural network algorithm, the highly nonlinear mapping between input and output can be simulated by deep learning algorithm effectively. At present, deep learning algorithms are widely used in electric load forecasting [7, 8], traffic speed prediction [9], energy management system [10], etc.
Stacked Denoising Autoencoder (SDAE) is a semi-supervised learning model. The input data is reconstructed from the bottom up, and the deep structure features of the data are continuously extracted while the effective information in the data is retained, and the re-expressed data is preferable for further processing [11]. Automatic encoder (AE) is the basic unit of SDAE, which is a single hidden layer neural network.
At present, automatic encoders are mostly used to reduce the dimension of input data [12]. It can, however, also be used to find structural features to enrich that information of the input data when the sparse constraints are added to the hiding layer, which means that most of the neuron outputs are 0. The relative entropy is used as sparsely restriction, and it can be depicted as below:
KL ρ ρ = ρ
Where ρ log
ρ
ρ ˆ
(1 ρ ) log
1
1
−
−
ρ
ρ ˆ
(1) represents the sparse target value and the value of the relative entropy function is 0.
ρ ˆ represents the average activation rate of neurons, when
ρ ρ ˆ
The Denoising Autoencoder (DAE) trains the automatic encoder with the contaminated data. Therefore, the model can not only filter out the noise in the data, but also extract the structure features of the data.

Available online at www.sciencedirect.com
Applied Energy Symposium and Forum, Renewable Energy Integration with Mini/Microgrids,
REM 2018, 29–30 September 2018, Rhodes, Greece
National Engineering Laboratory for Electric Vehicles, School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081,
China
Battery is the bottleneck technology of electric vehicles. The complex chemical reactions inside the battery are difficult to monitor directly. The establishment of a precise mathematical model for the battery is of great significance in ensuring the secure and stable operation of the battery management system. First of all, a data cleaning method based on machine learning is put forward, which is applicable to the characteristics of big data from batteries in electric vehicles. Secondly, this paper establishes a lithium-ion battery model based on deep learning algorithm and the error of model based on different algorithms is compared. The data of electric buses are used for validating the effectiveness of the model. The result shows that the data cleaning method achieves good results, in the case of the terminal voltage missing, the mean absolute percentage error of filling is within 4%, and the battery modeling method in this paper is able to simulate the battery characteristics accurately, and the mean absolute percentage error of the terminal voltage estimation is within 2.5%.
Copyright © 2018 Elsevier Ltd. All rights reserved.
Selection and peer-review under responsibility of the scientific committee of the Applied Energy Symposium and Forum,
Renewable Energy Integration with Mini/Microgrids, REM 2018.
Keywords: electric vehicle; lithium-ion power battery; modeling; battery management; bigdata; deeplearning;
The battery and its management system are the most significant components of the electric vehicles (EVs), with great impact on the efficiency and safety of the EVs. The research of the battery and its management system mainly focus on the parameter identification, State of Charge (SoC) estimation and fault detection based on the equivalent
* Corresponding author. Tel.: +86-010-68914842; fax: +86-010-68914842.
E-mail address: hwhebit@bit.edu.cn
1876-6102 Copyright © 2018 Elsevier Ltd. All rights reserved.
Selection and peer-review under responsibility of the scientific committee of the Applied Energy Symposium and Forum, Renewable Energy
Integration with Mini/Microgrids, REM 2018.
2
169 circuit model and electrochemical model [1]. However, these models cannot adapt to changes of battery life and environment, which have effect on the normal operation of the battery management system [2]. Therefore, in order to improve the safety and stability of the battery management system, it is extremely important to put forward a battery model that is able to work stably in whole life cycle and multi-variable environment.
In previous work, the neural network algorithm has been widely used in battery modeling. A black box model for the battery based on neural network algorithm is established by Kang L W et al. [3], who designed a model that can accurately represent the highly nonlinear mapping between the input and output of the battery, but a tremendous amount of data is needed for improving the accuracy and adaptability of the model. However, with the increase of data volume, the training process of neural network becomes complicated, and there is a serious overfitting problem in the case of poor data quality.
For improving the accuracy of the battery model, Haq I N et al [4] used the Support Vector Regression (SVR) algorithm to model the battery, and achieved effective results in experimental stage. However, as the amount of training data increasing, the training time of the model increases explosively, making it difficult to adapt to the big data environment.
Based on the aforementioned discussion, the contributions of this paper are in 3-fold: (1) A data cleaning method based on machine learning algorithm is proposed to improve the quality of the data; (2) A lithium-ion battery model based on deep learning algorithm is established ; (3) The error of model based on different algorithms is compared.
2. Methodology
2.1. Short description of SVR
The SVR is an algorithm for solving the regression problem which adheres to the principle of minimizing structural risks [5]. SVR makes use of a kernel function to map data into high-dimensional space, where a line can be used to fit the data points. The loss function of SVR allows the model to have an acceptable error for the training data, meanwhile,
SVR uses slack variables in the training process, which will prevent the model from being affected by the dead pixel data. Since SVR algorithm can effectively prevent the model from over-fitting, it obtain a better effect than Back
Propagation neural network when processing the data which is polluted by noise or bad points [6].
2.2. Short description of SDAE
In contrast to neural network algorithm, the highly nonlinear mapping between input and output can be simulated by deep learning algorithm effectively. At present, deep learning algorithms are widely used in electric load forecasting [7, 8], traffic speed prediction [9], energy management system [10], etc.
Stacked Denoising Autoencoder (SDAE) is a semi-supervised learning model. The input data is reconstructed from the bottom up, and the deep structure features of the data are continuously extracted while the effective information in the data is retained, and the re-expressed data is preferable for further processing [11]. Automatic encoder (AE) is the basic unit of SDAE, which is a single hidden layer neural network.
At present, automatic encoders are mostly used to reduce the dimension of input data [12]. It can, however, also be used to find structural features to enrich that information of the input data when the sparse constraints are added to the hiding layer, which means that most of the neuron outputs are 0. The relative entropy is used as sparsely restriction, and it can be depicted as below:
KL ρ ρ = ρ
Where ρ log
ρ
ρ ˆ
(1 ρ ) log
1
1
−
−
ρ
ρ ˆ
(1) represents the sparse target value and the value of the relative entropy function is 0.
ρ ˆ represents the average activation rate of neurons, when
ρ ρ ˆ
The Denoising Autoencoder (DAE) trains the automatic encoder with the contaminated data. Therefore, the model can not only filter out the noise in the data, but also extract the structure features of the data.

170
3
Extreme Learning Machine (ELM) is a kind of machine learning algorithm to solve the problem of regression, the core of the ELM algorithm is the linear regression layer, in which the regression problem is converted to the problem of finding least-squares solution for linear system, the weight matrix can be obtained directly by the formula below:
ˆ
+ (2)
Where X and T respectively represent the input and the output data,
ˆ is weight of the network.
SDAE-ELM is one of the most commonly models in deep learning, training the SDAE layer-by-layer with he nonlabeled data , and then using the label data to train the ELM directly.
2.3. Model development
Battery data is characterized by large volume, low value density and error, etc. Therefore, the data cleaning is an important process before modeling the battery. But the operating conditions of EVs are complex and changeable and the battery is a highly nonlinear system, the data preprocessing method based on statistical methods can hardly work effectively, so a data cleaning method for the big data of electric vehicles based on machine learning algorithm is proposed in this section.
4 Shuangqi Li / Energy Procedia 00 (2018) 000–000
U = ( , , ) (3)
The method for model training is shown in sections 2-2.In the process of the training, the residual A and residual
B are recorded. When the residual A is less than the critical value or reaches the maximum iterations, the parameters of the network are assigned to the black box model. Residual B is used to determine whether the model is over-fitting.
3. Result and discussion
3.1. Basis data and the result of data cleaning
This paper collects and stores a large amount of operational data including the Terminal voltage, the SoC, the temperature and the current information for the battery of electrical buses. But limited by the calculating ability of the simulation environment, only a small part of the data is selected to test the models and algorithms proposed in this paper.
Fig. 1. The process of data cleaning.
The SVR algorithm is used to fill in the missing data (The current or the terminal voltage), a complete battery state is formed and the data is sent back to the database for further mining. The process of data cleaning in showed in Figure
1.
Fig. 2. Black-box model for the battery.
The chemical reaction in Lithium-ion battery is extremely complex and it is difficult to be monitored directly, so a black-box model is established for the battery in this paper. The modeling process is shown in Figure 2.The current, temperature, SoC are used as inputs, and the terminal voltage of the battery is used as output in this paper, the purpose of the neural network is to approximate the function:
Fig 3. The data used in this paper.

Author name / Energy Procedia 00 (2018) 000–000 3
Extreme Learning Machine (ELM) is a kind of machine learning algorithm to solve the problem of regression, the core of the ELM algorithm is the linear regression layer, in which the regression problem is converted to the problem of finding least-squares solution for linear system, the weight matrix can be obtained directly by the formula below:
ˆ
+ (2)
Where X and T respectively represent the input and the output data,
ˆ is weight of the network.
SDAE-ELM is one of the most commonly models in deep learning, training the SDAE layer-by-layer with he nonlabeled data , and then using the label data to train the ELM directly.
2.3. Model development
Battery data is characterized by large volume, low value density and error, etc. Therefore, the data cleaning is an important process before modeling the battery. But the operating conditions of EVs are complex and changeable and the battery is a highly nonlinear system, the data preprocessing method based on statistical methods can hardly work effectively, so a data cleaning method for the big data of electric vehicles based on machine learning algorithm is proposed in this section.
4
171
U = ( , , ) (3)
The method for model training is shown in sections 2-2.In the process of the training, the residual A and residual
B are recorded. When the residual A is less than the critical value or reaches the maximum iterations, the parameters of the network are assigned to the black box model. Residual B is used to determine whether the model is over-fitting.
3. Result and discussion
3.1. Basis data and the result of data cleaning
This paper collects and stores a large amount of operational data including the Terminal voltage, the SoC, the temperature and the current information for the battery of electrical buses. But limited by the calculating ability of the simulation environment, only a small part of the data is selected to test the models and algorithms proposed in this paper.
Fig. 1. The process of data cleaning.
The SVR algorithm is used to fill in the missing data (The current or the terminal voltage), a complete battery state is formed and the data is sent back to the database for further mining. The process of data cleaning in showed in Figure
1.
Fig. 2. Black-box model for the battery.
The chemical reaction in Lithium-ion battery is extremely complex and it is difficult to be monitored directly, so a black-box model is established for the battery in this paper. The modeling process is shown in Figure 2.The current, temperature, SoC are used as inputs, and the terminal voltage of the battery is used as output in this paper, the purpose of the neural network is to approximate the function:
Fig 3. The data used in this paper.

172
5
As shown in Figure 3, after eliminating the charging data in the database, this paper selects 15000 sets of data for model training and testing, among which 1-10000 are used as training data and 10001-15000 are used as test data for the Black-box Model of the battery.
In the experiment of missing data filling, the loss ratio of the data are 1/5, and the missing variables are terminal voltage. The SVR algorithm is used to fill in the terminal voltage in this paper.
Owing to SVR’s higher robustness to the noise and the error in the data, it performs well on the experimental data, the maximum relative error is within 4%.
3.2. Result for the battery modeling
The battery is modeled based on the method mentioned in Section 2-5, and the test-data are used to verify the accuracy of the model, and the error of the model established by different algorithms is shown in Table 1, the accuracy of the black-box model is shown in figure 4.
Table 1. The error of different algorithms.
Model
BP neural network
ELM
SVR
SDAE+ELM
MAPE(%)
4 .15
3.68
3.17
2.42
Std
2.3782
2.6414
1.7283
1.5452
6 Shuangqi Li / Energy Procedia 00 (2018) 000–000 to extract the deep structure features of the data effectively, and the results show that the deep-learning algorithm is able to reduce the error of the model and achieve a high-precision simulation for the dynamic characteristics of the battery effectively, and mean absolute percentage error of the model is within 2.5%.
Future work can be conducted by the collection and storage of the big data, the benefit of improving the quality of data are much greater than that of the algorithms, and high-quality data is the basis for precise modeling.
References
[1] Li J, Yang Q, Robinson F, et al. Design and test of a new droop control algorithm for a SMES/battery hybrid energy storage system[J]. Energy,
2017, 118: 1110-1122.
[2] Li J, Xiong R, Mu H, et al. Design and real-time test of a hybrid energy storage system in the microgrid with the benefit of improving the battery lifetime[J]. Applied Energy, 2018, 218: 470-478.
[3] Kang L W, Zhao X, Ma J. A new neural network model for the state-of-charge estimation in the battery degradation process[J]. Applied Energy,
2014, 121(5):20-27.
[4] Haq I N, Saputra R H, Edison F, et al. State of charge (SoC) estimation of LiFePO4 battery module using support vector regression[C]. Electric
Vehicular Technology and Industrial, Mechanical, Electrical and Chemical Engineering. IEEE, 2016.
[5] Awad M, Khanna R. Support Vector Regression[J]. Neural Information Processing Letters & Reviews, 2007, 11(10):203--224.
[6] Jain R K, Smith K M, Culligan P J, et al. Forecasting energy consumption of multi-family residential buildings using support vector regression:
Investigating the impact of temporal and spatial monitoring granularity on performance accuracy[J]. Applied Energy, 2014, 123(3):168-178.
[7] Chen K, Chen K, Wang Q, et al. Short-term Load Forecasting with Deep Residual Networks[J]. IEEE Transactions on Smart Grid, 2018,
PP(99):1-1.
[8] Wang H Z, Li G Q, Wang G B, et al. Deep learning based ensemble approach for probabilistic wind power forecasting[J]. Applied Energy, 2017,
188:56-70.
[9] Wang J, Gu Q, Wu J, et al. Traffic Speed Prediction and Congestion Source Exploration: A Deep Learning Method[C]. IEEE, International
Conference on Data Mining. IEEE, 2017:499-508.
[10] Wu J, He H, Peng J, et al. Continuous reinforcement learning of energy management with deep Q network for a power split hybrid electric bus[J]. Applied Energy, 2018, 222:799-811.
[11] Wang H, Shi X, Yeung D Y. Relational stacked denoising autoencoder for tag recommendation[C]. Twenty-Ninth AAAI Conference on
Artificial Intelligence. AAAI Press, 2015:3052-3058.
[12] Vincent P, Larochelle H, Lajoie I, et al. Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local
Denoising Criterion[J]. Journal of Machine Learning Research, 2010, 11(12):3371-3408.
Fig. 4. The output of the black-box model for battery.
The BP neural network and the ELM algorithm are difficult to fit the complex functions, but compared with BP neural network, ELM algorithm is faster, so it can be used with other algorithms to form a complex model. The SDAE-
ELM model is able to extract the deep structure features of the data, so its accuracy and stability have been significantly improved.
The error of the model can be further used to mine the fault information of the power battery and its management system, and provide a data foundation for fault diagnosis.
4. Conclusions and outlook
This paper presents a lithium-ion battery model based on deep learning algorithm, which can adapt to the big data environment. Based on machine learning algorithm, the data cleaning method achieves good results, in the case of the terminal voltage missing, the mean absolute percentage error of filling is within 4%, which has a greater effect on promoting the overall quality of the data set. The battery modeling method based on deep-learning algorithm is able

Author name / Energy Procedia 00 (2018) 000–000 5
As shown in Figure 3, after eliminating the charging data in the database, this paper selects 15000 sets of data for model training and testing, among which 1-10000 are used as training data and 10001-15000 are used as test data for the Black-box Model of the battery.
In the experiment of missing data filling, the loss ratio of the data are 1/5, and the missing variables are terminal voltage. The SVR algorithm is used to fill in the terminal voltage in this paper.
Owing to SVR’s higher robustness to the noise and the error in the data, it performs well on the experimental data, the maximum relative error is within 4%.
3.2. Result for the battery modeling
The battery is modeled based on the method mentioned in Section 2-5, and the test-data are used to verify the accuracy of the model, and the error of the model established by different algorithms is shown in Table 1, the accuracy of the black-box model is shown in figure 4.
Table 1. The error of different algorithms.
Model
BP neural network
ELM
SVR
SDAE+ELM
MAPE(%)
4 .15
3.68
3.17
2.42
Std
2.3782
2.6414
1.7283
1.5452
6
173 to extract the deep structure features of the data effectively, and the results show that the deep-learning algorithm is able to reduce the error of the model and achieve a high-precision simulation for the dynamic characteristics of the battery effectively, and mean absolute percentage error of the model is within 2.5%.
Future work can be conducted by the collection and storage of the big data, the benefit of improving the quality of data are much greater than that of the algorithms, and high-quality data is the basis for precise modeling.
References
[1] Li J, Yang Q, Robinson F, et al. Design and test of a new droop control algorithm for a SMES/battery hybrid energy storage system[J]. Energy,
2017, 118: 1110-1122.
[2] Li J, Xiong R, Mu H, et al. Design and real-time test of a hybrid energy storage system in the microgrid with the benefit of improving the battery lifetime[J]. Applied Energy, 2018, 218: 470-478.
[3] Kang L W, Zhao X, Ma J. A new neural network model for the state-of-charge estimation in the battery degradation process[J]. Applied Energy,
2014, 121(5):20-27.
[4] Haq I N, Saputra R H, Edison F, et al. State of charge (SoC) estimation of LiFePO4 battery module using support vector regression[C]. Electric
Vehicular Technology and Industrial, Mechanical, Electrical and Chemical Engineering. IEEE, 2016.
[5] Awad M, Khanna R. Support Vector Regression[J]. Neural Information Processing Letters & Reviews, 2007, 11(10):203--224.
[6] Jain R K, Smith K M, Culligan P J, et al. Forecasting energy consumption of multi-family residential buildings using support vector regression:
Investigating the impact of temporal and spatial monitoring granularity on performance accuracy[J]. Applied Energy, 2014, 123(3):168-178.
[7] Chen K, Chen K, Wang Q, et al. Short-term Load Forecasting with Deep Residual Networks[J]. IEEE Transactions on Smart Grid, 2018,
PP(99):1-1.
[8] Wang H Z, Li G Q, Wang G B, et al. Deep learning based ensemble approach for probabilistic wind power forecasting[J]. Applied Energy, 2017,
188:56-70.
[9] Wang J, Gu Q, Wu J, et al. Traffic Speed Prediction and Congestion Source Exploration: A Deep Learning Method[C]. IEEE, International
Conference on Data Mining. IEEE, 2017:499-508.
[10] Wu J, He H, Peng J, et al. Continuous reinforcement learning of energy management with deep Q network for a power split hybrid electric bus[J]. Applied Energy, 2018, 222:799-811.
[11] Wang H, Shi X, Yeung D Y. Relational stacked denoising autoencoder for tag recommendation[C]. Twenty-Ninth AAAI Conference on
Artificial Intelligence. AAAI Press, 2015:3052-3058.
[12] Vincent P, Larochelle H, Lajoie I, et al. Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local
Denoising Criterion[J]. Journal of Machine Learning Research, 2010, 11(12):3371-3408.
Fig. 4. The output of the black-box model for battery.
The BP neural network and the ELM algorithm are difficult to fit the complex functions, but compared with BP neural network, ELM algorithm is faster, so it can be used with other algorithms to form a complex model. The SDAE-
ELM model is able to extract the deep structure features of the data, so its accuracy and stability have been significantly improved.
The error of the model can be further used to mine the fault information of the power battery and its management system, and provide a data foundation for fault diagnosis.
4. Conclusions and outlook
This paper presents a lithium-ion battery model based on deep learning algorithm, which can adapt to the big data environment. Based on machine learning algorithm, the data cleaning method achieves good results, in the case of the terminal voltage missing, the mean absolute percentage error of filling is within 4%, which has a greater effect on promoting the overall quality of the data set. The battery modeling method based on deep-learning algorithm is able