Design and Simulation of an Accurate Neural Network ...

International Review of Automatic Control (I.RE.A.CO.), Vol. 10, N. 2 ISSN 1974-6059 March 2017

Design and Simulation of an Accurate Neural Network State-of-Charge Estimator for Lithium Ion Battery Pack Cheddadi Youssef, Diouri Omar, Gaga Ahmed, Errahimi Fatima, Es-Sbai Najia Abstract – State of charge (SOC) estimation is an important parameter that has to be determined by the battery management system, especially in electric vehicle and smart grid applications. SOC is an unmeasured parameter. Therefore, it must be inferred from other measured quantities from each battery such as discharge current, battery terminal voltage and temperature. There are various methods to estimate the battery’s SOC: Modeless approaches, Data driven nonlinear models and Model based observers. In this work, we adopted the second approach. An optimal feed forward-neural-network (FFNN) based battery model was suggested to simulate the complete dynamic electrical features of the battery and estimate accurately its SOC. Different charge/discharge current profiles are taken into account during training steps to improve the neural network model robustness and estimation accuracy. The obtained results show that FFNN, which is trained with an importance sampling data, is an accurate estimator for SOC. Copyright © 2017 Praise Worthy Prize S.r.l. - All rights reserved.

Keywords: Lithium-Ion Battery, SOC Estimation, Feed-Forward Neural Network, Battery Management System, Importance Sampling, Training Algorithm, Electric Vehicle, Micro-Grid

Nomenclature BMS SOC ANN FFNN EV SOH DOD KF EKF UKF CC MSE

reliability for the battery [3]. Accelerated aging, performance deterioration and even dangerous incidents are some of the many famous issues associated with defective state-of-charge estimation. BMS is an electronic system responsible for all the control and management operations of various battery parameters such as state of charge, State of Health (SOH), Deep of Discharge (DOD)…etc. It is considered the heart of the energy storage system in any application especially electrified vehicles. The SOC estimation is one of the key tasks of BMS [2] [3]. There exist many SOC estimation approaches that can be summarized as follows: Modeless approaches, i.e. columbic counting [5] [6]; Data driven nonlinear models, i.e. artificial neural network; and Model based observers, i.e. unscented Kalman filter. In Fig. 1, we can see the summary of the three mandatory approaches to estimate the battery SOC [7]. The first approach is Coulomb counting or current integration method, which not only needs to integrate real time current measurement, but it also requires accurate initialization to precisely estimate SOC. The second approach is machine learning or data driven approaches that include the artificial neural network method. Learning machine techniques need to acquire measurements from the real environment to teach the model how to act as the real model behavior. Consequently, its performance relies on the database collected from the real world measurements; the

Battery Management System State of Charge Artificial Neural Network Feed-Forward Neural Network Electric Vehicle State of Health Deep of Discharge Kalman Filter Extended KF Unscented KF Constant Current Mean Square Error

I.

Introduction

Thanks to its high power density, high energy density, capacity for fast charging[1], long life and environmental friendliness[2]; Lithium ion has emerged as one of the favored types of electrochemical batteries in the last decades. It is used in many fields of application such as micro grids, portable electronics, telecommunication and Electric Vehicles as a rechargeable energy storage system. With the rising importance of Lithium ion battery in both the automotive industry and the smart grid sector, it has become of critical importance to develop an effective battery management system (BMS) with robust control and smart management algorithms that deliver an accurate state of charge (SOC) and guarantee safety and Copyright © 2017 Praise Worthy Prize S.r.l. - All rights reserved

https://doi.org/10.15866/ireaco.v10i2.11957

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accuracy of its estimation depends strongly on both the quantity, and the quality of the data that is trained with. The third approach is nonlinear Observer techniques. These techniques demand sophisticated models that mimic the electrochemical behavior of the battery under a diversity of operating conditions. Generally, to develop a nonlinear Observer based SOC estimator, an equivalent circuit model is used, and SOC is considered as one of the states to be estimated in the dynamic battery model. For instance, when using the Kalman filtering to yield an SOC estimator, we encounter some difficulties caused by unknown noise, the inaccurate battery model, the nonlinear and the time-dependent characteristics of the batteries [9] [12].

non-linear NN for SOC estimation. In this paper, we propose an artificial neural network algorithm to improve the design and performance of the neural network in order to estimate accurately the SOC of the battery. In this SOC estimation method based on the feed forward neural network model, we take the terminal voltage, charge/discharge current as an input vector and the SOC as an output signal.

II.

Feed Forward Neural Network Estimator Theory

Artificial neural networks (ANNs) are universal approximators that can model any nonlinear function with desired accuracies [18]. The ANN is a mathematical model that consists of many nonlinear artificial neurons running in parallel, which may be created as one layer or multiple layers [25]. It is widely used in function fitting, data clustering and pattern recognition [11]. Feedforward neural networks are one of the most commonly used networks that have good performance prediction.

Fig. 1. A summary scheme of SOC estimation approaches

The major difference between machine learning and observer methods is that machine learning uses data regression methods but its implementation is not too complex, and few computational resources are needed. Another way would be to adopt a more advanced and complex model, at higher system identification but this raises computational cost. In some references a hybrid method associates between artificial neural networks (ANN) and observer methods or coulomb counting method to estimate the SOC parameter, for example in [28] the KF(kalman filter), In [8] particle filter, In [9] UKF (unscented KF), in [10] EKF (Extended KF) are discussed. The SOC estimation method based on the neural network model is proposed in many specific studies [8] [9] [13] [14] [15] [17]. In SOC estimation methods based on the neural network model, different algorithms of artificial neural networks are used such as BackPropagation neural network [13], Feed-Forward neural network algorithm [9] and Wavelet neural network [19]. In order to estimate state of energy, Dong G. [21] use a hybrid method based on the wavelet neural network model and a particle filter. The Wavelet NN based battery model was used to simulate the dynamic electrical characteristics of batteries and the particle filter to suppress the measurement noises. One approach that yields a better estimation model is to use a data driven learning machine approach. For example, Dang et al. [13] used a dual NN system in which the first NN, a linear one, was designed to emulate an equivalent circuit model, and its output was used as the input of a second

Fig. 2. General architecture of FFNN

In general, an artificial neural network contains three main layers; an input layer with nodes to symbolize the input variables, one or more hidden layers with nodes to reproduce the nonlinearity between the system input and output and an output layer to represent the system output variable. In this work, we applied feed forward NN because of its easy implementation and ability for nonlinear simulation [17] and [22]. The processing operation occurs at each neuron of both the hidden layer and output layer through the activation function. Additionally, the activation functions of the two layers in the proposed neural network are hyperbolic logarithmic sigmoid functions, as written in Equation (1):

Flog sig  u  

1 1  exp    u 

(1)

where β is a slope parameter. In the output layer, the linear transfer function is used as an activation function for regression and fitting problems, as:

Flin  u   u

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(2)

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Thus, the mathematical equation of the hidden layer or output of the FFNN can be expressed as in Eq. (3):

 m H i  F  X iWij  b j  i 1





  ,

j  1, 2,...n

(3)



where Xi is the output from the ith node at the previous layer, Wij is the weight of the interconnection from the ith node of the previous layer to jth node of the present layer, and bj is the bias. The training data determine the bias bj and the net weight Wij by using the LevenbergMarquardt algorithm.

Fig. 4. External structure of FFNN battery estimator model

The learning methodology in the neural network can be summarized in three main points. Firstly, we collect a large amount of current and voltage measurement data. Secondly, we use an effective architecture to identify the relationship between the data and the SOC function through the interconnection structure between the different layers of the network. Thirdly, we update the released relationship continuously by adding new data so that the error value is decreased and tends to zero. More details are given in the following sections.

III. Simulation Methodology III.1. Battery Charge and Discharge Process It is Obvious that the relationship between the SOC value and the battery terminal voltage is very difficult to perceive due to the complex nature of electrochemical reactions and process inside the battery during its lifespan[18]. As mentioned earlier in this paper, neural network SOC estimation belongs to data driven methods, which have the advantage that they are black box models. Hence, they determine the variation of internal parameters from the external data measurements. Consequently modeling the complex Equivalent Circuit battery model is not required. However, we need to know some essential battery characteristics during the charge and discharge process. In this study, we used a 12V/ 60Ah lithium ion battery which is widely used in micro-grid and electrified vehicle applications. The voltage curves during the discharge process with different constant current (CC) values are shown in Fig. 3. We can obviously see the nonlinearity between the terminal voltage and SOC. In addition, the lithium ion battery is fully discharged in 5, 19… or n hours depending on the rate C/n that defines the CC of discharge or charge process.

III.3. Training and Test Data In theory, it has been confirmed that a function of any shape could be fitted with just two hidden layers [24], but the number of neurons in each layer remains an issue. Therefore, we adopt a constructive training algorithm for the feed-forward neural network SOC estimation using a Matlab script in order to determine the effective interconnection structure, sufficient neurons number of hidden layers that find the best data fit. The adopted algorithm begins the training phase with a simple structure that contains low dimension inputs and targets, and then the structure complexity rises with the addition of new inputs/targets vector. The constructed approach followed in the training algorithm is described essentially in five steps that could be presented clearly in the flow chart in Fig. 5. In this simulation, we adopt different current profiles for training our neural network model. The first type profile is constant current to fully charge and discharge the battery. The second is based on partially discharging and charging battery with constant amplitude values, the third is a partially charge/discharge profile with variable pulses as shown in Fig. 6. The diversity of the training data quality with importance sampling is assured in order to take into consideration the extreme real life conditions. Therefore, the robustness and the accuracy of the FFNN response will be enhanced even under other kinds of data tests. In order to test the SOC estimation method proposed or any other neural network based SOC estimation, several measurements and simulations must be taken. Lithium-ion battery pack voltages and currents are measured by a battery testing system that includes a battery pack, high sampling current and voltage sensors and a programmable DC electronic load.

Fig. 3. Discharge voltage curves at different CC

III.2. Methodology In this paper, FFNN is used to directly find the estimated SOC. Fig. 4 shows the external organization of the adopted FFNN model; where the inputs are the battery voltage and the current load, while the output is represented by the battery SOC.

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With the aim of testing the validity and reliability of the SOC estimation method proposed in this paper, Matlab/Simulink software is used to simulate the neural network model and test it with data that is different from the training data.

Fig. 8. The simulation schematic in Matlab software

IV.

Result and Discussion

To avoid under-fitting, we trained the neural network to a reasonable Mean Square error that is adequate to mimic the dynamics of the battery. Depending on the chemical nature and the actual application, it is suitable to keep the battery SOC limited within a safe operation area, for lithium ion in a 20% to 90% range [29]. A battery SOC that verifies the above condition enhances its life span and protects the battery from any damage like deep discharge and overload. As we can see in Fig. 9, three results of different constant current discharges show the complete similarity between the reference and the estimated SOC during the whole discharge process, except the exponential zone between 80% and 90 % of the SOC. The transient zone involves a small error that does not exceed 5% in its maximum value with a mean square error of 3 %. The same results are shown in Fig. 10 and Fig. 11, for variable current discharge profiles with different magnitudes. The maximum error in the exponential zone does not surpass 6%. When the battery switches from discharge or charge state, an unexpected change of current causes a large magnitude change in the voltage. This phenomenon is called the Hysteresis phenomenon, which is considered one of the most important effects that characterize the battery dynamic. This effect was observed in the battery voltage response due to the internal parameters, more precisely the internal ohmic resistance [20]. When the battery is not connected, the electrochemical effect of the internal battery imposes a slow voltage change. In Comparison with [9] results, we find that this FFNN model reached a maximum error of 10% between the actual and the estimated SOC value model with 4% of MS error in the neural network training phase while in our optimal FFNN we reached a maximum error lower than 6 % with a trained MS error that doesn’t exceed 3 %. To achieve this optimal result, several architectures of feed forward neural network with different hidden neuron numbers are investigated and simulated. If we decrease the training MS error more by adding new neurons, the neural network risk to limit its

Fig. 5. The neural network algorithm Flow-chart

Fig. 6. Different current pulse profile used for training

Fig. 7. A synoptic diagram of Battery test system

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generalization ability and the over-fitting problem will be encountered. The Table I illustrates the over-fitting problem; for example, the use of 18 neurons in hidden layers instead of 16 neurons, increased the maximum error more than two times even if the mean square error doesn’t exceed 1%. The above comparison shows that 16 is confirmed to be a reasonable number of the hidden neurons for actual SOC estimation.

Fig. 10. SOC estimation results test for different current discharge pulse

Fig.11. Zoom in critical area of SOC estimation

The number of hidden neurons is determined through the adaptive algorithm written in Matlab script, which is discussed in the flowchart of the previous section. TABLE I THE RESULTS OF DIFFERENT HIDDEN NEURON NUMBER Hidden neuron MS error Max error number 14 3,4 % 10 % 16 3% 6% 18 1% 13 %

V.

Conclusion

In this paper, a state of charge estimation for lithium ion batteries based on the feed-forward neural network has been designed and performed. Belonging to data driven learning machine approach, the FFNN is characterized by precise estimation, simple implementation and not need modeling the complex Equivalent Circuit battery model compared to other SOC

Fig. 9. Estimated SOC for different constant current

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[15] S. Sepasi, R. Ghorbani, and B.-Y. Liaw, A novel on-board stateof-charge estimation method for aged Li-ion batteries based on model adaptive extended Kalman filter. Journal of Power Sources 245 (2014) 337-344. [16] A.-A. Hussein, Derivation and Comparison of Open loop and Closed-loop Neural Network Battery State-of-Charge Estimators, Energy Procedia 75 (2015) 1856 – 1861. [17] I.W. Sandberg, Nonlinear dynamical systems: feedforward neural network perspectives, John Wiley & Sons (2001), pp. 1–15. [18] Wu, Ji, Zhang, Chenbin, et Chen, Zonghai. An online method for lithium-ion battery remaining useful life estimation using importance sampling and neural networks. Applied Energy, 2016, vol. 173, p. 134-140. [19] Chen, Yuehui, Yang, Bo, et Dong, Jiwen. Time-series prediction using a local linear wavelet neural network. Neurocomputing, 2006, vol. 69, no 4, p. 449-465. [20] Roscher, Michael A. et Sauer, Dirk Uwe. Dynamic electric behavior and open-circuit-voltage modeling of LiFePO 4-based lithium ion secondary batteries. Journal of Power Sources, 2011, vol. 196, no 1, p. 331-336. [21] Dong, G., Zhang, X., Zhang, C., & Chen, Z. (2015). A method for state of energy estimation of lithium-ion batteries based on neural network model. Energy, 90, 879-888. [22] Sadowsky JS. A new method for Viterbi decoder simulation using importance sampling. IEEE Trans Commun 1990.38:1341–51. [23] Biondini G. An introduction to rare event simulation and importance sampling. In: Venu Govindaraju VVR,Rao CR, editors. Handbook of Statistics, 2 (Elsevier: 2015. p. 29–68 ) [24] Hartman, Eric J., Keeler, James D., et Kowalski, Jacek M. Layered neural networks with Gaussian hidden units as universal approximations. Neural computation, 1990, vol. 2, no 2, p. 210215.. [25] Firat, Mahmut et Gungor, Mahmud. Generalized regression neural networks and feed forward neural networks for prediction of scour depth around bridge piers. Advances in Engineering Software, 2009, vol. 40, no 8, p. 731-737. [26] P. Spagnol, S. Rossi, S.-M. Savaresi, Kalman filter SOC estimation for Li-ion batteries, Control Applications (CCA), IEEE International Conference on, IEEE, 2008, pp. 587–592. [27] Cheddadi, Youssef, GAGA, Ahmed, Errahimi, Fatima, et al. Design of an energy management system for an autonomous hybrid micro-grid based on Labview IDE. 3rd International Renewable and Sustainable Energy Conference (IRSEC). IEEE, 2015. p. 1-6. [28] Di Domenico, Domenico, Fiengo, Giovanni, et Stefanopoulou, Anna. Lithium-ion battery state of charge estimation with a Kalman filter based on an electrochemical model, IEEE International Conference on Control Applications, 2008. p. 702707. [29] Li, X., Koseki, H., Thermal Analysis on Lithium Primary Batteries, (2014) International Journal on Energy Conversion (IRECON), 2 (4), pp. 133-136. [30] Malik, F., Lehtonen, M., Saarijärvi, E., Safdarian, A., A Feasibility Study of Fast Charging Infrastructure for EVs on Highways, (2014) International Review of Electrical Engineering (IREE), 9 (2), pp. 341-350.

estimation approaches. It has been understood that the proposed estimator is accurate thanks to the combination between a considerable number of hidden layers and a smart training strategy for the neural network model that relies on importance data sampling. The robustness of the proposed estimator has been tested with load current profiles different from those trained with. The simulation results prove that the performance of the proposed neural network has a reliable SOC estimation performance for online application. Hence, the main contribution of this work resides in a suggestion of optimal Feed-Forward NN with 16 hidden neurons engaged for estimating lithium ion battery SOC, with utilization of Importance sampling to precisely select both current and voltage for the FFNN model input. For future work SOC estimation under real-life loading conditions to fit Electric vehicle application will be studied with the implementation of an adaptive filter to cancel all kind of FFNN error estimation.

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Authors’ information Sidi Mohamed Ben Abdellah University, Faculty of Sciences and Technology, Laboratory of Renewables Energy and Intelligent Systems, Road Imouzzer, BP 2202, Fez, Morocco. Youssef Cheddadi is an Industrial Engineer graduated in 2013 from National School of Applied Sciences Agadir, Morocco. Currently, he is a PhD Student and Researcher in Renewable Energy and Intelligent Systems Laboratory at Faculty of science and Technology, Sidi Mohammed Ben Abdellah University of Fez, Morocco. His research interests: Power electronics, Electric Vehicle, Renewable energy, Control theory and embedded systems.

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Omar Diouri is an Engineer in Electronics Systems and Telecommunications in 2013 graduated from Faculty of Sciences and Technology, University of Fez, Morocco. Actually, he is a PhD Student and Researcher in Renewable Energy and Intelligent Systems Laboratory at Sidi Mohammed Ben Abdellah University of Fez, Morocco. Research interests: Power electronics, Electronics, Renewable energy and Energy Efficiency, Sustainable and Human Development. Ahmed Gaga received his Engineer degree in electronic and embedded systems from university of sciences and technology, Fez, Morocco in 2013. Currently, he is a research professor at UPF University in Fez. He works in Electrical Engineering department, especially renewable energies and intelligent systems Laboratory. His research interest is Electronics; Microcontroller based embedded systems, Photovoltaic applications and Control theory. Najia Es-Sbai Professor in the Electric Engineering Department at the Faculty of Sciences and Technology, University of Sidi Mohamed Ben Abdellah (USMBA- Fez Morocco), since 1995. She completed his Ph.D. at USMBA in 2002. Her main research concerns area of nanostructures. In recent years, she focused on image processing, especially detection of emotions. She is involved in the supervision of three PhD thesis concerning the management and optimization of energy flows in a smart grid DC isolated, intelligent inverters, and DC-DC converters for energy optimization. She writes and co-writes more than 40 papers in national and international journals. Ms. ES-SBAI served on a dozen committees of conference programs. She was deputy head of the electrical engineering department at the Faculty of Science and Technology of Fez. She is currently a Deputy Director of the Renewable Energies and Intelligent Systems Laboratory. Fatima Errahimi, Professor and researcher at the Faculty of Sciences and Technology in Sidi Mohammed Ben Abdellah University of Fez. She got PhD degree on Automatic Control Systems and robotics in 2004. Her special fields of interest include the integration of renewable energy sources in building energy management, optimization control for demand side units in households and smart grid. Ms. ERRAHIMI participated in many research projects among which the IRESEN one on low cost CPV in Morocco. She supervised many PhD thesis and several post graduate students.

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