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Evaluation of batteries residual energy for battery pack recycling: Proposition of stack stress-coupled-AI approach
Journal of Energy Storage 26 (2019) 101001
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Evaluation of batteries residual energy for battery pack recycling: Proposition of stack stress-coupled-AI approach Akhil Garga, Li Weia, Ankit Goyalb, Xujian Cuic, Liang Gaoa,
T
⁎
a
State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Hubei, China University of Amsterdam, Institute of Physics, Science Park 904, Amsterdam, the Netherlands c Intelligent Manufacturing Key Laboratory of Ministry of Education, Shantou University, China b
A R T I C LE I N FO
A B S T R A C T
Keywords: Energy storage Battery pack recycling Residual energy Genetic programming
It is predicted that by 2025, approximately 1 million metric tons of spent battery waste will be accumulated. How to reasonably and effectively evaluate the residual energy of the lithium-ion batteries embedded in hundreds in packs used in Electric Vehicles (EVs) grows attention in the field of battery pack recycling. The main challenges of evaluation of the residual energy come from the uncertainty of thermo-mechanical-electrochemical behavior of battery. This motivates the notion of facilitating research on establishing a model which can detect and predict the state of battery based on parameters enable to be measured, such as voltage and stack stress. Thus, the present work proposes a stack stress-coupled-artificial intelligence approach for analyzing the residual energy (remaining) in the batteries. Experiments are designed and performed to verify the fundamentals. A robust model is formulated based on artificial intelligence approach of genetic programming. The findings in the study can provide an optimized recycling strategy for spent batteries by accurately predicting the state of battery based on stack stress.
1. Introduction Given with the limited amount of traditional energy, the society is working hard to weaken its dependence on fossil fuels and finding alternative energy sources. In the face of deteriorating environment, people are looking forward to other options which are environmentallyfriendly and sustainable. With the emergence of lithium-ion battery powered EVs, it looks promising to solve this problem. [1,2]. However, there are many problems in lithium-ion batteries during its practical applications. For example, the detection of battery state of health (SOH) and state of charge (SOC), battery aging mechanism investigation, and recycling or reuse strategies of spent batteries. Among these problems, battery pack recycling accounts for the major problem and a hot scenario for the next years. By 2025, approximately 1 million metric tons of spent battery waste will be accumulated. How to reasonably and effectively evaluate the residual energy of the lithium-ion batteries used in Electric Vehicles (EVs) grows attention in the field of battery pack recycling. The works on recycling of single cell battery has been paid great attention due to its diverse usage. The major works on recycling of single cell battery is to recover the materials such as cobalt, lithium, etc. by use of hydro-metallurgical methods. Till now, for the whole pack recycling, there is hardly any research being carried out in context of its
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automation or efficient recycling. The problem is attributed to hundreds of batteries embedded in series of parallel configurations in these packs. This problem of whole pack recycling could be very challenging because there shall be need of development of an intelligent model to sort out the batteries effectively based on the accurate prediction of residual energy. The main challenges of evaluation of the residual energy come from the uncertainty of thermo-mechanical-electrochemical behavior of battery. Thus, an empirical model which can reflect and predict the real time state of health of batteries in EVs also reflects the prediction of residual energy in the batteries. In this perspective, literature has been conducted. Based on the state-of-the-art studies, the battery modeling methods are summarized as shown in Fig. 1 According to different kinds of battery models, the estimation technologies can be classified into three major types, including (1) Coulomb Counting Method (CCM) [3] (2) Black-box Battery Model (BBM) [4] and (3) State-space Battery Model (SBM) [5]. (1) CCM is largely relying on the accuracy of the current measurement. In practical scenarios of uncertain disturbances, this open-loop estimation method usually gives bad results with an accumulation of measurement errors resulting from ammeters [6,7]. The estimation errors are even higher when the working temperature is too high or too low. (2) For Black-box Battery Models, Artificial intelligence (AI)-based approaches,
Corresponding author. E-mail address: [email protected] (L. Gao).
https://doi.org/10.1016/j.est.2019.101001 Received 2 August 2019; Received in revised form 18 September 2019; Accepted 5 October 2019 2352-152X/ © 2019 Elsevier Ltd. All rights reserved.
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Fig. 1. Classification of Battery Modeling procedures based on the literature.
are discussed in the end.
such as Artificial Neural Networks (ANN), Fuzzy Logic (FL), Support Vector machine (SVM), are often employed to formulate the mathematical models. The black-box battery model can provide a good SOC estimation based on the nonlinear relationships established from a given data set. Zenati et al. conducted experiments on battery aging in different operating conditions and combine impedance measurements with the fuzzy logic inference for estimation of either SOC or SOH [8]. Awadallah and Venkatesh developed a SOC estimation model using Adaptive Neuro-Fuzzy Inference Systems (ANFIS) and compared it to the traditional Coulomb counting method [9]. (3) State-space battery models based on Kalman filtering are popular in the estimation of battery SOC since it is an online close-loop algorithm. A state-space model has been established by Ting et al. through mathematical derivations to simulate the complex behaviors of a battery system [10]. The results indicate that there exist four state variables relevant to the battery model. Thus, the different models discussed above mainly rely on electrical parameters. These models are imprecise because the measurement of electrical parameters in real time is hard and generate noisy data resulting from instantaneous variations occurring in complex driving conditions. In addition, these electrical parameters-based models usually require great computing efforts and do not perform well with the aging of batteries. Thus, a simpler estimation method is expected with acceptable accuracy guaranteed. A coupling between mechanical stress and chemical characteristics of lithium-ion pouch batteries is observed in the work of Cannarella and Arnold [11]. The correlation is concluded to be electrode volume change induced by chemical reactions in lithium-ion batteries. The irreversible stress produced in the charging process is related to the energy loss inside the batteries through charging-discharging cycles [12]. In addition, some recent studies [13,14] have explored the means of establishing the fundamental and empirical relationship between the battery residual energy and the mechanical parameters such as the mechanical stress and strain when the battery is subjected to sudden compression or three-point bending tests. The studies were proved to be useful which compliments easier and accurate evaluation. Therefore, to address these problems related to recycling purpose, this paper proposes the Stack stress-coupled-AI approach for analyzing the residual energy in the batteries. Experiments based on the static loading on the Li-ion batteries is firstly conducted and its capacity shall be measured. The current study aims to determine the relationship between stress and capacity quantitatively. Based on the measured experimental data, the AI approach of genetic programming (GP) is then applied to formulate a functional relationship of capacity as a function of design variables (stress, temperature, voltage). Conclusions
2. Research problem statement An effective and efficient analysis of residual energy (remaining capacity) is an important problem for purpose of recycling of battery packs used in EVs. Finding of residual energy is related to SOH/SOC of the battery. The battery SOH is defined as the ratio of the current available full-charge capacity of to its original nominal capacity when it is freshly new while the SOC is the ratio of the residual capacity to the current full-charge capacity. For instance, a battery of 1 Ah nominal capacity with an 80% SOH and 60% SOC has a remaining capacity of 0.48 Ah. In addition, the main parameter used in this study is stack stress which linked to the capacity fade of batteries through particles exfoliation and the growth of solid electrolyte interface (SEI). The current research aims to quantitatively determine the relationship between inputs and output (see Fig. 2) during the charging-discharging cycles. An AI approach of GP framework is proposed to build battery models based on the experiment data and numerous analysis results are discussed. 3. Experimental details Battery charge-discharge experimental tests are carried out to collect data for parameters such as capacity, stack stress, and other key parameters. The schematic computer layout of the experimental is shown in Fig. 3 Battery testing Equipment in laboratory (Fig. 4) can charge and discharge batteries and measure data. 3.1. Experimental system The experimental system is built using the following devices: (1) (2) (3) (4) (5) (6)
Stress sensors and steel container - Fig. 4(a) Charge and discharge module - Fig. 4(b). Stress data collecting device - Fig. 4(c). Neware battery system- Fig. 4(d) Lithium-ion battery (refer to Table 1) A computer connecting to the sensors and the electronic load (see in Fig. 4(d)).
In this study, three different values of stack stress are applied. There are two batteries in the 3 kg group and 6 kg group and 3 in the 9 kg group. The battery is fixed in steel container and different initial stresses are set by tightening the screws. Neware battery system 2
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Fig. 2. Illustration of research problem undertaken in this study.
capacity as the output [15]. Table 3 shows part of the experimental data.
connects the positive and negative poles of the battery to charge and discharge the battery. Charge and discharge module can collect data of open circuit voltage (OCV) and capacity. Stress sensor can record and transfer stress data to stress data acquisation system. When the experiment is set up, Neware battery system periodically fully charges each battery under constant current and constant voltage at different initial stress and then power down them for a period of time before it discharges the battery. The concrete steps of charging-discharging are listed in Table 2. In order to obtain a series of experimental data, these four steps are recycled for many times. To increase the accuracy of the model, voltage is also included during the modelling process. Therefore, the OCV is collected when the cell is full-charged and discharged respectively as experimental data in the charge-discharge test. Hence, 4 parameters are defined as inputs of the model including initial stress, real-time stress, full-charged OCV and discharged OCV while the cell
3.2. Experimental findings A total of 1510 groups of data is collected in the experiment. The pareto chart of collected experimental data is shown in Fig. 5(a). It is clear that the capacity values are mainly distributed in the range [2.412–2.531] with minor values in low-value range and high-value range, which shows an approximate normal distribution. Fig. 5(b) shows the cell capacity of the complete experimental data set. It can be observed that the two curves for the 6 kg group and the 9 kg group almost overlap while the curve for the 3 kg group deviates from the previous two. However, the three curves show similar trend but with the difference on the cycle number. The 3 kg group curve seems likely
Fig. 3. Schematic computer layout of the conducted experimental tests. 3
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Fig. 4. Lithium-ion battery testing system measuring the stack stress.
under 3 different initial loads share a similar changing mode. On the general, the capacity experiences an increase and then drops down to a value lower than the initial value in the first period, which is around 100 cycles. In the second period, the capacity undergoes a similar change as it does in the first period. The peak value in the second period is lower than that in the first period. Fig. 5(d) shows the real-time stress with adjusted experimental data set. The real-time stress here is the average stress value of batteries from the same group. It can be seen that real-time stress of batteries with similar original load differs from each other from the same group. This phenomenon appears in all the three groups and no reason has been found to account for that. The average value is aimed to reduce the influence of this difference on the model. From Fig. 5(d), one can conclude that the real-time stress of the 6 kg group and 9 kg group share a declining trend on the general though the decrease in within a small range. Nevertheless, the real-time stress does not follow any patterns yet changes in a small amplitude of fluctuation.
Table 1 Li-ion battery specifications. Parameters
Values
Operating voltage Cell type Cell size Cell capacity Nominal voltage Internal resistance
2.75–4.2 Type 18,650 18 × 65 mm 2600 mAh 3.6–3.7 V 45.6 mΩ
Table 2 Charging- discharging steps and process.
Step Step Step Step
1 2 3 4
status
Condition1
Condition2
Cut-off condition
Full charge Rest discharge Rest
0.5C-rate 30 min 0.5C-rate 30 min
4.2V —— —— ——
Current <=0.05 Step time >=30 Voltage <=2.75 Step time >=30
A mins V mins
4. Modeling method of genetic programming to overlap with the other two curves if the other two curves are leftshifted for around 25 cycles. This difference on the cycle number appears probably because the batteries used in the 3 kg group test were hold for about one month after undergoing around 20 cycles in another experiment. In other words, the batteries in the 3 kg group test actually started from cycle number around 20. Therefore, the original experimental data set is adjusted by deleting about 30 cycles from the 6 kg group and the 9 kg group in the aim of keeping consistency among the three groups of tests. As shown in Fig 5(c), the capacities of batteries
Genetic programming (GP) is an artificial intelligence (AI) algorithm that can generate symbolic functional expressions based on only the given data [16]. The evolutionary system can mimic the behaviors of the targeted complicated system [17,18]. A lot of researches related to modeling problems in energy storage system verifies the practicality of the modeling method of GP [19–23]. Compared to other modeling methods, GP has advantages from different aspects: (1) GP works without any assumptions about the structure or the form 4
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Table 3 Partial data collection for the battery cell capacity. Input Sample number
1 2 3 4 5 6 7 8 9 … 397 398 399 400 401 402 403 404 405 … 808 809 810 811 812 813 814 815 816 …
Initial stress (kg)
Real-time stress (kg)
Output Discharged OCV (V)
3 3 3 3 3 3 3 3 3 … 6 6 6 6 6 6 6 6 6 … 9 9 9 9 9 9 9 9 9 …
5.358 5.399 5.416 5.441 5.482 5.515 5.534 5.552 5.573 … 6.365 6.369 6.367 6.398 6.387 6.396 6.398 6.390 6.373 … 10.838 10.832 10.824 10.836 10.838 10.832 10.824 10.822 10.817 …
3.433 3.441 3.432 3.438 3.438 3.440 3.440 3.434 3.431 … 3.431 3.432 3.427 3.429 3.431 3.431 3.426 3.425 3.427 … 3.437 3.438 3.440 3.439 3.439 3.440 3.441 3.433 3.434 …
Fullcharged OCV (V)
Cell capacity (Ah)
4.170 4.171 4.177 4.169 4.182 4.182 4.181 4.182 4.182 … 4.181 4.182 4.181 4.181 4.181 4.181 4.182 4.182 4.182 … 4.199 4.178 4.178 4.178 4.178 4.178 4.177 4.178 4.178 …
2.480 2.473 2.523 2.497 2.487 2.483 2.519 2.506 2.499 … 2.498 2.494 2.515 2.503 2.498 2.495 2.519 2.520 2.514 … 2.505 2.497 2.485 2.487 2.484 2.479 2.475 2.515 2.508 …
of the solution. (2) GP is suitable for complex modeling problems. It can generate different kinds of models, i.e. linear models, non-linear models, fraction expressions, etc. Another commonly used modeling method, response surface method (RSM), is usually undertaken to solve simple modeling problems with polynomial equations (quadratic equation is the preference). However, the models formed based on RSM does not perform well on data obtained from complex and non-linear systems. (3) The solution of GP is explicit in different forms such as mathematical expressions and programs. (4) GP avoids falling into local optimal solution due to the fitness evaluation criteria. In this study, GP is adopted on the modeling process with the experimental data from the battery charge-discharge tests as in Section 3.
4.2. Data modelling process of GP The modelling process is carried out in MATLAB R2014b. The experimental data is imported into GP. 80% of data obtained from experiment is used for training process to produce models while the remaining is used to validate the models. The settings are done based on trial-and-error approach. Additionally, the parameters setting of GP is shown in Table 4. The primitive set including the terminal set and the functional set as shown in Table 5. x1, x2, x3 and x4 in terminal sets represent initial stress, real-time stress, discharged OCV and fullcharged OCV respectively while y stands for the cell capacity. RMSE and MAPE i.e. objective functions. are chosen to measure the performance of the GP model.
Fig. 5. (a) Pareto chart showing the distribution of battery capacity in descending order of frequency, (b) Cell capacity under different initial stress with orginal data set, (c) Adjusted data set, and (d) Real-time stress of batteries under different initial stress.
5
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5. Analysis of the GP model
Table 4 Settings adopted in the GP simulation.
Model No. 10 is selected as the best model among the 15 simulation runs of GP. The post model analysis is then carried out to determine the insights into experimental process.
Terminal set Kind of primitive Variables Constant values Population size Number of generations Number of simulations run Maximum depth of each tree Maximum number of genes Crossover Mutation Reproduction
Contents x1, x2, x3, x4, y [−30,30] 1000 500 15 3 3 0.85 0.1 0.05
5.1. Accuracy of the model Fig. 6 shows the distribution of the experimental data and that of the estimated data. The orange dots, representing the estimated capacity, covers the majority of the blue dots, which represents the experimental capacity. Fig. 6 and Table 6 indicates that the GP model can estimate output values with a small deviation from the experimental value.
Table 5 Functional set adopted in the GP simulation.
5.2. Normal distribution design
Function set Kind of primitive Arithmetic Mathematical Others
In addition to the accuracy, the robustness of the GP model also needs to be tested. In general, it describes a model's behavior to the uncertain changes in the assumptions made in about the research problem. If the model is able to give the same or similar conclusions regardless of insignificant changes, the model has certain robustness. In context of statistical modeling, the robustness of a mathematical model is usually referred as the model insensitivity to small fluctuations of inputs. Therefore, the robustness of the GP model is checked by carrying out the normal distribution design of the inputs/design variables. Another reason for the normal distribution design is that the GP model's behavior to other values of inputs in the same range is unknown since the model is trained with discrete data from the experiment. It can be concluded from Fig. 6 that 99.7% of the simulation data are within the range between the minimum value and the maximum value in the experimental data. The designed normal distributed data is imported as the input of the GP model to obtain the results. Fig. 7 shows the distribution of the capacity resulting from the normal distribution of the inputs.
Contents +,-,×,÷ plog, square, tan, exp, ppower iflte
4.3. Selection of the best model 15 separate models are obtained from the 15 simulation runs of GP. From each run, the best model is selected based on the minimum training error. The performances of the models are shown in Table 6. Four indexes including RMSE of training data, RMSE of testing data, MAPE of training data and that of testing data is taken into consideration to rate the models performance. As shown in Table 6, model No. 10 is selected to be the final model based on the best overall performance. The explicit mathematical form of model No. 10 is given in the expression below:
y = 35.2566 + (−0.78285)*((ppower (x1, (−1.862822))) 5.3. Parametric analysis and interaction analysis
/((x2) − ((13.523072)))) + (−188057.4712) *(ppower (p log(x3), (( −25.564417)) + ((−29.035220)))) + (−14.9941)*(ppower (p log(x3), iflte (x2 , x3 , x2 , x3)))
Parametric analysis is an important statistical tool to construct parametric relations between the dependent variables and the design/ input. To put it in a simple way, parametric analysis studies the effect of one factor on the result with fixed values of the other factors [24]. In this analysis, a parametric analysis is performed on the model to investigate the effects of each input on the capacity. The procedures of the parametric analysis adopted in this study are quite simple. One of the three inputs is kept at original values from the experiment (except for initial stress which ranges from 1 kg to 9 kg), meanwhile, other inputs
(1)
where x1 stands for initial stress, x2 for real-time stress, x3 for discharged OCV and y for cell capacity. The model does not contain x4 which means in this model full-charged OCV is irrelevant to cell capacity.
Table 6 Table showing RMSE and MAPE of GP models. Model no.
Training RMSE
Training MAPE
Test RMSE
Test MAPE
No. of nodes
Depth
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
0.027819405 0.043407467 0.028001262 0.029210342 0.029732218 0.028011787 0.028760727 0.02976328 0.027797614 0.029278796 0.029260204 0.028129783 0.027828703 0.029307769 0.028115021
0.876872213 1.409253851 0.883017382 0.938521700 0.946714858 0.889760054 0.931690487 0.959625076 0.871798585 0.939758314 0.945840772 0.891899949 0.890114967 0.937481526 0.885670788
0.05379104 0.07307797 0.05227433 0.04601589 0.03948816 0.05278484 0.0468868 0.04274756 0.05736355 0.04592892 0.04767038 0.04935389 0.04880876 0.04466128 0.04929874
1.893350308 2.595386675 1.822398032 1.573353958 1.304235636 1.847449836 1.562191494 1.443789464 2.031403674 1.554991867 1.631234291 1.696395712 1.659535806 1.515255554 1.703485292
23 21 21 20 19 24 19 16 23 21 25 27 22 21 22
5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
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Fig. 6. Comparison of experimental capacity and estimated capacity from the GP model.
(1) GP model generates relationship with higher fitting accuracy for prediction of capacity as a function of real-time stress under varied applied initial stresses. Thus, the GP model can be used for sorting out the batteries based on their residual energy prediction during the battery pack recycling process. (2) When the initial applied stress value is low, the higher capacity value gets when the value of real-time stress is higher. (3) The interaction discharged OCV and either of the other two independent variables can be ignored. (4) The findings in the study can provide an optimized recycling strategy for spent batteries.
are set at their average values. Then, the three inputs are imported into the GP model for plot of response capacity values. Based on the results obtained, it can concluded that the initial stress and capacity are nonlinear negative correlations. In addition, the non-linear positive correlations can be found between real-time stress and capacity. In order to study the comprehensive effect of multi-parameters on capacity, the interaction analysis is performed. Results show that when the initial stress is low, the higher capacity value gets when the value of real-time stress is higher. However, the capacity does not seem to fluctuate at all in other situations. Furthermore, the interaction between initial stress and discharged OCV is negligible.
The application of GP approach for developing model for residual energy prediction could also be applicable to hybrid energy systems. The hybrid energy systems have great potential in the application of port terminals. The main parts of hybrid energy storage system include energy consuming system based on the grid, battery energy storage system, and renewable energy system which may include solar energy, wind energy, and tidal energy etc. In this case, the energy consumption
6. Conclusions A battery residual energy (remaining life detection) framework is proposed to provide a recycling strategy for spent batteries in EVs. Experiments are performed and AI method of GP was used to generate the mathematical model for predicting the cell capacity. The main results are shown as follows:
Fig. 7. Distribution of cell capacity data calculated using the GP model. 7
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which rely on electricity generated by combustion of fossil fuels could be reduced to benefit the environment. In addition, cost of energy consumption will also descend. However, the real application of such hybrid system in ports is still on the stage of development. There are several problems need to be focused: (1) Battery energy storage system robust design with an intelligent management and monitoring system. (2) Cleaner energy transfer efficiency and the cost. (3) Long-term effective electricity power conversion agreement between government and port operators. Further, the algorithms based on advanced machine learning principles [25–28] considering uncertainties in the design variables and the system can also be considered.
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Declaration of Competing Interest All Authors declares there is no conflict of interest. Acknowledgements This work was supported by the National Natural Science Foundation of China [grant numbers 51675196 and 51721092], the Program for HUST Academic Frontier Youth Team [grant number 2017QYTD04] and the Program for HUST Graduate Innovation and Entrepreneurship Fund[grant number 2019YGSCXCY037]. References [1] A.C.R. Teixeira, J.R. Sodré, Impacts of replacement of engine powered vehicles by electric vehicles on energy consumption and CO2 emissions, Transp. Res. Part D 59 (2018) 375–384. [2] J. Hofmann, D. Guan, K. Chalvatzis, et al., Assessment of electrical vehicles as a successful driver for reducing CO2 emissions in china, Appl. Energy 184 (2016) 995–1003. [3] J.H. Aylor, A. Thieme, B.W. Johnso, A battery state-of-charge indicator for electric wheelchairs, IEEE Trans. Indust. Electron. 39 (5) (1992) 398–409. [4] D. Yang, Y. Wang, R. Pan, et al., State-of-health estimation for the lithium-ion battery based on support vector regression, Appl. Energy (2017). [5] I. Baghdadi, O. Briat, P. Gyan, et al., State of health assessment for lithium batteries based on voltage–time relaxation measure, Electrochim. Acta 194 (2016) 461–472. [6] L.W. Juang, P.J. Kollmeyer, R. Zhao, et al., coulomb counting state-of-charge algorithm for electric vehicles with a physics-based temperature dependent battery model, Energy Conversion Congress and Exposition, IEEE, 2015, pp. 5052–5059. [7] M.H. Chang, H.P. Huang, S.W. Chang, A new state of charge estimation method for LiFePO4 battery packs used in robots, Energies 6 (4) (2013) 2007–2030. [8] A. Zenati, P. Desprez, H. Razik, Estimation of the SOC and the SOH of li-ion batteries, by combining impedance measurements with the fuzzy logic inference, IECON 2010 -, Conference on IEEE Industrial Electronics Society, IEEE, 2010, pp. 1773–1778. [9] M.A. Awadallah, B. Venkatesh, Accuracy improvement of SOC estimation in
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Journal of Energy Storage journal homepage: www.elsevier.com/locate/est
Evaluation of batteries residual energy for battery pack recycling: Proposition of stack stress-coupled-AI approach Akhil Garga, Li Weia, Ankit Goyalb, Xujian Cuic, Liang Gaoa,
T
⁎
a
State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Hubei, China University of Amsterdam, Institute of Physics, Science Park 904, Amsterdam, the Netherlands c Intelligent Manufacturing Key Laboratory of Ministry of Education, Shantou University, China b
A R T I C LE I N FO
A B S T R A C T
Keywords: Energy storage Battery pack recycling Residual energy Genetic programming
It is predicted that by 2025, approximately 1 million metric tons of spent battery waste will be accumulated. How to reasonably and effectively evaluate the residual energy of the lithium-ion batteries embedded in hundreds in packs used in Electric Vehicles (EVs) grows attention in the field of battery pack recycling. The main challenges of evaluation of the residual energy come from the uncertainty of thermo-mechanical-electrochemical behavior of battery. This motivates the notion of facilitating research on establishing a model which can detect and predict the state of battery based on parameters enable to be measured, such as voltage and stack stress. Thus, the present work proposes a stack stress-coupled-artificial intelligence approach for analyzing the residual energy (remaining) in the batteries. Experiments are designed and performed to verify the fundamentals. A robust model is formulated based on artificial intelligence approach of genetic programming. The findings in the study can provide an optimized recycling strategy for spent batteries by accurately predicting the state of battery based on stack stress.
1. Introduction Given with the limited amount of traditional energy, the society is working hard to weaken its dependence on fossil fuels and finding alternative energy sources. In the face of deteriorating environment, people are looking forward to other options which are environmentallyfriendly and sustainable. With the emergence of lithium-ion battery powered EVs, it looks promising to solve this problem. [1,2]. However, there are many problems in lithium-ion batteries during its practical applications. For example, the detection of battery state of health (SOH) and state of charge (SOC), battery aging mechanism investigation, and recycling or reuse strategies of spent batteries. Among these problems, battery pack recycling accounts for the major problem and a hot scenario for the next years. By 2025, approximately 1 million metric tons of spent battery waste will be accumulated. How to reasonably and effectively evaluate the residual energy of the lithium-ion batteries used in Electric Vehicles (EVs) grows attention in the field of battery pack recycling. The works on recycling of single cell battery has been paid great attention due to its diverse usage. The major works on recycling of single cell battery is to recover the materials such as cobalt, lithium, etc. by use of hydro-metallurgical methods. Till now, for the whole pack recycling, there is hardly any research being carried out in context of its
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automation or efficient recycling. The problem is attributed to hundreds of batteries embedded in series of parallel configurations in these packs. This problem of whole pack recycling could be very challenging because there shall be need of development of an intelligent model to sort out the batteries effectively based on the accurate prediction of residual energy. The main challenges of evaluation of the residual energy come from the uncertainty of thermo-mechanical-electrochemical behavior of battery. Thus, an empirical model which can reflect and predict the real time state of health of batteries in EVs also reflects the prediction of residual energy in the batteries. In this perspective, literature has been conducted. Based on the state-of-the-art studies, the battery modeling methods are summarized as shown in Fig. 1 According to different kinds of battery models, the estimation technologies can be classified into three major types, including (1) Coulomb Counting Method (CCM) [3] (2) Black-box Battery Model (BBM) [4] and (3) State-space Battery Model (SBM) [5]. (1) CCM is largely relying on the accuracy of the current measurement. In practical scenarios of uncertain disturbances, this open-loop estimation method usually gives bad results with an accumulation of measurement errors resulting from ammeters [6,7]. The estimation errors are even higher when the working temperature is too high or too low. (2) For Black-box Battery Models, Artificial intelligence (AI)-based approaches,
Corresponding author. E-mail address: [email protected] (L. Gao).
https://doi.org/10.1016/j.est.2019.101001 Received 2 August 2019; Received in revised form 18 September 2019; Accepted 5 October 2019 2352-152X/ © 2019 Elsevier Ltd. All rights reserved.
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Fig. 1. Classification of Battery Modeling procedures based on the literature.
are discussed in the end.
such as Artificial Neural Networks (ANN), Fuzzy Logic (FL), Support Vector machine (SVM), are often employed to formulate the mathematical models. The black-box battery model can provide a good SOC estimation based on the nonlinear relationships established from a given data set. Zenati et al. conducted experiments on battery aging in different operating conditions and combine impedance measurements with the fuzzy logic inference for estimation of either SOC or SOH [8]. Awadallah and Venkatesh developed a SOC estimation model using Adaptive Neuro-Fuzzy Inference Systems (ANFIS) and compared it to the traditional Coulomb counting method [9]. (3) State-space battery models based on Kalman filtering are popular in the estimation of battery SOC since it is an online close-loop algorithm. A state-space model has been established by Ting et al. through mathematical derivations to simulate the complex behaviors of a battery system [10]. The results indicate that there exist four state variables relevant to the battery model. Thus, the different models discussed above mainly rely on electrical parameters. These models are imprecise because the measurement of electrical parameters in real time is hard and generate noisy data resulting from instantaneous variations occurring in complex driving conditions. In addition, these electrical parameters-based models usually require great computing efforts and do not perform well with the aging of batteries. Thus, a simpler estimation method is expected with acceptable accuracy guaranteed. A coupling between mechanical stress and chemical characteristics of lithium-ion pouch batteries is observed in the work of Cannarella and Arnold [11]. The correlation is concluded to be electrode volume change induced by chemical reactions in lithium-ion batteries. The irreversible stress produced in the charging process is related to the energy loss inside the batteries through charging-discharging cycles [12]. In addition, some recent studies [13,14] have explored the means of establishing the fundamental and empirical relationship between the battery residual energy and the mechanical parameters such as the mechanical stress and strain when the battery is subjected to sudden compression or three-point bending tests. The studies were proved to be useful which compliments easier and accurate evaluation. Therefore, to address these problems related to recycling purpose, this paper proposes the Stack stress-coupled-AI approach for analyzing the residual energy in the batteries. Experiments based on the static loading on the Li-ion batteries is firstly conducted and its capacity shall be measured. The current study aims to determine the relationship between stress and capacity quantitatively. Based on the measured experimental data, the AI approach of genetic programming (GP) is then applied to formulate a functional relationship of capacity as a function of design variables (stress, temperature, voltage). Conclusions
2. Research problem statement An effective and efficient analysis of residual energy (remaining capacity) is an important problem for purpose of recycling of battery packs used in EVs. Finding of residual energy is related to SOH/SOC of the battery. The battery SOH is defined as the ratio of the current available full-charge capacity of to its original nominal capacity when it is freshly new while the SOC is the ratio of the residual capacity to the current full-charge capacity. For instance, a battery of 1 Ah nominal capacity with an 80% SOH and 60% SOC has a remaining capacity of 0.48 Ah. In addition, the main parameter used in this study is stack stress which linked to the capacity fade of batteries through particles exfoliation and the growth of solid electrolyte interface (SEI). The current research aims to quantitatively determine the relationship between inputs and output (see Fig. 2) during the charging-discharging cycles. An AI approach of GP framework is proposed to build battery models based on the experiment data and numerous analysis results are discussed. 3. Experimental details Battery charge-discharge experimental tests are carried out to collect data for parameters such as capacity, stack stress, and other key parameters. The schematic computer layout of the experimental is shown in Fig. 3 Battery testing Equipment in laboratory (Fig. 4) can charge and discharge batteries and measure data. 3.1. Experimental system The experimental system is built using the following devices: (1) (2) (3) (4) (5) (6)
Stress sensors and steel container - Fig. 4(a) Charge and discharge module - Fig. 4(b). Stress data collecting device - Fig. 4(c). Neware battery system- Fig. 4(d) Lithium-ion battery (refer to Table 1) A computer connecting to the sensors and the electronic load (see in Fig. 4(d)).
In this study, three different values of stack stress are applied. There are two batteries in the 3 kg group and 6 kg group and 3 in the 9 kg group. The battery is fixed in steel container and different initial stresses are set by tightening the screws. Neware battery system 2
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Fig. 2. Illustration of research problem undertaken in this study.
capacity as the output [15]. Table 3 shows part of the experimental data.
connects the positive and negative poles of the battery to charge and discharge the battery. Charge and discharge module can collect data of open circuit voltage (OCV) and capacity. Stress sensor can record and transfer stress data to stress data acquisation system. When the experiment is set up, Neware battery system periodically fully charges each battery under constant current and constant voltage at different initial stress and then power down them for a period of time before it discharges the battery. The concrete steps of charging-discharging are listed in Table 2. In order to obtain a series of experimental data, these four steps are recycled for many times. To increase the accuracy of the model, voltage is also included during the modelling process. Therefore, the OCV is collected when the cell is full-charged and discharged respectively as experimental data in the charge-discharge test. Hence, 4 parameters are defined as inputs of the model including initial stress, real-time stress, full-charged OCV and discharged OCV while the cell
3.2. Experimental findings A total of 1510 groups of data is collected in the experiment. The pareto chart of collected experimental data is shown in Fig. 5(a). It is clear that the capacity values are mainly distributed in the range [2.412–2.531] with minor values in low-value range and high-value range, which shows an approximate normal distribution. Fig. 5(b) shows the cell capacity of the complete experimental data set. It can be observed that the two curves for the 6 kg group and the 9 kg group almost overlap while the curve for the 3 kg group deviates from the previous two. However, the three curves show similar trend but with the difference on the cycle number. The 3 kg group curve seems likely
Fig. 3. Schematic computer layout of the conducted experimental tests. 3
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Fig. 4. Lithium-ion battery testing system measuring the stack stress.
under 3 different initial loads share a similar changing mode. On the general, the capacity experiences an increase and then drops down to a value lower than the initial value in the first period, which is around 100 cycles. In the second period, the capacity undergoes a similar change as it does in the first period. The peak value in the second period is lower than that in the first period. Fig. 5(d) shows the real-time stress with adjusted experimental data set. The real-time stress here is the average stress value of batteries from the same group. It can be seen that real-time stress of batteries with similar original load differs from each other from the same group. This phenomenon appears in all the three groups and no reason has been found to account for that. The average value is aimed to reduce the influence of this difference on the model. From Fig. 5(d), one can conclude that the real-time stress of the 6 kg group and 9 kg group share a declining trend on the general though the decrease in within a small range. Nevertheless, the real-time stress does not follow any patterns yet changes in a small amplitude of fluctuation.
Table 1 Li-ion battery specifications. Parameters
Values
Operating voltage Cell type Cell size Cell capacity Nominal voltage Internal resistance
2.75–4.2 Type 18,650 18 × 65 mm 2600 mAh 3.6–3.7 V 45.6 mΩ
Table 2 Charging- discharging steps and process.
Step Step Step Step
1 2 3 4
status
Condition1
Condition2
Cut-off condition
Full charge Rest discharge Rest
0.5C-rate 30 min 0.5C-rate 30 min
4.2V —— —— ——
Current <=0.05 Step time >=30 Voltage <=2.75 Step time >=30
A mins V mins
4. Modeling method of genetic programming to overlap with the other two curves if the other two curves are leftshifted for around 25 cycles. This difference on the cycle number appears probably because the batteries used in the 3 kg group test were hold for about one month after undergoing around 20 cycles in another experiment. In other words, the batteries in the 3 kg group test actually started from cycle number around 20. Therefore, the original experimental data set is adjusted by deleting about 30 cycles from the 6 kg group and the 9 kg group in the aim of keeping consistency among the three groups of tests. As shown in Fig 5(c), the capacities of batteries
Genetic programming (GP) is an artificial intelligence (AI) algorithm that can generate symbolic functional expressions based on only the given data [16]. The evolutionary system can mimic the behaviors of the targeted complicated system [17,18]. A lot of researches related to modeling problems in energy storage system verifies the practicality of the modeling method of GP [19–23]. Compared to other modeling methods, GP has advantages from different aspects: (1) GP works without any assumptions about the structure or the form 4
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Table 3 Partial data collection for the battery cell capacity. Input Sample number
1 2 3 4 5 6 7 8 9 … 397 398 399 400 401 402 403 404 405 … 808 809 810 811 812 813 814 815 816 …
Initial stress (kg)
Real-time stress (kg)
Output Discharged OCV (V)
3 3 3 3 3 3 3 3 3 … 6 6 6 6 6 6 6 6 6 … 9 9 9 9 9 9 9 9 9 …
5.358 5.399 5.416 5.441 5.482 5.515 5.534 5.552 5.573 … 6.365 6.369 6.367 6.398 6.387 6.396 6.398 6.390 6.373 … 10.838 10.832 10.824 10.836 10.838 10.832 10.824 10.822 10.817 …
3.433 3.441 3.432 3.438 3.438 3.440 3.440 3.434 3.431 … 3.431 3.432 3.427 3.429 3.431 3.431 3.426 3.425 3.427 … 3.437 3.438 3.440 3.439 3.439 3.440 3.441 3.433 3.434 …
Fullcharged OCV (V)
Cell capacity (Ah)
4.170 4.171 4.177 4.169 4.182 4.182 4.181 4.182 4.182 … 4.181 4.182 4.181 4.181 4.181 4.181 4.182 4.182 4.182 … 4.199 4.178 4.178 4.178 4.178 4.178 4.177 4.178 4.178 …
2.480 2.473 2.523 2.497 2.487 2.483 2.519 2.506 2.499 … 2.498 2.494 2.515 2.503 2.498 2.495 2.519 2.520 2.514 … 2.505 2.497 2.485 2.487 2.484 2.479 2.475 2.515 2.508 …
of the solution. (2) GP is suitable for complex modeling problems. It can generate different kinds of models, i.e. linear models, non-linear models, fraction expressions, etc. Another commonly used modeling method, response surface method (RSM), is usually undertaken to solve simple modeling problems with polynomial equations (quadratic equation is the preference). However, the models formed based on RSM does not perform well on data obtained from complex and non-linear systems. (3) The solution of GP is explicit in different forms such as mathematical expressions and programs. (4) GP avoids falling into local optimal solution due to the fitness evaluation criteria. In this study, GP is adopted on the modeling process with the experimental data from the battery charge-discharge tests as in Section 3.
4.2. Data modelling process of GP The modelling process is carried out in MATLAB R2014b. The experimental data is imported into GP. 80% of data obtained from experiment is used for training process to produce models while the remaining is used to validate the models. The settings are done based on trial-and-error approach. Additionally, the parameters setting of GP is shown in Table 4. The primitive set including the terminal set and the functional set as shown in Table 5. x1, x2, x3 and x4 in terminal sets represent initial stress, real-time stress, discharged OCV and fullcharged OCV respectively while y stands for the cell capacity. RMSE and MAPE i.e. objective functions. are chosen to measure the performance of the GP model.
Fig. 5. (a) Pareto chart showing the distribution of battery capacity in descending order of frequency, (b) Cell capacity under different initial stress with orginal data set, (c) Adjusted data set, and (d) Real-time stress of batteries under different initial stress.
5
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5. Analysis of the GP model
Table 4 Settings adopted in the GP simulation.
Model No. 10 is selected as the best model among the 15 simulation runs of GP. The post model analysis is then carried out to determine the insights into experimental process.
Terminal set Kind of primitive Variables Constant values Population size Number of generations Number of simulations run Maximum depth of each tree Maximum number of genes Crossover Mutation Reproduction
Contents x1, x2, x3, x4, y [−30,30] 1000 500 15 3 3 0.85 0.1 0.05
5.1. Accuracy of the model Fig. 6 shows the distribution of the experimental data and that of the estimated data. The orange dots, representing the estimated capacity, covers the majority of the blue dots, which represents the experimental capacity. Fig. 6 and Table 6 indicates that the GP model can estimate output values with a small deviation from the experimental value.
Table 5 Functional set adopted in the GP simulation.
5.2. Normal distribution design
Function set Kind of primitive Arithmetic Mathematical Others
In addition to the accuracy, the robustness of the GP model also needs to be tested. In general, it describes a model's behavior to the uncertain changes in the assumptions made in about the research problem. If the model is able to give the same or similar conclusions regardless of insignificant changes, the model has certain robustness. In context of statistical modeling, the robustness of a mathematical model is usually referred as the model insensitivity to small fluctuations of inputs. Therefore, the robustness of the GP model is checked by carrying out the normal distribution design of the inputs/design variables. Another reason for the normal distribution design is that the GP model's behavior to other values of inputs in the same range is unknown since the model is trained with discrete data from the experiment. It can be concluded from Fig. 6 that 99.7% of the simulation data are within the range between the minimum value and the maximum value in the experimental data. The designed normal distributed data is imported as the input of the GP model to obtain the results. Fig. 7 shows the distribution of the capacity resulting from the normal distribution of the inputs.
Contents +,-,×,÷ plog, square, tan, exp, ppower iflte
4.3. Selection of the best model 15 separate models are obtained from the 15 simulation runs of GP. From each run, the best model is selected based on the minimum training error. The performances of the models are shown in Table 6. Four indexes including RMSE of training data, RMSE of testing data, MAPE of training data and that of testing data is taken into consideration to rate the models performance. As shown in Table 6, model No. 10 is selected to be the final model based on the best overall performance. The explicit mathematical form of model No. 10 is given in the expression below:
y = 35.2566 + (−0.78285)*((ppower (x1, (−1.862822))) 5.3. Parametric analysis and interaction analysis
/((x2) − ((13.523072)))) + (−188057.4712) *(ppower (p log(x3), (( −25.564417)) + ((−29.035220)))) + (−14.9941)*(ppower (p log(x3), iflte (x2 , x3 , x2 , x3)))
Parametric analysis is an important statistical tool to construct parametric relations between the dependent variables and the design/ input. To put it in a simple way, parametric analysis studies the effect of one factor on the result with fixed values of the other factors [24]. In this analysis, a parametric analysis is performed on the model to investigate the effects of each input on the capacity. The procedures of the parametric analysis adopted in this study are quite simple. One of the three inputs is kept at original values from the experiment (except for initial stress which ranges from 1 kg to 9 kg), meanwhile, other inputs
(1)
where x1 stands for initial stress, x2 for real-time stress, x3 for discharged OCV and y for cell capacity. The model does not contain x4 which means in this model full-charged OCV is irrelevant to cell capacity.
Table 6 Table showing RMSE and MAPE of GP models. Model no.
Training RMSE
Training MAPE
Test RMSE
Test MAPE
No. of nodes
Depth
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
0.027819405 0.043407467 0.028001262 0.029210342 0.029732218 0.028011787 0.028760727 0.02976328 0.027797614 0.029278796 0.029260204 0.028129783 0.027828703 0.029307769 0.028115021
0.876872213 1.409253851 0.883017382 0.938521700 0.946714858 0.889760054 0.931690487 0.959625076 0.871798585 0.939758314 0.945840772 0.891899949 0.890114967 0.937481526 0.885670788
0.05379104 0.07307797 0.05227433 0.04601589 0.03948816 0.05278484 0.0468868 0.04274756 0.05736355 0.04592892 0.04767038 0.04935389 0.04880876 0.04466128 0.04929874
1.893350308 2.595386675 1.822398032 1.573353958 1.304235636 1.847449836 1.562191494 1.443789464 2.031403674 1.554991867 1.631234291 1.696395712 1.659535806 1.515255554 1.703485292
23 21 21 20 19 24 19 16 23 21 25 27 22 21 22
5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
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Fig. 6. Comparison of experimental capacity and estimated capacity from the GP model.
(1) GP model generates relationship with higher fitting accuracy for prediction of capacity as a function of real-time stress under varied applied initial stresses. Thus, the GP model can be used for sorting out the batteries based on their residual energy prediction during the battery pack recycling process. (2) When the initial applied stress value is low, the higher capacity value gets when the value of real-time stress is higher. (3) The interaction discharged OCV and either of the other two independent variables can be ignored. (4) The findings in the study can provide an optimized recycling strategy for spent batteries.
are set at their average values. Then, the three inputs are imported into the GP model for plot of response capacity values. Based on the results obtained, it can concluded that the initial stress and capacity are nonlinear negative correlations. In addition, the non-linear positive correlations can be found between real-time stress and capacity. In order to study the comprehensive effect of multi-parameters on capacity, the interaction analysis is performed. Results show that when the initial stress is low, the higher capacity value gets when the value of real-time stress is higher. However, the capacity does not seem to fluctuate at all in other situations. Furthermore, the interaction between initial stress and discharged OCV is negligible.
The application of GP approach for developing model for residual energy prediction could also be applicable to hybrid energy systems. The hybrid energy systems have great potential in the application of port terminals. The main parts of hybrid energy storage system include energy consuming system based on the grid, battery energy storage system, and renewable energy system which may include solar energy, wind energy, and tidal energy etc. In this case, the energy consumption
6. Conclusions A battery residual energy (remaining life detection) framework is proposed to provide a recycling strategy for spent batteries in EVs. Experiments are performed and AI method of GP was used to generate the mathematical model for predicting the cell capacity. The main results are shown as follows:
Fig. 7. Distribution of cell capacity data calculated using the GP model. 7
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which rely on electricity generated by combustion of fossil fuels could be reduced to benefit the environment. In addition, cost of energy consumption will also descend. However, the real application of such hybrid system in ports is still on the stage of development. There are several problems need to be focused: (1) Battery energy storage system robust design with an intelligent management and monitoring system. (2) Cleaner energy transfer efficiency and the cost. (3) Long-term effective electricity power conversion agreement between government and port operators. Further, the algorithms based on advanced machine learning principles [25–28] considering uncertainties in the design variables and the system can also be considered.
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Declaration of Competing Interest All Authors declares there is no conflict of interest. Acknowledgements This work was supported by the National Natural Science Foundation of China [grant numbers 51675196 and 51721092], the Program for HUST Academic Frontier Youth Team [grant number 2017QYTD04] and the Program for HUST Graduate Innovation and Entrepreneurship Fund[grant number 2019YGSCXCY037]. References [1] A.C.R. Teixeira, J.R. Sodré, Impacts of replacement of engine powered vehicles by electric vehicles on energy consumption and CO2 emissions, Transp. Res. Part D 59 (2018) 375–384. [2] J. Hofmann, D. Guan, K. Chalvatzis, et al., Assessment of electrical vehicles as a successful driver for reducing CO2 emissions in china, Appl. Energy 184 (2016) 995–1003. [3] J.H. Aylor, A. Thieme, B.W. Johnso, A battery state-of-charge indicator for electric wheelchairs, IEEE Trans. Indust. Electron. 39 (5) (1992) 398–409. [4] D. Yang, Y. Wang, R. Pan, et al., State-of-health estimation for the lithium-ion battery based on support vector regression, Appl. Energy (2017). [5] I. Baghdadi, O. Briat, P. Gyan, et al., State of health assessment for lithium batteries based on voltage–time relaxation measure, Electrochim. Acta 194 (2016) 461–472. [6] L.W. Juang, P.J. Kollmeyer, R. Zhao, et al., coulomb counting state-of-charge algorithm for electric vehicles with a physics-based temperature dependent battery model, Energy Conversion Congress and Exposition, IEEE, 2015, pp. 5052–5059. [7] M.H. Chang, H.P. Huang, S.W. Chang, A new state of charge estimation method for LiFePO4 battery packs used in robots, Energies 6 (4) (2013) 2007–2030. [8] A. Zenati, P. Desprez, H. Razik, Estimation of the SOC and the SOH of li-ion batteries, by combining impedance measurements with the fuzzy logic inference, IECON 2010 -, Conference on IEEE Industrial Electronics Society, IEEE, 2010, pp. 1773–1778. [9] M.A. Awadallah, B. Venkatesh, Accuracy improvement of SOC estimation in
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