Concept Review of a Cloud-Based Smart Battery Management System for Lithium-Ion Batteries: Feasibility, Logistics, and Functionality
Abstract
:1. Introduction
2. Review of the Current Battery Management System (BMS)
- Cell monitoring: This function involves the acquisition of the current, voltage, and temperature of each cell in the system. The measurements need to reach a certain degree of accuracy, as the data are often used to perform other functions in the BMS. Moreover, the BMS is also responsible for communication.
- Cell balancing: The imbalance of cell capacity can be caused by inconsistency in capacity and material quality during operation. Cell balancing is to redistribute the energy and maintain all cells at a similar SOC level. There are two cell balancing approaches: active and passive balancing. Active balancing is to transfer excess energy in higher SOC cells into lower SOC cells until they reach the same level. Passive balancing dissipates excess energy of higher SOC cells directly into heat to be removed. The function of cell balancing is necessary because battery capacity and lifetime will be reduced without it.
- Battery safety and protection: A main function of the BMS is to ensure the safety of the battery and protect it from operating at conditions that are harmful to both the battery and the users. Hazardous conditions are sometimes caused by the chemical characteristics of the battery. Fault diagnosis is a significant function of the BMS to ensure safe operation. Fault diagnosis algorithms detect the fault, identify its location and type, and perform necessary actions to reduce the effect of the fault. Fault diagnosis methods can be categorized into model-based, knowledge-based, and data-driven methods. The BMS also sets safety limits to protect the battery from working beyond the safe operating range of current, voltage, and temperature.
- State estimation: This mainly refers to the estimation of SOC and SOH. An accurate estimation of the battery SOC is necessary because it enables long battery life, prevention from battery failure, efficient operation, and accurate calculations of SOH and cell balancing. SOC estimation methods can be classified into the look-up table method, the coulomb counting method, model-based estimation methods, data-driven estimation methods, and the hybrid method. SOH estimation is crucial in selected energy management strategies to prolong battery life and appropriately arrange for the replacement of the battery. SOH can be estimated by direct measurement methods, indirect analysis methods, adaptive algorithms, and data-driven methods. Despite the importance of state estimation, the SOC and SOH values cannot be measured directly from the battery. Therefore, algorithms need to be developed to estimate the SOC and SOH of the battery based on the measurable data.
- Thermal management: The performance of the battery is usually affected by its temperature due to the effect of temperature on degradation and internal resistance. The battery thermal management system (BTMS) can help decrease maximum battery temperature and temperature differences inside the pack. There are three classes of BTMS, including active, passive, and hybrid. Active BTMS are air-based, liquid-based, and thermoelectric, whereas passive BTMS are phase change material (PCM)-based and heat-pipe-based. Hybrid BTMS use combinations of active and passive approaches such as PCM with air circulation, PCM with liquid circulation, and PCM with heat pipe. Without proper thermal management, the battery pack is susceptible to thermal runaway propagation as overheating is a direct trigger of thermal runaway.
3. Concept of the Cloud-Based Smart BMS
4. Perspectives on the Functionality and Usability of Cloud-Based Smart BMS
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
- Evans, A.; Strezov, V.; Evans, T.J. Assessment of utility energy storage options for increased renewable energy penetration. Renew. Sustain. Energy Rev. 2012, 16, 4141–4147. [Google Scholar] [CrossRef]
- Markovic, D.S.; Zivkovic, D.; Branovic, I.; Popovic, R.; Cvetkovic, D. Smart power grid and cloud computing. Renew. Sustain. Energy Rev. 2013, 24, 566–577. [Google Scholar] [CrossRef]
- Bose, B.K. Power Electronics, Smart Grid, and Renewable Energy Systems. Proc. IEEE 2017, 105, 2011–2018. [Google Scholar] [CrossRef]
- Barton, J.P.; Infield, D.G. Energy Storage and Its Use with Intermittent Renewable Energy. IEEE Trans. Energy Convers. 2004, 19, 441–448. [Google Scholar] [CrossRef]
- Nasiri, A. Integrating energy storage with renewable energy systems. In Proceedings of the 2008 34th Annual Conference of IEEE Industrial Electronics, Orlando, FL, USA, 9–12 November 2008; pp. 17–18. [Google Scholar]
- Roberts, B.P.; Sandberg, C. The Role of Energy Storage in Development of Smart Grids. Proc. IEEE 2011, 99, 1139–1144. [Google Scholar] [CrossRef]
- Scrosati, B.; Garche, J. Lithium batteries: Status, prospects and future. J. Power Sources 2010, 195, 2419–2430. [Google Scholar] [CrossRef]
- U.S Energy Information Administration. Battery Storage in the United States: An Update on Market Trends. 2020. Available online: https://www.eia.gov/analysis/studies/electricity/batterystorage/pdf/battery_storage.pdf (accessed on 8 November 2020).
- Li, X.; Wang, C. Engineering nanostructured anodes via electrostatic spray deposition for high performance lithium ion battery application. J. Mater. Chem. A 2013, 1, 165–182. [Google Scholar] [CrossRef]
- Liu, Y.; Liu, H.; An, L.; Zhao, X.; Liang, G. Blended spherical lithium iron phosphate cathodes for high energy density lithium–ion batteries. Ionics 2019, 25, 61–69. [Google Scholar] [CrossRef]
- Fang, R.; Chen, K.; Yin, L.; Sun, Z.; Li, F.; Cheng, H.M. The Regulating Role of Carbon Nanotubes and Graphene in Lithium-Ion and Lithium-Sulfur Batteries. Adv. Mater. 2018, 31, 1800863. [Google Scholar] [CrossRef]
- Tran, M.-K.; Cunanan, C.; Panchal, S.; Fraser, R.; Fowler, M. Investigation of Individual Cells Replacement Concept in Lithium-Ion Battery Packs with Analysis on Economic Feasibility and Pack Design Requirements. Processes 2021, 9, 2263. [Google Scholar] [CrossRef]
- Tran, M.-K.; Sherman, S.; Samadani, E.; Vrolyk, R.; Wong, D.; Lowery, M.; Fowler, M. Environmental and Economic Benefits of a Battery Electric Vehicle Powertrain with a Zinc–Air Range Extender in the Transition to Electric Vehicles. Vehicles 2020, 2, 398–412. [Google Scholar] [CrossRef]
- Cunanan, C.; Tran, M.-K.; Lee, Y.; Kwok, S.; Leung, V.; Fowler, M. A Review of Heavy-Duty Vehicle Powertrain Technologies: Diesel Engine Vehicles, Battery Electric Vehicles, and Hydrogen Fuel Cell Electric Vehicles. Clean Technol. 2021, 3, 474–489. [Google Scholar] [CrossRef]
- Tran, M.-K.; Fowler, M. Sensor Fault Detection and Isolation for Degrading Lithium-Ion Batteries in Electric Vehicles Using Parameter Estimation with Recursive Least Squares. Batteries 2020, 6, 1. [Google Scholar] [CrossRef] [Green Version]
- Hu, X.; Zhang, K.; Liu, K.; Lin, X.; Dey, S.; Onori, S. Advanced Fault Diagnosis for Lithium-Ion Battery Systems: A Review of Fault Mechanisms, Fault Features, and Diagnosis Procedures. IEEE Ind. Electron. Mag. 2020, 14, 65–91. [Google Scholar] [CrossRef]
- Liu, K.; Li, K.; Peng, Q.; Zhang, C. A brief review on key technologies in the battery management system of electric vehicles. Front. Mech. Eng. 2019, 14, 47–64. [Google Scholar] [CrossRef] [Green Version]
- Gabbar, H.A.; Othman, A.M.; Abdussami, M.R. Review of Battery Management Systems (BMS) Development and Industrial Standards. Technologies 2021, 9, 28. [Google Scholar] [CrossRef]
- Cui, Y.; Zuo, P.; Du, C.; Gao, Y.; Yang, J.; Cheng, X.; Yin, G. State of health diagnosis model for lithium ion batteries based on real-time impedance and open circuit voltage parameters identification method. Energy 2018, 144, 647–656. [Google Scholar] [CrossRef]
- Tran, M.-K.; Mathew, M.; Janhunen, S.; Panchal, S.; Raahemifar, K.; Fraser, R.; Fowler, M. A comprehensive equivalent circuit model for lithium-ion batteries, incorporating the effects of state of health, state of charge, and temperature on model parameters. J. Energy Storage 2021, 43, 103252. [Google Scholar] [CrossRef]
- Sui, X.; He, S.; Vilsen, S.B.; Meng, J.; Teodorescu, R.; Stroe, D.-I. A review of non-probabilistic machine learning-based state of health estimation techniques for Lithium-ion battery. Appl. Energy 2021, 300, 117346. [Google Scholar] [CrossRef]
- Kim, T.; Makwana, D.; Adhikaree, A.; Vagdoda, J.; Lee, Y. Cloud-Based Battery Condition Monitoring and Fault Diagnosis Platform for Large-Scale Lithium-Ion Battery Energy Storage Systems. Energies 2018, 11, 125. [Google Scholar] [CrossRef] [Green Version]
- Li, W.; Rentemeister, M.; Badeda, J.; Jöst, D.; Schulte, D.; Sauer, D.U. Digital twin for battery systems: Cloud battery management system with online state-of-charge and state-of-health estimation. J. Energy Storage 2020, 30, 101557. [Google Scholar] [CrossRef]
- Yang, S.; Zhang, Z.; Cao, R.; Wang, M.; Cheng, H.; Zhang, L.; Jiang, Y.; Li, Y.; Chen, B.; Ling, H.; et al. Implementation for a cloud battery management system based on the CHAIN framework. Energy AI 2021, 5, 100088. [Google Scholar] [CrossRef]
- Akdere, M.; Giegerich, M.; Wenger, M.; Schwarz, R.; Koffel, S.; Fuhner, T.; Waldhor, S.; Wachtler, J.; Lorentz, V.R.H.; Marz, M. Hardware and software framework for an open battery management system in safety-critical applications. In Proceedings of the IECON 2016—42nd Annual Conference of the IEEE Industrial Electronics Society, Florence, Italy, 23–26 October 2016; pp. 5507–5512. [Google Scholar]
- Lelie, M.; Braun, T.; Knips, M.; Nordmann, H.; Ringbeck, F.; Zappen, H.; Sauer, D. Battery Management System Hardware Concepts: An Overview. Appl. Sci. 2018, 8, 534. [Google Scholar] [CrossRef] [Green Version]
- Rahimi-Eichi, H.; Ojha, U.; Baronti, F.; Chow, M.-Y. Battery Management System: An Overview of Its Application in the Smart Grid and Electric Vehicles. IEEE Ind. Electron. Mag. 2013, 7, 4–16. [Google Scholar] [CrossRef]
- Xing, Y.; Ma, E.W.M.; Tsui, K.L.; Pecht, M. Battery Management Systems in Electric and Hybrid Vehicles. Energies 2011, 4, 1840–1857. [Google Scholar] [CrossRef]
- Rezvanizaniani, S.M.; Liu, Z.; Chen, Y.; Lee, J. Review and recent advances in battery health monitoring and prognostics technologies for electric vehicle (EV) safety and mobility. J. Power Sources 2014, 256, 110–124. [Google Scholar] [CrossRef]
- Kim, T.; Qiao, W.; Qu, L. Online SOC and SOH estimation for multicell lithium-ion batteries based on an adaptive hybrid battery model and sliding-mode observer. In Proceedings of the 2013 IEEE Energy Conversion Congress and Exposition, Denver, CO, USA, 14–18 September 2013; pp. 292–298. [Google Scholar]
- Lu, L.; Han, X.; Li, J.; Hua, J.; Ouyang, M. A review on the key issues for lithium-ion battery management in electric vehicles. J. Power Sources 2013, 226, 272–288. [Google Scholar] [CrossRef]
- Shen, M.; Gao, Q. A review on battery management system from the modeling efforts to its multiapplication and integration. Int. J. Energy Res. 2019, 43, 5042–5075. [Google Scholar] [CrossRef]
- Plett, G.L. Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs. J. Power Sources 2004, 134, 277–292. [Google Scholar] [CrossRef]
- Plett, G.L. Sigma-point Kalman filtering for battery management systems of LiPB-based HEV battery packs. J. Power Sources 2006, 161, 1369–1384. [Google Scholar] [CrossRef]
- Kim, I.-S. A Technique for Estimating the State of Health of Lithium Batteries Through a Dual-Sliding-Mode Observer. IEEE Trans. Power Electron. 2010, 25, 1013–1022. [Google Scholar]
- Tran, M.-K.; Mevawala, A.; Panchal, S.; Raahemifar, K.; Fowler, M.; Fraser, R. Effect of integrating the hysteresis component to the equivalent circuit model of Lithium-ion battery for dynamic and non-dynamic applications. J. Energy Storage 2020, 32, 101785. [Google Scholar] [CrossRef]
- Tran, M.-K.; DaCosta, A.; Mevawalla, A.; Panchal, S.; Fowler, M. Comparative Study of Equivalent Circuit Models Performance in Four Common Lithium-Ion Batteries: LFP, NMC, LMO, NCA. Batteries 2021, 7, 51. [Google Scholar] [CrossRef]
- Li, J.; Wang, L.; Lyu, C.; Liu, E.; Xing, Y.; Pecht, M. A parameter estimation method for a simplified electrochemical model for Li-ion batteries. Electrochim. Acta 2018, 275, 50–58. [Google Scholar] [CrossRef]
- Kumaresan, K.; Sikha, G.; White, R.E. Thermal Model for a Li-Ion Cell. J. Power Sources 2008, 155, A164–A171. [Google Scholar] [CrossRef]
- Prada, E.; Di Domenico, D.; Creff, Y.; Bernard, J.; Sauvant-Moynot, V.; Huet, F. Simplified Electrochemical and Thermal Model of LiFePO4-Graphite Li-Ion Batteries for Fast Charge Applications. J. Electrochem. Soc. 2012, 159, A1508–A1519. [Google Scholar] [CrossRef] [Green Version]
- Hu, X.; Feng, F.; Liu, K.; Zhang, L.; Xie, J.; Liu, B. State estimation for advanced battery management: Key challenges and future trends. Renew. Sustain. Energy Rev. 2019, 114, 109334. [Google Scholar] [CrossRef]
- Song, Y.; Liu, D.; Yang, C.; Peng, Y. Data-driven hybrid remaining useful life estimation approach for spacecraft lithium-ion battery. Microelectron. Reliab. 2017, 75, 142–153. [Google Scholar] [CrossRef]
- Deng, Z.; Yang, L.; Cai, Y.; Deng, H.; Sun, L. Online available capacity prediction and state of charge estimation based on advanced data-driven algorithms for lithium iron phosphate battery. Energy 2016, 112, 469–480. [Google Scholar] [CrossRef]
- Tran, M.-K.; Panchal, S.; Chauhan, V.; Brahmbhatt, N.; Mevawalla, A.; Fraser, R.; Fowler, M. Python-based scikit-learn machine learning models for thermal and electrical performance prediction of high-capacity lithium-ion battery. Int. J. Energy Res. 2021, 12, 2825. [Google Scholar] [CrossRef]
- Wang, D.; Yang, F.; Tsui, K.-L.; Zhou, Q.; Bae, S.J. Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Spherical Cubature Particle Filter. IEEE Trans. Instrum. Meas. 2016, 65, 1282–1291. [Google Scholar] [CrossRef]
- Ma, G.; Zhang, Y.; Cheng, C.; Zhou, B.; Hu, P.; Yuan, Y. Remaining useful life prediction of lithium-ion batteries based on false nearest neighbors and a hybrid neural network. Appl. Energy 2019, 253, 113626. [Google Scholar] [CrossRef]
- Hannan, M.A.; Hoque, M.M.; Hussain, A.; Yusof, Y.; Ker, P.J. State-of-the-Art and Energy Management System of Lithium-Ion Batteries in Electric Vehicle Applications: Issues and Recommendations. IEEE Access 2018, 6, 19362–19378. [Google Scholar] [CrossRef]
- Tran, M.-K.; Fowler, M. A Review of Lithium-Ion Battery Fault Diagnostic Algorithms: Current Progress and Future Challenges. Algorithms 2020, 13, 62. [Google Scholar] [CrossRef] [Green Version]
- Wu, C.; Zhu, C.; Ge, Y.; Zhao, Y. A Review on Fault Mechanism and Diagnosis Approach for Li-Ion Batteries. J. Nanomater. 2015, 2015, 631263. [Google Scholar] [CrossRef] [Green Version]
- Wang, Q.; Ping, P.; Zhao, X.; Chu, G.; Sun, J.; Chen, C. Thermal runaway caused fire and explosion of lithium ion battery. J. Power Sources 2012, 208, 210–224. [Google Scholar] [CrossRef]
- Marcicki, J.; Onori, S.; Rizzoni, G. Nonlinear Fault Detection and Isolation for a Lithium-Ion Battery Management System. In Proceedings of the ASME 2010 Dynamic Systems and Control Conference, ASMEDC, Cambridge, MA, USA, 12 September 2010; Volume 1, pp. 607–614. [Google Scholar]
- Zheng, C.; Ge, Y.; Chen, Z.; Huang, D.; Liu, J.; Zhou, S. Diagnosis Method for Li-Ion Battery Fault Based on an Adaptive Unscented Kalman Filter. Energies 2017, 10, 1810. [Google Scholar] [CrossRef] [Green Version]
- Liu, Z.; He, H.; Ahmed, Q.; Rizzoni, G. Structural Analysis Based Fault Detection and Isolation Applied for A Lithium-Ion Battery Pack. IFAC-PapersOnLine 2015, 48, 1465–1470. [Google Scholar] [CrossRef]
- Kim, J.; Cho, B.H. An innovative approach for characteristic analysis and state-of-health diagnosis for a Li-ion cell based on the discrete wavelet transform. J. Power Sources 2014, 260, 115–130. [Google Scholar] [CrossRef]
- Shang, Y.; Lu, G.; Kang, Y.; Zhou, Z.; Duan, B.; Zhang, C. A multi-fault diagnosis method based on modified Sample Entropy for lithium-ion battery strings. J. Power Sources 2020, 446, 227275. [Google Scholar] [CrossRef]
- Naha, A.; Khandelwal, A.; Agarwal, S.; Tagade, P.; Hariharan, K.S.; Kaushik, A.; Yadu, A.; Kolake, S.M.; Han, S.; Oh, B. Internal short circuit detection in Li-ion batteries using supervised machine learning. Sci. Rep. 2020, 10, 1301. [Google Scholar] [CrossRef] [PubMed]
- Zhao, Y.; Liu, P.; Wang, Z.; Zhang, L.; Hong, J. Fault and defect diagnosis of battery for electric vehicles based on big data analysis methods. Appl. Energy 2017, 207, 354–362. [Google Scholar] [CrossRef]
- Feng, F.; Hu, X.; Hu, L.; Hu, F.; Li, Y.; Zhang, L. Propagation mechanisms and diagnosis of parameter inconsistency within Li-Ion battery packs. Renew. Sustain. Energy Rev. 2019, 112, 102–113. [Google Scholar] [CrossRef]
- Xiong, R.; Li, L.; Tian, J. Towards a smarter battery management system: A critical review on battery state of health monitoring methods. J. Power Sources 2018, 405, 18–29. [Google Scholar] [CrossRef]
- Xiong, R.; Ma, S.; Li, H.; Sun, F.; Li, J. Toward a Safer Battery Management System: A Critical Review on Diagnosis and Prognosis of Battery Short Circuit. iScience 2020, 23, 101010. [Google Scholar] [CrossRef]
- Beam, A.L.; Kohane, I.S. Big Data and Machine Learning in Health Care. JAMA 2018, 319, 1317–1318. [Google Scholar] [CrossRef]
- Heaton, J.B.; Polson, N.G.; Witte, J.H. Deep learning for finance: Deep portfolios. Appl. Stoch. Models Bus. Ind. 2017, 33, 3–12. [Google Scholar] [CrossRef]
- Ciolacu, M.; Tehrani, A.F.; Beer, R.; Popp, H. Education 4.0—Fostering student’s performance with machine learning methods. In Proceedings of the 2017 IEEE 23rd International Symposium for Design and Technology in Electronic Packaging (SIITME), Constanta, Romania, 25–28 October 2017; pp. 438–443. [Google Scholar]
- Nuhic, A.; Terzimehic, T.; Soczka-Guth, T.; Buchholz, M.; Dietmayer, K. Health diagnosis and remaining useful life prognostics of lithium-ion batteries using data-driven methods. J. Power Sources 2013, 239, 680–688. [Google Scholar] [CrossRef]
- Klass, V.; Behm, M.; Lindbergh, G. A support vector machine-based state-of-health estimation method for lithium-ion batteries under electric vehicle operation. J. Power Sources 2014, 270, 262–272. [Google Scholar] [CrossRef]
- Li, X.; Yuan, C.; Li, X.; Wang, Z. State of health estimation for Li-Ion battery using incremental capacity analysis and Gaussian process regression. Energy 2020, 190, 116467. [Google Scholar] [CrossRef]
- Eddahech, A.; Briat, O.; Bertrand, N.; Delétage, J.-Y.; Vinassa, J.-M. Behavior and state-of-health monitoring of Li-ion batteries using impedance spectroscopy and recurrent neural networks. Int. J. Electr. Power Energy Syst. 2012, 42, 487–494. [Google Scholar] [CrossRef]
- Chemali, E.; Kollmeyer, P.J.; Preindl, M.; Emadi, A. State-of-charge estimation of Li-ion batteries using deep neural networks: A machine learning approach. J. Power Sources 2018, 400, 242–255. [Google Scholar] [CrossRef]
- Yang, R.; Xiong, R.; Ma, S.; Lin, X. Characterization of external short circuit faults in electric vehicle Li-ion battery packs and prediction using artificial neural networks. Appl. Energy 2020, 260, 114253. [Google Scholar] [CrossRef]
- Dai, H.; Zhao, G.; Lin, M.; Wu, J.; Zheng, G. A Novel Estimation Method for the State of Health of Lithium-Ion Battery Using Prior Knowledge-Based Neural Network and Markov Chain. IEEE Trans. Ind. Electron. 2019, 66, 7706–7716. [Google Scholar] [CrossRef]
- Khang, T.D.; Vuong, N.D.; Tran, M.-K.; Fowler, M. Fuzzy C-Means Clustering Algorithm with Multiple Fuzzification Coefficients. Algorithms 2020, 13, 158. [Google Scholar] [CrossRef]
- Singh, P.; Vinjamuri, R.; Wang, X.; Reisner, D. Fuzzy logic modeling of EIS measurements on lithium-ion batteries. Electrochim. Acta 2006, 51, 1673–1679. [Google Scholar] [CrossRef]
- Martinez, D.A.; Poveda, J.D.; Montenegro, D. Li-Ion battery management system based in fuzzy logic for improving electric vehicle autonomy. In Proceedings of the 2017 IEEE Workshop on Power Electronics and Power Quality Applications (PEPQA): Bogota, Colombia, 30 May–1 June 2017; pp. 1–6. [Google Scholar]
BMS Components | Advantages |
---|---|
Hardware | Potentially smaller devices Significantly greater computational capability Virtually unlimited data storage |
Software | More efficient operational control and optimization Better and more interactive monitoring and visualization More accurate and reliable prognostics and diagnostics |
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Tran, M.-K.; Panchal, S.; Khang, T.D.; Panchal, K.; Fraser, R.; Fowler, M. Concept Review of a Cloud-Based Smart Battery Management System for Lithium-Ion Batteries: Feasibility, Logistics, and Functionality. Batteries 2022, 8, 19. https://doi.org/10.3390/batteries8020019
Tran M-K, Panchal S, Khang TD, Panchal K, Fraser R, Fowler M. Concept Review of a Cloud-Based Smart Battery Management System for Lithium-Ion Batteries: Feasibility, Logistics, and Functionality. Batteries. 2022; 8(2):19. https://doi.org/10.3390/batteries8020019
Chicago/Turabian StyleTran, Manh-Kien, Satyam Panchal, Tran Dinh Khang, Kirti Panchal, Roydon Fraser, and Michael Fowler. 2022. "Concept Review of a Cloud-Based Smart Battery Management System for Lithium-Ion Batteries: Feasibility, Logistics, and Functionality" Batteries 8, no. 2: 19. https://doi.org/10.3390/batteries8020019
APA StyleTran, M. -K., Panchal, S., Khang, T. D., Panchal, K., Fraser, R., & Fowler, M. (2022). Concept Review of a Cloud-Based Smart Battery Management System for Lithium-Ion Batteries: Feasibility, Logistics, and Functionality. Batteries, 8(2), 19. https://doi.org/10.3390/batteries8020019