# A New Charging Algorithm for Li-Ion Battery Packs Based on Artificial Neural Networks

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Background

#### 2.1. Charging Methods

#### 2.2. Balancing Methodologies

## 3. Proposed Approach

#### 3.1. Artificial Neural Networks

_{k}) in real-time. To perform an efficient and optimized control of the battery charging process, the proposed algorithm combines two FFNNs. The first FFNN (Figure 2a) is responsible for the determination of the charging current (${i}_{k}$). This FFNN uses the following three inputs to accurately determine the charging current: the difference between the full charge voltage ($fcV$) and the average cell voltage of the pack (${\Psi}_{k}$), which is represented by ${\gamma}_{k}$; the maximum difference between cell voltages (${\varphi}_{k}$); the pack temperature (${T}_{k}$), where $k$ is the instant of time. The second FFNN (Figure 2b) estimates the balancing orders (θ

_{k}) using the following three inputs: the average cell voltage of the pack (${\Psi}_{k}$), the voltage of each individual cell (${V}_{i,k}$), and the standard deviation of the pack voltage (${\delta}_{k}$).

_{1}training dataset was created with real data obtained through CC charging method wherein the Li-ion battery was subjected to different current values (from 0.25 A to 2 A with a 0.25 A step). Additionally, to improve the FFNN

_{1}generalization capability, the training dataset was enriched with artificial data created according to security and operational guidelines. The obtained training dataset is represented in Figure 3, by a three-dimensional map that correlates: the maximum difference between cells voltages (${\varphi}_{k}$), the pack temperature (${T}_{k}$ and the difference between the full charge voltage and the average cell voltage of the pack (${\gamma}_{k}$).

_{2}training process, the dataset was created according to Equations (1) and (2), such that when the voltage of each individual cell (${V}_{i,k}$) exceeds the average cell voltage of the pack (${\gamma}_{k}$) plus the standard deviation of the pack voltage (${\delta}_{k}$) multiplied by coefficient $\alpha $, the cell is considered balanced.

#### 3.2. Training Approach

_{lb}and x

_{ub}), the number of agents in the population, maximum number of iterations, and PSO control parameters.

_{t}is the number of samples of the training dataset.

## 4. System Description and Experimental Results

#### 4.1. System Description

^{®}software running on a desktop workstation. This type of centralized control architecture allows great flexibility in the development of new charging algorithms with cell balancing strategies.

^{®}TMS320F28069 microcontroller.

^{®}ISL94212 device that allows a chain connection of up to 14 devices. Each device allows the simultaneous monitoring of 12 battery cells. In this study, the implemented system had 24 cells in series, resulting in a nominal power of 230 W/h.

_{bal}resistor (33Ohm) until all cells in the pack have reached the same voltage. However, the ISL94212 device only allows four temperature sensors. Therefore, using the capabilities of the TMS320F28069 microcontroller, a temperature acquisition system was developed to enable the use of a larger number of temperature sensors (in this case 12 sensors, 1 sensor for each 2 cells). Lastly, the power unit was the Magna-Power Electronics

^{®}DC SL 500-5.2 programmable power supply, which communicated through Standard Commands for Programmable Instruments (SCPI) with the main control unit.

- Stage 1. In this stage, all the system variables are initialized, and all the communication ports are configured. Additionally, the number of ISL94212 devices connected in the chain is checked and the initial voltages and temperatures of each battery pack is acquired.
- Stage 2. The stop criteria, represented by Equations (10) and (11), is checked to verify if the battery packs are in charging condition or already fully charged.

- Stage 3. In this stage, the input data of the FFNNs is obtained. To accomplish this, the voltages and temperatures of each battery pack connected in the chain are acquired. When the acquisition process is complete, the data is passed through a moving average filter with the last six measured data points to reduce noise.
- Stage 4. In this stage, the FFNN
_{1}is executed, and the calculated charging current is communicated to the power unit through the SCPI. - Stage 5. Finally, FFNN
_{2}is executed and then the output response value is evaluated in a comparison stage that normalizes the orders to binary values (0 or 1). Afterwards, the balancing orders are performed using the acquisition and balancing unit. The system then waits 60 s for the balancing process to finish in order to not compromise the accuracy of the next measurement process.

#### 4.2. Experimental Results

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

- Khan, A.B.; Choi, W. Optimal Charge Pattern for the High-Performance Multistage Constant Current Charge Method for the Li-Ion Batteries. IEEE Trans. Energy Convers.
**2018**, 33, 1132–1140. [Google Scholar] [CrossRef] - Wu, T.; Liu, X.; Zhang, X.; Lu, Y.; Wang, B.; Deng, Q.; Yang, Y.; Wang, E.; Lyu, Z.; Li, Y.; et al. Full Concentration Gradient-Tailored Li-Rich Layered Oxides for High-Energy Lithium-Ion Batteries. Adv. Mater.
**2021**, 33, 2001358. [Google Scholar] [CrossRef] [PubMed] - El Kharbachi, A.; Zavorotynska, O.; Latroche, M.; Cuevas, F.; Yartys, V.; Fichtner, M. Exploits, Advances and Challenges Benefiting beyond Li-Ion Battery Technologies. J. Alloys Compd.
**2020**, 817, 153261. [Google Scholar] [CrossRef] - Li, S.; Leng, D.; Li, W.; Qie, L.; Dong, Z.; Cheng, Z.; Fan, Z. Recent Progress in Developing Li2S Cathodes for Li–S Batteries. Energy Storage Mater.
**2020**, 27, 279–296. [Google Scholar] [CrossRef] - Su, Y.S.; Fu, Y.; Cochell, T.; Manthiram, A. A Strategic Approach to Recharging Lithium-Sulphur Batteries for Long Cycle Life. Nat. Commun.
**2013**, 4, 2985. [Google Scholar] [CrossRef] - Ren, D.; Feng, X.; Lu, L.; He, X.; Ouyang, M. Overcharge Behaviors and Failure Mechanism of Lithium-Ion Batteries under Different Test Conditions. Appl. Energy
**2019**, 250, 323–332. [Google Scholar] [CrossRef] - Mandli, A.R.; Ramachandran, S.; Khandelwal, A.; Kim, K.Y.; Hariharan, K.S. Fast Computational Framework for Optimal Life Management of Lithium Ion Batteries. Int. J. Energy Res.
**2018**, 42, 1973–1982. [Google Scholar] [CrossRef] - Rodrigues, M.T.F.; Son, S.-B.; Colclasure, A.M.; Shkrob, I.A.; Trask, S.E.; Bloom, I.D.; Abraham, D.P. How Fast Can a Li-Ion Battery Be Charged? Determination of Limiting Fast Charging Conditions. ACS Appl. Energy Mater.
**2021**, 4, 1063–1068. [Google Scholar] [CrossRef] - Liu, K.; Zou, C.; Li, K.; Wik, T. Charging Pattern Optimization for Lithium-Ion Batteries With an Electrothermal-Aging Model. IEEE Trans. Ind. Inform.
**2018**, 14, 5463–5474. [Google Scholar] [CrossRef] - Lin, Q.; Wang, J.; Xiong, R.; Shen, W.; He, H. Towards a Smarter Battery Management System: A Critical Review on Optimal Charging Methods of Lithium Ion Batteries. Energy
**2019**, 183, 220–234. [Google Scholar] [CrossRef] - Wang, S.C.; Liu, Y.H. A PSO-Based Fuzzy-Controlled Searching for the Optimal Charge Pattern of Li-Ion Batteries. IEEE Trans. Ind. Electron.
**2015**, 62, 2983–2993. [Google Scholar] [CrossRef] - Faisal, M.; Hannan, M.A.; Ker, P.J.; Rahman, M.S.A.; Begum, R.A.; Mahlia, T.M.I. Particle Swarm Optimised Fuzzy Controller for Charging–Discharging and Scheduling of Battery Energy Storage System in MG Applications. Energy Rep.
**2020**, 6, 215–228. [Google Scholar] [CrossRef] - Kalogiannis, T.; Hosen, M.S.; Gandoman, F.H.; Sokkeh, M.A.; Jaguemont, J.; Berecibar, M.; van Mierlo, J. Multi-Objective Particle Swarm Optimization and Training of Datasheet-Based Load Dependent Lithium-Ion Voltage Models. Int. J. Electr. Power Energy Syst.
**2021**, 133, 107312. [Google Scholar] [CrossRef] - Chen, L.R.; Hsu, R.C.; Liu, C.S. A Design of a Grey-Predicted Li-Ion Battery Charge System. IEEE Trans. Ind. Electron.
**2008**, 55, 3692–3701. [Google Scholar] [CrossRef] - Chen, L.-R.; Hsu, R.C.; Liu, C.S.; Yang, H.-Y.; Chu, N.-Y. A Grey-Predicted Li-Ion Battery Charge System. In Proceedings of the 30th Annual Conferenceof the IEEE industrlal Electronics Society, Busan, South Korea, 2–6 November 2004; pp. 502–507. [Google Scholar]
- Li, C.Y.; Liu, G.P. Optimal Fuzzy Power Control and Management of Fuel Cell/Battery Hybrid Vehicles. J. Power Sources
**2009**, 192, 525–533. [Google Scholar] [CrossRef] - Mansiri, K.; Sukchai, S.; Sirisamphanwong, C. Fuzzy Control Algorithm for Battery Storage and Demand Side Power Management for Economic Operation of the Smart Grid System at Naresuan University, Thailand. IEEE Access
**2018**, 6, 32440–32449. [Google Scholar] [CrossRef] - Faisal, M.; Hannan, M.A.; Ker, P.J.; Lipu, M.S.H.; Uddin, M.N. Fuzzy-Based Charging—Discharging Controller for Lithium-Ion Battery in Microgrid Applications. IEEE Trans. Ind. Appl.
**2021**, 57, 4187–4195. [Google Scholar] [CrossRef] - Liu, Y.-H.; Teng, J.-H.; Lin, Y.-C. Search for an Optimal Rapid Charging Pattern for Lithium–Ion Batteries Using Ant Colony System Algorithm. IEEE Trans. Ind. Electron.
**2005**, 52, 1328–1336. [Google Scholar] [CrossRef] - Guo, Z.; Liaw, B.Y.; Qiu, X.; Gao, L.; Zhang, C. Optimal Charging Method for Lithium Ion Batteries Using a Universal Voltage Protocol Accommodating Aging. J. Power Sources
**2015**, 274, 957–964. [Google Scholar] [CrossRef] - Lee, C.; Chang, T.; Hsu, S.; Jiang, J. Taguchi-Based PSO for Searching an Optimal Four-Stage Charge Pattern of Li-Ion Batteries. J. Energy Storage
**2019**, 21, 301–309. [Google Scholar] [CrossRef] - Amanor-boadu, J.M.; Guiseppi-Elie, A.; Sánchez-Sinencio, E. Search for Optimal Pulse Charging Parameters for Li-Ion Polymer Batteries Using Taguchi Orthogonal Arrays. IEEE Trans. Ind. Electron.
**2018**, 65, 8982–8992. [Google Scholar] [CrossRef] - Perez, H.E.; Hu, X.; Dey, S.; Moura, S.J. Optimal Charging of Li-Ion Batteries with Coupled Electro-Thermal-Aging Dynamics. IEEE Trans. Veh. Technol.
**2017**, 66, 7761–7770. [Google Scholar] [CrossRef] - Zou, C.; Hu, X.; Wei, Z.; Wik, T.; Egardt, B. Electrochemical Estimation and Control for Lithium-Ion Battery Health-Aware Fast Charging. IEEE Trans. Ind. Electron.
**2018**, 65, 6635–6645. [Google Scholar] [CrossRef] - Wang, S.; Kuang, K.; Han, X.; Chu, Z.; Lu, L.; Ouyang, M. A Model-Based Continuous Differentiable Current Charging Approach for Electric Vehicles in Direct Current Microgrids. J. Power Sources
**2021**, 482, 229019. [Google Scholar] [CrossRef] - Chen, J.C.; Chen, T.L.; Liu, W.J.; Cheng, C.C.; Li, M.G. Combining Empirical Mode Decomposition and Deep Recurrent Neural Networks for Predictive Maintenance of Lithium-Ion Battery. Adv. Eng. Inform.
**2021**, 50, 101405. [Google Scholar] [CrossRef] - Yang, Q.; Xu, J.; Li, X.; Xu, D.; Cao, B. State-of-Health Estimation of Lithium-Ion Battery Based on Fractional Impedance Model and Interval Capacity. Int. J. Electr. Power Energy Syst.
**2020**, 119, 105883. [Google Scholar] [CrossRef] - Rastegarpanah, A.; Hathaway, J.; Ahmeid, M.; Lambert, S.; Walton, A.; Stolkin, R. A Rapid Neural Network–Based State of Health Estimation Scheme for Screening of End of Life Electric Vehicle Batteries. Proc. Inst. Mech. Eng. Part I J. Syst. Control Eng.
**2020**, 235, 330–346. [Google Scholar] [CrossRef] - Chandran, V.; Patil, C.K.; Karthick, A.; Ganeshaperumal, D.; Rahim, R.; Ghosh, A. State of Charge Estimation of Lithium-Ion Battery for Electric Vehicles Using Machine Learning Algorithms. World Electr. Veh. J.
**2021**, 12, 38. [Google Scholar] [CrossRef] - Ashraf, A.; Bangyal, W.H.; Rauf, H.T.; Pervaiz, S.; Ahmad, J. Training of Artificial Neural Network Using New Initialization Approach of Particle Swarm Optimization for Data Classification. In Proceedings of the 2020 International Conference on Emerging Trends in Smart Technologies (ICETST), Karachi, Pakistan, 26–27 March 2020. [Google Scholar] [CrossRef]
- Hussein, A.A.H.; Batarseh, I. A Review of Charging Algorithms for Nickel and Lithium Battery Chargers. IEEE Trans. Veh. Technol.
**2011**, 60, 830–838. [Google Scholar] [CrossRef] - Shen, W.; Vo, T.T.; Kapoor, A. Charging Algorithms of Lithium-Ion Batteries: An Overview. In Proceedings of the 2012 7th IEEE Conference on Industrial Electronics and Applications (ICIEA), Singapore, 18–20 July 2012; pp. 1567–1572. [Google Scholar] [CrossRef]
- Keil, P.; Jossen, A. Charging Protocols for Lithium-Ion Batteries and Their Impact on Cycle Life—An Experimental Study with Different 18650 High-Power Cells. J. Energy Storage
**2016**, 6, 125–141. [Google Scholar] [CrossRef] - Velho, R.; Beirão, M.; Calado, M.D.R.; Pombo, J.; Fermeiro, J.; Mariano, S. Management System for Large Li-Ion Battery Packs with a New Adaptive Multistage Charging Method. Energies
**2017**, 10, 605. [Google Scholar] [CrossRef] - Liu, C.L.; Wang, S.C.; Chiang, S.S.; Liu, Y.H.; Ho, C.H. PSO-Based Fuzzy Logic Optimization of Dual Performance Characteristic Indices for Fast Charging of Lithium-Ion Batteries. In Proceedings of the 2013 IEEE 10th International Conference on Power Electronics and Drive Systems (PEDS), Kitakyushu, Japan, 22–25 April 2013; pp. 474–479. [Google Scholar] [CrossRef]
- Mathieu, R.; Briat, O.; Gyan, P.; Vinassa, J.M. Fast Charging for Electric Vehicles Applications: Numerical Optimization of a Multi-Stage Charging Protocol for Lithium-Ion Battery and Impact on Cycle Life. J. Energy Storage
**2021**, 40, 102756. [Google Scholar] [CrossRef] - Yin, M.; Cho, J.; Park, D. Pulse-Based Fast Battery IoT Charger Using Dynamic Frequency and Duty Control Techniques Based on Multi-Sensing of Polarization Curve. Energies
**2016**, 9, 209. [Google Scholar] [CrossRef] - Chen, L.-R. A Design of an Optimal Battery Pulse Charge System by Frequency-Varied Technique. IEEE Trans. Ind. Electron.
**2007**, 54, 398–405. [Google Scholar] [CrossRef] - Chen, L.R. Design of Duty-Varied Voltage Pulse Charger for Improving Li-Ion Battery-Charging Response. IEEE Trans. Ind. Electron.
**2009**, 56, 480–487. [Google Scholar] [CrossRef] - Notten, P.H.L.; Op, J.H.G.; Beek, J.R.G. Van Boostcharging Li-Ion Batteries: A Challenging New Charging Concept. J. Power Sources
**2005**, 145, 89–94. [Google Scholar] [CrossRef] - Amietszajew, T.; Mcturk, E.; Fleming, J.; Bhagat, R. Understanding the Limits of Rapid Charging Using Instrumented Commercial 18650 High-Energy Li-Ion Cells. Electrochim. Acta
**2018**, 263, 346–352. [Google Scholar] [CrossRef] - Liu, J.; Duan, Q.; Chen, H.; Sun, J.; Wang, Q. An Optimal Multistage Charge Strategy for Commercial Lithium Ion Battery. Sustain. Energy Fuels
**2018**, 2, 1726–1736. [Google Scholar] [CrossRef] - Santucci, A.; Sorniotti, A.; Lekakou, C. Power Split Strategies for Hybrid Energy Storage Systems for Vehicular Applications. J. Power Sources
**2014**, 258, 395–407. [Google Scholar] [CrossRef] [Green Version] - Cho, S.Y.; Lee, I.O.; Baek, J.I.; Moon, G.W. Battery Impedance Analysis Considering DC Component in Sinusoidal Ripple-Current Charging. IEEE Trans. Ind. Electron.
**2016**, 63, 1561–1573. [Google Scholar] [CrossRef] - Chen, L.R.; Wu, S.L.; Shieh, D.T.; Chen, T.R. Sinusoidal-Ripple-Current Charging Strategy and Optimal Charging Frequency Study for Li-Ion Batteries. IEEE Trans. Ind. Electron.
**2013**, 60, 88–97. [Google Scholar] [CrossRef] - Gallardo-Lozano, J.; Romero-Cadaval, E.; Milanes-Montero, M.I.; Guerrero-Martinez, M.A. Battery Equalization Active Methods. J. Power Sources
**2014**, 246, 934–949. [Google Scholar] [CrossRef] - Cao, J.; Schofield, N.; Emadi, A. Battery Balancing Methods: A Comprehensive Review. In Proceedings of the 2008 IEEE Vehicle Power and Propulsion Conference, Harbin, China, 3–5 September 2008; pp. 3–8. [Google Scholar] [CrossRef]
- Daowd, M.; Omar, N.; van den Bossche, P.; van Mierlo, J. A Review of Passive and Active Battery Balancing Based on MATLAB/Simulink. Int. Rev. Electr. Eng.
**2011**, 6, 2974–2989. [Google Scholar] [CrossRef] - Qi, J.; Dah-Chuan Lu, D. Review of Battery Cell Balancing Techniques. In Proceedings of the 2014 Australasian Universities Power Engineering Conference (AUPEC), Perth, Australia, 28 September–1 October 2014; pp. 1–6. [Google Scholar] [CrossRef]
- Raman, S.R.; Xue, X.D.; Cheng, K.W.E. Review of Charge Equalization Schemes for Li-Ion Battery and Super-Capacitor Energy Storage Systems. In Proceedings of the 2014 International Conference on Advances in Electronics, Computers and Communications, Bangalore, India, 10–11 October 2014. [Google Scholar]
- Kim, M.-Y.; Kim, C.-H.; Kim, J.-H.; Moon, G.-W. A Chain Structure of Switched Capacitor for Improved Cell Balancing Speed of Lithium-Ion Batteries. IEEE Trans. Ind. Electron.
**2014**, 61, 3989–3999. [Google Scholar] [CrossRef] - Ye, Y.; Cheng, K. An Automatic Switched-Capacitor Cell Balancing Circuit for Series-Connected Battery Strings. Energies
**2016**, 9, 138. [Google Scholar] [CrossRef] [Green Version] - Ho, K.C.; Liu, Y.H.; Ye, S.P.; Chen, G.J.; Cheng, Y.S. Mathematical Modeling and Performance Evaluation of Switched-Capacitor-Based Battery Equalization Systems. Electronics
**2021**, 10, 2629. [Google Scholar] [CrossRef] - Ye, Y.; Cheng, K.W.E. Modeling and Analysis of Series-Parallel Switched-Capacitor Voltage Equalizer for Battery/Supercapacitor Strings. IEEE J. Emerg. Sel. Top. Power Electron.
**2015**, 3, 977–983. [Google Scholar] [CrossRef] - Xu, B.; Liu, L.; Wang, S.; Lin, Z.; Mai, R. A Series-Parallel Resonance-Switched-Capacitor Equalizer for the Hybrid Energy Storage System Based on Cascade Utilization. In Proceedings of the 2021 IEEE 2nd China International Youth Conference on Electrical Engineering (CIYCEE), Chengdu, China, 15–17 December 2021; pp. 1–6. [Google Scholar] [CrossRef]
- Zhang, Y.; Yang, R. An Improved Buck-Boost Circuit Equalization Method for Series Connected Battery Packs. In Proceedings of the 2021 IEEE 4th International Electrical and Energy Conference (CIEEC), Wuhan, China, 28–30 May 2021. [Google Scholar] [CrossRef]
- Wu, Q.; Gao, M.; Lin, H.; Dong, Z. A Bimodal Multichannel Battery Pack Equalizer Based on a Quasi-Resonant Two-Transistor Forward Converter. Energies
**2021**, 14, 1112. [Google Scholar] [CrossRef] - Tang, S.; Yang, Y. Why Neural Networks Apply to Scientific Computing? Theor. Appl. Mech. Lett.
**2021**, 11, 100242. [Google Scholar] [CrossRef] - SAMSUNG. Specification of Product for Lithium-Ion Rechargeable Cell-Model: ICR18650-26H; Samsung SDI Co., Ltd.: Yongin-si, Korea, 2011. [Google Scholar]
- Nunes, H.G.G.; Pombo, J.A.N.; Bento, P.M.R.; Mariano, S.J.P.S.; Calado, M.R.A. Collaborative Swarm Intelligence to Estimate PV Parameters. Energy Convers. Manag.
**2019**, 185, 866–890. [Google Scholar] [CrossRef] - Nguyen, D.; Widrow, B. Improving the Learning Speed of 2-Layer Neural Networks by Choosing Initial Values of the Adaptive Weights. In Proceedings of the 1990 IJCNN International Joint Conference on Neural Networks, San Diego, CA, USA, 17–21 June 1990. [Google Scholar]
- Ruan, D.; Montero, J.; Lu, J.; Martinez, L.; D’hondt, P.; Kerre, E.E. Computational Intelligence in Decision and Control. In Proceedings of the 8th International FLINS Conference, Madrid, Spain, 21–24 September 2008; Volume 21, p. 24. [Google Scholar] [CrossRef] [Green Version]

**Figure 2.**Representation of the proposed architectures: (

**a**) implemented FFNN to estimate the charging current; (

**b**) implemented FFNN to estimate the balancing orders.

**Figure 9.**(

**a**,

**b**) current profiles for the proposed algorithm and the multistage method, respectively; (

**c**,

**d**) charging parameters for the proposed algorithm and the multistage method, respectively.

**Figure 11.**Number of cells balanced during charge for: (

**a**) proposed algorithm; (

**b**) multistage algorithm.

Charging Time (h) | Difference Between Cell Voltages (V) | Temperature Increase (°C) | |
---|---|---|---|

Multistage with five current levels | 2.42 | 0.01 | 13.84 |

Proposed charging Algorithm | 2.23 | 0.01 | 9.28 |

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |

© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Faria, J.P.D.; Velho, R.L.; Calado, M.R.A.; Pombo, J.A.N.; Fermeiro, J.B.L.; Mariano, S.J.P.S.
A New Charging Algorithm for Li-Ion Battery Packs Based on Artificial Neural Networks. *Batteries* **2022**, *8*, 18.
https://doi.org/10.3390/batteries8020018

**AMA Style**

Faria JPD, Velho RL, Calado MRA, Pombo JAN, Fermeiro JBL, Mariano SJPS.
A New Charging Algorithm for Li-Ion Battery Packs Based on Artificial Neural Networks. *Batteries*. 2022; 8(2):18.
https://doi.org/10.3390/batteries8020018

**Chicago/Turabian Style**

Faria, João P. D., Ricardo L. Velho, Maria R. A. Calado, José A. N. Pombo, João B. L. Fermeiro, and Sílvio J. P. S. Mariano.
2022. "A New Charging Algorithm for Li-Ion Battery Packs Based on Artificial Neural Networks" *Batteries* 8, no. 2: 18.
https://doi.org/10.3390/batteries8020018