The Series and Parallel configuration of batteries combination is the most common pack design for delivering the required energy and capacity for Electric Vehicles. However, this combination is hard configured and inflexible to follow the degradation rate of the cells. This problem can be more evident in Second Life Batteries (SLB), which are found in stationary systems and low-speed vehicles. Therefore, this paper presents a self-re-configurable BMS to control and manage a pack of SLBs with relays that can handle the pack’s configuration. The system was built and tested using six different approaches to calculate the performance and efficiency of the pack. The results suggested that a Series configuration ACSC with relays that enable and disable the cells with upper voltage thresholds is the fastest method for charging SLB efficiently. However, Parallel connection was the technique that could reduce the voltage deviation. Despite that, the six methods are suitable for charging the SLB, and the application will depend on the need.
HolechekJLGeliHMESawalhahMN, et al. A global assessment: can renewable energy replace fossil fuels by 2050?Sustainability2022; 14(8): 4792.
2.
LiuWPlackeTChauKT.Overview of batteries and battery management for electric vehicles. Energy Rep2022; 8: 4058–4084.
3.
HasibSAIslamSChakraborttyRK, et al. A comprehensive review of available battery datasets, RUL prediction approaches, and advanced battery management. IEEE Access2021; 9: 86166–86193.
4.
ChenWLiangJYangZ, et al. A review of lithium-ion battery for electric vehicle applications and beyond. Energy Proc2019; 158: 4363–4368.
5.
NeubauerJPesaranA.The ability of battery second use strategies to impact plug-in electric vehicle prices and serve utility energy storage applications. J Power Sources2011; 196(23): 10351–10358.
6.
SaxenaSLe FlochCMacDonaldJ, et al. Quantifying EV battery end-of-life through analysis of travel needs with vehicle powertrain models. J Power Sources2015; 282: 265–276.
7.
Canals CasalsLEtxandi-SantolayaMBibiloni-MuletPA, et al. Electric vehicle battery health expected at end of life in the upcoming years based on UK data. Batteries2022; 8(10): 164.
8.
HuXDengXWangF, et al. A review of second-life lithium-ion batteries for stationary energy storage applications. Proc IEEE Inst Electr Electron Eng2022; 110(6): 735–753.
9.
MubengaNSStuartT.Capacity measurements for second life EV batteries. Electricity J2022; 3(3): 396–409.
10.
HuXZhangKLiuK, et al. Advanced fault diagnosis for lithium-ion battery systems: s review of fault mechanisms, fault features, and diagnosis procedures. IEEE Ind Electron Mag2020; 14(3): 65–91.
11.
ZhangZKongXZhengY, et al. Real-time diagnosis of micro-short circuit for Li-ion batteries utilizing low-pass filters. Energy2019; 166: 1013–1024.
12.
DongTWangYPengP, et al. Electrical-thermal behaviors of a cylindrical graphite-nca li-ion battery responding to external short circuit operation Li-ion battery responding to external short circuit operation. Int J Energy Res2019; 43(4): 1444–1459.
13.
OuyangDChenMLiuJ, et al. Investigation of a commercial lithium-ion battery under overcharge/over-discharge failure conditions. RSC Adv2018; 8(58): 33414–33424.
14.
Juarez-RoblesDVyasAAFearC, et al. Overcharge and aging analytics of Li-ion cells. J Electrochem Soc2020; 167(9): 090547.
15.
HabibAKMAHasanMKIssaGF, et al. Lithium-ion battery management system for electric vehicles: constraints, challenges, and recommendations. Batteries2023; 9(3): 152.
16.
OmaribaZBZhangLSunD.Review of battery cell balancing methodologies for optimizing battery pack performance in electric vehicles. IEEE Access2019; 7: 129335–129352.
17.
ZuoHLiangJZhangB, et al. Intelligent estimation on state of health of lithium-ion power batteries based on failure feature extraction. Energy2023; 282: 128794.
18.
DengYYingHEJ, et al. Feature parameter extraction and intelligent estimation of the state-of-health of lithium-ion batteries. Energy2019; 176: 91–102.
19.
BaumannMWildfeuerLRohrS, et al. Parameter variations within Li-ion battery packs – theoretical investigations and experimental quantification. J Energy Storage2018; 18: 295–307.
20.
ChangLMaCZhangC, et al. Correlations of lithium-ion battery parameter variations and connected configurations on pack statistics. Appl Energy2023; 329: 120275.
21.
Cordoba-ArenasAOnoriSRizzoniG.A control-oriented lithium-ion battery pack model for plug-in hybrid electric vehicle cycle-life studies and system design with consideration of health management. J Power Sources2015; 279: 791–808.
22.
ChangLMaCLuanC, et al. Influence of the assembly method on the cell current distribution of series–parallel battery packs based on connector resistance. Front Energy Res2022; 10: 804303. DOI: 10.3389/fenrg.2022.804303.
23.
WangYZhaoYZhouS, et al. Impact of individual cell parameter difference on the performance of series-parallel battery packs. ACS Omega2023; 8(11): 10512–10524.
24.
FillAKochSBirkeKP.Algorithm for the detection of a single cell contact loss within parallel-connected cells based on continuous resistance ratio estimation. J Energy Storage2020; 27: 101049.
25.
KimTQiaoWQuL.Self-reconfigurable multicell batteries. In: 2011 IEEE energy conversion congress and exposition, 2011, p.35493555. IEEE.
26.
MuhammadSRafiqueMULiS, et al. Reconfigurable battery systems: a survey on hardware architecture and research challenges. ACM Trans Des Autom Electron Syst2019; 24(2): 127.
27.
PanYFengXZhangM, et al. Internal short circuit detection for lithium-ion battery pack with parallel-series hybrid connections. J Clean Prod2020; 255: 120277.
28.
XuCZhangFFengX, et al. Experimental study on thermal runaway propagation of lithium-ion battery modules with different parallel-series hybrid connections. J Clean Prod2021; 284: 124749.
29.
MaMLiXGaoW, et al. Multi-fault diagnosis for series-connected lithium-ion battery pack with reconstruction-based contribution based on parallel PCA-KPCA. Appl Energy2022; 324: 119678.
30.
GotzJGalvãoJSilveiraA, et al. Intelligent management for second-life lithium-ion batteries with backup cells. In: International conference on Flexible automation and intelligent manufacturing: establishing bridges for more sustainable manufacturing systems, 2024, pp.10111018. Cham: Springer Nature.
31.
GotzJEspoladorJGuerreroG, et al. Machine learning for forecasting and predicting failures in lithium-ion batteries. In: International conference on flexible automation and intelligent manufacturing, 2023, pp.537–545. Cham: Springer International Publishing.
32.
GotzJGuerreroGEspoladorJ, et al. Application of anomaly detection algorithms in lithium-ion battery packs - a case study. In: International conference on flexible automation and intelligent manufacturing, 2023, pp.753–760. Cham: Springer International Publishing.
33.
TeodorescuRSuiXVilsenSB, et al. Smart battery technology for lifetime improvement. Batteries2022; 8(10): 169.
