Topology-aware predictive maintenance via joint replacement and reconfiguration for BESSs under heterogeneous capacity degradation

Abstract

Efficient maintenance of battery energy storage systems (BESS) is critical for reliable operation under large-scale renewable integration. In modular architectures, heterogeneous degradation creates strong interactions between component health and system topology, meaning system capacity and risk depend not only on individual states but also on dynamic module configuration. Conventional maintenance policies often assume a fixed mapping from component states to system performance and thus fail to address topology-induced bottlenecks and imbalance-driven risks, which frequently leads to premature replacements or hidden safety hazards. This study proposes a novel predictive maintenance (PdM) framework that synergistically coordinates module replacement and structural reconfiguration. Unlike existing approaches, our method treats maintenance as a coupled decision process where replacement restores component health while reconfiguration reshapes the topology to mitigate degradation constraints. The problem is formulated as a nested mixed-integer nonlinear programming (MINLP) model. To solve this computationally challenging problem, we develop a tailored Bayesian Hierarchical Optimization with Adaptive Large Neighborhood Search (BHO-ALNS) algorithm that efficiently explores the joint decision space of replacement thresholds and reconfiguration actions. Numerical experiments based on data-driven degradation simulations demonstrate that the proposed strategy significantly outperforms various common conventional schemes. Specifically, compared to the fixed-threshold replacement-only strategy frequently employed in real-world maintenance practice, our approach increases net economic benefit by approximately 23%, while simultaneously enhancing capacity utilization and mitigating safety risks. These findings highlight the necessity of jointly managing component degradation and system configuration, offering a paradigm shift from passive component renewal to active structural adaptation in BESS.

Graphical abstract

Keywords

BESS / PdM / heterogeneous degradation / replacement and reconfiguration / MINLP

References

[1] Chang X, Zhao Y M, Yuan B, Fan M, Meng Q, Guo Y G, Wan L J, (2024). Solid-state lithium-ion batteries for grid energy storage: opportunities and challenges. Science China. Chemistry, 67( 1): 43–66

[2] Chen Z, Wang Z, Wu W, Xia T, Pan E, (2025). Neural representation-based wiener process with meta-learning for battery RUL prediction under time-varying degradation rate. IEEE Transactions on Instrumentation and Measurement, 74: 1–13

[3] Dao C D, Zuo M J, (2017). Selective maintenance of multi-state systems with structural dependence. Reliability Engineering & System Safety, 159: 184–195

[4] Du M, Zhang Y, Miao Q, (2025). Remaining useful life prediction of lithium battery based on deep reinforcement learning fusion network. Reliability Engineering & System Safety, 264: 111392

[5] Edge J S, O’Kane S, Prosser R, Kirkaldy N D, Patel A N, Hales A, Ghosh A, Ai W, Chen J, Yang J, Li S, Pang M C, Bravo Diaz L, Tomaszewska A, Marzook M W, Radhakrishnan K N, Wang H, Patel Y, Wu B, Offer G J, (2021). Lithium ion battery degradation: what you need to know. Physical Chemistry Chemical Physics, 23( 14): 8200–8221

[6] Farakhor A, Wu D, Wang Y, Fang H, (2023). A novel modular, reconfigurable battery energy storage system: Design, control, and experimentation. IEEE Transactions on Transportation Electrification, 9( 2): 2878–2890

[7] Fioravanti R, Kumar K, Nakata S, Chalamala B, Preger Y, (2020). Predictive-maintenance practices: For operational safety of battery energy storage systems. IEEE Power & Energy Magazine, 18( 6): 86–97

[8] Forouzanfar M, Gagliardi G, Tedesco F, Casavola A, (2025). Integrated model-based control allocation strategies oriented to predictive maintenance of saturated actuators. IEEE Transactions on Automation Science and Engineering, 22: 1045–1056

[9] Han W, Wik T, Kersten A, Dong G, Zou C, (2020). Next-generation battery management systems: Dynamic reconfiguration. IEEE Industrial Electronics Magazine, 14( 4): 20–31

[10] Han X, Lu L, Zheng Y, Feng X, Li Z, Li J, Ouyang M, (2019). A review on the key issues of the lithium ion battery degradation among the whole life cycle. eTransportation, 1: 100005

[11] Hassan Q, Viktor P, Al-Musawi T J, Ali B M, Algburi S, Alzoubi H M, Al-Jiboory A K, Sameen A Z, Salman H M, Jaszczur M, (2024). The renewable energy role in the global energy transformations. Renewable Energy Focus, 48: 100545

[12] He L, Yang Z, Gu Y, Liu C, He T, Shin K G, (2018). SoH-aware reconfiguration in battery packs. IEEE Transactions on Smart Grid, 9( 4): 3727–3735

[13] Hendel G, (2022). Adaptive large neighborhood search for mixed integer programming. Mathematical Programming Computation, 14( 2): 185–221

[14] Hittinger E, Ciez R E, (2020). Modeling costs and benefits of energy storage systems. Annual Review of Environment and Resources, 45( 1): 445–469

[15] Hong J, Li K, Liang F, Yang H, Hou Y, Ma F, Wang F, Zhang X, Zhang H, Zhang C, (2024). Research on battery inconsistency evaluation based on improved local outlier factor and fuzzy matrix. Journal of Energy Storage, 104: 114572

[16] Hu X, Deng X, Wang F, Deng Z, Lin X, Teodorescu R, Pecht M G, (2022). A review of second-life lithium-ion batteries for stationary energy storage applications. Proceedings of the IEEE, 110( 6): 735–753

[17] Inaolaji A, Wu X, Roychowdhury R, Smith R, (2024). Optimal allocation of battery energy storage systems for peak shaving and reliability enhancement in distribution systems. Journal of Energy Storage, 95: 112305

[18] Kasuya M, Jin L, (2025). Condition-based maintenance policy for deteriorating systems based on Wiener process with heterogeneous spare unit population and partial observation. Reliability Engineering & System Safety, 262: 111137

[19] Olde Keizer M C A, Flapper S D P, Teunter R H, (2017). Condition-based maintenance policies for systems with multiple dependent components: A review. European Journal of Operational Research, 261( 2): 405–420

[20] Kim K, Choi J I, (2023). Effect of cell-to-cell variation and module configuration on the performance of lithium-ion battery systems. Applied Energy, 352: 121888

[21] Kim S H, Shin Y J, (2023). Optimize the operating range for improving the cycle life of battery energy storage systems under uncertainty by managing the depth of discharge. Journal of Energy Storage, 73: 109144

[22] Kroposki B, Johnson B, Zhang Y, Gevorgian V, Denholm P, Hodge B M, Hannegan B, (2017). Achieving a 100% renewable grid: Operating electric power systems with extremely high levels of variable renewable energy. IEEE Power & Energy Magazine, 15( 2): 61–73

[23] Lawder M T, Suthar B, Northrop P W C, De S, Hoff C M, Leitermann O, Crow M L, Santhanagopalan S, Subramanian V R, (2014). Battery energy storage system (BESS) and battery management system (BMS) for grid-scale applications. Proceedings of the IEEE, 102( 6): 1014–1030

[24] Lin Y, Shao Y, Yang C, Guo W, Liu W, Zheng C, Gao X, (2024). Evaluation and optimization for integrated photo-voltaic and battery energy storage systems under time-of-use pricing in the industrial park. Journal of Energy Storage, 100: 113614

[25] Liu X, Hu Z, Wang X, Xie M, (2024). Capacity degradation assessment of lithium-ion battery considering coupling effects of calendar and cycling aging. IEEE Transactions on Automation Science and Engineering, 21( 3): 3052–3064

[26] Liu X, Zheng Z, Büyüktahtakın İ E, Zhou Z, Wang P, (2021). Battery asset management with cycle life prognosis. Reliability Engineering & System Safety, 216: 107948

[27] Ma C, Wang Q, Cai Z, Si S, Zhao J, (2021). Component reassignment for reliability optimization of reconfigurable systems considering component degradation. Reliability Engineering & System Safety, 215: 107867

[28] Mara S T, Norcahyo R, Jodiawan P, Lusiantoro L, Rifai A P, (2022). A survey of adaptive large neighborhood search algorithms and applications. Computers & Operations Research, 146: 105903

[29] Meng Y, Qi F, Liu B, Yin Z, Ye S, Zhang H, (2025). Joint optimization of maintenance strategy and buffer capacity for a multi-state buffered serial system considering self-repair actions. Reliability Engineering & System Safety, 262: 111252

[30] Mo H, Li C, Liu N, Zhao B, Dong H, Liu H, Zio E, (2025). Energy storage systems for carbon neutrality: Challenges and opportunities. Frontiers of Engineering Management, 12( 2): 305–329

[31] Nyamathulla S, Dhanamjayulu C, (2024). A review of battery energy storage systems and advanced battery management system for different applications: Challenges and recommendations. Journal of Energy Storage, 86: 111179

[32] Okumuşoğlu B C, Basciftci B, Kocuk B, (2024). An integrated predictive maintenance and operations scheduling framework for power systems under failure uncertainty. INFORMS Journal on Computing, 36( 5): 1335–1358

[33] Qian Z, Chen Z, Pan E, (2026). A transfer condition-focused model for battery capacity forecast. Reliability Engineering & System Safety, 267: 111928

[34] See K W, Wang G, Zhang Y, Wang Y, Meng L, Gu X, Zhang N, Lim K C, Zhao L, Xie B, (2022). Critical review and functional safety of a battery management system for large-scale lithium-ion battery pack technologies. International Journal of Coal Science & Technology, 9( 1): 36

[35] Shahriari B, Swersky K, Wang Z, Adams R P, De Freitas N, (2016). Taking the human out of the loop: A review of Bayesian optimization. Proceedings of the IEEE, 104( 1): 148–175

[36] Shi Y, Xu B, Wang D, Zhang B, (2018). Using battery storage for peak shaving and frequency regulation: Joint optimization for superlinear gains. IEEE Transactions on Power Systems, 33( 3): 2882–2894

[37] Tan J, Chen X, Bu Y, Wang F, Wang J, Huang X, Hu Z, Liu L, Lin C, Meng C, Lin J, Xie S, Xu J, Jing R, Zhao Y, (2024). Incorporating FFTA-based safety assessment of lithium-ion battery energy storage systems in multi-objective optimization for integrated energy systems. Applied Energy, 367: 123472

[38] Tembo P M, Dyer C, Subramanian V, (2024). Lithium-ion battery recycling—A review of the material supply and policy infrastructure. NPG Asia Materials, 16( 1): 43

[39] Tian J, Fan Y, Pan T, Zhang X, Yin J, Zhang Q, (2024). A critical review on inconsistency mechanism, evaluation methods and improvement measures for lithium-ion battery energy storage systems. Renewable & Sustainable Energy Reviews, 189: 113978

[40] Tian Z, Zhang H, (2022). Wind farm predictive maintenance considering component level repairs and economic dependency. Renewable Energy, 192: 495–506

[41] Wang L Y, Yin G, Ding Y, Zhang C, (2022). State of health based battery reconfiguration for improved energy efficiency. Control Theory and Technology, 20( 4): 443–455

[42] Wang S, Zhao X, Tian Z, Zuo M J, (2021). Optimum component reassignment for balanced systems with multi-state components operating in a shock environment. Reliability Engineering & System Safety, 210: 107514

[43] Wang S, Zhao X, Wu C, Wang X, (2023). Joint optimization of multi-stage component reassignment and preventive maintenance for balanced systems considering imperfect maintenance. Reliability Engineering & System Safety, 237: 109367

[44] Wang Y, Li X, Chen J, Liu Y, (2022). A condition-based maintenance policy for multi-component systems subject to stochastic and economic dependencies. Reliability Engineering & System Safety, 219: 108174

[45] Wang Y, Xiang H, Soo Y Y, Fan X, (2025). Aging mechanisms, prognostics and management for lithium-ion batteries: Recent advances. Renewable & Sustainable Energy Reviews, 207: 114915

[46] Wu Q, Feng Q, Ren Y, Xia Q, Wang Z, Cai B, (2021). An intelligent preventive maintenance method based on reinforcement learning for battery energy storage systems. IEEE Transactions on Industrial Informatics, 17( 12): 8254–8264

[47] Wu W, Chen Z, Liu W, Zhou D, Xia T, Pan E, (2025). Battery health prognosis in data-deficient practical scenarios via reconstructed voltage-based machine learning. Cell Reports. Physical Science, 6( 2): 102442

[48] Wu Y, Liu Z, Li B, Liu J, Zhang L, (2022). Energy management strategy and optimal battery capacity for flexible PV-battery system under time-of-use tariff. Renewable Energy, 200: 558–570

[49] Xie L, Ustolin F, Lundteigen M A, Li T, Liu Y, (2022). Performance analysis of safety barriers against cascading failures in a battery pack. Reliability Engineering & System Safety, 228: 108804

[50] Xie Y, Wang S, Li R, Ren D, Yi M, Xu C, Han X, Lu L, Friess B, Offer G, Ouyang M, (2022). Inhomogeneous degradation induced by lithium plating in a large-format lithium-ion battery. Journal of Power Sources, 542: 231753

[51] Yan X, Li J, Liu N, (2025). Refined multi-state modeling based battery energy storage system reliability indicators and evaluation. Applied Energy, 393: 126038

[52] Yang L, (2025a). An evidence-based likelihood approach for the reliability of a complex system with overlapped failure data. Computers & Industrial Engineering, 201: 110893

[53] Yang L, Wang J, Xie M, (2025b). Graph-based reliability evaluation of a reconfigurable multi-stage system using sequential unconnected path sets. Reliability Engineering & System Safety, 261: 111093

[54] Yang L, Zhang X, Lu Z, Fu Y, Moens D, Beer M, (2024). Reliability evaluation of a multi-state system with dependent components and imprecise parameters: A structural reliability treatment. Reliability Engineering & System Safety, 250: 110240

[55] You H, Wang X, Zhu J, Jiang B, Han G, Wei X, Dai H, (2024). Investigation of lithium-ion battery nonlinear degradation by experiments and model-based simulation. Energy Storage Materials, 65: 103083

[56] Yu S, You L, Zhou S, (2023). A review of optimization modeling and solution methods in renewable energy systems. Frontiers of Engineering Management, 10( 4): 640–671

[57] Zhao X, Chen P, Tang L C, (2025). Condition-based maintenance via Markov decision processes: A review. Frontiers of Engineering Management, 12( 2): 330–342

[58] Zhen M, Chen Z, Lu B, Chen Z, Pan E, (2025). Net benefit-oriented condition-based maintenance for lithium-ion battery packs in SGLS systems: Combining degradation updating and decision-making. Computers & Industrial Engineering, 200: 110850

[59] Zheng Y, Zhao J, Song Y, Luo F, Meng K, Qiu J, Hill D J, (2018). Optimal operation of battery energy storage system considering distribution system uncertainty. IEEE Transactions on Sustainable Energy, 9( 3): 1051–1060

[60] Zhou K, Zhang Z, Liu L, Yang S, (2022). Energy storage resources management: Planning, operation, and business model. Frontiers of Engineering Management, 9( 3): 373–391

RIGHTS & PERMISSIONS

Higher Education Press