What is it about?
Large battery energy storage systems are increasingly built from many battery units that differ in size, state of charge, health, and available power. Managing these units efficiently is difficult because using too many batteries increases cycling, while uneven use can accelerate aging and communication delays can distort distributed power allocation. This study develops an echelon energy management strategy that first selects only the battery units needed for each operating step and then allocates power among them according to both their aging condition and adjustable capacity. A fixed-time proportional optimal consensus method enables this allocation to be carried out in a distributed manner within a bounded convergence time. The strategy also corrects delay-induced steady-state errors and explicitly limits each battery unit according to its actual available power, helping prevent overcharging or over-discharging. The method was evaluated through simulations of a 32-unit multi-battery system and hardware experiments using four battery units under changing power commands, parameter uncertainties, and temporary unit unavailability.
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Why is it important?
Conventional multi-battery management strategies often optimize only one aspect of operation, such as state of charge, available capacity, or battery aging. This can improve one objective while worsening another, and consensus-based methods may also suffer from power-allocation errors when communication delays are present. The proposed strategy addresses these issues together: it reduces unnecessary battery activation, balances aging and adjustable capacity, preserves accurate total-power tracking, and enforces individual output limits. In the simulations, the proposed approach improved aging balance by about 43% and adjustable-capacity balance by about 96% compared with single-objective strategies, while also reducing equivalent cycling intensity. Hardware tests further showed that the strategy maintained aging balance and improved adjustable-capacity balance by 78%, while respecting battery power limits under the tested conditions. These features are important for operating large battery fleets more sustainably without sacrificing control accuracy or safety. Echelon_Energy_Management_Strat…
Perspectives
What I find particularly valuable about this work is that it treats battery lifetime, usable flexibility, control accuracy, and operational safety as interconnected problems rather than separate objectives. From my perspective, the echelon idea is especially practical because it avoids involving every battery unit in every control action, while the proportional consensus mechanism distributes the required power according to the condition and capability of each active unit. The combination of distributed optimization, explicit power constraints, and correction for communication-delay effects also helps bridge theoretical consensus control with realistic battery-management requirements. A useful next step is to evaluate the approach under more challenging operating conditions, including larger state-estimation errors, measurement noise, long-term physical battery degradation, packet loss, time-varying communication disturbances, and network interference. Its scalability also suggests potential extensions to large battery clusters, power-system dispatch, and virtual power plant applications. Echelon_Energy_Management_Strat…
Trustworthy AI & Resilient CPES | IEEE PES Representative to the Systems Council | SysC Distinguished Lecturer | Clarivate HCR Yang Li
Northeast Electric Power University
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This page is a summary of: Echelon Energy Management Strategy for Multi-Battery Energy Storage Systems Based on Fixed-Time Proportional Optimal Consensus, IEEE Transactions on Energy Conversion, January 2026, Institute of Electrical & Electronics Engineers (IEEE),
DOI: 10.1109/tec.2026.3743895.
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