In this paper, we propose a battery management algorithm to optimize the lifetimes of retired lithium batteries with heterogeneous states of health in a battery energy storage system under dynamic power demand. A battery energy storage system allows for the use of retired lithium batteries for applications such as backup power in homes, data centers, etc. In a battery energy storage system, a battery pack consists of several retired batteries connected in parallel or in series to fulfill the required power demand. Owing to the retired batteries’ different capacity levels, i.e., states of health, a scheduling strategy is required to turn battery cells inside the battery pack on and off such that the secondary lifetimes of the retired batteries are extended. To establish the optimal scheduling policy, it is necessary to determine the correct states of each battery cell, including the state of charge and the state of health. To that end, the proposed algorithm first estimates the state of charge and state of health for all cells based on data measured using an extended Kalman filter. Then, a deep reinforcement learning scheduling algorithm is implemented to connect/disconnect the battery cells to/from the battery pack based on their states. Via simulation, we show that the proposed algorithm estimates the state of charge and state of health of each battery cell with low error and extends the lifetime of battery packs by 20.6%, compared to methods proposed in previous works.

In this paper, we propose a battery management algorithm to maximize the lifetime of a parallel-series connected battery pack with heterogeneous states of health in a battery energy storage system. The growth of retired lithium-ion batteries from electric vehicles increases the applications for battery energy storage systems, which typically group multiple individual batteries with heterogeneous states of health in parallel and series to achieve the required voltage and capacity. However, previous work has primarily focused on either parallel or series connections of batteries due to the complexity of managing diverse battery states, such as state of charge and state of health. To address the scheduling in parallel-series connections, we propose a cooperative multi-agent deep Q network framework that leverages multi-agent deep reinforcement learning to observe multiple states within the battery energy storage system and optimize the scheduling of cells and modules in a parallel-series connected battery pack. Our approach not only balances the states of health across the cells and modules but also enhances the overall lifetime of the battery pack. Through simulation, we demonstrate that our algorithm extends the battery pack’s lifetime by up to 16.27% compared to previous work and exhibits robustness in adapting to various power demand conditions.