Deep Reinforcement Learning-Based Battery Management Algorithm for Retired Electric Vehicle Batteries with a Heterogeneous State of Health in BESSs

• Published in: Energies (Basel) Vol. 17; no. 1; p. 79
• Main Authors: Doan, Nhat Quang, Shahid, Syed Maaz, Choi, Sung-Jin, Kwon, Sungoh
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-01-2024
• Subjects: Algorithms; battery energy storage system; battery management system; Deep learning; deep reinforcement learning; Electric vehicles; Electrocutions; Energy storage; extended Kalman filter; Lithium; Machine learning; Power supply; retired lithium-ion batteries; Scheduling; SOH estimation
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en17010079

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.