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Active equalization control method for battery pack based on Double-DQN
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AI
DOI：10.1016/j.est.2024.111361.png)
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Abstract
En 中文
In response to the issues of inconsistency and component loss during battery balancing, an active balancing scheme has been designed to control the battery balancing process. Firstly, a balancing circuit based on redundant batteries is proposed, which leverages the principle of single-cell parallel voltage division to enhance the consistency of the battery pack. To mitigate the problem of component loss, this study proposes a DoubleDQN-based balancing control strategy that effectively reduces the losses during battery balancing by considering the impact of the battery pack's state of charge (SOC) and state of health (SOH). Simulation results demonstrate that the proposed scheme not only reduces the inconsistency of the battery pack but also significantly decreases the component losses. Additionally, a hardware prototype has been developed to evaluate the efficacy of the proposed balancing scheme.
Keywords:
Machine learning
Active equalization
Redundant battery
Optimum control
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9.8
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2.3W
Citations:
10.1W
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