Abstract：The performance of lithium-ion battery energy storage systems degrades severely in low- temperature environments. Electrochemical reactions slow down, internal resistance increases, and available capacity drops, posing a major challenge for applications in cold climates. A critical yet often overlooked factor is the strategy for setting the heating target temperature. Conventional approaches typically rely on a fixed temperature value, determined by general engineering experience and maintained throughout the discharge cycle. Such a static method fails to account for the dynamic changes in the battery's internal states during operation. Consequently, energy utilization is suboptimal, excessive heating energy is consumed, and overall system efficiency is compromised. This study introduces a dynamic optimization method for the low-temperature heating target with a dual adaptive unscented Kalman filter (DAUKF) and a long short-term memory (LSTM) neural network. An electro-thermal coupling model is constructed to capture the intricate interactions between electrical and thermal behaviors. Since the state of charge (SOC) and state of temperature (SOT) are highly nonlinear and tightly coupled parameters, a DAUKF-based joint estimation framework is proposed. Compared to the conventional dual extended Kalman filter (DEKF), the DAUKF demonstrates superior accuracy and robustness under strongly nonlinear, time-varying conditions, effectively mitigating error accumulation and state drift. Then, an efficiency-temperature mapping model is constructed using an LSTM network to predict the optimal discharge temperature for each SOC level. It overcomes the limitations of fixed empirical settings by learning the complex, non-linear relationship between SOC, temperature, and discharge efficiency from extensive experimental data. The LSTM captures the battery's efficiency dynamics during low-temperature discharge, enabling real-time prediction of the optimal heating target. Its strong generalization capability allows it to adapt to varying operational scenarios. Finally, a closed-loop dynamic control framework is constructed that operates cyclically through three core stages: state estimation, temperature prediction, and feedback-based optimization. The DAUKF continuously provides real-time SOC and SOT estimates that are fed into the LSTM model, which then generates the optimal heating target temperature in real time. This process enables multi-stage, full-process dynamic temperature regulation, where the heating target adjusts intelligently based on the battery's actual condition. As a result, the battery is maintained at the most energy-efficient temperature at any given time, enhancing discharge perfor- mance while minimizing heating energy consumption. Experimental validation was conducted under various low-temperature discharge scenarios. The results confirm that the proposed DAUKF-LSTM collaborative optimization method significantly improves battery discharge efficiency. The average energy-efficiency gain consistently outperforms that of traditional constant- temperature control strategies. This paper contributes a novel and effective paradigm for intelligent thermal management in cold-region energy storage applications. By enabling dynamic, self-adaptive temperature control, it paves the way for more efficient and reliable next-generation smart energy systems.
刘沛津, 孙文池, 姚倩. 基于DAUKF-LSTM协同的锂离子电池储能系统低温加热目标温度动态优化[J]. 电工技术学报, 2026, 41(14): 4974-4987.
Liu Peijin, Sun Wenchi, Yao Qian. Dynamic Optimization of Low-Temperature Heating Target for Energy Storage Systems Based on DAUKF-LSTM Estimation. Transactions of China Electrotechnical Society, 2026, 41(14): 4974-4987.
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