Lithium-ion batteries in cold climates suffer from significant performance degradation, such as reduced available power and life cycle deterioration. To address this problem, a reconfigurable battery system (RBS) based self-heating method is proposed in this article. This innovative approach leverages a switch array integrated within the RBS, achieving self-heating with a fast temperature rise and low energy loss. Additionally, employing ac heating current with high frequency further mitigates damage to the battery. The modularized three-switch reconfigurable topology is proposed, and the ac-heating principle is designed. Also, based on the frequency-dependent characteristics of the battery impedance, the heating strategy is developed to accurately control the heating current. The experimental results demonstrate the efficient heating capability: the battery can be heated from −30 °C to 0 °C within 237 s by consuming only 6.27% of nominal capacity, and the battery capacity fade rate is only 0.47% after 200 heating cycles.

To achieve fine control of multilayer temperature uniformity and energy consumption in a battery thermal management system (BTMS), a model predictive control (MPC) based on the reduced-order model and the heat generation previewer is proposed in this work. A direct contact liquid cooling battery pack is adopted to verify the control strategy. The control-oriented reduced-order model is developed for online multilayer temperature distribution acquisition. A heat generation predictor coupling with a dual neural network is integrated into the MPC controller to provide accurate future disturbances preview. The results indicate that the BTMS can be controlled to the target temperature with less overshoot. Besides, the temperature difference of the cell, module, and pack level can be limited to 0.8 °C, 1 °C, and 2 °C, respectively, decreasing the state of health difference among the cells. For energy consumption, the proposed method improves up to 56.48%.

Battery performance is significantly reduced at low temperatures, posing a challenge. To overcome this issue, the reconfigurable battery system (RBS) based hybrid self-heating (HSH) method is proposed in this article. This innovative approach leverages the flexible mode-switching characteristics of the RBS, achieving HSH with a high temperature rise rate and minimal energy loss. Additionally, employing square ac heating current with high frequency and low amplitude further mitigates damage to the battery. The physical configuration of the RBS- based HSH method is designed, and the modularized three-switch reconfigurable topology is proposed. Furthermore, the heating strategy is developed to further reduce battery fading. The experimental results demonstrate the efficient heating capability of this approach: the battery can be rapidly heated from –20 °C to 10 °C in just 239 s, consuming only 6.29% of the nominal capacity.

Accurate and reliable state-of-health (SOH) estimation is an important topic in battery management. Single data-driven model based SOH estimation suffers significant discrepancy problems over different cases. Moreover, existing ensemble based SOH estimation methods suffer serious problems, such as insufficient diversity of base models, complicated weight calculation, and severe overfitting. To address these problems, a stacking-based ensemble learning method for SOH estimation is proposed in this article. A second-level learner is used to integrate three heterogeneous base models without any weight calculation step. Fused datasets are generated by cross validation, maximizing the model generalization. Comprehensive validations are performed on batteries with two different cathode materials using two training strategies. The results show that the proposed ensemble method outperforms not only all base models (29% better than the optimal base model), but also the average method (more than 32%) and the state-of-the-art ensemble method (more than 44%).

Battery performance will be dramatically reduced at low temperatures. To solve this problem, a hybrid self-heating method (HSHM) for batteries used at low temperature is proposed in this article. The HSHM owns features of low cost, high temperature rise rate, low energy loss, etc., which has the potential to be widely used to heat batteries. The physical and electric configuration of the HSHM is designed, and the working principles are analyzed. The heating strategy of the HSHM is then introduced to illustrate the heating performance. To validate the proposed method, the experimental workbench is established. Experimental results show that even with smaller heating current, the heating speed of the HSHM is faster, and less energy is needed to heat the battery. Compared with the traditional self-heating method, the performance of the HSHM is improved by 1.3 times for the temperature rise rate and improved by 55.6% for the energy loss rate, respectively.