Temperature is a crucial parameter that determines the safety and reliability of lithium-ion batteries (LIBs) in electric vehicles and energy storage systems. Estimating LIBs temperature for battery management system state monitoring and thermal control, especially the core temperature is essential. However, the core temperature cannot be obtained directly and must be estimated via other measurable variables. To this end, this paper proposes a method to estimate the core temperature of LIBs based on the long short-term memory (LSTM) method combined with transfer learning (TL). Through this method, the relationship between core temperature and measured variables can be obtained. Moreover, the TL procedure, with its fine-tuning strategy, can substantially reduce the computation burden when used to estimate the core temperature of other LIBs via a reduced training set. In addition, the applicability of the proposed method is verified via a long period cycle test. The experimental results demonstrate that the proposed method can precisely estimate the LIB core temperature in ambient temperatures of between -10°C and 55°C in highly dynamic driving cycles and aging cycle tests, and the maximum RMSE is 0.3302°C. The LSTM-TL method has a high accuracy compared with other widely used methods.

The integrated community energy system (ICES) can realize the economic and environmental protection. As the critical equipment for ICES, energy router (ER) can realize the flexible transformation of different energy carriers. However, the efficiencies of the energy conversion devices in ER are usually assumed to be constant for the sake of simplicity, which may result in out-of-order operation schemes and redundant costs of ICES. In addition, the forms of multiple energy coupling of ICES are complex, and the conventional methods are difficult to coordinate the multiple energy demands. This paper proposes a demand side management (DSM) method for ICES. Firstly, a dynamic ER model is built to determine the energy conversion efficiency with the variation in load rate. The energy hub model provides a mathematical representation of ER, considering energy demand and the availability of different energy sources. Then, the DR considering renewable energy output is proposed, where a real-time price strategy is established and determined by the power of load demand and generation of renewable energy. Finally, a multi-objective operation optimization strategy based on low-carbon and the economy is established. The model involves the device constraint in the process of energy production and energy transmission. The simulation results of the proposed model under different scenarios are compared. The results show that the model effectively improves the economic benefits of ICES and reduces carbon emissions, and alleviates the pressure on the energy supply.

The charging capability of lithium-ion batteries (LiBs) is inherently limited at low temperatures, resulting in less charging current rates. In this regard, preheating LiBs before charging has been suggested as a solution, however, this requires separate control and additional switching mechanisms. Moreover, this approach cannot simultaneously produce alternating current (AC) and direct current (DC), resulting in reduced charging efficiencies. To overcome these challenges, this research proposes a self-heating and charging coordinated strategy. Furthermore, we propose a dual-active-bridge LC (DAB-LC) converter designed to facilitate simultaneous AC and DC output. A dual-closed-loop controller ensures that the converter can output the maximum acceptable DC of LiBs at the current temperature and state of charge (SOC) and superimposes adaptive AC within the battery safety threshold, thereby substantially enhancing the charging speed. Experimental results demonstrate that the proposed strategy can charge LiBs to 80% SOC in 1.55 h at -10°C, which is 6.65-times faster than the conventional little-current charging method.

The hybrid modular DC transformer (HMDCT) combining the series resonant dual active bridge (SR-DAB) and phase-shift dual active bridge (PS-DAB) has the advantages of high efficiency and bidirectional power regulation. However, due to the unregulated SR-DAB resonant tank inertia and limited PS-DAB output power, the dynamic response of HMDCT is slower than the pure PS-DAB based DC transformer, which could lead to a larger output overshoot. To address the problems, first, a novel trajectory-switching-modulation (TSM) method is proposed to control the SR-DAB resonant tank to track the desired state-trajectory rapidly by using SR-DAB switch-states. Compared with the conventional LLC trajectory control, TSM is more suitable for the SR-DAB in the bidirectional HMDCT because TSM is available under bidirectional power flow without changing the topology or increasing magnetizing losses. Then, a TSM based fast power transients control strategy is proposed to improve the bidirectional HMDCT dynamic performance by controlling the PS-DAB and SR-DAB simultaneously. Besides, PS-DAB current stress can also be reduced during the load transients. Experimental results show that the proposed strategy has better dynamic performance than the classic proportional-integral feedforward based strategy.

The ${{LLC}}$ converter-based dc transformer is popular for its stiff voltage gain, very high efficiency, and simple control. To avoid excessive start-up inrush current and hardware overdesign, the fixed-frequency variable duty-cycle soft start-up strategy is often adopted. However, in this conventional strategy, the optimal current-limiting curve (OCC) is derived by the challenging numerical model iterative algorithm, which is difficult to model, calculate online, and adapt to different operating conditions. To solve this problem, a closed-form OCC (COCC)-based soft start-up strategy is proposed. The COCC is derived by state-trajectory analysis and can be quickly calculated through the simple closed-form equation, providing greater practicality and flexibility while accurately maximizing the utilization of the allowable peak current. Furthermore, the closed-form equation makes it possible to estimate the start-up capability under the variable duty-cycle strategy. The calculation method of the maximum allowable start-up load is proposed, offering an important basis for the design of distribution system restoration schemes. Finally, a 50-kW dc transformer is built to verify the effectiveness of the proposed strategy under various conditions.