目的 随着用电设备的增加与自然灾害的频发，对配电网弹性的需求不断提升。为此，提出了一种基于台风灾害下优化分布式资源协同配置的策略。 方法 首先，充分考虑灾前电动汽车用户在台风灾害下参与供电恢复的潜力，并以此构建台风灾害下的电网故障模型和电动汽车灾前调度模型。然后，以配电网负荷缺失成本和电网故障风险成本最低为目标，建立了分布式新能源最优配置模型。最后，以IEEE 33节点系统为例，设置了3种场景并进行了对比研究。 结果 3种场景的对比分析结果表明，所提策略能实现负荷缺失量与支路累计越限功率和两者加权综合值最低。其中，负荷缺失量减少百分比为95.53%，支路累计越限功率和减少率为29.23%。 结论 在台风灾害下，所提策略可以有效恢复系统失负荷，提高电网弹性，并降低系统运行风险。

Objectives With the increase of electrical equipment and frequent natural disasters, the demand for distribution network resilience is increasing. Therefore, a strategy of optimizing distributed resource cooperative allocation based on typhoon disaster is proposed. Methods First of all, the power supply recovery potential of electric vehicle users participating in typhoon disasters is fully considered, and the power grid failure and electric vehicle pre-disaster scheduling model under typhoon disaster is constructed. Then, aiming at the lowest load loss cost and network fault risk cost, the optimal allocation model of distributed new energy is established. Finally, taking IEEE 33 node system as an example, three scenarios are set up for comparative study. Results The comparative analysis results of the three scenarios show that the proposed strategy can achieve the lowest weighted comprehensive value of load loss and sum of branch cumulative over-limit power. Among them, the percentage of load loss reduction is 95.53%, and the reduction rate of the sum of branch cumulative over-limit power is 29.23%. Conclusions In the case of typhoon disaster, the proposed strategy can effectively restore the system load loss, improve the elasticity of the power grid, and reduce the operation risk of the system.

电动汽车（electric vehicle，EV）既是交通网的车流负荷，又是电网的用电负荷，它的出行以及充电行为会对交通网和电网的运行规律造成影响。针对电动汽车单体进行随机抽样的传统预测方法，未能从电动汽车群体的移动运行状态考虑，提出了一种基于交通均衡理论的电动汽车充电负荷预测方法。首先在时间和空间两个维度上，分别对电动汽车和路网进行建模，采用A*算法构建电动汽车出行原目的地（origin-destination，OD）对的有效的路径集。计及用户有限理性，引入分时电价制定了一种计及交通流量约束的充电站电价，并结合随机效用理论与分时电价，建立了半动态交通均衡模型来求解交通流的分配。随后基于组合荷电状态（calculation method of combined state of charge，CSOC）的概率数值计算方法对电动汽车充电负荷进行求解。最后，基于上海市新能源汽车数据，分别以改进的13节点路网和上海市某区域路网分析验证了所提充电负荷预测方法的有效性，结果表明所提方法可以降低充电成本，缓解充电站充电压力。

Electric vehicles (EVs) are both traffic loads of the transport network and electricity loads of power grid, and their travelling as well as charging behaviours will have an impact on the operating laws of the transport network and power grid. Aiming at the traditional EV prediction method of random sampling for a single EV unit, which fails to consider the operation state from the movement of EV groups, in this paper, a method for forecasting electric vehicle charging loads that leverages traffic equilibrium theory is proposed. Firstly, models for electric vehicles and road networks are constructed in both time and space dimensions, and an efficient path set for origin-destination (OD) pairs of electric vehicles is generated using the A* algorithm. Taking into account users' limited rationality, a charging station electricity pricing, which incorporates traffic flow constraints, is formulated by introducing time-of-use (TOU) pricing. Furthermore, a semi-dynamic traffic equilibrium model is developed by integrating stochastic utility theory with TOU pricing to address the traffic flow allocation. then based on the probabilistic numerical calculation method of combined state of charge (CSOC), the charging load of electric vehicles is solved. Finally, based on the data of new energy vehicles in Shanghai, the effectiveness of the proposed charging load prediction method is verified by analysing the improved 13 nodes road network and a regional road network in Shanghai, the results show that the proposed method can reduce the charging cost and alleviate the charging pressure of charging stations.

在交通网-新型配电网耦合的背景下，考虑极端灾害事件造成道路受损的影响，提出极端灾害后交通网-新型配电网多时段供电恢复策略。首先，考虑灾害造成的道路修复状态以及交通流量变化，建立动态元胞传输模型（dynamic cell transmission model，DCTM），通过出行车辆历史决策行为得到配电网抢修车辆及移动式储能车（mobile energy storage vehicles，MESVs）行程时间。其次，引入流量状态因子描述交通路网中元胞的流量传递过程，根据路径流量的分布状况确定道路路口的分流因数取值，优化抢修车辆及MESVs的行驶路径。然后，以负荷削减量及应急资源调度成本最小为目标，建立考虑道路流量变化、抢修车辆和MESVs行驶路径优化的配电网混合整数线性规划模型，提出了交通网-新型配电网多时段供电恢复策略。最后，通过算例验证了所提方法对提高新型配电网供电恢复的有效性。

In the context of the coupling between the transportation network and the new distribution network, considering the impact of extreme disaster events on road damage, a multi period power restoration strategy for the transportation network and the new distribution network after extreme disasters is proposed. Firstly, considering the road repair status and traffic flow changes caused by disasters, a dynamic cell transmission model (DCTM) is established to obtain the travel time of distribution network repair vehicles and mobile energy storage vehicles (MESVs) through the historical decision-making behavior of travel vehicles. Secondly, the flow state factor is introduced to describe the flow transmission process of cells in the transportation network, the diversion factor value of road intersections is determined based on the distribution of path flow, and the driving path of repair vehicles and MESVs is optimized. Then, with the goal of minimizing load reduction and emergency resource scheduling costs, a mixed integer linear programming model for the distribution network considering changes in road flow, optimization of the driving paths of repair vehicles and MESVs is established, and a multi-period power supply recovery strategy for the transportation network new distribution network is proposed. Finally, the effectiveness of the proposed method in improving the restoration of power supply in the new distribution network is verified through numerical examples.

目的 锂离子电池模型作为电池管理系统的核心技术之一，对电池性能优化和寿命延长起着至关重要的作用。为了便于在不同场景下选择合适的模型，系统总结了当前锂离子电池不同类型的建模方式，并进行了对比分析。 方法 首先，阐述了锂离子电池的工作原理，强调了精确建模的重要性；然后，根据不同应用场景全面总结了当前广泛采用的锂离子电池模型，并分析讨论了一系列新型机器学习电池模型；最后，探讨了锂离子电池建模技术面临的挑战及未来的研究趋势。 结论 传统电池模型均存在一定局限性，而数据驱动模型在处理复杂系统时往往具有更独特的优势，未来研究需要在模型复杂度和实用性之间找到平衡。研究结果为锂离子电池在储能系统中的应用和未来发展提供了参考。

Objectives As one of the core technologies of battery management system (BMS), the research on lithium-ion battery model plays a vital role in optimizing battery performance and extending battery life. In order to facilitate the selection of appropriate models in different scenarios, different types of modeling methods for lithium-ion batteries are systematically summarized and compared. Methods Firstly, the working principle of lithium-ion battery is explained, and the importance of accurate modeling is emphasized. Then, the current widely used lithium-ion battery models is comprehensively summarized according to different application scenarios, and a series of novel machine learning battery models are analyzed and discussed. Finally, the challenges of lithium-ion battery modelling techniques and future research trends are discussed. Conclusions It is found that traditional battery models all have certain limitations, while data-driven models often have more unique advantages in dealing with complex systems. Future research needs to find a balance between model complexity and usability. The research results provide a reference for application and future development of lithium-ion batteries in energy storage systems.

Thermo-electrochemical cells (or thermocells) represent a promising technology to convert waste heat energy into electrical energy, generating power with minimal material consumption and a limited carbon footprint. Recently, the adoption of ionic liquids has pushed both the operational temperature range and the power output of thermocells. This research discusses the design challenges and the key performance limitations that need to be addressed to deploy the thermocells in real-world applications. For this purpose, a unique up-scaled design of a thermocell is proposed, in which the materials are selected according to the techno-economic standpoint. Specifically, the electrolyte is composed of EMI-TFSI ionic liquid supplemented by [Co(ppy)]3+/2+ redox couples characterized by a positive Seebeck coefficient (1.5 mV/K), while the electrodes consist of carbon-based materials characterized by a high surface area. Such electrodes, adopted to increase the rate of the electrode reactions, lead to a thermoelectric performance one order of magnitude greater than the Pt electrode-based counterpart. However, the practical applications of thermocells are still limited by the low power density and low voltage that can be generated.