The bidirectional cell-to-cell equalization methods perform well to prevent the series-connected batteries from overcharging and undercharging. However, it remains a challenge to achieve fast equalization and high equalization efficiency with low complexity. To address the issue, we propose a new bidirectional cell-to-cell active equalization method using a multiwinding transformer. The new method allows the energy to transfer directly from the highest voltage cell to the lowest one by fly-back operation or forward operation which gives a short balancing path and guarantees a fast equalization speed. We adopt the bidirectional switches of low conduction loss for high equalization efficiency. Since the bidirectional switches are turned on or off simultaneously, a common driving circuit for the bidirectional switch has been designed to reduce the number of the driving circuits by half. The experiments with a six 50-Ah Lithium-ion battery strings are conducted and the results have demonstrated that our new approach has achieved a good overall performance of equalization in terms of speed, efficiency, and complexity of the circuit.

With the increasing share of renewable energy in the grid and the enhanced flexibility of the future power system, it is imperative for thermal power companies to explore alternative strategies. The flexible transformation of thermal power units is an effective strategy to address the previously mentioned challenges; however, the factors influencing the diffusion of this technology merit further investigation, yet they have been seldom examined by scholars. To address this gap, this issue is examined using an evolutionary game model of multi-agent complex networks, and a more realistic group structure is established through heterogeneous group differentiation. With factors such as group relationships, diffusion paths, compensation electricity prices, and subsidy intensities as variables, several diffusion scenarios are developed for research purposes. The results indicate that when upper-level enterprises influence the decision-making of lower-level enterprises, technology diffusion is significantly accelerated, and enhanced communication among thermal power enterprises further promotes diffusion. Among thermal power enterprises, leveraging large and medium-sized enterprises to promote the flexibility transformation of units proves to be an effective strategy. With regard to factors like the compensation price for depth peak shaving, the initial application ratio of groups, and the intensity of government subsidies, the compensation price emerges as the key factor. Only with a high compensation price can the other two factors effectively contribute to promoting technology diffusion.

Founded on bounded rationality and limited information, evolutionary game theory has been preliminarily applied in many fields, such as electricity market (EM). To address the complex behavioral decision-making issues in the more-common three-population multi-strategy evolutionary game (3PmSEG) scenarios in EM. This paper explores the long-term evolutionarily stable equilibrium (ESE) characteristics of general 3PmSEG systems with the aim of systematically investigating the evolution process of long-term on-grid bidding of a generation-side EM based on these features. First, the long-term ESE characteristics of general three-population two-strategy and three-strategy evolutionary games are thoroughly investigated. Complete relative net payoff (RNP) parameters are defined for these games. Then, the modeling idea of general 3PmSEGs is elaborated. Research shows that the game can be guided to evolve toward an expected long-term ESE point by properly adjusting its RNP parameters. To verify this, finally, the long-term on-grid bidding of power generation is investigated for a tripartite generation-side EM. The case study reveals that effective government supervision can effectively promote new energy accommodation of the market. Overall, the models developed in this paper are relatively universal and practical, which can provide some theoretical and methodological references for complex evolutionary game issues in related fields.

This article proposes a flyback-based topology with battery switching units to address the issues of the inconsistency among battery packs and battery failure, thus avoiding the significant waste associated with replacing the entire battery pack as usual. To achieve efficient and fast equalization under complex imbalanced conditions, the proposed topology can switch between buck–boost and flyback mode with multipath balancing channels and arbitrary cell(s) to cell(s) balancing modes flexibly. Also, a fuzzy control strategy is adopted to realize efficient transmission and distribution of the energy. Furthermore, a series of influencing factors on balancing performance are analyzed mathematically and in simulation according to the established model. Finally, to validate the effectiveness of the proposed method, experiments based on a four-battery pack are carried out and results indicate that the proposed approach not only realizes arbitrary battery cell switching functionality but also achieves high efficiency and rapid equalization.

This article presents a path planning strategy with ant colony algorithm for series connected batteries. The motive of this paper is the increasing need for efficient and fast equalization for Lithium-ion batteries. There are many great papers on the design of the equalization circuits. However, they lack the part of path planning strategy for the balancing circuits. To solve this issue, we adopt the graph model to represent the balancing paths among different battery cells and then construct two optimal models based on the best efficiency and speed, respectively. Finally, ant colony algorithm is used to solve these two models. This makes it possible to achieve different goals according to the practical operating conditions. We validate the function of the proposed path planning strategy through an example of 13 series connected battery balancing system.

This paper presents a multimodal adaptive active equalization topology to improve battery balancing performance and extend the lifespan of energy storage systems. The proposed topology utilizes a single inductor as the energy storage component, enabling direct energy transfer between arbitrary cells in a battery pack under various imbalanced conditions, offering greater flexibility and faster balancing speeds. Then, an equivalent model of the topology is developed, and its balancing performance including power and efficiency is analyzed across different imbalanced scenarios. To further optimize the balancing process, a deep reinforcement learning-based is integrated, dynamically determining the optimal balancing path and adjusting the duty cycle in real time. Simulation results indicate that the proposed DQN-based strategy achieves the shortest balancing time among the compared methods under multiple operating conditions. Finally, closed-loop validation in a hardware-in-the-loop (HIL) environment on a four-cell SW18650-26HPA ternary lithium battery pack confirms the method’s effectiveness, demonstrating flexible cell switching and fast, efficient equalization.