For the secure usage of battery charging and discharging within electric vehicles, the study of cell pack equalization technology is essential. Therefore, in this paper, an improved Bidirectional Cuk equalizer (BCEQ) structure based on a variable-domain fuzzy PID (VFPID) control equalization strategy is recommended in stages. With the new equalization topology, only half of the capacitive and inductive components are needed to transfer energy between any two individual cells in the power supply. In addition, the proposed VFPID control strategy further improves the efficiency of the equalization model by dynamically adjusting the magnitude of the equalization current parameters. Through simulation experiments, the improved topology was capable of substantially lessening the equalization time and increasing energy utilization by more than 4%. In comparison with the fuzzy PID (FPID) algorithm, around 27.3% faster equalization times can be achieved with the VFPID algorithm; the VFPID algorithm also performed well under the Dynamic Stress Test (DST) condition, demonstrating that the topology and equalization strategy suggested in this paper can successfully address the inconsistency of the FPID algorithm. It has been revealed that the topology and equalization methodology offered in this paper is effective in solving the battery pack inconsistency.

During the planning and construction of electric vehicle charging stations (EVCSs), consideration of the long-term operating revenue loss for investors is often lacking. To address this issue, this study proposes an EVCS planning method that takes into account the potential loss of long-term operating revenues associated with charging facilities. First, the method combines the Bass model with electric vehicle (EV) user travel characteristics to generate a charging load dataset. Then, the cost of profit loss—which represents the EVCS utilization rate—is incorporated into the construction of the objective function. Additionally, a parallel computing method is introduced into the solution algorithm to generate the EVCS planning scheme. Finally, the cost-to-profit ratio of the EVCSs is used as a filtering condition to obtain the optimal EVCS planning scheme. The results show that the EVCS planning scheme considering the profit loss reduces the annual comprehensive cost by 24.25% and 16.93%, and increases the net profit by 22.14% and 24.49%, respectively, when compared to the traditional planning scheme under high and low oil prices. In particular, the charging station strategy proposed in this study has the best effect in the case of high oil prices.

Driven by the rapid advancement of Vehicle-to-Grid (V2G) and Grid-to-Vehicle (G2V) technologies, improving power quality and system stability during charging and discharging has become a research focus. To address this, this paper proposes a Model Predictive Control (MPC) strategy for Active Neutral-Point Voltage Balancing (ANPVB) in a single-phase three-level converter used in on-board V2G chargers. Traditional converters rely on passive balancing using redundant vectors, which cannot ensure neutral-point (NP) voltage stability under sudden load changes or frequent power fluctuations. To solve this issue, an auxiliary leg is introduced into the converter topology to actively regulate the NP voltage. The proposed method avoids complex algorithm design and weighting factor tuning, simplifying control implementation while improving voltage balancing and dynamic response. The results show that the proposed Model Predictive Current Control-based ANPVB (MPCC-ANPVB) and Model Predictive Direct Power Control-based ANPVB (MPDPC-ANPVB) strategies maintain the NP voltage within ±0.7 V, achieve accurate power tracking within 50 ms, and reduce the total harmonic distortion of current (THDi) to below 1.89%. The proposed strategies are tested in both V2G and G2V modes, confirming improved power quality, better voltage balance, and enhanced dynamic response.

Series-connected lithium-ion battery packs are widely used in electric vehicles (EVs). However, inevitable inconsistency among cells can cause charge imbalance, accelerated aging, and reduced system safety. To improve the consistency of series-connected battery packs under complex EV operating conditions, this study proposes a hierarchical active balancing system. Bidirectional Buck–Boost converters are employed for intra-group balancing, and distributed flyback converters are used for inter-group balancing. A multi-stage coordinated balancing control strategy is further developed to reduce control complexity and improve balancing efficiency. A 16-cell series-connected battery pack model is established in MATLAB R2024a/Simulink and evaluated under resting, charging, and discharging conditions. The results show that, compared with the conventional single-layer Buck–Boost balancing topology, the proposed method reduces the balancing time by 58.09%, 57.97%, and 58.06%, respectively. These results indicate that the proposed system can effectively improve the consistency and balancing performance of series-connected battery packs, providing a scalable solution for EV battery management systems.