The supercapacitor tram charges its supercapacitors as passengers enter and exit each station, taking advantage of the short parking time, usually within 30 seconds. However, using a single DC-DC converter for energy conversion would be costly and unreliable. To address this issue, this paper proposes a method that involves the parallel connection of multiple DC-DC converters in coordination with supercapacitors on supercapacitor trams to achieve power balancing. The paper begins by developing a system model for the power supply system and explaining its motivation. Next, a design for a parallel connection of multiple DC-DC converters is presented to prevent excessive current in any one converter and achieve current balancing among them, thereby reducing the system load. Finally, a cooperative control method is proposed to achieve current balancing at each DC-DC converter. The proposed method’s effectiveness has been demonstrated through extensive experiments and simulations.

The estimation of the state-of-charge (SOC) in lithium-ion batteries has garnered significant attention, with current research primarily concentrating on individual batteries. In practice, however, lithium-ion batteries often require connection to a balancing circuit to correct battery imbalance. In such cases, the system topology differs from that of an individual battery. Therefore, it is essential to account for this change. This article proposes a method for estimating the SOC of lithium-ion battery cells within reconfigurable circuits. We established a switching system model for lithium-ion batteries in reconfigurable circuits. We then design a nonlinear switching observer and examine its stability. Additionally, we conducted extensive experiments to evaluate the proposed observer’s performance and compared it with other observers.

In electric vehicles equipped with hybrid energy storage systems, it is crucial to achieve fast and precise regulation of the DC bus voltage to match the target reference. Existing techniques often struggle to avoid voltage overshoots while pursuing fast stabilization, which leads to significant fluctuations in the DC bus voltage. To overcome this challenge, this paper presents an advanced energy management strategy using predictive setpoint modulation. We develop an innovative set-point modulation technique that achieves efficient regulation of the DC bus voltage by utilizing a feed-forward compensator in combination with parallel operation of multiple DC-DC converters and supercapacitors (SCs). This configuration not only ensures voltage stability, but also optimizes the charge/discharge cycle management of the supercapacitors, which improves the energy utilization efficiency and overall system performance. Laboratory experimental results show that our strategy significantly reduces DC bus voltage fluctuations and maintains stable system operation under various load conditions, outperforming conventional technologies. In addition, our technology has the advantages of fast response and easy integration into existing systems, providing an efficient and reliable solution for energy management in electric vehicles.

State of charge (SOC) estimation of lithium-ion batteries has been extensively studied, and most of the existing research focuses on SOC estimation of individual lithium-ion battery. In practical applications, however, lithium-ion batteries are connected to a balancing circuit to eliminate imbalances between batteries. When a balancing circuit is activated, the state space equation of its equivalent circuit will change. In this paper, we propose a switched extended Kalman filter method for SOC estimation of lithium-ion battery balance systems. The switching system model is established by combining the Li-ion battery equivalent circuit model and the switching resistance balance circuit. A switching extended Kalman filter is designed to estimate the SOC of a switching system.

In the field of energy storage, precise estimation of the state-of-charge (SOC) of lithium-ion batteries is crucial for maximizing their efficiency and extending their lifespan. Existing research has predominantly focused on SOC estimation for individual battery cells. However, in practical applications, a balancing circuit is typically integrated into the battery management system (BMS) to mitigate cell imbalance. When the balancing circuit is activated, the battery cell transitions into a new operational mode, rendering conventional SOC estimation techniques ineffective. Reconfigurable circuits, recognized for their adaptability across diverse application environments, present a novel approach for SOC estimation in lithium-ion batteries, leveraging their dynamic reconfiguration capabilities. This paper introduces an innovative method for SOC estimation of reconfigurable lithium-ion batteries, employing an extended Kalman filter (EKF) within the context of reconfigurable circuits. The proposed methodology begins with the design of a switching system for lithium-ion batteries, facilitating equivalent circuit modeling. Subsequently, a nonlinear observer, based on the extended Kalman filter, is developed to estimate the SOC. An experimental platform was also constructed to validate the feasibility and efficiency of the proposed method. Experimental results demonstrate that this approach significantly enhances the accuracy and robustness of SOC estimation compared to traditional methods.

In the current era, a Li-ion battery pack, typically comprised of multiple cells, can offer higher voltage and output power. This plays a crucial role in various applications, including electric vehicles and energy storage. Accurate estimating the battery pack's state of charge (SOC) is crucial to offer users a clearer understanding of the battery status and to alleviate range anxiety. In the industry, it's common practice to precisely estimate the SOC for each cell, enabling an accurate assessment of the battery pack's overall SOC. However, most current methods for estimating the SOC in battery packs are centralized. In such cases, a problem with estimating the SOC of a single cell can greatly impact the overall SOC estimation of the entire battery pack. Likewise, if centralized equipment encounters issues, the SOC estimation for the entire battery pack is likely to be interrupted. This paper presents a distributed method for estimating battery pack SOC, utilizing a digital twin-based simulation platform. In the following, the each node that measures the SOC of cell is regarded as an agent that can communicate. Through communication among agents, each agent can converge to a reliable battery pack SOC estimation. In the event of a sudden issue arising in the SOC estimation of a cell, the proposed method can still uphold a dependable estimate of the battery pack's SOC, thereby bolstering the overall robustness of the SOC estimation system for the entire battery pack.

To ensure optimal functionality of the super-capacitor management system in practical applications, the accurate and robust state of charge (SOC) estimation is crucial, particularly to account for aging effects and varying operating conditions. This paper proposes a switched system-based approach for the co-estimation of SOC and parameters of supercapacitors coupled with balancing resistor circuits. A switched model incorporating an equivalent circuit model is developed to accommodate the activation of equalization within a series-connected supercapacitor pack. The method combines a modified recursive least squares (RLS) algorithm with a switching sliding mode observer (SMO) for real-time parameter adaptation and SOC estimation. The experimental verification under a multi-balancing charging scenario demonstrates sig-nificant enhancements in accuracy and robustness compared to traditional methods employing fixed model configurations and parameters.

With the rapid development of Autonomous Rail Rapid Transit technology, lithium-ion battery as its main power source, the research of its charging technology has become particularly important. At present, the charging scheme of lithium-ion battery mainly includes two ways: single high-power charging and multiple low-power charging modules in parallel. However, the single high-power charging scheme has the problems of high cost and low efficiency, and the traditional parallel charging method lacks effective module management strategy, resulting in unbalanced load between modules during charging, affecting charging efficiency a nd safety. A iming at the shortcomings of current research, this paper proposes a parallel charging scheme of multiple DC-DC modules based on cooperative control. By designing a closed-loop control system of current inner loop and voltage outer loop, the precise control and cooperative work of parallel modules are realized. The experimental results show that the scheme not only improves the charging efficiency, but also ensures the stability and safety of the charging process, which provides an effective and reliable solution for the lithium-ion battery charging of the intelligent rail train.

This paper explores the estimation of the State of Charge (SoC) of lithium-ion batteries. Currently, the majority of research efforts focus on the SoC estimation of individual lithium-ion batteries. However, in practical scenarios, lithium-ion batteries are commonly connected with balancing circuits to address battery imbalances. Upon activation of the equalization circuit, the battery's system dynamics transition to a new mode. Therefore, it is difficult f o r c l assical S o C estimation algorithms to accurately estimate the real SoC value. In this paper, we employ a switched system methodology to estimate the battery's SoC. We describe the switched system of the Thevenin equivalent circuit model of a lithium-ion battery using a switched resistance balance circuit. Then we use the method of nonlinear switching observer to analyze the convergence and divergence. Finally, we set up an experimental platform and verify the performance of the observer through several sets of experiments.

With rubber wheels instead of steel wheels and no need to be guided by steel rails, Autonomous rail Rapid Transit(ART), is gradually coming into people's lives. However, ART still occupies existing lanes and the relatively small station spacing of ART means that ART needs to be started and stopped frequently, all of which can lead to fluctuations in DC bus voltage during ART operation, making it difficult for loads (such as motors, air conditioners, and sensors) to operate properly. Therefore, this paper proposes the use of predictive set-point modulation to suppress DC bus voltage fluctuations. The predictive set-point modulation method is able to predict the direction of DC bus voltage changes prospectively, and then adjust the voltage preset value to balance the fluctuation of the output voltage, optimizing the closed-loop system's transient dynamic performance. Moreover, since lithium ion battery has high power and high energy consumption, using only one DC-DC circuit can reduce system reliability and cost. Therefore, we propose to use parallel DC-DC modules to balance the excessive power of the battery and verify the feasibility of the proposed method through simulation experiments. The experiments show that the proposed method can effectively suppress the DC bus voltage fluctuation and improve the system reliability.

The State-of-Charge (SOC) estimation for super-capacitors has been thoroughly examined in the literature, while the majority of the research to far is concentrating on the SOC estimation of single supercapacitor units. Nevertheless, the system dynamics of the battery may shift to a different system when utilizing the recently suggested reconfigurable circuit, suggesting that the straightforward use of current SOC estimate techniques is not possible. In order to assess the battery's state of charge (SOC), we use a switching systems technique in this paper. We establish the supercapacitor's RC model with a reconfigurable circuit and carefully investigate the continuity of the state and observability of the switched system. Afterwards, we propose a switching observer and compare the performance of various observers, analyzing its convergence qualities. We compare the proposed observer with other observers through a hardware platform, and the experimental results prove the superiority of the proposed observer in SOC estimation.

Supercapacitors, leveraging their distinctive characteristics and advantages, have evolved into efficient energy storage solutions. State of Charge (SOC) is a crucial parameter for supercapacitors, and the estimation of SOC for individual supercapacitor cells has been extensively researched. In practical applications, it is common to assemble hundreds or even thousands of cells to form a supercapacitor pack, particularly in fields like electric vehicles a nd electric b uses. Therefore, estimating the SOC for the supercapacitor pack becomes imperative. In response to the demands for supercapacitor pack SOC$(SOC_{pack})$estimation and wireless management, this paper proposes a distributed method. After modeling the supercapacitor cells, the definition of$SOC_{pack}$is introduced. The SOC of a cell in the definition is estimated based on Kalman filter. The proposed distributed method relies on wireless communication and computational updates between cells. Through iterative processes, it ultimately converges to the estimated$SOC_{pack}$. Finally, we conducted simulation experiments to analyze the performance of the proposed method under various communication conditions, thereby validating its effectiveness and robustness.