• Energy Research
• 2021-2025close

The modular multilevel converter is capable to reach high-voltage levels with high flexibility, high reliability, and high power quality as it became the standard solution for high-power high-voltage applications that operate with fixed frequency. However, in machine-drive applications, the modular multilevel converter shows critical problems since an extremely high submodule-capacitor voltage ripple occurs in the machine start-up and at low-speed operation, which can damage the converter. Recently, a new converter solution named modular multilevel series converter was proposed as a promising alternative for high-power machine-drive applications since it presented many important structural and operational advantages in relation to the modular multilevel converter such as the reduced number of submodule capacitors and the low submodule-capacitor voltage ripple at low frequencies. Even though the modular multilevel series converter presented a reduced number of capacitors, the size of these capacitors was not analyzed. This paper presents a detailed comparison analysis of the performance of the modular multilevel converter and the modular multilevel series converter at variable-frequency operation, which is based on the proposed analytical description of the submodule-capacitor voltage ripple in such topologies. This analysis concludes that the new modular multilevel series converter can be designed with smaller capacitors in comparison to the modular multilevel converter if these converters are used to drive electrical machines that operate within a range of low-frequency values. In other words, the modular multilevel series converter experiences extremely low submodule-capacitor voltage ripple at very low frequencies, which means that this converter solution presents high performance in the electrical machine start-up and at low-speed operation.

Abstract Current battery management systems (BMSs) in automotive applications monitor and control batteries in a relatively simple, conservative manner, with limited capabilities of sensing, estimation, proactive controls, and fault diagnosis. With ever-increasing computing power onboard and/or in the cloud, enhanced environmental perception and vehicular communications, emerging electrified vehicles and smart grids provide unprecedented opportunities for designing and developing next-generation smart BMSs. However, three entrenched technical challenges need to be addressed, including 1) limited knowledge of battery internal states and parameters; 2) poor adaptability to extreme operating conditions; and 3) lack of efficient predictive maintenance, resulting in great concern for battery safety and economy. This paper aims to present some critical insights into possible solutions to the three challenges. First, the multi-physics coupled battery modeling concept is introduced to emphasize that looking at mechanical-electrochemical-thermal-aging dynamics is critically important for devising revolutionary BMS algorithms. Second, electrothermal modeling, advanced optimization routines, and predictive control with vehicular autonomy and connectivity facilitate innovative designs in dynamically hysteresis-aware thermal management, heat transfer under extreme fast charging, and preheating in a cold climate. Third, battery models and machine learning are complementary and can be very useful for improving battery remaining useful life prediction and fault diagnosis, achieving high-efficiency predictive maintenance.

A modular multilevel converter’s (MMC’s) submodule (SM)-capacitor voltage will increase under unbalanced grid conditions. Depending on the imbalance level, the voltage ripple can be considerably high, and it can exceed the pre-defined safe limits. If this occurs, the converter will trip, which can lead to serious stability problems for the grid. This paper first proposes an analytical solution for deriving the three-phase imbalanced SM ripple of an MMC under an unbalanced grid. With this analytical tool, the imbalance mechanism of the SM voltage ripple can be easily understood. What is more, the symmetrical component method is first applied to analyze the three-phase SM capacitor ripple, and the positive-/negative-/zero-sequence components of the three-phase SM voltage ripple are easily identified by the proposed analytical method. Then, based on this powerful analytical tool, the proper circulating-current profile to be injected can be obtained, allowing for the right compensation of the voltage ripple. Based on this approach, two new voltage ripple compensation methods are proposed in this paper. Simulations were carried out to validate the analytical description of the submodule-capacitor voltage ripple proposed in this paper. Moreover, simulation and experimental results are provided to validate the new compensation techniques introduced in this paper.

Technologies that accelerate the delivery of reliable battery-based energy storage will not only contribute to decarbonization such as transportation electrification, smart grid, but also strengthen the battery supply chain. As battery inevitably ages with time, losing its capacity to store charge and deliver it efficiently. This directly affects battery safety and efficiency, making related health management necessary. Recent advancements in automation science and engineering raised interest in AI-based solutions to prolong battery lifetime from both manufacturing and management perspectives. This paper aims at presenting a critical review of the state-of-the-art AI-based manufacturing and management strategies towards long lifetime battery. First, AI-based battery manufacturing and smart battery to benefit battery health are showcased. Then the most adopted AI solutions for battery life diagnostic including state-of-health estimation and ageing prediction are reviewed with a discussion of their advantages and drawbacks. Efforts through designing suitable AI solutions to enhance battery longevity are also presented. Finally, the main challenges involved and potential strategies in this field are suggested. This work will inform insights into the feasible, advanced AI for the health-conscious manufacturing, control and optimization of battery on different technology readiness levels.

Accurate estimation of the state of health (SOH) of batteries is essential for maximizing the lifetime of the battery and improving the safety and economy of any energy storage system. Data-driven methods can use measurement data to effectively estimate the SOH, but the estimation performance depends on the relevance between the selected feature and SOH. In this article, fuzzy entropy (FE) of battery voltage is proposed as a new feature for SOH estimation and validated on Li-ion batteries. Compared with the traditional sample entropy, the FE can capture the variation of voltage during the battery degradation more efficiently in terms of the parameter selection, data noise, data size, and test condition. Moreover, the aging temperature variation is involved in the established SOH estimator as the temperature is a disturbance variable in the real applications. The FE-SOH is used as the input–output data pair of the support vector machine, and a single-temperature model, a full-temperature model, and a partial-temperature model are established. As a result, the FE-based method has better estimation accuracy under aging temperature variation. The FE-based method also decreases the dependence on the size of the required training data. Finally, the effectiveness of the proposed method is verified by experimental results.

Current pulses are convenient to be actively implemented by a Battery Management System (BMS). However, the Short-Term Features (STF) from current pulses originate from various sensors with uneven qualities, which hinders one powerful and strong learner with STF for the battery SOH estimation. This paper thus proposes an optimized weak learner formulation procedure for Lithium-ion (Li-ion) battery SOH estimation, which further enables the automatic initialization and integration of the weak learners with STF into an efficient SOH estimation framework. A Pareto Front-based Selection Strategy (PFSS) is designed to select the representative solutions from the non-dominated solutions fed by a Knee point driven Evolutionary Algorithm (KnEA), which guarantees both the diversity and accuracy of the weak learners. Afterwards, the weak learners, whose coefficients are obtained by Self-adaptive Differential Evolution (SaDE), are integrated by a weight-based structure. The proposed method utilizes the weak learners with STF to boost the overall performance of SOH estimation. The validation of the proposed method is proved by LiFePO4/C batteries under accelerated cycling ageing test including one mission profile providing Primary Frequency Regulation (PFR) service to the grid and one constant current profile.

Abstract With the large-scale integration of the distribution generations (DGs) and the increasing medium-voltage and low-voltage DC power demands, multi-terminal hybrid AC/DC microgrid has drawn great attention from researchers around the world. In order to reduce the number of power conversion stages and meet DC transmission demands under different DC voltage levels, this paper proposes a four-terminal interconnection scheme of the hybrid AC/DC microgrid, connecting one medium-voltage AC (MVAC) terminal, one medium-voltage DC (MVDC) terminal and two low-voltage DC (LVDC) terminals. The proposed interconnection scheme includes a modular multilevel converter (MMC) as the main interlinking converter of the MVAC grid and MVDC microgrid, and a series of dual active bridges (DAB) converters as two isolated LV DC microgrid interfaces. It has more flexibility for power supplies, especially MVDC transmission, and a more robust tolerance for unequal power distribution between the two LVDC Microgrids. To realize the DC capacitor voltage balancing control, an improved energy control method is proposed in this paper. The proposed method keeps DC capacitor voltage balance and AC current zero on the MVDC transmission lines, which contributes to the stability of the MVDC microgrid. In addition, the symmetry of the AC currents is also guaranteed with this control method. Validation results of a four-terminal hybrid AC/DC microgrid verify the effectiveness of the proposed microgrid and control scheme.