The transportation sector, a significant contributor to carbon dioxide emissions as of 2020, confronts a pressing challenge in mitigating pollution. Electric Vehicles (EVs) present a promising solution, offering a cleaner alternative; however, their limited travel range poses a constraint. Hybrid Electric Vehicles (HEVs) and Hybrid Energy Storage System Electric Vehicles (HESS EVs) emerge as economically feasible compromises. Nonetheless, the effective management of energy and the optimization of power source size remain crucial challenges for both HEVs and HESS EVs. Among various Energy Management Strategies (EMS), the Fuzzy Logic Controller (FLC) stands out for its performance, simplicity, and real-time applicability. This article comprehensively explores the diverse applications of FLC as an EMS in both HEVs and HESS EVs, providing a comparative analysis with other EMS methods and delving into the advantages and challenges associated with each approach. A detailed examination of various FLC types employed as EMS has been conducted, drawing insights from a multitude of references. Each class of FLC EMS is scrutinized, presenting a broad overview of proposed methodologies within each category. By providing this comprehensive information, the article equips readers with foundational knowledge and insights for the continued development of FLC EMS in hybrid electric and hybrid energy storage system electric vehicles.

The transportation sector contributes up to 35% of carbon dioxide pollution. Electric Vehicles (EVs) offer a pollution-free alternative but face a crucial challenge in their battery-based Energy Storage System (ESS). The solution to the battery issues is combining it with other ESS with high power density called a Hybrid Energy Storage System (HESS). Energy Management Strategy (EMS) is used to distribute the power demand in the HESS. Low Pass Filters (LPFs) are one type of EMS that can be used to ensure the smooth flow of power between different energy storage elements. This article focuses on the pivotal role of Low Pass Filters (LPFs) within HESS for EVs, facilitating seamless power flow. The novelty lies in the comprehensive review of LPFs in this context, shedding light on their impact on energy management. Four LPF architecture classes are discussed: fixed cut-off, optimal cut-off, adaptive cut-off, and combination, referencing prior research. Additionally, a critical examination of challenges and limitations is provided, offering insights for researchers and practitioners.

The global promotion of electric vehicle usage is a response to the challenges of energy crises and climate change. However, significant drawbacks remain, particularly related to the high costs and limitations of onboard energy storage. To mitigate these issues, the concept of Hybrid Energy Storage Systems, which integrate batteries with complementary energy storage solutions, has gained attention. This study focuses on the optimal sizing and energy management strategy of Hybrid Energy Storage Systems, which are critical for enhancing the performance and viability of these systems in electric vehicles. An Improved Low-Pass Filter is introduced to address limitations of conventional low-pass filters, including phase shift effects and state of charge limitations while optimizing the cut-off frequency and ensuring adaptability to varying driving conditions. Particle Swarm Optimization is employed to achieve optimal sizing of the Hybrid Energy Storage Systems and to develop an efficient energy management system. The performance of the Improved Low-Pass Filter is validated against a Fuzzy Logic Controller and a conventional low-pass filter across various driving cycles. Simulation results demonstrate that the proposed Improved Low-Pass Filter significantly enhances performance, achieving up to an 8.345% improvement in city driving conditions compared to battery-only electric vehicles.

The challenge in controlling a manipulator robot is to model the system to obtain an efficient control system design. One approach that can be used to model the dynamics of a manipulator robot is data-driven modeling. However, in its implementation, data-driven modeling is highly sensitive to sensor noise, which significantly affects the accuracy of the system identification. In addition, the existing approach yields only a generalized form of the differential equation for each joint, which has not been divided into inertial, Coriolis, and gravitational variables that can be used for other purposes. In this study, a LASSO model selection criteria with a variable segregation algorithm (LMSCVS) is proposed to derive the dynamic equation of a 3-DoF manipulator robot, segregating the generalized form variables into Coriolis and centrifugal, inertia, and gravitational variables. Additionally, a Dynamic Expression Nonlinearization(DEx-N) algorithm is introduced to generate nonlinear candidates more efficiently to express the dynamics of the robot manipulator. The experimental results on the ROB3 hardware demonstrate that the proposed method successfully discovers mathematical equations, resulting in higher accuracy and sparsity compared to the previous method. The processing time of the proposed method is also significantly faster. Based on these results, the proposed method has a better performance in identifying real systems that usually have noise in the sensor data and in discovering the equation of robot manipulator dynamics for broader purposes.

In this study, the system was designed for monitoring the voltage condition of each battery’s cell. The method used in this study is by using passive shunt resistor balancing method. An electronic circuit was designed in order to balance the value of the able voltage at the battery cells using resistors and then to remove the excess voltage. The result showed that the electrical circuit was capable to balance the voltage of each cell. Based on experiment with several values of load, it is can concluded that 0.1C rate of discharge has the best performance, because it does not affect battery voltage characteristic significantly which leads to better sensor reads.