Regenerative Braking System Research Articles

Development of Deep Learning-Based Algorithm for Extracting Abnormal Deceleration Patterns

A smart regenerative braking system for EVs can reduce unnecessary brake operations by assisting in the braking of a vehicle according to the driving situation, road slope, and driver’s preference. Since the strength of regenerative braking is generally determined based on calibration data determined during the vehicle development process, some drivers could encounter inconveniences when the regenerative braking is activated differently from their driving habits. In order to solve this problem, various deep learning-based algorithms have been developed to provide driving stability by learning the driving data. Among those artificial intelligence algorithms, anomaly detection algorithms can successfully separate the deceleration data in abnormal driving situations, and the resulting refined deceleration data can be used to train the regression model to achieve better driving stability. This study evaluates the performance of a personalized driving assistance system by applying driver characteristic data, obtained through an anomaly detection algorithm, to vehicle control.

Blockchain-Powered Energy Optimization in Metro Networks: A Case Study on Electric Braking

As urban populations continue to expand, the need for efficient and sustainable metro systems has become increasingly pressing. Traditional energy management methods, while somewhat effective, often fall short in fully utilizing the potential of regenerative braking systems within metro networks. These conventional approaches, which rely heavily on centralized control and energy storage systems, encounter scalability, security, and transparency limitations. Additionally, inefficient management of energy recovery data can result in significant energy losses and higher operational costs. In response to these challenges, this study proposes a blockchain-based solution utilizing Proof-of-Work (PoW) algorithms to optimize energy recovery, particularly during electric braking in metro systems. The developed model securely and transparently validates energy recovery events in real-time, eliminating the need for centralized oversight. By customizing the PoW algorithm, we achieved a balance between computational efficiency and strong security, making this solution scalable and practical for large metro networks. Initial simulations demonstrated a 12-15% improvement in energy recovery efficiency and a 10% reduction in operational costs compared to traditional systems. Furthermore, the comparison between net energy gains and the energy expended by the PoW process highlights the transformative potential of blockchain technologies in metro transportation, offering a pathway to more sustainable and environmentally friendly urban mobility solutions.

Optimization and control for a novel uncoupled regenerative braking system of three-axle electric bus

Optimization and control for a novel uncoupled regenerative braking system of three-axle electric bus

Predictive Energy Demand and Optimization in Metro Systems Using AI and IoT Technologies

Introduction: With the rapid urbanization of modern cities, metro systems have become indispensable for efficient mobility. However, the increasing demand for public transportation has led to rising energy consumption, posing significant challenges for operational sustainability. Current energy management strategies in metro networks rely on static models and centralized systems, which often fail to adapt to real-time fluctuations in energy demand, leading to inefficiencies and wasted resources. Methods: This paper proposes an innovative approach to optimizing energy demand in metro systems by integrating Artificial Intelligence (AI) and the Internet of Things (IoT). By leveraging real-time data collected from IoT sensors deployed throughout the metro network, we apply machine learning algorithms such as Random Forests and Neural Networks to dynamically predict energy demand. These predictions enable metro operators to adjust energy consumption in real-time, thus improving overall system efficiency and reducing operational waste. Our approach was validated using data from the Parisian metro system through extensive simulations. Results: The results of simulations demonstrate significant improvements in energy efficiency. Optimized energy demand management led to a reduction in wasted energy during metro operations, particularly through the utilization of regenerative braking systems. Conclusions: Our findings suggest that integrating AI and IoT technologies into metro systems significantly improves energy efficiency by enabling dynamic energy demand prediction and real-time adjustment of energy consumption. The proposed system is scalable and adaptable, making it suitable for application in metro networks globally, thereby enhancing energy efficiency and supporting sustainable transport initiatives.

Enhancing Electric Bicycle Efficiency: Investigating Regenerative Braking and Pedal Charging Systems

Electric bicycles (e-bikes) are gaining global popularity as part of the broader shift towards electric vehicles (EVs). This research presents a regenerative electric bicycle designed to maximize energy efficiency, aiming for a top speed of 45 km/h and a range of 50 km. The bicycle incorporates two key energy recovery systems: a downhill regenerative system that captures braking energy and converts it into electricity, and a pedal charging system that converts pedaling kinetic energy to recharge the battery. A digital signal processing system seamlessly switches between charging and electric drive based on rider input. This study examines the effectiveness of these systems. The regenerative braking system achieved an impressive 30.9% efficiency in capturing energy during testing. The pedal charging system initially demonstrated an efficiency of 2.98%. MATLAB simulations revealed clear trends in force and power requirements across different grades (0°, 2.5°, 5°, 7.5°, and 10°), highlighting the crucial role of aerodynamic drag and gradient resistance in influencing performance and energy efficiency. While controlled testing environments require further real-world evaluation, these findings underscore the promising potential of regenerative braking and pedal charging for enhancing electric bicycle efficiency. This research paves the way for the development of more efficient and accessible electric bicycles, promoting sustainable transportation solutions through the selection of durable, efficient, and environmentally friendly components.

Optimization of Energy Involved in Rolling Stock of a Sub-Urban Rail Transport System

Objectives: This research is based on the real-world study of Mumbai Metro, which focuses on enhancing the efficiency of metro rail systems by optimizing the timetable to synchronize train movements. The primary goal is to minimize total energy consumption by maximizing the exchange of regenerative-braking energy. Methods: Mumbai Metro Rolling Stock is having regenerative braking systems, allowing surplus energy regenerated during braking to be transferred to other trains in the same electrical rail section. To achieve this optimization, a graphical method is used to coordinate the time synchronization of the braking train arriving at a station with the accelerating train departing from the same or different station. This coordination ensures that regenerative-braking energy is effectively utilized and distributed among trains within the system. By quantifying the energy savings resulting from synchronization, this model provides an incentive for implementing more efficient train scheduling practices. The application of this model was demonstrated through the design of schedules for the Mumbai Metro. The implementation resulted in measurable energy savings, showcasing a clear correlation between train movement synchronization and reduced energy consumption. Importantly, this optimization strategy does not compromise overall passenger service quality and can be implemented with relatively low investment costs. In conclusion, adjusting the timetable in force based on principles of time synchronization and energy optimization has the potential to yield considerable energy savings for metro rail systems. Findings: This research emphasizes the importance of strategic train scheduling in maximizing the efficiency and sustainability of public transportation networks using regeneration of electrical energy. By implementing the optimized timetable, could lead to approximately a 5.03 % increase in regenerative energy utilization which would save 2.2 % in total energy consumption. Novelty: This study introduces a novel method to assess energy cooperation between trains using timetable optimization because this is the first real-world study of its kind conducted on any Indian metro system in which if one train is in braking mode then another train in the same electrical section should be in motoring mode so as to avoid the losses of the regenerated energy. Keywords: Optimization, Headway, Dwell time, Coasting period, Traction energy

Energy harvesting for automated storage and retrieval system with sustainable configuration of storage assignment and input/output point

The warehouse automation market has experienced significant growth due to the necessity for quick responses to customer needs. The adoption of Automated Storage and Retrieval System (AS/RS) aims to enhance operational efficiency and expedite order fulfillment, although environmental considerations are frequently overlooked. This study introduces the implementation of energy harvesting using Regenerative Braking System (RBS) on AS/RS to minimize the carbon emission impact. The best configuration of storage assignments and Input/Output (I/O) points is examined to improve travel time, response time, and carbon emission as sustainability indicators. This study employs a discrete-event simulation mimicking the AS/RS and warehouse environment under uncertainty. Simulation-based experiment was performed under 96 different scenarios and the result was assessed through statistical tests revealing the main and interaction effects between factors to performance indicators, including the trade-off between them. The result reveals that the implementation of RBS in AS/RS can result in 13% energy saving on average or equal to additional travel range of 28,800 m indicating the suitability adoption towards green operation. However, the lowest carbon emission is followed by higher travel time and response time. Thus, metamodel-based optimization was also performed via desirability function analysis. The optimization result reveals that the sustainable AS/RS configuration is obtained with a single-side for I/O point, non-class for storage classification, closest open location with column-order for slot selection, and closest open location with row-order for retrieval selection.

Effects of stress-relief and natural aging on the geometric tolerances and functional requirements of ferritic nitrocarburized gray cast iron brake rotors

Gray cast iron (GCI) brake rotors are prone to corrosion, particularly in electric vehicles (EVs), owing to their regenerative braking systems. Ferritic nitrocarburizing (FNC) heat treatment has been used to improve the corrosion resistance and cleanability of GCI rotors. However, pre-existing residual stresses in the rotors can lead to distortion after FNC processing, thereby affecting the functional and tolerance requirements. This study investigated the effects of stress-relief (SR) heat treatment and natural aging (NA) on the natural frequency, damping properties, surface roughness, and geometric tolerances of GCI brake rotors before and after FNC treatment. The GCI brake rotors were subjected to SR and NA processes, followed by FNC treatment. The natural frequency, damping Q-factor, surface roughness, and geometric tolerances were measured and analyzed using two-way ANOVA at a 95% confidence level (α = 0.05). The results showed that SR heat treatment significantly reduced the effect of residual stresses and improved geometric tolerances compared to NA. The FNC treatment increased the surface roughness and slightly improved the damping properties but had an insignificant effect on the natural frequency. The combination of SR and FNC treatments provided the best overall performance in terms of quality, geometric tolerance, and surface finish. This study highlights the importance of SR heat treatment for optimizing the manufacturing process of FNC-treated GCI brake rotors for EVs.

An Efficient Regenerative Braking System for Electric Vehicles Based on a Fuzzy Control Strategy

An Efficient Regenerative Braking System for Electric Vehicles Based on a Fuzzy Control Strategy

Implementation of Regenerative Brake System Controller by Using Mini Controller Arduino

The key requirement for electric vehicles is efficient braking. The objective of this study is to provide a detailed description of the regenerative braking system utilizing various power flow regulators. This study utilizes a buck-boost type Boost Converter. There are two methods employed to modify the voltage derived from the fluctuating input produced during regenerative braking: one to decrease it and the other to boost it. Subsequently, a voltage sensor detects the resulting output voltage, which is then regulated using an Arduino microcontroller. The examination findings indicate that the buck-boost converter performs well, maintaining an output voltage within the range of 39-40 volts. This allows it to function well even when there are fluctuations in the input voltage. The voltage value can be utilized for the purpose of charging a battery for a 36 Volt electric motor. The findings demonstrate the efficacy of utilizing a Buck-Boost Converter regulator. Furthermore, it could charge the battery within a span of 8 seconds, making it a viable option for electric vehicles as an alternative to regenerative braking for battery recharging.

A Study on an Energy-Regenerative Braking Model Using Supercapacitors and DC Motors

This study presents an energy regeneration model and some theory required to construct a regeneration braking system. Due to the effects of carbon dioxide (CO2) emissions, there is increasing interest in the use of electric vehicles (EVs), electric bikes, electric bicycles, electric buses and electric aircraft globally. In order to promote the use of electric transportation systems, there is a need to underscore the impact of net zero emissions. The development of EVs requires regenerating braking system. This study presents the advantages of regenerative braking. This system is globally seen in applications such as electric cars, trams, and trains. In this study, the design specification, design methodology, testing configurations, Simulink model, and recommendations will be outlined. A unique element of this work is the practical experiment that was carried out using 1.5 Amps with no load and 2.15 Amps with a load. The discharge voltage was purely from the 22 W bulb load connected to the capacitor bank as we limited this study to the use of 1.5 Amps and it took 15 min for a full discharge cycle, after which no charge was left in the capacitor bank. The results showed that the discharge rate and charging rate for the regenerative braking system were effective but could be improved. The objective of this paper is to investigate how a supercapacitor works alongside a battery in regenerative braking applications. This study demonstrates that the superconductor used can deliver maximum power when required. Also, it can also withstand elevated peaks in charging or discharging current via the supercapacitor. Combining a battery with a supercapacitor reduces the abrupt load on the battery by shifting it to the capacitor. When these two combinations are used in tandem, the battery pack’s endurance and lifespan are both boosted.

Enhancing Estimating the Charge Level in Electric Vehicles: Leveraging Force Fluctuation and Regenerative Braking Data

Accurate determination of the state of charge is vital to optimize the performance and lifespan of electric vehicle batteries. Traditional methods which rely on battery models and direct measurements can be error-prone due to fluctuating operating conditions and battery degradation over time. Regenerative braking systems are crucial in electric and hybrid vehicles for improving energy efficiency by transforming kinetic energy into electrical energy during braking. However, force fluctuation is a challenge that can affect the performance and comfort of regenerative braking. It is known to us that electric motors and generators used in regenerative braking have non- linear torque characteristics, especially at low speeds, leading to inconsistent braking force. Variations in road conditions, such as wet or uneven surfaces, can affect the grip of the tires, leading to fluctuations in deceleration. Interactions of regenerative braking system with conventional friction brakes can cause force fluctuations, especially during the transition between the two systems. This study introduces an improved state of charge estimation technique based on force fluctuation and a regenerative braking system. This research shows that this approach significantly enhances state of charge accuracy compared to traditional methods, especially in urban driving conditions with frequent braking. The findings underscore the potential of using regenerative braking as well as force fluctuation condition data as a valuable input for state of charge estimation, ultimately leading to better battery management and an extended electric vehicle range.

A comparative analysis of power control strategies for a plug-in extended-range FCHEV with battery and PEMFC durability considerations

ABSTRACT Fuel cell hybrid electric vehicles employ fuel cells along with batteries or ultracapacitors as power sources, and energy management systems can control their power split. This study investigates energy management strategies for lightweight plug-in extended-range fuel cell hybrid electric vehicles to optimise fuel economy, extend component lifespans, and enhance vehicle efficiency. Comparisons between power follower and dynamic programming-based strategies are conducted, focusing on incorporating PEMFC health constraints to prolong stack durability via on-off binary mode operation. Using the New European Driving Cycle, performance evaluation is conducted on a parallel architecture featuring a 16-kW motor, regenerative braking system, eight 420-Wh batteries in series, and six 1-kW fuel cell stacks. Dynamic programming strategies demonstrated potential improvements, with savings in hydrogen consumption of 62–72 L per driving cycle and SOC levels of 3.9%. Moreover, optimised battery lifespan enabled the system to achieve approximately 18,087 -18,745 charge/discharge cycles. Implementing PEMFC health constraints facilitated reductions in average accumulated active time to 205 seconds per driving cycle and startup/shutdown cycle frequency to 36.5 cycles per hour, promoting enhanced system durability and operational efficiency.

Enhanced Mileage and Efficiency in Lightweight EVs Through Innovative Regenerative Braking and Speed Control with Solar-PV Integration

This study introduces a novel way to regenerative braking based on a Brushless DC (BLDC) motor. The preferred way for braking is to use a variable Stator voltage from a multi-cell battery system DC-DC buck converter. The proposed brake system's performance was evaluated using a simulation. According to simulation results, the proposed regenerative braking system is both practical and effective. Furthermore, this study introduces the most essential technique for regenerative braking, which employs a BLDC motor to increase the mileage of lightweight electric vehicles

Reliability, Availability, and Maintainability Assessment of a Mechatronic System Based on Timed Colored Petri Nets

The mechatronic industry is currently subject to huge changes challenging it to offer products matching individual customer requirements at competitive prices. The design of such products calls for sophisticated and complex components integration following different technologies. Since we are on the cusp of the Fourth Industrial Revolution, in which the world of mechatronic production, network connectivity, the Internet of Things, and cyber-physical systems are correlated, the complexity of these systems increases exponentially, and we are talking about advanced mechatronic systems. To assist these changes, various methods, sweeping all project phases, are used by business houses. Predictive dependability assessment in the earlier design stage is considered a powerful metric used to evaluate the performances of different kinds of mechatronic products before the production phase. Altogether, dependability analysis ties the design directly to the desired functionality, operability, and integrity of the system. This paper explores an approach to assessing the dependability attributes, reliability, availability, and maintainability (RAM), of repairable mechatronic systems based on timed colored Petri nets and a Monte Carlo simulation, integrating simultaneously diverse components technologies: mechanical, electronic, and software. The proposed approach is tested taking the case of a regenerative braking system. The methodology appears to be efficient for evaluating predictive RAM indicators (MTTFF, MTTR, MTBF…) for the whole system and for each individual component separately.

PLL Technique of Regenerative Braking System Capable of Responding to Immediate Input Change

PLL Technique of Regenerative Braking System Capable of Responding to Immediate Input Change

Technical and economic feasibility study on reducing CO2 emissions of Dutch beam trawlers

This paper examines the technical and economic influence of CO2 reduction measures on the design and operation of Dutch beam trawlers. This is done by means of a parametric model used to assess the influence on the overall design of the vessel. Technical feasibility is determined by meeting operational effectiveness requirements, maximum added draught, maximum added length, and a reduction of CO2 emission by at least 40%. Secondly the model evaluates the new energy carrier and fish storage layout as a result of additional required volume. Additional volume is gained within the net store, fish hold, or by hull extension. Additionally, various propeller configurations, waste heat recovery, and regenerative braking systems are explored to reduce energy consumption. The economic performance is assessed using yearly operational requirements, capital expenses of configuration, and total cost of ownership.

Integrated Vehicle-Following Control for Four-Wheel Independent Drive Based on Regenerative Braking System Control Mechanism for Battery Electric Vehicle Conversion Driven by PMSM 30 kW

Integrated Vehicle-Following Control for Four-Wheel Independent Drive Based on Regenerative Braking System Control Mechanism for Battery Electric Vehicle Conversion Driven by PMSM 30 kW

Analysis of Combined Braking Torque on The Regenerative Anti-Lock Braking System in The Quarter Electric Vehicle Model

Analysis of Combined Braking Torque on The Regenerative Anti-Lock Braking System in The Quarter Electric Vehicle Model

REGENERATIVE BRAKING WITH POWER MONITORING IN ELECTRIC VEHICLES

The duration of battery charging and the scarcity of charging stations are the main obstacles to the widespread usage of electric vehicles. We thus suggest a regenerative breaking system here. Every time the brakes are applied, a car might produce energy thanks to this mechanism. More power is produced with stronger brakes. In a braking drum, we employ a friction lining arrangement. The friction liner on a rotating drum does not wear down the drum. When the brakes are applied, the friction lining within the drum touches it, moving the motors attached to it in the same direction. This process uses the motors as a dynamo to produce electricity. This method so provides a regenerative braking system by enabling the automobile battery to be charged each time the brakes are used. It affects us both in different ways.