Browse Topic: Regenerative braking
This study investigates an optimal control strategy for a battery electric vehicle (BEV) equipped with a high-speed motor and a continuously variable transmission (CVT). The proposed dual-motor powertrain model activates only one motor at a time, with Motor A routed through a CVT and Motor B through a fixed gear. To improve energy efficiency, two optimization methods are evaluated: a quasi-steady-state map-based approach and a dynamic programming (DP) method. The DP approach applies Bellman’s principle to derive the globally optimal CVT ratio and motor torque trajectory over the WLTC cycle. Simulation results demonstrate that the DP method significantly improves overall efficiency compared to traditional control logic. Furthermore, the study proposes using DP-derived maps to refine practical control strategies, offering a systematic alternative to conventional experimental calibration.
Increasing the mission capability of ground combat and tactical vehicles can lead to new concepts of operation that enhance safety and effectiveness of warfighters. High-temperature power electronics enabled by wide-bandgap semiconductors such as silicon carbide can provide the required power density to package new capabilities into space-constrained vehicles and provide features including silent mobility, boost acceleration, regenerative braking, adaptive cooling, and power for future protection systems and command and control (C2) on the move. An architecture using high voltage [1] would best satisfy the ever-increasing power demands to enable defense against unmanned aerial systems (UAS) and offensive directed energy (DE) systems for advanced survivability and lethality capabilities.
As the ICE vehicle changes into the EV, we can use regenerative brake. It can improve not only the energy consumption but also reduce the hydraulic brake usage. The less hydraulic brake usage mitigates the heat loading on the brake disc. From this reason, the lightweight brake can be used in the EV. However, when the lightweight brake is applied, the brake NVH can be increased. The optimization design of the lightweight brake should be done to prevent the brake NVH. In this paper, the optimal brake disc thickness and brake interfaces are determined by using of disc heat capacity analysis. The lightweight brake should be optimized by using of the brake squeal analysis. We can verify the results from both analysis and test. Finally, we can have the lightweight brake, which is competitive in terms of cost, weight and robust to the brake NVH.
The use of drum brakes in Battery Electric Vehicles (BEVs) offers numerous benefits, including energy efficiency, reduced brake dust emissions, and reliable performance under challenging weather conditions. The capability of regenerative braking reduces the friction brake application frequency in BEVs and therefore the brakes can be prone to corrosion and performance degradation especially considering conventional disc brake systems. The closed design of a drum brake prevents corrosion of the friction-components by sealing out water, dirt or snow. A common sealing concept is performed with a labyrinth between the gap of the rotating drum and the axle mounted backplate. A hermetical isolation of water and snow ingress into the drum cannot be achieved with this concept, so additional aerodynamic measures are necessary to deflect the air/water path and protect the inner brake components. Additionally, interfaces like wheel cylinders, electric park brake parts, brake shoe pins, and axle
From humble Chevrolet Bolts to six-figure Lucid Airs, every EV can reverse its electric motors to slow the vehicle while harvesting energy for the battery, the efficient tag-team process known as regenerative braking. Today's EVs do this so well that traditional friction brakes, which clamp onto a spinning wheel rotor or drum, can seem an afterthought. Witness Volkswagen's decision to equip its ID.4 with old-fashioned rear drum brakes, with VW claiming drums reduce EV rolling resistance and offer superior performance after long periods of disuse.
Lithium-ion cells operate under a narrow range of voltage, current, and temperature limits, which requires a battery management system (BMS) to sense, control, and balance the battery pack. The state of power (SOP) estimation is a fundamental algorithm of the BMS. It operates as a dynamic safety limit, preventing rapid ageing and optimizing power delivery. SOP estimation relies on predictive algorithms to determine charge and discharge power limits sustainable within a specified time frame, ensuring the cell design constraints are not violated. This paper explores various approaches for real-time deployment of SOP estimation algorithms for a high-power lithium-ion battery (LIB) with a low-cost microcontroller. The algorithms are based on a root-finding approach and a first-order equivalent circuit model (ECM) of the battery. This paper assesses the practical application of the algorithm with a focus on processor execution time, flash memory and RAM allocation using a processor-in-the
This paper aims at analysing the effect of regeneration braking on the amount of energy harnessed during vehicle braking, coasting and its effect on the drive train components like gear, crown wheel pinion, spider gear & bearing etc. Regenerative braking systems (RBS) is an effective method of recovering the kinetic energy of the vehicle during braking condition and using this to recharge the batteries. In Battery Electric Vehicles (BEV), this harnessed energy is used for controlled charging of the high voltage batteries which will help in increasing the vehicle range eventually. Depending on the type of the powertrain architecture, components between motor output to the wheels will vary, i.e., in an e-axle, motor is coupled with a gear box which will be connected with differential and the wheels. Whereas in case of a central drive architecture, motor is coupled with gearbox which is connected with a propeller shaft and then the differential and to the wheels. All the components
Good driving practices, encompassing actions like maintaining smooth acceleration, sustaining a consistent speed, and avoiding aggressive maneuvers, can yield several benefits. These practices enhance energy efficiency, reduce accident risks, and significantly lower maintenance costs. Consequently, the presence of a system capable of providing actionable insights to promote such driving behavior is crucial. Addressing this need, the Drive-GPT model is introduced, representing an AI-based generative pre-trained transformer. Within this study, the transformative potential of deep learning networks, specifically based on transformers, is showcased in capturing the typical driving patterns exhibited by individuals in diverse road, traffic, weather, and vehicle health scenarios. The model's training dataset comprises an extensive 90 million data points from multivariate time series originating from telematics systems in 100 vehicles traversing eight distinct Indian cities over a six-month
In recent years, global warming, depletion of fossil fuels, and reducing pollution have become increasingly prominent issues, resulting in demand for environmentally-friendly two-wheeled vehicles capable of reducing CO2 emissions. However, it remains necessary to meet customers’ expectations by providing smaller drivetrains, lighter vehicles, and support for long-distance riding, among other characteristics. In the face of this situation, hybrid electric vehicle (HEV) systems are considered to be the most realistic method for creating environmentally-friendly powertrains and are widely used. This research introduces a hybrid electric two-wheeled vehicle fitted with an electrical variable transmission (EVT) system, a completely new type of electrical transmission that meets the aforementioned needs, achieving enhanced fuel efficiency with a compact drivetrain. The EVT system comprises double rotors installed inside the stator. The hybrid electric two-wheeled vehicle equipped with the
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