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3 results on '"dual-motor energy recovery"'

1. Efficiency Optimization and Control Strategy of Regenerative Braking System with Dual Motor

Author

Yang Yang, Qiang He, Yongzheng Chen, and Chunyun Fu

Subjects
electric vehicle, dual-motor energy recovery, regenerative braking system, cvt speed ratio control, motor minimum loss, energy consumption and efficiency characteristics, braking force distribution, Technology
Abstract

The regenerative braking system of electric vehicles can not only achieve the task of braking but also recover the braking energy. However, due to the lack of in-depth analysis of the energy loss mechanism in electric braking, the energy cannot be fully recovered. In this study, the energy recovery problem of regenerative braking using the independent front axle and rear axle motor drive system is investigated. The accurate motor model is established, and various losses are analyzed. Based on the principle of minimum losses, the motor control strategy is designed. Furthermore, the power flow characteristics in electric braking are analyzed, and the optimal continuously variable transmission (CVT) speed ratio under different working conditions is obtained through optimization. To understand the potential of dual-motor energy recovery, a regenerative braking control strategy is proposed by optimizing the dynamic distribution coefficient of the dual-electric mechanism and considering the restrictions of regulations and the I curve. The simulation results under typical operating conditions and the New York City Cycle (NYCC) proposed conditions indicate that the improved strategy has higher joint efficiency. The energy recovery rate of the proposed strategy is increased by 1.18% in comparison with the typical braking strategy.

Published
2020
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2. Energy Storage Systems for Electric Vehicles.

Subjects
History of engineering & technology, AC-AC converters, CFD, CVT speed ratio control, EV batteries, Electric Truck Simulator, Electric Vehicle (EV), Kalman filter, Lithium-ion battery, MATLAB, MeshWorks, SEI forming additives, SLI battery, Simscape, Traveling Salesman Problem (TSP), Vehicle Routing Problem (VRP), adaptive EKF SOC estimation, adaptive equivalent consumption minimization strategy (A-ECMS), adaptive model-based algorithm, artificial intelligence, artificial neural networks, automated guided vehicle, autoregressive models of nonstationary signals, available capacity, battery, battery capacity, battery chargers, battery life, battery mechanical aging, battery modelling and simulation, battery recycling, battery reusing, battery sizing, battery testing cycler, battery thermal management, battery thermal model, braking force distribution, braking intention, butyronitrile, cabin heating, cell expansion, coulomb counting, diffusion induced stress, driving conditions identification, dual-motor energy recovery, echelon utilization, electric bus, electric buses, electric vehicle, electric vehicles, electrified propulsion, electro-hydraulic braking, electrochemical-thermal model, electrode particle model, energy consumption and efficiency characteristics, energy efficiency, energy management, energy storage applications, environmental conditions, equalization algorithm, estimation, fast charging, force, fuel cell, fuel cell hybrid electric vehicle, fuzzy unscented Kalman filtering algorithm, global optimization, heat and mass transfer, hybrid electric vehicles (HEVs), hybrid energy storage system, hybrid vehicles, hydrostatic stress influence on diffusion, improved second-order RC equivalent circuit, iron phosphate, joint estimation, latent heat storage, layered bidirectional equalization, least squares support vector machines (LSSVM), least-energy routing algorithm, li-ion battery, linear observer SOC estimation, linear quadratic estimator, lithium ion battery, lithium-ion batteries, lithium-ion battery, lithium-ion cobalt battery, lithium-ion polymer battery, lowest instantaneous energy costs, metallic phase change material, metric evaluation, micro-channel cooling plate, mode switching, model-based design, motor minimum loss, multi-objective energy management, new energy vehicle, nitrile-based solvents, non-aqueous electrolyte, oil-electric-hydraulic hybrid system, open circuit voltage, open-circuit voltage, optimal energy management, particle swarm optimization (PSO), performance degradation modelling, pontryagin's minimum principle (PMP), power batteries, power battery, power conversion harmonics, power distribution, powertrain optimization, recurrent-neural-network (RNN), regenerative braking system, resistance, retired batteries, square root cubature Kalman filter, state of charge, state of energy, state of health, state-of-charge, state-of-charge estimation (SOC), state-of-health, temperature, thermal analysis, thermal energy storage, torque coordinated control, waveforms modeling, wireless power transmission
Abstract

Summary: The global electric car fleet exceeded 7 million battery electric vehicles and plug-in hybrid electric vehicles in 2019, and will continue to increase in the future, as electrification is an important means of decreasing the greenhouse gas emissions of the transportation sector. The energy storage system is a very central component of the electric vehicle. The storage system needs to be cost-competitive, light, efficient, safe, and reliable, and to occupy little space and last for a long time. It should also be produced and disposed of in an environmentally friendly manner. This leaves many research challenges, and the purpose of this book is therefore to provide a platform for sharing the latest findings on energy storage systems for electric vehicles (electric cars, buses, aircraft, ships, etc.) Research in energy storage systems requires several sciences working together, and this book therefore include contributions from many different disciplines; this covers a wide range of topics, e.g. battery-management systems, state-of-charge and state-of-health estimation, thermal-battery-management systems, power electronics for energy storage devices, battery aging modelling, battery reuse and recycling, etc.

3. Efficiency Optimization and Control Strategy of Regenerative Braking System with Dual Motor

Author

Yongzheng Chen, Yang Yang, Chunyun Fu, and Qiang He

Subjects
0209 industrial biotechnology, Control and Optimization, business.product_category, Computer science, 020209 energy, Energy Engineering and Power Technology, 02 engineering and technology, lcsh:Technology, dual-motor energy recovery, 020901 industrial engineering & automation, electric vehicle, regenerative braking system, CVT speed ratio control, motor minimum loss, energy consumption and efficiency characteristics, braking force distribution, Control theory, Electric vehicle, cvt speed ratio control, 0202 electrical engineering, electronic engineering, information engineering, Electrical and Electronic Engineering, Engineering (miscellaneous), Continuously variable transmission, Energy recovery, lcsh:T, Renewable Energy, Sustainability and the Environment, Motor drive, Axle, Regenerative brake, business, Energy (signal processing), Energy (miscellaneous)
Abstract

The regenerative braking system of electric vehicles can not only achieve the task of braking but also recover the braking energy. However, due to the lack of in-depth analysis of the energy loss mechanism in electric braking, the energy cannot be fully recovered. In this study, the energy recovery problem of regenerative braking using the independent front axle and rear axle motor drive system is investigated. The accurate motor model is established, and various losses are analyzed. Based on the principle of minimum losses, the motor control strategy is designed. Furthermore, the power flow characteristics in electric braking are analyzed, and the optimal continuously variable transmission (CVT) speed ratio under different working conditions is obtained through optimization. To understand the potential of dual-motor energy recovery, a regenerative braking control strategy is proposed by optimizing the dynamic distribution coefficient of the dual-electric mechanism and considering the restrictions of regulations and the I curve. The simulation results under typical operating conditions and the New York City Cycle (NYCC) proposed conditions indicate that the improved strategy has higher joint efficiency. The energy recovery rate of the proposed strategy is increased by 1.18% in comparison with the typical braking strategy.

Published
2020

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