Regenerative Braking Potential and Energy Simulations for a Plug-In Hybrid Electric Vehicle Under Real Driving Conditions

Author(s):  
L. A. S. B. Martins ◽  
J. M. O. Brito ◽  
A. M. D. Rocha ◽  
J. J. G. Martins

There are several possible configurations and technologies for the powertrains of electric and hybrid vehicles, but most of them will include advanced energy storage systems comprising batteries and ultra-capacitors. Thus, it will be of capital importance to evaluate the power and energy involved in braking and the fraction that has the possibility of being regenerated. The Series type Plug-in Hybrid Electric Vehicle (S-PHEV), with electric traction and a small Internal Combustion Engine ICE) powering a generator, is likely to become a configuration winner. The first part of this work describes the model used for the quantification of the energy flows of a vehicle, following a particular route. Normalised driving-cycles used in Europe and USA and real routes and traffic conditions were tested. The results show that, in severe urban driving-cycles, the braking energy can represent more than 70% of the required useful motor-energy. This figure is reduced to 40% in suburban routes and to a much lower 18% on motorway conditions. The second part of the work consists on the integration of the main energy components of an S-PHEV into the mathematical model. Their performance and capacity characteristics are described and some simulation results presented. In the case of suburban driving, 90% of the electrical motor-energy is supplied by the battery and ultra-capacitors and 10% by the auxiliary ICE generator, while on motorway these we got 65% and 35%, respectively. The simulations also indicate an electric consumption of 120 W.h/km for a small 1 ton car on a suburban route. This value increases by 11% in the absence of ultra-capacitors and a further 28% without regenerative braking.

Author(s):  
H Yeo ◽  
H Kim

A regenerative braking algorithm and a hydraulic module are proposed for a parallel hybrid electric vehicle (HEV) equipped with a continuous variable transmission (CVT). The regenerative algorithm is developed by considering the battery state of charge, vehicle velocity and motor capacity. The hydraulic module consists of a reducing valve and a power unit to supply the front wheel brake pressure according to the control algorithm. In addition, a stroke simulator is designed to provide a similar pedal operation feeling. In order to evaluate the performance of the regenerative braking algorithm and the hydraulic module, a hardware-in-the-loop simulation (HILS) is performed. In the HILS system, the brake system consists of four wheel brakes and the hydraulic module. Dynamic characteristics of the HEV are simulated using an HEV simulator. In the HEV simulator, each element of the HEV powertrain such as internal combustion engine, motor, battery and CVT is modelled using MATLAB SIMULINK. In the HILS, a driver operates the brake pedal with his or her foot while the vehicle speed is displayed on the monitor in real time. It is found from the HILS that the regenerative braking algorithm and the hydraulic module suggested in this paper provide a satisfactory braking performance in tracking the driving schedule and maintaining the battery state of charge.


The emissions from the internal combustion (IC) engine vehicle causes pollution which increases the carbon footprints in the environment which causes global warming. In ICE vehicle only 20 % of the energy produced by it is used to run the vehicle and rest 80 % of it get wasted. The emerging technology of Hybrid Electric vehicle (HEV) has become the feasible solution for the modern world as it lessens the carbon emission and augments the fuel performance of vehicle. The role of power electronic converters is very crucial in designing the configuration of HEVs. The performance of the converter is employed for realizing the features of electric traction motor drive. The paper analyses the performance of a small car powered by gasoline based internal combustion engine, series hybrid electric vehicle (SHEV) and parallel hybrid electric vehicle (PHEV) drive train. The simulation has been performed on Advanced Vehicle Simulator (ADVISOR) platform. Different types of HEVs configuration has been analyzed by considering three different driving schedules such as CYC_UDDS, CYC_NEDC and CYC_URBAN_INDIA. The gradability and acceleration test has also been carried out in all category of test vehicles and result is demonstrated by examining vehicle emission at each driving cycle


2013 ◽  
Vol 288 ◽  
pp. 142-147 ◽  
Author(s):  
Shang An Gao ◽  
Xi Ming Wang ◽  
Hong Wen He ◽  
Hong Qiang Guo ◽  
Heng Lu Tang

Fuel cell hybrid electric vehicle (FCHEV) is one of the most efficient technologies to solve the problems of the energy shortage and the air pollution caused by the internal-combustion engine vehicles, and its performance strongly depends on the powertrains’ matching and its energy control strategy. The theoretic matching method only based on the theoretical equation of kinetic equilibrium, which is a traditional method, could not take fully use of the advantages of FCHEV under a certain driving cycle because it doesn’t consider the target driving cycle. In order to match the powertrain that operates more efficiently under the target driving cycle, the matching method based on driving cycle is studied. The powertrain of a fuel cell hybrid electric bus (FCHEB) is matched, modeled and simulated on the AVL CRUISE. The simulation results show that the FCHEB has remarkable power performance and fuel economy.


2016 ◽  
Vol 78 (6) ◽  
Author(s):  
Mohd Sabirin Rahmat ◽  
Fauzi Ahmad ◽  
Ahmad Kamal Mat Yamin ◽  
Noreffendy Tamaldin ◽  
Vimal Rau Aparow ◽  
...  

This paper provided a validated modeling and a simulation of a 6 degree freedom vehicle longitudinal model and drive-train component in a series hybrid electric vehicle. The 6-DOF vehicle dynamics model consisted of tire subsystems, permanent magnet synchronous motor which acted as the prime mover coupled with an automatic transmission, hydraulic brake subsystem, battery subsystem, alternator subsystem and internal combustion engine to supply the rotational input to the alternator. A speed and torque tracking control systems of the electric power train were developed to make sure that the power train was able to produce the desired throttle torque in accelerating the vehicle. A human-in-the-loop-simulation was utilized as a mechanism to evaluate the effectiveness of the proposed hybrid electric vehicle. The proposed simulation was used as the preliminary result in identifying the capability of the vehicle in terms of the maximum speed produced by the vehicle and the capability of the alternator to recharge the battery. Several tests had been done during the simulation, namely sudden acceleration, acceleration and braking test and unbounded motion. The results of the simulation showed that the proposed hybrid electric vehicle can produce a speed of up to 70 km/h with a reasonable charging rate to the battery. The findings from this study can be considered in terms of design, optimization and implementation in a real vehicle.


2018 ◽  
Vol 10 (9) ◽  
pp. 168781401879776 ◽  
Author(s):  
Jianjun Hu ◽  
Zhihua Hu ◽  
Xiyuan Niu ◽  
Qin Bai

To improve the fuel efficiency and battery life-span of plug-in hybrid electric vehicle, the energy management strategy considering battery life decay is proposed. This strategy is optimized by genetic algorithm, aiming to reduce the fuel consumption and battery life decay of plug-in hybrid electric vehicle. Besides, to acquire better drive-cycle adaptability, driving patterns are recognized with probabilistic neural network. The standard driving cycles are divided into urban congestion cycle, highway cycle, and urban suburban cycle; the optimized energy management strategies in three representative driving cycles are established; meanwhile, a comprehensive test driving cycle is constructed to verify the proposed strategies. The results show that adopting the optimized control strategies, fuel consumption, and battery’s life decay drop by 1.9% and 3.2%, respectively. While using the drive-cycle recognition, the features of different driving cycles can be identified, and based on it, the vehicle can choose appropriate control strategy in different driving conditions. In the comprehensive test driving cycle, after recognizing driving cycles, fuel consumption and battery’s life decay drop by 8.6% and 0.3%, respectively.


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