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Yang Y.,Chongqing University | Gong H.,SAIC | Qin D.-T.,Chongqing University | Su L.,Chongqing Changan New Energy Automobile Co. | Lin G.-Y.,Chang'an University
Zhongguo Gonglu Xuebao/China Journal of Highway and Transport | Year: 2014

In order to improve the economy of full hybrid electric vehicle (HEV) equipped with continuously variable transmission (CVT), system efficiency optimization models for various operating modes were built with consideration of efficiency of engine, integrated starter and generator (ISG), battery and CVT. Two driving schemas, charge consumption plan and charge growth plan, were proposed. For the target of instantaneous optimization of system efficiency, the energy management strategy was established. The cycle simulation model of full HEV with CVT was established using MATLAB/Simulink. The simulation was performed under NEDC working condition. The results show that fuel consumption of full HEV with CVT by employing the control strategy proposed in this paper decreases by 10.42%, compared with the strategy in ADVISOR, and compared with the power equivalent traditional vehicle, decreases by 39.77%. The control strategy proposed in the article can also control the battery state of charge within a reasonable range of operation.


Piao C.-H.,Chongqing University of Posts and Telecommunications | Yu Q.-F.,Chongqing University of Posts and Telecommunications | Duan C.-X.,Chongqing University of Posts and Telecommunications | Su L.,Chongqing Changan New Energy Automobile CO. | Zhang Y.,Chongqing University of Posts and Telecommunications
Journal of Electrical Engineering and Technology | Year: 2014

The offline verification of state of charge estimation, power estimation, fault diagnosis and emergency control of battery management system (BMS) is one of the key technologies in the field of electric vehicle battery system. It is difficult to test and verify the battery management system software in the early stage, especially for algorithms such as system state estimation, emergency control and so on. This article carried out the virtual environment modeling for verification of battery management system. According to the input/output parameters of battery management system, virtual environment is determined to run the battery management system. With the integration of the developed BMS model and the external model, the virtual environment model has been established for battery management system in the vehicle's working environment. Through the virtual environment model, the effectiveness of software algorithm of BMS was verified, such as battery state parameters estimation, power estimation, fault diagnosis, charge and discharge management, etc.


Piao C.-H.,Chongqing University of Posts and Telecommunications | Hu Z.-H.,Chongqing University of Posts and Telecommunications | Su L.,Chongqing Changan New Energy Automobile CO. | Zhao J.-F.,Chongqing University of Posts and Telecommunications
Journal of Electrical Engineering and Technology | Year: 2016

A novel battery SOH estimation algorithm based on outlier detection has been presented. The Battery state of health (SOH) is one of the most important parameters that describes the usability state of the power battery system. Firstly, a battery system model with lifetime fading characteristic was established, and the battery characteristic parameters were acquired from the lifetime fading process. Then, the outlier detection method based on angular distribution was used to identify the outliers among the battery behaviors. Lastly, the functional relationship between battery SOH and the outlier distribution was obtained by polynomial fitting method. The experimental results show that the algorithm can identify the outliers accurately, and the absolute error between the SOH estimation value and true value is less than 3%. © The Korean Institute of Electrical Engineers.


Qin D.,Chongqing University | Du B.,Chongqing University | Duan Z.,Chongqing Changan New Energy Automobile Co. | Liu Y.,Chongqing University | Lin Y.,Chongqing University
Qiche Gongcheng/Automotive Engineering | Year: 2013

To improve the shift quality of a specific hybrid electric vehicle with AMT, its two typical working modes are studied with corresponding shift control strategies proposed based on the features of its structure and working modes. A motor/engine's electronic throttle combined speed control strategy is adopted for engine drive mode, while a shift control strategy of motor speed active regulation without clutch disengagement is used for engine/motor hybrid drive mode. The control strategies are verified by both the simulations and bench tests. The results show that the control strategies adopted can effectively reduce shift shock, shorten power interruption time and improve shift quality.


Zeng Y.-P.,Chongqing University | Zeng Y.-P.,Nanchang Institute of Technology | Qin D.-T.,Chongqing University | Du C.-H.,Chongqing Changan New Energy Automobile Co. | Liu Y.-G.,Chongqing University
Zhongguo Gonglu Xuebao/China Journal of Highway and Transport | Year: 2016

In order to improve the catalytic converter's efficiency of plug-in hybrid electric vehicle, the additional cost function was proposed to achieve temperature compensation when the catalyst temperature deviated from the suitable temperature range. On the basis of this, the engine fuel consumption and the additional cost when catalytic converter temperature deviated from its suitable temperature range were considered as the objective function, the Hamilton function was established according to Pontryagin minimum principle, and the real-time control strategy considering converter's efficiency was obtained after solving the objective function. This real-time control strategy was compared with the real-time control strategy without considering the converter's efficiency. The results show that comparing to the real-time control strategy without considering the converter's efficiency, the one considering converter's efficiency increases by 2.2% in terms of fuel consumption, but HC emission, CO emissions and NOx emissions decrease by 17.9%, 25.9% and 24.5%, respectively. Therefore, the real-time control strategy considering the converter's efficiency can greatly improve the emission performance of vehicle with little cost of fuel economy. © 2016, Editorial Department of China Journal of Highway and Transport. All right reserved.


Zeng Y.,Chongqing University | Qin D.,Chongqing University | Su L.,Chongqing Changan New Energy Automobile Co. | Yao M.,Chongqing University
Qiche Gongcheng/Automotive Engineering | Year: 2016

Base on the vehicle powertrain system model, engine thermodynamics and fuel consumption model, and cabin thermal exchange model, CD-CS mode control strategy, Pontryagin's minimum principle (PMP)-based comprehensive real-time heating control strategy and PMP-based active comprehensive real-time heating control strategy are studied with consideration of cabin heating power demand. Then the above-mentioned three control strategies are simulated on Matlab/Simulink platform. The results show that under the commuter driving cycle composed of UDDS and HWFET, compared with CD-CS mode control strategy and PMP-based comprehensive real=time heating control strategy, the engine fuel consumption with PMp-based active comprehensive real-time heating control strategy reduces by 10.9% and 4.97% respectively. © 2016, SAE-China. All right reserved.


Ye X.,Xi'an Jiaotong University | Qin D.-T.,Xi'an Jiaotong University | Hu M.-H.,Xi'an Jiaotong University | Duan Z.-H.,Chongqing Changan New Energy Automobile Co. | Chen Q.-H.,Xi'an Jiaotong University
Chongqing Daxue Xuebao/Journal of Chongqing University | Year: 2010

The system efficiency of ISG (integrated starter/generator) type medium hybrid electric vehicle (HEV) is optimized by combining transient and global optimization method under given driving mode, and energy management strategy of the hybrid system is optimized to reach the highest hybrid system efficiency. In order to avoid dynamic fuel increasing caused by engine throttle mutation during driving mode shifting or acceleration, closed-loop control of inertia moment of engine and compensation control of motor torque are adopted to coordinate the medium hybrid system to suppress over fuel injection of the ICE caused by engine throttle mutation. Model of medium hybrid electric vehicle is built and simulation is carried out. The results show that the throttle coordinated control can improve fuel economy, under the condition of guaranteed dynamic performance.


Fu H.,Chongqing Changan New Energy Automobile CO. | Feng C.,Chongqing Changan New Energy Automobile CO. | Xue S.,Chongqing Changan New Energy Automobile CO.
IEEE Transportation Electrification Conference and Expo, ITEC Asia-Pacific 2014 - Conference Proceedings | Year: 2014

This paper presents a vibration suppression control method for a motor-gearbox directly coupled integrated powertrain of electric and hybrid vehicles. The integrated powertrain is modeled as a two-mass system. Based on the analysis of the system dynamics, the oscillation of torsional angle and relative speed difference needs to be controlled to improve driving performance. To this end, an optimal LQR controller with a reduced-order observer is proposed to suppress the vibration caused by the oscillation of torsional angle in the system. A computer simulation model is developed to verify the effectiveness of the proposed control scheme. The simulation results illustrate that the estimated states are close to the reference states. It is also shown that the oscillation of the system is significantly suppressed. And both torsional angle and relative speed difference are well controlled under different gear ratios, which is beneficial for enhancing shift quality. Therefore, the proposed vibration control scheme is suitable for the integrated powertrain of electric and hybrid vehicles. © 2014 IEEE.


Qin D.-T.,Chongqing University | Zhao X.-Q.,Chongqing University | Su L.,Chongqing Changan New Energy Automobile Co. | Yang G.-L.,Chongqing University
Zhongguo Gonglu Xuebao/China Journal of Highway and Transport | Year: 2015

To further improve the economy of a novel plug-in hybrid electric vehicle (PHEV), a variable parameter energy management strategy was proposed based on driving cycle and trip distance, which would influence the vehicle economy. To reduce the influence of driving cycle, fuzzy Euclid's degree of nearness was used to establish the driving cycle recognition control strategy based on typical driving cycles. To reduce the influence of trip distance, the vehicle trip distance mode recognition control strategy was proposed by using fuzzy recognition, with the trip distance and the power battery SOC of vehicle at start as inputs and the trip distance mode as output. Eventually, the simulation for energy management strategy was carried out for analysis. The results show that compared with the constant parameter energy management strategy, the equivalent fuel consumption of PHEV under the condition of the variable parameter energy management in the same distance of new European driving cycle (NEDC) can be reduced by more than 5%. The variable parameter energy management strategy can be used to improve the economy of PHEV. ©, 2015, Xi'an Highway University. All right reserved.


Piao C.,Chongqing University | Piao C.,Chongqing ChangAn New Energy Automobile Co. | Yang X.,Chongqing University | Teng C.,Chongqing University | Yang H.Q.,Chongqing ChangAn New Energy Automobile Co.
OPEE 2010 - 2010 International Conference on Optics, Photonics and Energy Engineering | Year: 2010

Based on artificial neural networks and Thenvenin model, this paper uses an improved model predicting state of charge. We combine artificial neural networks model with Thevenin model, and predict state of charge in real time at the same time. When the difference between the predictive value of artificial neural networks model and the predictive value of Thevenin model is more than 10%, we revised the predictive value of artificial neural networks model by weighted average value. The results show that it can reduce the error of artificial neural networks model obvious and the average error is 4. 72%. It is lower independence on initial state of charge than artificial neural networks model. © 2010 IEEE.

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