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Adaptive Sliding-Mode With Hysteresis Control Strategy for Simple Multimode Hybrid Energy Storage System in Electric Vehicles EI SCIE Scopus
期刊论文 | 2017 , 64 (2) , 1404-1414 | IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS | IF: 7.05
WoS CC Cited Count: 12 SCOPUS Cited Count: 17
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Abstract :

In this paper, a simple multimode hybrid energy storage system (HESS) is proposed for electric vehicles (EVs). Compared to the improved semiactive HESS, only two switches are added in the main circuit topology of the multimode HESS, thereby achieving the operating modes can be actively switched. The mode switch strategy is designed according to the driving modes of the EV and the status of the power sources. To improve the overall system efficiency of the multimode HESS, the boost converter will operate at the peak efficiency to convey the energy from the battery to the supercapacitor (SC). An adaptive sliding-mode control (ASMC) with hysteresis control (HC) strategy is also developed by combining practical application of the multimode HESS. Simulations and experiments are presented to verify the effectiveness of the proposed multimode HESS and its ASMC strategy. Simulated results showthat themultimode HESS can select suitable operating modes in corresponding conditions. Compared to the total sliding-mode control strategy, experimental results demonstrate that the ASMC with HC strategy can improve the operating stability of the multimode HESS under different operating modes. The multimode HESS can not only switch to suitable operating modes, but also avoid the excessive output power of the battery to meet different power demands of the load. In addition, the SC absorbs all the braking energy such that the battery safety can be effectively ensured.

Keyword :

electric vehicles (EVs) Adaptive sliding-mode control (ASMC) mode switch strategy boost converter hybrid energy storage system (HESS)

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GB/T 7714 Wang, Bin , Xu, Jun , Wai, Rong-Jong et al. Adaptive Sliding-Mode With Hysteresis Control Strategy for Simple Multimode Hybrid Energy Storage System in Electric Vehicles [J]. | IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS , 2017 , 64 (2) : 1404-1414 .
MLA Wang, Bin et al. "Adaptive Sliding-Mode With Hysteresis Control Strategy for Simple Multimode Hybrid Energy Storage System in Electric Vehicles" . | IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 64 . 2 (2017) : 1404-1414 .
APA Wang, Bin , Xu, Jun , Wai, Rong-Jong , Cao, Binggang . Adaptive Sliding-Mode With Hysteresis Control Strategy for Simple Multimode Hybrid Energy Storage System in Electric Vehicles . | IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS , 2017 , 64 (2) , 1404-1414 .
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A Method to Simultaneously Detect the Current Sensor Fault and Estimate the State of Energy for Batteries in Electric Vehicles EI SCIE PubMed Scopus
期刊论文 | 2016 , 16 (8) | SENSORS | IF: 2.677
WoS CC Cited Count: 6
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Abstract :

Recently, State of energy (SOE) has become one of the most fundamental parameters for battery management systems in electric vehicles. However, current information is critical in SOE estimation and current sensor is usually utilized to obtain the latest current information. However, if the current sensor fails, the SOE estimation may be confronted with large error. Therefore, this paper attempts to make the following contributions: Current sensor fault detection and SOE estimation method is realized simultaneously. Through using the proportional integral observer (PIO) based method, the current sensor fault could be accurately estimated. By taking advantage of the accurate estimated current sensor fault, the influence caused by the current sensor fault can be eliminated and compensated. As a result, the results of the SOE estimation will be influenced little by the fault. In addition, the simulation and experimental workbench is established to verify the proposed method. The results indicate that the current sensor fault can be estimated accurately. Simultaneously, the SOE can also be estimated accurately and the estimation error is influenced little by the fault. The maximum SOE estimation error is less than 2%, even though the large current error caused by the current sensor fault still exists.

Keyword :

battery model battery management systems proportional integral observer fault detection electric vehicle state of energy estimation

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GB/T 7714 Xu, Jun , Wang, Jing , Li, Shiying et al. A Method to Simultaneously Detect the Current Sensor Fault and Estimate the State of Energy for Batteries in Electric Vehicles [J]. | SENSORS , 2016 , 16 (8) .
MLA Xu, Jun et al. "A Method to Simultaneously Detect the Current Sensor Fault and Estimate the State of Energy for Batteries in Electric Vehicles" . | SENSORS 16 . 8 (2016) .
APA Xu, Jun , Wang, Jing , Li, Shiying , Cao, Binggang . A Method to Simultaneously Detect the Current Sensor Fault and Estimate the State of Energy for Batteries in Electric Vehicles . | SENSORS , 2016 , 16 (8) .
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Digital image correlation for large deformation applied in Ti alloy compression and tension test EI SCIE Scopus
期刊论文 | 2014 , 125 (18) , 5316-5322 | OPTIK | IF: 0.677
WoS CC Cited Count: 10
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Abstract :

In this paper, digital speckle correlation is used in the measurement of Ti alloy compression and tension test. The key technologies applied in the measurement are discussed in detail, including camera calibration with telephoto lens and digital image correlation in large deformation. Single camera self-calibration algorithm based on photogrammetry is proposed. In the algorithm, the interior parameters of camera are estimated without calibrated object, using the bundle adjustment technique, so the 3-D coordinates of calibration target points are not needed in advance to get a reliable camera calibration result. An updating reference image scheme could be employed to deal with large deformation situation. A large deformation measurement scheme, updating reference image scheme, is proposed in this paper. The un-deformed image is used as reference in correlation at first. Only for extremely large deformation field, in which iteration of correlation is not convergent, the reference image is updated to the image of previous deformed stage. Using this method, not only extremely large deformation can be measured successfully but also the accumulated error could be controlled. The 75 mm lens is calibrated in the measurement and compared the result with extensometer and un-calibrated image. Experimental results show that up to 150% tensile deformation and 50% compression deformation can be measured successfully. (c) 2014 Elsevier GmbH. All rights reserved.

Keyword :

Pattern recognition Digital image processing Target tracking Speckle

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GB/T 7714 Guo, Xiang , Liang, Jin , Xiao, Zhenzhong et al. Digital image correlation for large deformation applied in Ti alloy compression and tension test [J]. | OPTIK , 2014 , 125 (18) : 5316-5322 .
MLA Guo, Xiang et al. "Digital image correlation for large deformation applied in Ti alloy compression and tension test" . | OPTIK 125 . 18 (2014) : 5316-5322 .
APA Guo, Xiang , Liang, Jin , Xiao, Zhenzhong , Cao, Binggang . Digital image correlation for large deformation applied in Ti alloy compression and tension test . | OPTIK , 2014 , 125 (18) , 5316-5322 .
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Application of Parallel Chaos Optimization Algorithm for Plug-in Hybrid Electric Vehicle Design EI SCIE Scopus
期刊论文 | 2014 , 24 (1) | INTERNATIONAL JOURNAL OF BIFURCATION AND CHAOS | IF: 1.078
WoS CC Cited Count: 1 SCOPUS Cited Count: 2
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Abstract :

Plug-in hybrid electric vehicles (PHEVs) have been offered as alternatives that could greatly reduce fuel consumption relative to conventional vehicles. A successful PHEV design requires not only optimal component sizes but also proper control strategy. In this paper, a global optimization method, called parallel chaos optimization algorithm (PCOA), is used to optimize simultaneously the PHEV component sizes and control strategy. In order to minimize the cost, energy consumption (EC), and emissions, a multiobjective nonlinear optimization problem is formulated and recast as a single objective optimization problem by weighted aggregation. The driving performance requirements of the PHEV are considered as the constraints. In addition, to evaluate the objective function, the optimization process is performed over three typical driving cycles including Urban Dynamometer Driving Schedule (UDDS), Highway Fuel Economy Test (HWFET), and New European Driving Cycle (NEDC). The simulation results show the effectiveness of the proposed approach for reducing the fuel cost, EC and emissions while ensuring that the vehicle performance has not been sacrificed.

Keyword :

component sizes parallel chaos optimization algorithm (PCOA) Plug-in hybrid electric vehicle (PHEV) control strategy

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GB/T 7714 Wu, Xiaolan , Guo, Guifang , Xu, Jun et al. Application of Parallel Chaos Optimization Algorithm for Plug-in Hybrid Electric Vehicle Design [J]. | INTERNATIONAL JOURNAL OF BIFURCATION AND CHAOS , 2014 , 24 (1) .
MLA Wu, Xiaolan et al. "Application of Parallel Chaos Optimization Algorithm for Plug-in Hybrid Electric Vehicle Design" . | INTERNATIONAL JOURNAL OF BIFURCATION AND CHAOS 24 . 1 (2014) .
APA Wu, Xiaolan , Guo, Guifang , Xu, Jun , Cao, Binggang . Application of Parallel Chaos Optimization Algorithm for Plug-in Hybrid Electric Vehicle Design . | INTERNATIONAL JOURNAL OF BIFURCATION AND CHAOS , 2014 , 24 (1) .
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Design of a Novel Hybrid Power for EV EI CPCI-S Scopus
会议论文 | 2014 | IEEE Transportation Electrification Conference and Expo (ITEC Asia-Pacific)
SCOPUS Cited Count: 7
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Abstract :

A novel high efficient hybrid power with ultra-capacitor (UC) for electric vehicle (EV) is proposed in this paper. On this basis, the energy management strategies are designed according to various work modes of the EV. Two switch schemes could be achieved in the proposed hybrid power while discharging, and the UC would have the highest priority to recycle the energy in the recharge scheme. Simulation results show that the over-discharge of the batteries could be avoid and efficiency could be improved up to 3.5% in the proposed hybrid power, comparing to the equally improved hybrid power.

Keyword :

DC-DC converter energy management strategies electric vehicle (EV) hybrid power

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GB/T 7714 Wang, Bin , Xu, Jun , Cao, Binggang . Design of a Novel Hybrid Power for EV [C] . 2014 .
MLA Wang, Bin et al. "Design of a Novel Hybrid Power for EV" . (2014) .
APA Wang, Bin , Xu, Jun , Cao, Binggang . Design of a Novel Hybrid Power for EV . (2014) .
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Evaluation of Model Based State of Charge Estimation Methods for Lithium-Ion Batteries EI SCIE Scopus
期刊论文 | 2014 , 7 (8) , 5065-5082 | ENERGIES | IF: 2.072
WoS CC Cited Count: 32 SCOPUS Cited Count: 34
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Abstract :

Four model-based State of Charge (SOC) estimation methods for lithium-ion (Li-ion) batteries are studied and evaluated in this paper. Different from existing literatures, this work evaluates different aspects of the SOC estimation, such as the estimation error distribution, the estimation rise time, the estimation time consumption, etc. The equivalent model of the battery is introduced and the state function of the model is deduced. The four model-based SOC estimation methods are analyzed first. Simulations and experiments are then established to evaluate the four methods. The urban dynamometer driving schedule (UDDS) current profiles are applied to simulate the drive situations of an electrified vehicle, and a genetic algorithm is utilized to identify the model parameters to find the optimal parameters of the model of the Li-ion battery. The simulations with and without disturbance are carried out and the results are analyzed. A battery test workbench is established and a Li-ion battery is applied to test the hardware in a loop experiment. Experimental results are plotted and analyzed according to the four aspects to evaluate the four model-based SOC estimation methods.

Keyword :

sliding mode observer Luenberger observer proportional integral observer state of charge (SOC) battery management system (BMS) model-based estimation Kalman filter

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GB/T 7714 Zou, Zhongyue , Xu, Jun , Mi, Chris et al. Evaluation of Model Based State of Charge Estimation Methods for Lithium-Ion Batteries [J]. | ENERGIES , 2014 , 7 (8) : 5065-5082 .
MLA Zou, Zhongyue et al. "Evaluation of Model Based State of Charge Estimation Methods for Lithium-Ion Batteries" . | ENERGIES 7 . 8 (2014) : 5065-5082 .
APA Zou, Zhongyue , Xu, Jun , Mi, Chris , Cao, Binggang , Chen, Zheng . Evaluation of Model Based State of Charge Estimation Methods for Lithium-Ion Batteries . | ENERGIES , 2014 , 7 (8) , 5065-5082 .
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An online state of charge estimation method with reduced prior battery testing information EI SCIE Scopus
期刊论文 | 2014 , 63 , 178-184 | INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
WoS CC Cited Count: 28 SCOPUS Cited Count: 29
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Abstract :

An online State of Charge (SOC) estimation method with reduced prior battery testing information is proposed in this paper, in which no testing data obtained in laboratory is needed, including the relationship between the open circuit voltage (OCV) and the SOC. The first order RC battery model is utilized to interpret the characteristics of the lithium-ion battery. The genetic algorithm is introduced to carry out the online identification for the battery model. Parameters obtained by the identification are applied to the joint SOC estimation method to estimate the SOC of the battery. An experimental battery test workbench is established to validate the proposed method. Several drive cycle current profiles are scaled down and applied to the battery. The experiment results show that the parameters obtained by the proposed method could characterize the battery well, even for different drive cycles, and accurate SOC of the battery could be obtained online. (C) 2014 Elsevier Ltd. All rights reserved.

Keyword :

Online identification Genetic algorithm State of charge Battery management system Electric vehicle

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GB/T 7714 Xu, Jun , Cao, Binggang , Chen, Zheng et al. An online state of charge estimation method with reduced prior battery testing information [J]. | INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS , 2014 , 63 : 178-184 .
MLA Xu, Jun et al. "An online state of charge estimation method with reduced prior battery testing information" . | INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 63 (2014) : 178-184 .
APA Xu, Jun , Cao, Binggang , Chen, Zheng , Zou, Zhongyue . An online state of charge estimation method with reduced prior battery testing information . | INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS , 2014 , 63 , 178-184 .
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The State of Charge Estimation of Lithium-Ion Batteries Based on a Proportional-Integral Observer EI SCIE Scopus
期刊论文 | 2014 , 63 (4) , 1614-1621 | IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY | IF: 1.978
WoS CC Cited Count: 91 SCOPUS Cited Count: 104
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Abstract :

With the development of electric drive vehicles (EDVs), the state-of-charge (SOC) estimation for lithium-ion (Li-ion) batteries has become increasingly more important. Based on the analysis of some of the most popular model-based SOC estimation methods, the proportional-integral (PI) observer is proposed to estimate the SOC of lithium-ion batteries in EDVs. The structure of the proposed PI observer is analyzed, and the convergence of the estimation method with model errors is verified. To demonstrate the superiority and compensation properties of the proposed PI observer, the simple-structure RC battery model is utilized to model the Li-ion battery. To validate the results of the proposed PI-based SOC estimation method, the experimental battery test bench is established. In the validation, the urban dynamometer driving schedule (UDDS) drive cycle is utilized, and the PI-based SOC estimation results are found to agree with the reference SOC, generally within the 2% error band for both the known and unknown initial SOC cases.

Keyword :

Battery state of charge (SOC) electric vehicle sliding-mode observer lithium-ion (Li-ion) battery proportional-integral (PI) observer

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GB/T 7714 Xu, Jun , Mi, Chunting Chris , Cao, Binggang et al. The State of Charge Estimation of Lithium-Ion Batteries Based on a Proportional-Integral Observer [J]. | IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY , 2014 , 63 (4) : 1614-1621 .
MLA Xu, Jun et al. "The State of Charge Estimation of Lithium-Ion Batteries Based on a Proportional-Integral Observer" . | IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 63 . 4 (2014) : 1614-1621 .
APA Xu, Jun , Mi, Chunting Chris , Cao, Binggang , Deng, Junjun , Chen, Zheng , Li, Siqi . The State of Charge Estimation of Lithium-Ion Batteries Based on a Proportional-Integral Observer . | IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY , 2014 , 63 (4) , 1614-1621 .
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A Neural Network Enhanced Stereo Vision Obstacle Detection and Avoidance System for Unmanned Ground Vehicle CPCI-S
会议论文 | 2013 , 42 , 1-4 | 2nd International Conference on Advances in Computer Science and Engineering (CSE)
WoS CC Cited Count: 6
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Abstract :

This paper presents a neural network enhanced stereo vision obstacle detection and avoidance system for unmanned ground vehicle. We build a neural network to learn the mapping for the left image to the right image under the assumption of a flat road. Using the trained neural network we map the left image to the right directly and we get the left remapped image. So obstacles can be detected using correlation values between the right image and the left remapped image. With detection result the system tells the unmanned vehicle how to avoid obstacles. Our system does not require intrinsic calibration of stereo cameras and it does not perform the two IPM transforms. With neural network's parallel processing our system reduces the computation expense and increases the real-time performance. Experimental results show that our system is practicable.

Keyword :

stereo vision obstacle detection neural network unmanned ground vehicle

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GB/T 7714 Liu, Fanjun , Cao, Binggang . A Neural Network Enhanced Stereo Vision Obstacle Detection and Avoidance System for Unmanned Ground Vehicle [C] . 2013 : 1-4 .
MLA Liu, Fanjun et al. "A Neural Network Enhanced Stereo Vision Obstacle Detection and Avoidance System for Unmanned Ground Vehicle" . (2013) : 1-4 .
APA Liu, Fanjun , Cao, Binggang . A Neural Network Enhanced Stereo Vision Obstacle Detection and Avoidance System for Unmanned Ground Vehicle . (2013) : 1-4 .
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3D-modeling Depth Optimization Using a Neural Network and Image Segments EI CPCI-S Scopus
会议论文 | 2013 , 333-335 , 1096-1105 | 2nd International Conference on Measurement, Instrumentation and Automation (ICMIA 2013)
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Abstract :

We present a 3D(three-dimensional)-modeling disparity-map optimization algorithm using a neural network and image segments for stereo navigation. We decompose the optimization algorithm problem into two sub-problems: initial stereo matching and depth optimization. A two-step procedure is proposed to solve the sub-problems sequentially. The first step is a region based NCC(normalized cross-correlation) matching process. But we use fast Fourier transformation and inverse fast Fourier transformation to eliminate redundant calculations in NCC, and we create a high-confidence disparity map by cross checking. In the second step, the reference image (the left image of the inputted stereo pair) is segmented into regions according to homogeneous color. A neural network is then built to model the three dimensional surface and applied to refine disparities in each image segment. The experimental results obtained for Middlebury test datasets and real stereo road images indicate that our method is competitive with the best stereo matching algorithms currently available. In particular, the approach has significantly improved performance for road images used in navigation and the disparity maps recovered by our algorithm are similar to ground truth data.

Keyword :

depth optimization stereo vision neural network image segments

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GB/T 7714 Liu, Fanjun , Cao, Binggang . 3D-modeling Depth Optimization Using a Neural Network and Image Segments [C] . 2013 : 1096-1105 .
MLA Liu, Fanjun et al. "3D-modeling Depth Optimization Using a Neural Network and Image Segments" . (2013) : 1096-1105 .
APA Liu, Fanjun , Cao, Binggang . 3D-modeling Depth Optimization Using a Neural Network and Image Segments . (2013) : 1096-1105 .
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