scholarly journals A Novel Closed-Loop System for Vehicle Speed Prediction Based on APSO LSSVM and BP NN

Energies ◽  
2021 ◽  
Vol 15 (1) ◽  
pp. 21
Author(s):  
Xiaokai Guo ◽  
Xianguo Yan ◽  
Zhi Chen ◽  
Zhiyu Meng
Keyword(s):  
Fuel Consumption ◽  
Closed Loop ◽  
Fitness Function ◽  
Critical Role ◽  
Support Vector ◽  
Vehicle Speed ◽  
Prediction System ◽  
Energy Management Strategy ◽  
Training Time ◽  
Speed Prediction

Vehicle speed prediction plays a critical role in energy management strategy (EMS). Based on the adaptive particle swarm optimization–least squares support vector machine (APSO-LSSVM) algorithm with BP neural network (BPNN), a novel closed-loop vehicle speed prediction system is proposed. The database of a vehicle internet platform was adopted to construct a speed prediction model based on the APSO-LSSVM algorithm. Furthermore, a BPNN is established according to the local high-precision nonlinear fitting relationship between the predicted value and error so as to correct the prediction value. Then, the results are returned to the APSO-LSSVM model for calculating the minimum fitness function, thus obtaining a closed-loop prediction system. Finally, equivalent fuel consumption minimization strategy (ECMS) based EMS was performed. According to the simulation results, the RMSE performance is 0.831 km/h within 5 s, which is over 20% higher than other performances. Additionally, the training time is 15 min within 5 s, which is advantageous over BPNN. Furthermore, fuel consumption increases by 6.95% compared with the dynamic-programming algorithm and decreased by 5.6%~10.9% compared with the low accuracy of speed prediction. Overall, the proposed method is crucial for optimizing EMS as it is not only effective in improving prediction accuracy but also capable of reducing training time.

scholarly journals Dual-Input and Multi-Channel Convolutional Neural Network Model for Vehicle Speed Prediction

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7767
Author(s):  
Jiaming Xing ◽  
Liang Chu ◽  
Chong Guo ◽  
Shilin Pu ◽  
Zhuoran Hou
Keyword(s):  
Neural Network ◽  
Fuel Consumption ◽  
Network Architecture ◽  
Prediction Method ◽  
Support Vector ◽  
Vehicle Speed ◽  
Energy Management Strategy ◽  
Equivalent Fuel ◽  
Speed Prediction ◽  
Equivalent Fuel Consumption

With the development of technology, speed prediction has become an important part of intelligent vehicle control strategies. However, the time-varying and nonlinear nature of vehicle speed increases the complexity and difficulty of prediction. Therefore, a CNN-based neural network architecture with two channel input (DICNN) is proposed in this paper. With two inputs and four channels, DICNN can predict the speed changes in the next 5 s by extracting the temporal information of 10 vehicle signals and the driver’s intention. The prediction performances of DICNN are firstly examined. The best RMSE, MAE, ME and R2 are obtained compared with a Markov chain combined with Monte Carlo (MCMC) simulation, a support vector machine (SVM) and a single input CNN (SICNN). Secondly, equivalent fuel consumption minimization strategies (ECMS) combining different vehicle speed prediction methods are constructed. After verification by simulation, the equivalent fuel consumption of the simulation increases by only 4.89% compared with dynamic-programming-based energy management strategy and decreased by 5.40% compared with the speed prediction method with low accuracy.

DESIGN OF CONTROL SYSTEM FOR MOTOR IMAGERY BASED NEURO-AID APPLICATION

2019 ◽  
Vol 31 (04) ◽  
pp. 1950031
Author(s):  
Gauri Shanker Gupta ◽  
Maanvi Bhatnagar ◽  
Subhojit Ghosh ◽  
Rakesh Kumar Sinha
Keyword(s):  
Motor Imagery ◽  
Closed Loop ◽  
Open Loop ◽  
Stepper Motor ◽  
Support Vector ◽  
Loop Analysis ◽  
Training Time ◽  
Brain Signals ◽  
And Performance ◽  
Learning Machine

The application of Brain Computer Interface (BCI) for rehabilitation purpose has gained wide popularity in recent times. BCI for rehabilitation involves detection of brain signals, when the subject performs some sort of Motor Imagery (MI) task, for example, imagination of movement of limbs. Imagination of such movement causes desynchronization of neurons of one part of the brain gets within other parts synchronized. Band power features are best suited for quantification of the synchronization phenomenon. In the present work, extreme learning machine (ELM) and support vector machine (SVM) based classifiers are used to classify the test data. The classifier output is further used to generate control signals for driving a stepper motor, which may be used to drive some neuro-aid application device. In order to achieve a workable model for pragmatic applications, it is necessary to design a robust in nature stepper motor. Open loop analysis, closed loop analysis and performance analysis of motor with possible disturbances are carried out to evaluate the effectiveness of the proposed work. The maximum accuracy using ELM and SVM classifiers are achieved as 90% and 87.78% with a training time of 0.2496[Formula: see text]s and 3.964[Formula: see text]s, respectively. In the open loop and closed loop analysis, the desired angular movement (task imagined for rehabilitation) is achieved with an accuracy of 54.14% and 93.4%, respectively. These results suggest that a BCI system can be designed with higher efficiency with the help of MI data.

Eco-Driving of Connected and Automated Vehicle With Preceding Driver Behavior Prediction

2020 ◽  
Vol 143 (1) ◽  
Author(s):  
Mehmet Fatih Ozkan ◽  
Yao Ma
Keyword(s):  
Fuel Consumption ◽  
Traffic Safety ◽  
Fuel Economy ◽  
Vehicle Speed ◽  
Cruise Control ◽  
The Road ◽  
Comparison Results ◽  
Preceding Vehicle ◽  
Speed Prediction ◽  
On The Road

Abstract The development of vehicle connectivity and autonomy in the ground transportation sector is not only able to enhance traffic safety and driving comfort as well as fuel economy. This study presents a receding-horizon optimization-based control strategy integrated with the preceding vehicle speed prediction model to achieve an eco-driving strategy for connected and automated vehicles (CAVs). In the real traffic scenario where the CAV follows the preceding vehicle on the road, a gated recurrent unit (GRU) network is used to predict the behavior of the preceding vehicle by utilizing the historical inter-vehicle information collected through on-board sensors. Then, a nonlinear model predictive control (NMPC) algorithm is adopted for CAV to minimize the accumulated fuel consumption within the preview horizon. The NMPC approach solves the fuel-optimal speed profile of the CAV, considering a predicted short-term speed preview of the preceding vehicle. With the awareness of the preview speed conditions, the fuel consumption of the CAV is reduced by avoiding unnecessary braking and acceleration, especially during transient traffic conditions. The Pareto front framework is used to examine a trade-off between the vehicle speed prediction accuracy, computational burden, and the fuel consumption of the CAV in the proposed GRU-NMPC design. To analyze the effectiveness of the GRU-NMPC design, adaptive cruise control with constant time headway policy (ACC-CTH) is adopted as a benchmark control design. Comparison results show significant fuel economy improvement of the proposed design and expose possible fuel benefits from vehicle autonomy and sensor fusion technology.

Optimal Design of Propulsion System Configuration for Electrically Propelled Unmanned Surface Vehicle

2020 ◽  
pp. 1-10
Author(s):  
Liuqing Xiong ◽  
Haibo Gao ◽  
Rosemary Norman ◽  
Kayvan Pazouki ◽  
Zhiguo Lin ◽  
...  
Keyword(s):  
Fuel Consumption ◽  
Electric Propulsion ◽  
High Energy ◽  
Propulsion System ◽  
Support Vector ◽  
System Configuration ◽  
Energy Management Strategy ◽  
Equipment Selection ◽  
Unmanned Surface Vehicles ◽  
Multiobjective Particle Swarm Optimization

Unmanned surface vehicles (USVs) are vessels that operate without any crew on-board. There is an increased demand for USVs in recent years, particularly for the use of water quality monitoring and ocean data mapping. In China, USVs are widely used as a luring fish boat which acts as the assisting boat of light luring seine vessel. One of the main problems of such boat is that the traditional propulsion system is poorly matched with the high energy consumption that is required during certain specific operation, which results in poor vessel performance. A hybrid electric propulsion system configuration solution is proposed to increase the overall propulsion efficiency of such USVs. The typical operating profile was identified and a comprehensive simulation was conducted to demonstrate the compatibility during vessel operations. An intelligent equipment selection analysis was also carried out to recommend the optimal equipment selection by considering a multiobjective problem. The result shows that the configuration solution proposed can reduce fuel consumption and the optimal intelligent selection method can provide a suitable selection solution for decision makers. This article highlights an energy management strategy focusing on the threshold method based on support vector machine pattern recognition. A multiobjective particle swarm optimization algorithm based on the dynamic inertia weight and chaotic motion was used to optimize the equipment selection by considering fuel consumption and emissions. The proposed propulsion system configuration and equipment selection solution can be implemented for the design of USVs, which has a routine fixed operating pattern. Introduction Unmanned surface vehicles (USVs) are vessels that operate on water without any on-board personnel (Manley et al. 2008). Research on unmanned vessels is on the rise, especially for the purpose to enhance safety (Burmeister et al. 2014). USVs are typically used for vessels for specific missions, such as for scientific research, environmental missions, ocean resource exploration, and military uses (Liu et al. 2016). A large number of USVs are operated in the coastal region where emissions from the vessels have shown to have direct impact on the health of the population (Viana et al. 2014). In view of this, the selection of main propulsion system of such vessels is important, as the system plays the role of having the biggest impact on emissions. Tighter regulations arise from environmental concerns, which move design and operation to greener ships and more optimal designs. With the increase in concerns of climate change, new ship design is incorporating greener technology that promotes energy saving and emission reduction. The hybrid power system is one of the major developments toward the design of greener ships and has been gradually applied on offshore supply ships, inland river ships, and sightseeing ships (Qian et al. 2013; Gao et al. 2017; Xia et al. 2017).

scholarly journals Energy Management Strategy Based on a Novel Speed Prediction Method

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8273
Author(s):  
Jiaming Xing ◽  
Liang Chu ◽  
Zhuoran Hou ◽  
Wen Sun ◽  
Yuanjian Zhang
Keyword(s):  
Neural Network ◽  
Energy Management ◽  
Management Strategy ◽  
Prediction Method ◽  
Parallel Structure ◽  
Vehicle Speed ◽  
Energy Management Strategy ◽  
Highly Nonlinear ◽  
The Future ◽  
Speed Prediction

Vehicle speed prediction can obtain the future driving status of a vehicle in advance, which helps to make better decisions for energy management strategies. We propose a novel deep learning neural network architecture for vehicle speed prediction, called VSNet, by combining convolutional neural network (CNN) and long-short term memory network (LSTM). VSNet adopts a fake image composed of 15 vehicle signals in the past 15 s as model input to predict the vehicle speed in the next 5 s. Different from the traditional series or parallel structure, VSNet is structured with CNN and LSTM in series and then in parallel with two other CNNs of different convolutional kernel sizes. The unique architecture allows for better fitting of highly nonlinear relationships. The prediction performance of VSNet is first examined. The prediction results show a RMSE range of 0.519–2.681 and a R2 range of 0.997–0.929 for the future 5 s. Finally, an energy management strategy combined with VSNet and model predictive control (MPC) is simulated. The equivalent fuel consumption of the simulation increases by only 4.74% compared with DP-based energy management strategy and decreased by 2.82% compared with the speed prediction method with low accuracy.

scholarly journals Energy Management Strategy of a Hybrid Power System Based on V2X Vehicle Speed Prediction

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5370
Author(s):  
Ming Ye ◽  
Jing Chen ◽  
Xu Li ◽  
Kai Ma ◽  
Yonggang Liu
Keyword(s):  
Energy Management ◽  
Control Strategy ◽  
Model Simulation ◽  
Management Control ◽  
Vehicle Speed ◽  
Energy Management Strategy ◽  
State Data ◽  
Traffic State ◽  
Speed Prediction ◽  
Energy Economy

Energy consumption in vehicle driving is greatly influenced by traffic scenarios, and the intelligent traffic system (ITS) has a key role in solving the real-time optimal control of hybrid vehicles. To this end, a new energy management control strategy based on vehicle-to-everything (V2X) communication for vehicle speed prediction was proposed to dynamically adjust the engine and motor power output according to the traffic conditions. This study is based on intelligent network connectivity technology to obtain forward traffic state data and use a deep learning algorithm to model vehicle speed prediction using the traffic state data. The energy economy function was modeled using the MATLAB/Sinumlink platform and validated with a plug-in hybrid vehicle model simulation. The results indicate that the proposed strategy improves the vehicle energy economy by 13.02% and reduces CO2 emissions by 16.04% under real vehicle driving conditions, compared with the conventional logic threshold-based control strategy.

Stirling system optimization for series hybrid electric vehicles

2021 ◽  
pp. 095440702110180
Author(s):  
Charbel R Ghanem ◽  
Elio N Gereige ◽  
Wissam S Bou Nader ◽  
Charbel J Mansour
Keyword(s):  
Fuel Consumption ◽  
Hybrid Electric Vehicle ◽  
Combustion Engine ◽  
Optimization Technique ◽  
System Optimization ◽  
Energy Management Strategy ◽  
Fuel Savings ◽  
Auxiliary Power ◽  
Hybrid Electric ◽  
External Combustion

There have been many studies conducted to replace the conventional internal combustion engine (ICE) with a more efficient engine, due to increasing regulations over vehicles’ emissions. Throughout the years, several external combustion engines were considered as alternatives to these traditional ICEs for their intrinsic benefits, among which are Stirling machines. These were formerly utilized in conventional powertrains; however, they were not implemented in hybrid vehicles. The purpose of this study is to investigate the possibility of implementing a Stirling engine in a series hybrid electric vehicle (SHEV) to substitute the ICE. Exergy analysis was conducted on a mathematical model, which was developed based on a real simple Stirling, to pinpoint the room for improvements. Then, based on this analysis, other configurations were retrieved to reduce exergy losses. Consequently, a Stirling-SHEV was modeled, to be integrated as auxiliary power unit (APU). Hereafter, through an exergo-technological detailed selection, the best configuration was found to be the Regenerative Reheat two stages serial Stirling (RRe-n2-S), offering the best efficiency and power combination. Then, this configuration was compared with the Regenerative Stirling (R-S) and the ICE in terms of fuel consumption, in the developed SHEV on the WLTC. This was performed using an Energy Management Strategy (EMS) consisting of a bi-level optimization technique, combining the Non-dominated Sorting Genetic Algorithm (NSGA) with the Dynamic Programming (DP). This arrangement is used to diminish the fuel consumption, while considering the reduction of the APU’s ON/OFF switching times, avoiding technical issues. Results prioritized the RRe-n2-S presenting 12.1% fuel savings compared to the ICE and 14.1% savings compared to the R-S.

scholarly journals CAFD: Context-Aware Fault Diagnostic Scheme towards Sensor Faults Utilizing Machine Learning

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 617
Author(s):  
Umer Saeed ◽  
Young-Doo Lee ◽  
Sana Ullah Jan ◽  
Insoo Koo
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Diagnostic System ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Context Aware ◽  
Sensor Faults ◽  
Training Time ◽  
Low Intensity ◽  
Fault Diagnostic

Sensors’ existence as a key component of Cyber-Physical Systems makes it susceptible to failures due to complex environments, low-quality production, and aging. When defective, sensors either stop communicating or convey incorrect information. These unsteady situations threaten the safety, economy, and reliability of a system. The objective of this study is to construct a lightweight machine learning-based fault detection and diagnostic system within the limited energy resources, memory, and computation of a Wireless Sensor Network (WSN). In this paper, a Context-Aware Fault Diagnostic (CAFD) scheme is proposed based on an ensemble learning algorithm called Extra-Trees. To evaluate the performance of the proposed scheme, a realistic WSN scenario composed of humidity and temperature sensor observations is replicated with extreme low-intensity faults. Six commonly occurring types of sensor fault are considered: drift, hard-over/bias, spike, erratic/precision degradation, stuck, and data-loss. The proposed CAFD scheme reveals the ability to accurately detect and diagnose low-intensity sensor faults in a timely manner. Moreover, the efficiency of the Extra-Trees algorithm in terms of diagnostic accuracy, F1-score, ROC-AUC, and training time is demonstrated by comparison with cutting-edge machine learning algorithms: a Support Vector Machine and a Neural Network.

Hierarchical energy management strategy considering switching schedule for a dual-mode hybrid electric vehicle

2021 ◽  
pp. 095440702110297
Author(s):  
Hui Liu ◽  
Rui Liu ◽  
Riming Xu ◽  
Lijin Han ◽  
Shumin Ruan
Keyword(s):  
Fuel Consumption ◽  
Energy Management ◽  
Control Strategy ◽  
Power Distribution ◽  
Hybrid Electric Vehicle ◽  
Hierarchical Control ◽  
Management Problem ◽  
Energy Management Strategy ◽  
Dual Mode ◽  
Hybrid Electric

Energy management strategies are critical for hybrid electric vehicles (HEVs) to improve fuel economy. To solve the dual-mode HEV energy management problem combined with switching schedule and power distribution, a hierarchical control strategy is proposed in this paper. The mode planning controller is twofold. First, the mode schedule is obtained according to the mode switch map and driving condition, then a switch hunting suppression algorithm is proposed to flatten the mode schedule through eliminating unnecessary switch. The proposed algorithm can reduce switch frequency while fuel consumption remains nearly unchanged. The power distribution controller receives the mode schedule and optimizes power distribution between the engine and battery based on the Radau pseudospectral knotting method (RPKM). Simulations are implemented to verify the effectiveness of the proposed hierarchical control strategy. For the mode planning controller, as the flattening threshold value increases, the fuel consumption remains nearly unchanged, however, the switch frequency decreases significantly. For the power distribution controller, the fuel consumption obtained by RPKM is 4.29% higher than that of DP, while the elapsed time is reduced by 92.53%.

An application of backtracking search optimization–based least squares support vector machine for prediction of short-term wind speed

2019 ◽  
Vol 44 (3) ◽  
pp. 266-281 ◽  
Author(s):  
Zhongda Tian ◽  
Yi Ren ◽  
Gang Wang
Keyword(s):  
Support Vector Machine ◽  
Wind Speed ◽  
Prediction Model ◽  
Least Squares ◽  
Support Vector ◽  
Short Term ◽  
Backtracking Search ◽  
Wind Speed Prediction ◽  
Search Optimization ◽  
Speed Prediction

Wind speed prediction is an important technology in the wind power field; however, because of their chaotic nature, predicting wind speed accurately is difficult. Aims at this challenge, a backtracking search optimization–based least squares support vector machine model is proposed for short-term wind speed prediction. In this article, the least squares support vector machine is chosen as the short-term wind speed prediction model and backtracking search optimization algorithm is used to optimize the important parameters which influence the least squares support vector machine regression model. Furthermore, the optimal parameters of the model are obtained, and the short-term wind speed prediction model of least squares support vector machine is established through parameter optimization. For time-varying systems similar to short-term wind speed time series, a model updating method based on prediction error accuracy combined with sliding window strategy is proposed. When the prediction model does not match the actual short-term wind model, least squares support vector machine trains and re-establishes. This model updating method avoids the mismatch problem between prediction model and actual wind speed data. The actual collected short-term wind speed time series is used as the research object. Multi-step prediction simulation of short-term wind speed is carried out. The simulation results show that backtracking search optimization algorithm–based least squares support vector machine model has higher prediction accuracy and reliability for the short-term wind speed. At the same time, the prediction performance indicators are also improved. The prediction result is that root mean square error is 0.1248, mean absolute error is 0.1374, mean absolute percentile error is 0.1589% and R2 is 0.9648. When the short-term wind speed varies from 0 to 4 m/s, the average value of absolute prediction error is 0.1113 m/s, and average value of absolute relative prediction error is 8.7111%. The proposed prediction model in this article has high engineering application value.

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