scholarly journals Neural Network-Based Prediction of Vehicle Fuel Consumption Based on Driving Cycle Data

2022 ◽  
Vol 14 (2) ◽  
pp. 744
Author(s):  
Jakov Topić ◽  
Branimir Škugor ◽  
Joško Deur
Keyword(s):  
Neural Network ◽  
Fuel Consumption ◽  
Prediction Accuracy ◽  
Driving Cycle ◽  
Linear Regression Models ◽  
The Road ◽  
Routing Optimization ◽  
Fixed Dimension ◽  
Execution Speed ◽  
Road Slope

This paper deals with fuel consumption prediction based on vehicle velocity, acceleration, and road slope time series inputs. Several data-driven models are considered for this purpose, including linear regression models and neural network-based ones. The emphasis is on accounting for the road slope impact when forming the model inputs, in order to improve the prediction accuracy. A particular focus is devoted to conversion of length-varying driving cycles into fixed dimension inputs suitable for neural networks. The proposed prediction algorithms are parameterized and tested based on GPS- and CAN-based tracking data recorded on a number of city buses during their regular operation. The test results demonstrate that a proposed neural network-based approach provides a favorable prediction accuracy and reasonable execution speed, thus making it suitable for various applications such as vehicle routing optimization, synthetic driving cycle validation, transport planning and similar.

scholarly journals An Ecolevel Estimation Method of Individual Driver Performance Based on Driving Simulator Experiment

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Yiping Wu ◽  
Xiaohua Zhao ◽  
Ying Yao ◽  
Jian Rong
Keyword(s):  
Neural Network ◽  
Fuel Consumption ◽  
Prediction Accuracy ◽  
Driving Simulator ◽  
Model Simulation ◽  
Estimation Method ◽  
Training Sample ◽  
Backpropagation Neural Network ◽  
Driver Performance ◽  
Study Results

Accurately acquiring the ecolevel of individual driver performance is the precondition for more targeted ecodriving behavior optimization. Because of obvious advantage in mining hidden relationship, machine learning was adopted to explore the complicated relationship between driver performance and vehicle fuel consumption and thus to predict the ecolevel of individual driver performance in this study. Based on driving simulator tests, data of driver performance and vehicle fuel consumption were collected. The ecolevel was indicated as the ecoscore corresponding to vehicle fuel consumption. The model input was designed as 10 feature indexes of driver performance (e.g., percentage number, mean value, standard deviation, and power of applying acceleration pedal). The output was treated as ecoscore. Taking a number of one hundred of data segments in vehicle starting process as training sample, the optimal structure, functions, and learning rate of a backpropagation neural network model with three layers were obtained, after repeated model simulation experiments. The validation test of 16 sample data items showed that the mean prediction accuracy of our developed model was 92.89%. In addition, comparative analysis displayed that the performance of backpropagation neural network based model was better than linear regression based model and random forest based model, from the aspects of elapsed time and prediction accuracy in estimating the ecolevel of driver performance. The study results provide an effective method to grasp the ecolevel of driver performance and further contribute to driving behavior optimization towards vehicle fuel consumption and emissions reduction.

A driving-cycle predictive control approach for energy consumption optimization of hybrid electric vehicle

2021 ◽  
pp. 095440702110674
Author(s):  
Bingjiao Liu ◽  
Qin Shi ◽  
Zejia He ◽  
Yujiang Wei ◽  
Duoyang Qiu ◽  
...  
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Fuel Consumption ◽  
Control Strategy ◽  
Bp Neural Network ◽  
Driving Cycle ◽  
Electric System ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Hybrid Electric

This paper proposes an adaptive control strategy of fuel consumption optimization for hybrid electric vehicles (HEVs). The strategy combines a moving-horizon-based nonlinear autoregressive (NAR) algorithm, a backpropagation (BP) neural network algorithm, and an equivalent consumption minimization strategy (ECMS) method to reduce energy consumption. The moving-horizon-based NAR algorithm is applied to predict the short future driving cycle. The BP neural network algorithm is employed to recognize the driving cycle types, which provides the basis for the adaptive ECMS. Based on the abovementioned approach, the power split of the fuel and electric system is determined in advance, and the optimal control of energy efficiency is achieved. A driving experiment platform is established, taking a synthetic driving cycle composed of several standard driving cycles as the target cycle, and the control strategy is tested by the driver’s real operation. The results indicate that, compared with the basic ECMS, the A-ECMS with moving-horizon-based driving cycle prediction and recognition has better SOC (state of charge) retention and reduces the fuel consumption of the engine by 3.31%, the equivalent fuel consumption of the electric system by 0.9 L/100 km and the total energy consumption by 1 L/100 km. Adaptive ECMS based on driving cycle prediction and recognition is an effective method for the energy management of HEVs.

scholarly journals Study on Driving Cycle Synthesis Method for City Buses considering Random Passenger Load

2020 ◽  
Vol 2020 ◽  
pp. 1-21 ◽  
Author(s):  
Xiaodong Liu ◽  
Jian Ma ◽  
Xuan Zhao ◽  
Juan Du ◽  
Yanfeng Xiong
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Fuel Consumption ◽  
Real World ◽  
Markov Chain Model ◽  
Synthesis Method ◽  
Driving Cycle ◽  
Chain Model ◽  
Bus Stops ◽  
Road Slope

This paper proposes a novel driving cycle construction method in consideration of velocity, road slope, and passenger load, based on a real-world bus route with a plug-in hybrid electric bus (PHEB). The main purpose is to address the disadvantage that an inaccurate reflection of the real-world driving characteristics for city buses will be caused when ignoring the passenger load in the course of a driving cycle synthesis. Two contributions are supplemented to distinguish from the previous research. Firstly, a novel station-based method is proposed aiming at developing a driving cycle with high accuracy. The kinematic segments are partitioned according to the distance of adjacent bus stops, while a two-dimensional Markov chain Monte Carlo method is employed to synthesize driving cycle between each interval of adjacent bus stops. Secondly, the random passenger load for different bus stops is treated as a discrete Markov chain model, according to the correlation analysis of the measured passenger data which are distinguished for off-peak and peak hours. Meanwhile, Monte Carlo simulation and maximum likelihood estimation are utilized to determine the most likely number of passengers for each bus stop. At last, the fuel consumption of the PHEB is simulated with the best-synthesized driving cycle and contrasted to the mean fuel consumption of the later measured data which is composed of the velocity, road slope, and the passenger load. The results demonstrate that the synthesized driving cycle has a higher accuracy on fuel consumption estimation.

The Convolution Neural Network Combined with the HT Person Fit Statistic to Develop an APP for Detecting Dengue Fever in Children: Development and Usability Study (Preprint)

2019 ◽  
Author(s):  
CHIEN WEI ◽  
Chi Chow Julie ◽  
Chou Willy
Keyword(s):  
Neural Network ◽  
Dengue Fever ◽  
Prediction Accuracy ◽  
Early Stage ◽  
Family Members ◽  
Convolution Neural Network ◽  
Public Health Issue ◽  
Person Fit ◽  
Model Accuracy ◽  
Comparison Results

UNSTRUCTURED Backgrounds: Dengue fever (DF) is an important public health issue in Asia. However, the disease is extremely hard to detect using traditional dichotomous (i.e., absent vs. present) evaluations of symptoms. Convolution neural network (CNN), a well-established deep learning method, can improve prediction accuracy on account of its usage of a large number of parameters for modeling. Whether the HT person fit statistic can be combined with CNN to increase the prediction accuracy of the model and develop an application (APP) to detect DF in children remains unknown. Objectives: The aim of this study is to build a model for the automatic detection and classification of DF with symptoms to help patients, family members, and clinicians identify the disease at an early stage. Methods: We extracted 19 feature variables of DF-related symptoms from 177 pediatric patients (69 diagnosed with DF) using CNN to predict DF risk. The accuracy of two sets of characteristics (19 symptoms and four other variables, including person mean, standard deviation, and two HT-related statistics matched to DF+ and DF−) for predicting DF, were then compared. Data were separated into training and testing sets, and the former was used to predict the latter. We calculated the sensitivity (Sens), specificity (Spec), and area under the receiver operating characteristic curve (AUC) across studies for comparison. Results: We observed that (1) the 23-item model yields a higher accuracy rate (0.95) and AUC (0.94) than the 19-item model (accuracy = 0.92, AUC = 0.90) based on the 177-case training set; (2) the Sens values are almost higher than the corresponding Spec values (90% in 10 scenarios) for predicting DF; (3) the Sens and Spec values of the 23-item model are consistently higher than those of the 19-item model. An APP was subsequently designed to detect DF in children. Conclusion: The 23-item model yielded higher accuracy rates (0.95) and AUC (0.94) than the 19-item model (accuracy = 0.92, AUC = 0.90). An APP could be developed to help patients, family members, and clinicians discriminate DF from other febrile illnesses at an early stage.

scholarly journals An Effective Joint Soft-Sensing Strategy for Multi-Information under Diverse Vehicle Driving Scenarios

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 505
Author(s):  
Jianfeng Chen ◽  
Jiantian Sun ◽  
Shulin Hu ◽  
Yicai Ye ◽  
Haoqian Huang ◽  
...  
Keyword(s):  
Time Delay ◽  
Fast Response ◽  
Least Square ◽  
Accurate Information ◽  
Recursive Least Square ◽  
Soft Sensing ◽  
The Road ◽  
Vehicle Mass ◽  
Cubature Kalman Filter ◽  
Road Slope

A variety of accurate information inputs are of great importance for automotive control. In this paper, a novel joint soft-sensing strategy is proposed to obtain multi-information under diverse vehicle driving scenarios. This strategy is realized by an information interaction including three modules: vehicle state estimation, road slope observer and vehicle mass determination. In the first module, a variational Bayesian-based adaptive cubature Kalman filter is employed to estimate the vehicle states with the time-variant noise interference. Under the assumption of road continuity, a slope prediction model is proposed to reduce the time delay of the road slope observation. Meanwhile, a fast response nonlinear cubic observer is introduced to design the road slope module. On the basis of the vehicle states and road slope information, the vehicle mass is determined by a forgetting-factor recursive least square algorithm. In the experiments, a contrasted strategy is introduced to analyse and evaluate performance. Results declare that the proposed strategy is effective and has the advantages of low time delay, high accuracy and good stability.

scholarly journals Methods of Constructing Time Series for Predicting Local Time Scales by Means of a GMDH-Type Neural Network

2021 ◽  
Vol 11 (12) ◽  
pp. 5615
Author(s):  
Łukasz Sobolewski ◽  
Wiesław Miczulski
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Local Time ◽  
Time Scales ◽  
Time Scale ◽  
Prediction Accuracy ◽  
Prediction Horizon

Ensuring the best possible stability of UTC(k) (local time scale) and its compliance with the UTC scale (Universal Coordinated Time) forces predicting the [UTC-UTC(k)] deviations, the article presents the results of work on two methods of constructing time series (TS) for a neural network (NN), increasing the accuracy of UTC(k) prediction. In the first method, two prepared TSs are based on the deviations determined according to the UTC scale with a 5-day interval. In order to improve the accuracy of predicting the deviations, the PCHIP interpolating function is used in subsequent TSs, obtaining TS elements with a 1-day interval. A limitation in the improvement of prediction accuracy for these TS has been a too large prediction horizon. The introduction in 2012 of the additional UTC Rapid scale by BIPM makes it possible to shorten the prediction horizon, and the building of two TSs has been proposed according to the second method. Each of them consists of two subsets. The first subset is based on deviations determined according to the UTC scale, the second on the UTC Rapid scale. The research of the proposed TS in the field of predicting deviations for the Polish Timescale by means of GMDH-type NN shows that the best accuracy of predicting the deviations has been achieved for TS built according to the second method.

scholarly journals Neural Network Approach for Predicting Ship Speed and Fuel Consumption

2021 ◽  
Vol 9 (2) ◽  
pp. 119
Author(s):  
Lúcia Moreira ◽  
Roberto Vettor ◽  
Carlos Guedes Soares
Keyword(s):  
Neural Network ◽  
Fuel Consumption ◽  
Computing System ◽  
Fuel Oil ◽  
Network Computing ◽  
Peak Period ◽  
Neural Network Approach ◽  
Data Set ◽  
Navigation Data ◽  
Ship Speed

In this paper, simulations of a ship travelling on a given oceanic route were performed by a weather routing system to provide a large realistic navigation data set, which could represent a collection of data obtained on board a ship in operation. This data set was employed to train a neural network computing system in order to predict ship speed and fuel consumption. The model was trained using the Levenberg–Marquardt backpropagation scheme to establish the relation between the ship speed and the respective propulsion configuration for the existing sea conditions, i.e., the output torque of the main engine, the revolutions per minute of the propulsion shaft, the significant wave height, and the peak period of the waves, together with the relative angle of wave encounter. Additional results were obtained by also using the model to train the relationship between the same inputs used to determine the speed of the ship and the fuel consumption. A sensitivity analysis was performed to analyze the artificial neural network capability to forecast the ship speed and fuel oil consumption without information on the status of the engine (the revolutions per minute and torque) using as inputs only the information of the sea state. The results obtained with the neural network model show very good accuracy both in the prediction of the speed of the vessel and the fuel consumption.

The WLTC vs NEDC: A Case Study on the Impacts of Driving Cycle on Engine Performance and Fuel Consumption

2021 ◽  
Vol 18 (3) ◽  
pp. 9071-9081
Author(s):  
Jakub Lasocki
Keyword(s):  
Fuel Consumption ◽  
Combustion Engine ◽  
Engine Performance ◽  
Driving Cycle ◽  
Performance Characteristics ◽  
Pollutant Emission ◽  
Strong Impact ◽  
Light Duty ◽  
Light Duty Vehicles

The World-wide harmonised Light-duty Test Cycle (WLTC) was developed internationally for the determination of pollutant emission and fuel consumption from combustion engines of light-duty vehicles. It replaced the New European Driving Cycle (NEDC) used in the European Union (EU) for type-approval testing purposes. This paper presents an extensive comparison of the WLTC and NEDC. The main specifications of both driving cycles are provided, and their advantages and limitations are analysed. The WLTC, compared to the NEDC, is more dynamic, covers a broader spectrum of engine working states and is more realistic in simulating typical real-world driving conditions. The expected impact of the WLTC on vehicle engine performance characteristics is discussed. It is further illustrated by a case study on two light-duty vehicles tested in the WLTC and NEDC. Findings from the investigation demonstrated that the driving cycle has a strong impact on the performance characteristics of the vehicle combustion engine. For the vehicles tested, the average engine speed, engine torque and fuel flow rate measured over the WLTC are higher than those measured over the NEDC. The opposite trend is observed in terms of fuel economy (expressed in l/100 km); the first vehicle achieved a 9% reduction, while the second – a 3% increase when switching from NEDC to WLTC. Several factors potentially contributing to this discrepancy have been pointed out. The implementation of the WLTC in the EU will force vehicle manufacturers to optimise engine control strategy according to the operating range of the new driving cycle.

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