Nonlinear Autoregressive Neural Network Research Articles

Seamless Micro-Electro-Mechanical System-Inertial Navigation System/Polarization Compass Navigation Method with Data and Model Dual-Driven Approach.

The integration of micro-electro-mechanical system-inertial navigation systems (MEMS-INSs) with other autonomous navigation sensors, such as polarization compasses (PCs) and geomagnetic compasses, has been widely used to improve the navigation accuracy and reliability of vehicles in Internet of Things (IoT) applications. However, a MEMS-INS/PC integrated navigation system suffers from cumulative errors and time-varying measurement noise covariance in unknown, complex occlusion, and dynamic environments. To overcome these problems and improve the integrated navigation system's performance, a dual data- and model-driven MEMS-INS/PC seamless navigation method is proposed. This system uses a nonlinear autoregressive neural network (NARX) based on the Gauss-Newton Bayesian regularization training algorithm to model the relationship between the MEMS-INS outputs composed of the specific force and angular velocity data and the PC heading's angular increment, and to fit the integrated navigation system's dynamic characteristics, thus realizing data-driven operation. In the model-driven part, a nonlinear MEMS-INS/PC loosely coupled navigation model is established, the variational Bayesian method is used to estimate the time-varying measurement noise covariance, and the cubature Kalman filter method is then used to solve the nonlinear problem in the model. The robustness and effectiveness of the proposed method are verified experimentally. The experimental results show that the proposed method can provide high-precision heading information stably in complex, occluded, and dynamic environments.

Deep evolutionary fusion neural network: a new prediction standard for infectious disease incidence rates

BackgroundPreviously, many methods have been used to predict the incidence trends of infectious diseases. There are numerous methods for predicting the incidence trends of infectious diseases, and they have exhibited varying degrees of success. However, there are a lack of prediction benchmarks that integrate linear and nonlinear methods and effectively use internet data. The aim of this paper is to develop a prediction model of the incidence rate of infectious diseases that integrates multiple methods and multisource data, realizing ground-breaking research.ResultsThe infectious disease dataset is from an official release and includes four national and three regional datasets. The Baidu index platform provides internet data. We choose a single model (seasonal autoregressive integrated moving average (SARIMA), nonlinear autoregressive neural network (NAR), and long short-term memory (LSTM)) and a deep evolutionary fusion neural network (DEFNN). The DEFNN is built using the idea of neural evolution and fusion, and the DEFNN + is built using multisource data. We compare the model accuracy on reference group data and validate the model generalizability on external data. (1) The loss of SA-LSTM in the reference group dataset is 0.4919, which is significantly better than that of other single models. (2) The loss values of SA-LSTM on the national and regional external datasets are 0.9666, 1.2437, 0.2472, 0.7239, 1.4026, and 0.6868. (3) When multisource indices are added to the national dataset, the loss of the DEFNN + increases to 0.4212, 0.8218, 1.0331, and 0.8575.ConclusionsWe propose an SA-LSTM optimization model with good accuracy and generalizability based on the concept of multiple methods and multiple data fusion. DEFNN enriches and supplements infectious disease prediction methodologies, can serve as a new benchmark for future infectious disease predictions and provides a reference for the prediction of the incidence rates of various infectious diseases.

Power sharing enhancement through a decentralized droop-based control strategy in an islanded microgrid

A decentralized intelligent droop-based control strategy is proposed in this paper for the enhancement of equal active and reactive power sharing in an islanded inverter-based microgrid. Droop control is mostly preferred because it does not need communication facilities for its implementation. However, the presence of different feeder impedances for the different distributed generators (DGs) in a microgrid make reactive power sharing inaccurate. Furthermore, as a result of considerable load changes and different droop characteristics for the DGs, the inverter's output voltage and frequency are altered and these in turn alter the reactive and active power sharing respectively. With these conditions, the generalized droop control (GDC) strategy fails to effectively share load reactive and active power among the DGs. In order to extenuate these challenges, this paper presents a nonlinear autoregressive exogenous neural network droop-based control (NARX-NN DBC) strategy which does not depend on the varying line impedances, droop gains for the different DGs and is less affected by fluctuating loads in the grid. A microgrid, made up of two DGs and a load is modelled in MATLAB/Simulink environment and validation of the proposed control strategy is done through many simulations. Within the simulation runtime, NARX-NN DBC yielded a maximum frequency percentage deviation of 0.46% from the nominal value of 50 Hz whereas GDC yielded 0.62%. Regarding the voltage, NARX-NN DBC gave maximum deviation of 0.026% meanwhile GDC gave 0.079% from the nominal value for 380 V. In addition, during 0.57–0.64 s with load active power demand of 4.6 kW, NARX-NN DBC registered 0.43% power sharing error whereas GDC registered 6.5%. On the other hand, during 0.57–0.64 s, with 5kVAr load power demand, NARX-NN DBC registered 0.2% whereas, GDC registered 2%. These obtained results clearly show that NARX-NN DBC strategy has a better performance compared to GDC strategy with respect to power sharing in an autonomous microgrid.

Huizhou resident population, Guangdong resident population and elderly population forecast based on the NAR neural network Markov model

<abstract> <p>We propose a nonlinear auto regressive neural network Markov model (NARMKM) to predict the annual Huizhou resident population, Guangdong resident population and elderly population in China, and improve the accuracy of population forecasting. The new model is built upon the traditional neural network model and utilized matrix perturbation theory to study the natural and response characteristics of a system when the structural parameters change slightly. The delay order and hidden layer number of neurons has a greater effect the prediction result of NAR neural network model. Therefore, we make full use of prior information to constrain and test when making predictions. We choose reasonable parameter settings to obtain more reliable prediction results. Three experiments are conducted to validate the high prediction accuracy of the NARMKM model, with mean absolute percentage error (MAPE), root mean square error (RMSE), <italic>STD</italic> and <italic>R</italic><sup>2</sup>. These results demonstrate the superior fitting performance of the NARMKM model when compared to other six competitive models, including GM (1, 1), ARIMA, Multiple regression, FGM (1, 1), FANGBM and NAR. Our study provides a scientific basis and technical references for further research in the finance as well as population fields.</p> </abstract>

Retracted: Nonlinear Autoregressive Neural Network for Antimicrobial Waste Water Treatment

Retracted: Nonlinear Autoregressive Neural Network for Antimicrobial Waste Water Treatment

Coordinated adaptive cruise control with integration of driving behaviors based on prediction for surrounding vehicles status

The adaptive cruise is an important vehicle control process for unmanned vehicles. This paper studies coordinated adaptive cruise control (ACC) of energy-saving in an intelligent networked environment, aiming to integrate control for common driving behaviors (cruising, following, lane-changing) by hierarchical control. The decision-making layer for integrating three driving behaviors is designed to improve the economy and passing time. Respectively, cruising strategy is optimized by considering efficient working area of motor; following strategy is constructed by dynamically adjusting the following distance with the state changes of the surrounding vehicles; lane-changing strategy is developed by considering the motor working efficiency of target speed. To improve the predictability of decision-making, non-linear autoregressive neural network (NARX) is introduced to predict the surrounding vehicle state. Furthermore, receiving the control reference from the decision layer, the controller is designed to realizes the tracking control of vehicle speed and following distance based on model predictive control (MPC), which is co-optimized for tracking, economy, and comfort. Eventually, the effectiveness of the coordinated ACC control is verified based on the hardware in the loop (HiL) test. The experiment results show that the vehicles with coordinated ACC can shorten the passing time and reduce the driving energy consumption to a certain extent, especially in the medium and low speed conditions.

Nonlinear autoregression-based non-destructive evaluation approach for railway tracks using an ultrasonic fiber bragg grating array

The safe operation of railway systems relies on rapid and accurate non-destructive damage detection and evaluation techniques to help rule out potential threats brought by rail track damages. In this paper, an innovative fiber Bragg grating (FBG) array-based sensing system is developed for rail defect inspection by integrating the ultrasonic testing technology, and a nonlinear autoregressive neural network with exogenous inputs (NARX) is proposed for rail damage detection based on measured ultrasonic signals. The developed sensing system can realize the high-speed and multiplexed ultrasonic signals demodulation. The NARX is proposed to process the ultrasonic signals obtained by the developed sensing system, and a specified probabilistic damage sensitive feature (DSF) is tailored to indicate rail health status, derived from the probability density function (PDF) of NARX prediction errors. To verify the proposed sensing system and damage identification algorithm, experimental studies considering four rail conditions (i.e., intact, cracked, bump, and welded conditions) are conducted in this study. First, baseline NARX models are priorly trained using ultrasonic signals acquired on an intact rail segment; then, DSFs corresponding to each condition are calculated enabling effective rail damage diagnosis. To optimize the training parameters of NARX models, three different training functions are utilized to select the most appropriate parameters that are sensitive to rail damages. Last, large-scale testing is conducted, further proving the applicability and reliability of the proposed rail damage evaluation approach. Through this study, it is suggested that integrating high-performance sensing technologies with effective data processing tools would maximumly enable efficient and robust non-destructive testing, especially on important infrastructures such as rails.

การพยากรณ์ยอดขายและความต้องการสินค้าในอุตสาหกรรมผลิตอุปกรณ์ไฟฟ้า กรณีศึกษา บริษัทผลิตอุปกรณ์ไฟฟ้าแรงสูง

The purpose of this study is to find the product demand forecasting model from the artificial neural network method in a case study of a high-voltage equipment manufacturing company. And reduce fines for late delivery of goods by using the Matlab program. The forecasting model is established by using time series neural network method, and the nonlinear autoregressive neural network (NARX) function model is established by combining with external input data. Then the forecast deviation is the average deviation percentage error (MAPE) uses the least dynamics model. By using data that collects product sales from 2019 – 2022 total 48 set of data are divided into 2 data sets used in the study. by the first dataset is learning 36 datasets and the second dataset is learning 12 datasets. The model of this study was defined with the number of hidden layer nodes as 2, 6 and 10. The time delay was 2 and 3. Forecasting models are combined into 6 models. The best model result of each item from NARX forecast through the learning process Levenberg-Marquardt (trainlm), including models 1, 4, 5, 4, 3, 3, and 2 sequential The results show the least average absolute percentage error at 8.32, 5.85, 2.61, 3.39, 2.91, 2.19 and 7.76 and the results of the forecasting model were analyzed to reduce fines from selling all 7 items Able to reduce penalties for late delivery of 4 items showing the percentage of difference in cost reduction equal to 0.28% 0.24% 0.32% and 0.22%. It can be concluded that the prediction of the neural network inverted neural network model It can be used to forecast product sales and product demand as well. And reduce the cost of late delivery fines for better results.

Automated Pricing and Replenishment Decision Modeling for Supermarket Vegetables

This paper analyzes the historical sales data of vegetable products in a fresh food supermarket, and explores the automatic pricing and replenishment decision-making scheme of vegetable products in this supermarket. Firstly, we integrate and clean the attached data, and then we solve the characteristics and interrelationships of individual products and categories by establishing SARIMA model and Spearman's correlation coefficient model, and then we use nonlinear regression model, nonlinear autoregressive neural network model and optimization model to realize automatic pricing and replenishment decision-making for vegetable products.

Forecasting the consumptions of coagulation tests using a deep learning model.

Laboratory professionals aim to provide a reliable laboratory service using public resources efficiently while planning a test's procurement. This intuitive approach is ineffective, as seen in the COVID-19 pandemic, where the dramatic changes in admissions (e.g. decreased patient admissions) and the purpose of testing (e.g. D-dimer) were experienced. A model based on objective data was developed that predicts the future test consumption of coagulation tests whose consumptions were highly variable during the pandemic. Between December 2018 and July 2021, monthly consumptions of coagulation tests (PTT, aPTT, D-dimer, fibrinogen), total-, inpatient-, outpatient-, emergency-, non-emergency -admission numbers were collected. The relationship between input and output is modeled with an external input nonlinear autoregressive artificial neural network (NARX) using the MATLAB program. Monthly test consumption between January and July 2021 was used to test the power of the forecasting model.

Prediction of tail biting in pigs using partial least squares regression and artificial neural networks

Tail biting in pig farming causes economic losses as well as pain and suffering to the affected pigs. The aim of the study was to predict tail lesions to provide the farmer with the opportunity to intervene early. To do this, tail lesions were recorded during rearing (REA) and fattening (FAT) on two farms (Farm 1: 10 batches REA, 5 batches FAT; Farm 2: 7 batches REA, 4 batches FAT). In addition, tail posture, skin lesions, musculoskeletal system issues, treatment index for suckling, weight at weaning, water consumption, activity time, humidity, NH3 and CO2 concentrations were observed. Processed to daily values forming a time series, these data was used to predict the prevalence of tail lesions per pen using partial least squares (PLS) regression models, focused time-delay neural networks (FTDNN) and nonlinear autoregressive neural networks with exogenous inputs (NARX). After training, validating and testing the methods for Farm 1, the respective methods were applied to the data of Farm 2. In addition, it was investigated whether the prevalence of tail lesions per pen could be predicted for an offset of one to five days before with PLS. The networks were used to test whether a delay of one to five days before should be included for prediction. NARX achieved the best prediction performance on both farms in REA and FAT: accuracy between true and estimated values (R 0.85–0.97), recall (REC 0.63–0.97), false positive rate (FPR 0.01–0.27), precision (PRC 0.43–0.92), F1-score (0.54–0.94) and area under the precision-recall curve (AUPRC 0.66–0.97). PLS (R 0.49–0.81, REC 0.32–0.86, FPR 0.02–0.36, PRC 0.42–0.70, F1-score 0.40–0.77, AUPRC 0.43–0.85) and FTDNN (R 0.45–0.90, REC 0.19–1.00, FPR 0.00–0.97, PRC 0.30–0.84, F1-score 0.28–0.75, AUPRC 0.30–0.86) performed less accurately than NARX but still with sufficient prediction performance, whereby the results for Farm 2 were worse than for Farm 1. Therefore, a generalisation of the methods to other farms is possible. No noticeable difference was observed between offsets 1 to 5 and delays 1 to 5. PLS, FTDNN and NARX with all offsets and delays are suitable for predicting the prevalence of tail lesions per pen in several farms as well as in REA and FAT.

Mitigating network adaptation and QoT prediction challenges in WDM networks

Abstract The capacity and efficiency of optical communication networks have been completely transformed by wavelength division multiplexing (WDM) technology, which allows many wavelengths to be transmitted simultaneously over a single optical fibre. Conventional QoT prediction is based on analytical models that consider physical layer characteristics including dispersion, optical power and signal-to-noise ratio. But these models frequently oversimplify complex real-world situations, which reduces their accuracy for modern high-speed WDM networks. A data-driven solution is provided by machine learning(ML), which may boost the accuracy of QoT predictions by utilising real-time measurements, historical data and a variety of network situations. The creation of a ML-based framework for QoT prediction is investigated in the current research. This research proposes an effective ML-based routing computation model that uses a non-linear autoregressive recurrent neural network (ML-RCNA-RNN) to ensure QoT for every wavelength channel in high-capacity and high-speed WDM networks. Through simulations, more accurate QoT metrics, such as bit error rate (BER) 68.42 %, QoT prediction accuracy (Q-Factor) 5.9 %, network adaption time (ms) 48.3 %, latency (ms) 0.28 % and throughput (Gbps) 14.29 %, have been obtained compared to conventional QoT predictions. These results were obtained using Gaussian noise Python simulation (GNPy). As a result, the proposed framework that makes use of GNPy demonstrates that it substantially enhances optical communication networks’ performance and dependability. This facilitates the development of high-capacity, low-latency and reliable communication infrastructure, and makes it more adaptable and able to manage the complexity of high-speed WDM optical networks while preserving signal quality in the modern digital era.

Data driven modeling advancements for air temperature predictions in data centers

While data center cooling energy usage optimization studies have been performed through computational fluid dynamics/heat transfer (CFD/HT), and heuristic methods, data driven modeling techniques are now also being used for these applications. This paper investigates the air temperature prediction capabilities of static artificial neural network (ANN), Gaussian progress regression (GPR), support vector regression (SVR), relevance vector machine (RVM), linear regression, and regression trees; and transient long-short term memory (LSTM), and nonlinear autoregressive neural network with external input (NARX)) data driven modeling frameworks. The static study compared various models and found that GPR provided the best results (average error of 0.56 °C), closely followed by the ANN and SVR (average error of 0.60 °C and 0.68 °C respectively) methods. The transient study compared models based on an experimental data set and found that NARX outperforms LSTM for normal operations (0.83 °C and 1.07 °C average error respectively), and that data driven models are able to provide relatively good predictions, even if the input variables are slightly outside the training domain.

Time Series Modeling of Global Average Absolute Sea Level Change

This study aimed to demonstrate the effectiveness of time series models in modeling long-term records of global average absolute sea level changes from 1880 to 2014. Following the Box–Jenkins methodology, the ARIMA(0,1,2) model with drift was identified as the best-fit model for the time series due to its lowest AIC value. Using the LM algorithm, the results revealed that the NARNN model with 7 neurons in the hidden layer and 7 time delays exhibited the best performance among the nonlinear autoregressive neural network models, as indicated by its lower MSE. While ARIMA models excel in modeling linear problems within time series data, NARNN models are better suited for nonlinear patterns. However, a HYBRID model was explored, which combines the strengths of both ARIMA and NARNN models, offering the capability to address both linear and nonlinear aspects of time series data. The comparative analysis of this study demonstrated that the HYBRID model, with 6 neurons in the hidden layer and 7 time delays, outperformed the NARNN model with 7 neurons in the hidden layer and 7 time delays, as well as the ARIMA(0,1,2) model, with the lowest MSE in this study. These findings represent a significant step in time series forecasting by leveraging the strengths of both statistical and machine learning methods.

Using Interoperability between Mobile Robot and KNX Technology for Occupancy Monitoring in Smart Home Care.

It is important for older and disabled people who live alone to be able to cope with the daily challenges of living at home. In order to support independent living, the Smart Home Care (SHC) concept offers the possibility of providing comfortable control of operational and technical functions using a mobile robot for operating and assisting activities to support independent living for elderly and disabled people. This article presents a unique proposal for the implementation of interoperability between a mobile robot and KNX technology in a home environment within SHC automation to determine the presence of people and occupancy of occupied spaces in SHC using measured operational and technical variables (to determine the quality of the indoor environment), such as temperature, relative humidity, light intensity, and CO2 concentration, and to locate occupancy in SHC spaces using magnetic contacts monitoring the opening/closing of windows and doors by indirectly monitoring occupancy without the use of cameras. In this article, a novel method using nonlinear autoregressive Neural Networks (NN) with exogenous inputs and nonlinear autoregressive is used to predict the CO2 concentration waveform to transmit the information from KNX technology to mobile robots for monitoring and determining the occupancy of people in SHC with better than 98% accuracy.

Artificial intelligence models to evaluate the impact of climate change on groundwater resources

This study develops three different artificial intelligence (AI) models in order to investigate the effects of climate change on groundwater resources using historical records of precipitation, temperature and groundwater levels together with regional climate projections. In particular, the Non-linear Autoregressive Neural Network (NARX), the Long-Short Term Memory Neural Network (LSTM) and the Convolutional Neural Network (CNN) were compared. Considering an aquifer located in northern Italy as a case study, the neural networks were trained to replicate observed groundwater levels by taking as input precipitation and temperature records, and in the case of the NARX also antecedent groundwater levels, on a monthly scale. The trained networks were used to infer groundwater levels until the end of the century based on precipitation and temperature projections provided by an ensemble of 13 Regional Climate Models (RCMs) from the EURO-CORDEX initiative. Two emission pathways were considered: the RCP4.5 and RCP8.5. All the AI models show good performance metrics during the training phase, but NARXs perform poorly compared to the other models during validation and testing. For the future, the NARX and LSTM models predict a decline in groundwater levels, especially for the RCP8.5 scenario, while slight changes are expected using the CNN. As NARXs are not deep learning techniques and CNNs may not be able to extrapolate values outside the training range, LSTMs appear to be better suited for climate change impact evaluations.

State Machine-Based Approach for Estimating the Capacity Loss of Lithium-Ion Batteries

The use of Lithium-Ion Batteries (LIBs) have increased in recent years in many applications such as hybrid electrical vehicles (HEV), consumer electronic equipment, and electricity grid. The batteries undergo degradation during usage due to material aging and electrochemical processes, leading to efficiency reduction of battery-powered systems as well as catastrophic events. Several stress factors such as battery temperature, ambient temperature, and C-rate in the loading profiles influence the degradation. Therefore, predicting the health of the battery has gained attention. The service life can be extended or a system failure can be avoided by maintenance measures precisely matched to the function loss or by changing usage strategies. The State-of Health (SoH) condition of the battery can be determined by the application of lifetime models. Various health indicators such as remaining useful lifetime (RuL) and capacity fade are determined by the models based on the stress factors (utilization variables). For optimal use of the battery, it is helpful to develop an accurate lifetime model to represent the dynamic properties. However, models developed are less computationally efficient and unable to represent the non-linear degradation behavior well. The development of a precise model with correct parameterization is also costly. This is particularly true for models developed based on physical and chemical properties of the battery. In this contribution, an artificial neural network (ANN)-based state machine approach is introduced for capacity fade estimation. The degradation process is represented using three states modeling three different levels and the progression from the first state to the last. Capacity associated with each state is described using the non-linear auto regressive neural network with external input (NARX). The NARX is selected due to its ability to accurately model non linear behavior and time series data. Unlike known models, which are developed using analytical mathematical equations related to the battery properties, a combined machine learning approach is used here instead to learn the capacity behavior from historical data. Battery data sets from NASA are used for experimental verification. Based on the results, the estimated capacity fade show close proximity to actual capacity fade, with a low mean square error for different data sets. In addition, the estimated state progression follows the actual state progression.

Nonlinear Autoregressive Neural Network for Forecasting COVID-19 Confirmed Cases in Malaysia

A nonlinear autoregressive neural network (NARNN) model is a feedforward neural network for handling complex nonlinear time series problem. In this study, the tangent sigmoid (tansig) activation function with different number of past values and different number of hidden neurons for NARNN model is determined. The COVID-19 daily confirmed cases in Malaysia are collected with different amount of sample used which are 100, 500 and 900. Therefore, data of 100, 500 and 900 days prior to 21 September 2022 are extracted for the NARNN model training, validation and testing procedure. The lowest average mean squared error (MSE) are considered as the best combination. Result shown that the past value 1:10 and number of neurons of 10 when the sample size is 100. At sample size 500, past values of 1:10 and number of neurons of 8 enables the model to perform at its best. Whereas for sample size 900, network setting of 1:5 past value and 5 hidden neurons gives the least MSE. Multi-step ahead time series forecasting is conducted to forecast the number of COVID-19 confirmed cases in 7 days which is from 22 to 28 September 2022. The result shown for 7-days-ahead confirmed cases forecasting Malaysia datasets, the best forecasting outcome occurs when 900 samples are inputted.

Seamless MEMS-INS/Geomagnetic Navigation System Based on Deep-Learning Strong Tracking Square-Root Cubature Kalman Filter.

To suppress inertial navigation system drift and improve the seamless navigation capability of microelectromechanical system-inertial navigation systems/geomagnetic navigation systems (MEMS-INS/MNS) in geomagnetically unlocked environments, this paper proposes a hybrid seamless MEMS-INS/MNS strategy combining a strongly tracked square-root cubature Kalman filter with deep self-learning (DSL-STSRCKF). The proposed DSL-STSRCKF method consists of two innovative steps: (i) The relationship between the deep Kalman filter gain and the optimal estimation is established. In this paper, combining the two auxiliary methods of strong tracking filtering and square-root filtering based on singular value decomposition, the heading accuracy error of ST-SRCKF can reach 1.29°, which improves the heading accuracy by 90.10% and 9.20% compared to the traditional single INS and the traditional integrated navigation algorithm and greatly improves the robustness and computational efficiency. (ii) Providing deep self-learning capability for the ST-SRCKF by introducing a nonlinear autoregressive neural network (NARX) with exogenous inputs, which means that the heading accuracy can still reach 1.33° even during the MNS lockout period, and the heading accuracy can be improved by 89.80% compared with the single INS, realizing the continuous high-precision navigation estimation.

Real-Time Predictive Energy-Saving Control for Electric Vehicle Based on Road Slope Prediction

Predictive energy-saving control (PEC) is aimed at reducing energy consumption by designing the vehicle speed while considering future road and traffic information. In particular, the slope of the road ahead is necessary and critical for PEC. This paper proposes a road slope prediction method for production vehicles that uses the nonlinear autoregressive (NAR) neural network model based on road slope sensors. To adaptively balance the energy savings and trip time, this paper proposed a real-time variable weight PEC method for a four-wheel-drive (4WD) intelligent electric vehicle. The weight coefficients are automatically changed according to the characteristics of the road slope, where the vehicle energy-saving rate on the steep downhill road can be maximized. The results of real-time simulation on the dSPACE platform indicated that the road slope predictive model can be run in real time and adapted to changes in road slope and speed. The root mean square error (RMSE) of the predictive results is 0.3063. On a steep downhill road, the energy-saving rate of the proposed PEC method can reach 30.87% at a small expense of time of 3.75%. On uphill and flat roads, energy can be saved by 6.35% at a time cost of 3.0%. Compared with the PEC with constant weight factors, the two control objectives of energy savings and traveling time can be better balanced on various types of roads.