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A motor resistance parameter identification scheme was proposed for the speed prediction of a direct-torque-controlled (DTC) induction motor (IM) drive. The DTC IM drive was established on the basis of the stator's current and flux, with the stator current acquired from an IM using the Hall effect current sensor. Rotor speed prediction was achieved using the electromagnetic torque and rotor flux. The stator resistance parameter identification scheme was developed using the model reference adaptive system based on the motor's active power, and the adaptation mechanism was designed using the modified particle swarm optimization algorithm. The MATLAB\Simulink® toolbox was utilized to simulate this system, and all the control algorithms were realized using a TI DSP 6713 and F2812 micro-control card to validate this approach. Simulation and experimental results confirmed the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

At present, the design of modern vehicles requires improving driving performance while meeting emission standards, leading to increasingly complex power systems. In autonomous driving systems, accurate, real-time vehicle speed prediction is one of the key factors in achieving automated driving. Accurate prediction and optimal control based on future vehicle speeds are key strategies for dealing with ever-changing and complex actual driving environments. However, predicting driver behavior is uncertain and may be influenced by the surrounding driving environment, such as weather and road conditions. To overcome these limitations, we propose a real-time vehicle speed prediction method based on a lightweight deep learning model driven by big temporal data. Firstly, the temporal data collected by automotive sensors are decomposed into a feature matrix through empirical mode decomposition (EMD). Then, an informer model based on the attention mechanism is designed to extract key information for learning and prediction. During the iterative training process of the informer, redundant parameters are removed through importance measurement criteria to achieve real-time inference. Finally, experimental results demonstrate that the proposed method achieves superior speed prediction performance through comparing it with state-of-the-art statistical modelling methods and deep learning models. Tests on edge computing devices also confirmed that the designed model can meet the requirements of actual tasks. [ABSTRACT FROM AUTHOR]

An efficient energy management strategy (EMS) is significant to improve the economy of hybrid electric vehicles (HEVs). Herein, a power‐split HEV model is built and validated against test results, and then the EMS is proposed for this model based on vehicle speed prediction and deep reinforcement learning (DRL) algorithms. The rule‐based local controller and global optimal empirical knowledge are introduced to enhance the convergence speed. It is shown in the results that the twin delayed deep deterministic policy gradient algorithm (TD3) achieves more satisfactory performance on converge speed and energy efficiency. The networks of the DRL algorithm with continuous control update more robustly during iterations, in contrast to the discrete ones. Although the power‐split HEV with lower control dimension can reduce the learning burden for DRL EMS; however, the multidimensional control space shows greater optimization potential. As a result, the equivalent fuel consumption of TD3‐based EMS with multidimensional continuous control differences from the global optimal algorithm only by 4.92%. Herein, it is demonstrated in the results that long short‐term memory recurrent neural network (LSTM RNN) performs better for vehicle speed prediction than classical RNN and BP neural network, and the predictive vehicle speed feature helps improve fuel economy by 0.55%. [ABSTRACT FROM AUTHOR]

This work proposes a predictive equivalent consumption minimization (P-ECMS) strategy for a power-split hybrid electric vehicle (HEV) using predicted driving cycle speed based on Monte Carlo (MC) algorithm. The proposed P-ECMS fully takes advantage of the predicted speed profiles by the MC algorithm to optimally determine the power split among energy sources. In this study, to validate the workings of the MCbased P-ECMS scheme, a series of simulations under a total of seven replicated driving cycles including New European Driving Cycle (NEDC), Worldwide Harmonised Light Vehicles Test Procedure (WLTP), Urban Dynamometer Driving Schedule (UDDS), Highway Fuel Economy Test (HWFET), New York City Cycle (NYCC), California Unified Cycle (LA-92), and a combination of all (ALL-CYC) are conducted. The MC-based P-ECMS strategy is compared with a baseline ECMS in terms of fuel-saving. The fuel economy saving up to 6.01% under NEDC, 9.09% under WLTP, 6.33% under UDDS, 5.14% under HWFET, 1.96% under NYCC, 11.47% under LA-92, and 7.92% under ALL-CYC is achieved. The results in this article put forward that the proposed strategy delivers competitive fuel savings compared to the widely used baseline method. [ABSTRACT FROM AUTHOR]

To exploit the energy-saving potential and optimize the battery state of charge (SOC) maintaining capability of energy management strategies for fuel cell hybrid vehicles in specific driving scenarios, this study proposes a scenario-oriented adaptive equivalent consumption minimization strategy (SA-ECMS) based on a Nanjing-oriented driving cycle (NODC) and future speeds predicted via a hybrid neural network model. The proposed strategy determines the initial value of the equivalent factor (EF) and the proportional coefficient of the adaptive increment based on the NODC. Then, it periodically adjusts the EF via local optimization process according to the predicted speed to enhance scenario-specific adaptability and energy efficiency performance. Simulation results show that the hybrid neural network model achieves an average calculation time of 0.0033 s with a root-mean-square error of 0.85 m/s for 10 s prediction horizon, outperforming existing speed prediction models. Compared with the existing SOC feedback-based ECMS, the proposed SA-ECMS effectively suppresses the battery SOC within a narrower fluctuation range of −0.12% to 0.33%, achieves a deviation of only 0.0026 from the SOC reference value, and reduces the equivalent hydrogen-fuel consumption by 2.49% to 7.06 g/km. • A scenario-oriented driving cycle construction method is proposed. • A novel hybrid neural network model with higher prediction accuracy is proposed. • The scenario-oriented adaptability of the proposed strategy is enhanced. • Both battery SOC maintaining capability and fuel economy are improved. [ABSTRACT FROM AUTHOR]

Given the vehicle speed during actual driving, it is possible to apply an advanced energy management strategy for achieving better efficiency and less emission. We conducted a study to predict the future speed while driving of city buses, where only a few bus driving data and bus stop IDs are used without external complex traffic information. The speed prediction models were developed based on long time short memory (LSTM) and a gated recurrent unit (GRU), and a deep neural network (DNN) is also adopted for the bus stop ID processing. The performances of the models were analyzed and compared such that we found the LSTM-based model presents remarkable and practical prediction ability in accuracy and time spent. Adopting the proposed speed prediction model would make it a reality sooner, application of the optimal energy control strategy in the real world. [ABSTRACT FROM AUTHOR]

The future motion prediction of vehicles in the front is widely valued for its great potential to improve a vehicle's safety, fuel consumption, and efficiency. However, due to the uncertainty of a driver's driving intentions and vehicle dynamics, future motion prediction faces great challenges. In order to break the bottleneck in the prediction of leading vehicle motion, this paper proposes a prediction idea of decoupling the prediction of leading vehicle motion into vertical vehicle speed prediction based on the Gaussian process regression algorithm and horizontal heading angle prediction based on the long short-term memory method, which combines the predicted vehicle speed and heading angle to derive the future trajectory of the leading vehicle. Moreover, we propose a prediction algorithm of the leading vehicle motion based on the combination of driving intention recognition and multimodel prediction results by the Fuzzy C-means algorithm, which tries to solve the problem of the unclear driving intention of the predicted object and the nonlinearity between the future motion of the vehicle and the environment. Finally, the algorithm is validated using real vehicle data, proving that it has high prediction accuracy. [ABSTRACT FROM AUTHOR]

Accurate steady state speed prediction is one of the key indexes to improve vehicle safety. This paper presents a steady state speed prediction method for expressway vehicles under snow and ice conditions. Firstly, the torque balance equation of vehicle rollover time is established, and the critical state of vehicle stability is judged by the lateral acceleration of vehicle. Secondly, the steady-state speed prediction parameters, including friction coefficient, braking distance, driver reaction time and vehicle yaw Angle, were calculated according to the judgment results of stable critical state, and the steady-state speed prediction model was built according to the predicted parameters. Experimental results show that this method can accurately detect the critical state of vehicle stability, and the steady-state speed prediction results are basically consistent with the actual speed. [ABSTRACT FROM AUTHOR]

Transport practitioners need a universally applicable speed prediction model to estimate average speeds on any road. Average annual speed is a key input to the economic assessment of transport infrastructure where reliable estimates of future average speeds are necessary to calculate economic costs and benefits. The relationship between Annual Average Daily Traffic (AADT) and average annual speed was investigated on higher-order roads across South Africa, revealing a high level of variability in this correlation at different locations. This variation is influenced by road characteristics, such as alignment and cross-section, complicating the formulation of a universal speed prediction model. Two novel speed prediction models are proposed in this article that use AADT to forecast future average annual speed. The speeds of heavy vehicles and light vehicles can be estimated separately, as well as the average speed of all vehicles simultaneously. Both models are self-calibrating, accounting for the variation in the AADT–speed relationship. This calibration step is unique to speed prediction models and increases the reliability of these models to estimate future average speeds considerably. Furthermore, self-calibrating average annual speed prediction models are universally applicable and will simplify economic assessment of transport infrastructure. [ABSTRACT FROM AUTHOR]

Heavy rainfall events are becoming more common in many areas with escalating climate change, and these events can considerably affect travel speed and road safety. It is critical to understand when and how rainfall events affect congestion in the transportation network to help improve decision making for infrastructure planning and real-time operations. This study incorporates high-resolution rainfall and wind data into a travel speed prediction model, along with other data related to weather conditions, incidents, and real-time speeds, to assess if localized rainfall data can inform travel speed prediction during light and heavy rainfall events, and how this compares with the classical method of using a single city-wide rain gauge data point. The travel speed prediction model holistically selects the most related features from a high-dimensional feature space by modeling by wind direction, correlation analysis, and the least absolute shrinkage and selection operator (LASSO) to overcome overfitting issues and is applied to two urban arterials for case studies located in Pittsburgh, Pennsylvania. The results indicate that high-resolution rainfall features in many instances are better predictors of future rainfall on the target segments, leading to overall better prediction results (in 30-min lag time), when compared with models that use a single city-wide rain gauge. This has implications for other cities that are interested in improving travel speed prediction modeling and traffic modeling under increasing impacts from climate change and extreme weather. [ABSTRACT FROM AUTHOR]

Despite the increased interest in battery electric vehicles (BEV), limited range abilities unsettle customers, which is often related to range anxiety. A better understanding of energy consumption and the possibility to accurately predict the remaining battery energy along an upcoming route may help to reduce this stress perception by means of advanced in-vehicle information systems. Addressing the trend towards vehicles with on-board cloud communication and information systems, the present research focuses on electric powertrain consumption and speed profile forecasts. A meaningful prediction of a speed profile for a given route is a basic prerequisite for an accurate consumption forecast. This study proposes a methodology to derive such a speed profile from real-time traffic data obtained from HERE Technologies while considering individual driving style characteristics. Given the predicted speed profile, a detailed BEV consumption model which accounts for BEV specific energy management strategies and environmental factors is used to obtain a consumption forecast. Prediction uncertainties are analyzed and parameter sensitivities with respect to energy consumption are derived as a function of the route dependent mean vehicle speed. Within a field study with Mercedes Benz EQC experimental vehicles, covering thirty-two test cycles, it is shown that the proposed methodology can accurately predict energy consumption for long look-ahead horizons and significantly reduces the variance in prediction compared to a typical baseline strategy. [ABSTRACT FROM AUTHOR]

• A hierarchical predictive energy management is designed for fuel cell trucks under vehicle-following scenarios. • The speed planning considering future changes of leading vehicle is designed to ensure safe inter-vehicle distance and minimize power demand. • A driving-habit-conscious fuzzy C-means clustering enhanced Markov Chain speed prediction method is proposed to forecast future speed of leading vehicle. • The proposed hierarchical predictive energy management for host vehicle can reduce fuel cell degradation cost by 1.31–3.48% and total operation cost by 0.75–1.94%. Fuel cell hybrid electric heavy-duty vehicles play a key role to realize green and low-carbon travel. To cope with actual traffic environment, an advanced energy management strategy (EMS) is crucial to ensure vehicle's efficiency and economy. In this paper, a predictive co-optimization control method is designed to achieve speed planning and energy management for the host vehicle with a fuel cell/Li-ion battery hybrid energy storage system under vehicle-following scenarios. Firstly, the host vehicle obtains the real-time speed of leading vehicle by communication technology, and speed prediction for the leading vehicle is implemented by fuzzy C-means clustering enhanced Markov Chain (FCM-MC) considering driving habits. Secondly, based on speed prediction results, the speed planning which aims to ensure safe inter-vehicle distance and minimize the demanded vehicular power is implemented to derive the speed curve of host vehicle. Finally, according to speed planning results, predictive energy management is applied to achieve power allocation between fuel cell (FC) and battery. The simulation results denote that the proposed speed prediction method can decrease forecast error effectively. The speed planning ensures the safe inter-vehicle distance. With speed prediction accuracy improved, the proposed hierarchical energy management strategy can reduce fuel cell degradation cost by 1.31%-3.48% and total operation cost by 0.75%-1.94%. [ABSTRACT FROM AUTHOR]

Driving intention and speed prediction are essential factors in the energy management of plug-in hybrid electric vehicles (PHEVs). This paper proposes an improved energy management strategy for the subject vehicle by speed prediction fused with driving intention and LIDAR data in a vehicle-following scenario. A driving intention recognition model is developed based on the gated recurrent unit (GRU), which takes the vehicle speed, throttle opening, and brake pedal force of the subject vehicle as input. Then integrating the LIDAR point cloud data and driving intention result of the subject vehicle to achieve more accurate speed prediction, where joint probabilistic data association and interacting multiple models methods are used to process LIDAR data. The more accurate speed prediction is then applied to design a prediction-informed adaptive equivalent consumption minimization strategy (PIA-ECMS) for real-time energy management optimization. Experimental results demonstrate the recognition accuracy of up to 88%, indicating that the driver's driving intention can be identified effectively. The speed prediction has an error margin of no more than 5.9 km/h. Compared with existing adaptive ECMS without speed prediction, the proposed PIA-ECMS can enhance fuel economy by 1.3–2.7% while achieving better SOC charge sustainability. • Integration of driving intention recognition and LIDAR into energy management. • Speed prediction is fused by driving intention recognition and LIDAR. • A new prediction-informed adaptive ECMS to achieve better fuel economy. [ABSTRACT FROM AUTHOR]

• The greatest speed change occurred during the process of switching from the passenger lane to the truck lane. • The inner lane demonstrated higher mean speeds compared to the outer lane. • The 85th percentile speed of the adjacent main lane was lower than that of the acceleration lane. • The time-series speed prediction model using transformer networks could achieve an accuracy of 98.35% in predicting lane-level driving speed for freeway interchange areas. This study investigates the lane-level speed distribution in the freeway interchange area and develops a time-series speed prediction model using Transformer networks. The full-sample real-time speed data from interchange areas under heavy traffic was extracted by the YOLO v3 detection algorithm and Deep-Sort tracking algorithm based on Unmanned Aerial Vehicle (UAV) videos. A short-term prediction model of lane-level driving speed was constructed using the Time-Series Transformer (TST) framework. Results showed that the greatest magnitude of speed change occurred during the process of switching from the passenger lane to the truck lane. The inner lane consistently demonstrated higher mean speeds compared to the outer lane. The TST model proposed in this study could achieve an accuracy of 98.35% in predicting lane-level driving speed. These findings suggest the need to consider the speed transition between passenger and truck lanes in freeway engineering design, marking setting, and optimization of safety facilities. [Display omitted] [ABSTRACT FROM AUTHOR]

This paper adopts virtual tank experimental technology to study ship rapidity performance. Based on RANS method, a new method is put forward to predict the speed and power performance of full scale ship. The open water performance of propeller model, the resistance performance of ship model and the self-propulsion performance of ship model are numerically calculated. Through the analysis of rapidity numerical results of fishing ship and the change law of wake flow field, some conclusions can be drawn. The numerical prediction accuracy of propeller open water efficiency has a direct influence on total thrust efficiency. The interference between hull, propeller and rudder leads to the formation of a lower pressure zone on stern hull surface and rudder surface. The axial velocity and radial velocity are affected by the interference between the flow of hull stern and propeller, the absolute value of axial velocity on blade leaf is smaller than that on blade back, and the radial velocity induces vortices on the blade tip of the propeller. The increase of propeller rotation speed nearly has no effect on the free surface wave pattern. The prediction results deviations of full scale ship speed and propeller rotation speed between RANS method and EFD method are 0.04 kn and 0.4 r/min, and therefore they are negligible. The proposed method can provide reference for engineering application. [ABSTRACT FROM AUTHOR]

Weather information is frequently requested by travelers. Prior literature indicates that inclement weather is one of the most important factors contributing to traffic congestion and crashes. This paper proposes a methodology to use real-time weather information to predict future speeds. The reason for doing so is to ultimately have the capability to disseminate weather-responsive travel time estimates to those requesting information. Using a stratified sampling technique, cases with different weather conditions (precipitation levels) were selected and a linear regression model (called the base model) and a statistical learning model [using support vector machines for regression (SVR)] were used to predict 30-min-ahead speeds. One of the major inputs into a weather-responsive short-term speed prediction method is weather forecasts; however, weather forecasts may themselves be inaccurate. The effects of such inaccuracies are assessed by means of simulations. The predictive accuracy of the SVR models show that statistical learning methods may be useful in bringing together streaming forecasted weather data and real-time information on downstream traffic conditions to enable travelers to make informed choices. [ABSTRACT FROM AUTHOR]

Accurate ship speed prediction is not only important for economic benefits and emission reduction, but also plays a vital role in making satisfactory routing plans. To investigate a speed prediction method of the largest bulk carriers ever constructed, we firstly compare several widely-used data-driven methods and then pick out the most appropriate model for the prediction task. Being as the input attributes of the data-driven models, the metocean data are collected from forecasting products and carefully cleaned up by data pre-processing steps. However, we have observed from literatures that the data for training are mostly collected from observational systems, such as the noon-reports, sensor measurements, on-board monitoring systems and reanalysis products, which may have certain differences with the data from forecasting systems. To figure out whether the data inconsistency problem will affect the model effectiveness on prediction, exhaustive experiments of a sensitivity study are carried out by utilizing observational data for training models while testing the prediction performance with forecasting data. • Various data-driven models are used for predicting the speed of a Valemax vessel. • The sensitivity of using inconsistent met-ocean data for training is investigated. • Keeping data consistency is significant to obtain satisfactory and reliable results. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In this paper, an energy management strategy (EMS) based on model predictive control (MPC) is proposed to minimize fuel cost, electricity usage and battery ageing. To fulfil the MPC framework, a novel speed predictor with a variable horizon based on a K-means algorithm and a radius basis function neural network, which contains various predictive submodels, is designed to cope with different input drive states. In addition, a Q-learning algorithm is applied to construct an adaptive multimode state-of-charge (SOC) reference generator, which takes advantage of velocity forecasts for each prediction horizon. The algorithm fully considers the model nonlinearities and physical constraints and requires less computational effort. Based on the SOC reference and predictive velocity, the MPC problem is formulated to coordinate fuel consumption and battery degradation. Moreover, considering the influence of real-time traffic information, a traffic model that simulates actual road conditions is constructed in VISSIM to evaluate the performance of the proposed EMS. The simulation results show that the proposed speed predictor can effectively improve the predictive accuracy, and the multimode control laws based on drive condition classification present superior adaptability in SOC reference generation compared to single-mode law. With the aforementioned two improvements, the proposed EMS achieves desirable performance in fuel economy and battery lifetime extension. • Cost-optimal problem is built for coordinating fuel economy and battery lifetime. • A novel speed predictor with variable horizon is constructed. • Q-learning algorithm is applied as the adaptive multimode SOC reference generator. • A traffic model is constructed in VISSIM to evaluate the performance of the proposed EMS. • Influences of SOC reference and predictive speed accuracy are discussed in depth. [ABSTRACT FROM AUTHOR]

Forecasting future driving conditions such as acceleration, velocity, and driver behaviors can greatly contribute to safety, mobility, and sustainability issues in the development of new energy vehicles (NEVs). In this brief, a review of existing velocity prediction techniques is studied from the perspective of traffic flow and vehicle lateral dynamics for the first time. A classification framework for velocity prediction in NEVs is presented where various state-of-the-art approaches are put forward. Firstly, we investigate road traffic flow models, under which a driving-scenario-based assessment is introduced. Secondly, vehicle speed prediction methods for NEVs are given where an extensive discussion on traffic flow model classification based on traffic big data and artificial intelligence is carried out. Thirdly, the influence of vehicle lateral dynamics and correlation control methods for vehicle speed prediction are reviewed. Suitable applications of each approach are presented according to their characteristics. Future trends and questions in the development of NEVs from different angles are discussed. Finally, different from existing review papers, we introduce application examples, demonstrating the potential applications of the highlighted concepts in next-generation intelligent transportation systems. To sum up, this review not only gives the first comprehensive analysis and review of road traffic network, vehicle handling stability, and velocity prediction strategies, but also indicates possible applications of each method to prospective designers, where researchers and scholars can better choose the right method on velocity prediction in the development of NEVs. [ABSTRACT FROM AUTHOR]