Energy-efficient Speed Research Articles

Data-driven energy-efficient speed planning and adaptive car-following control for commuter plug-in hybrid electric vehicles

This paper proposes a hierarchical adaptive eco-driving control scheme for commuter plug-in hybrid electric vehicles (PHEVs), which contains the data-driven speed planner in the upper layer and the speed tracking controller in the lower layer. Considering that the speed planning for PHEVs with the goals of fuel economy and comfort involving both coupled motion and powertrain dynamics can result in an enormous computational burden, a data-driven speed planner is designed based on traffic information of the commuter PHEV, where the energy consumption and battery state are described through two models by the neural networks, respectively. As such, the suggested speed ensuring fuel economy and comfort for the vehicle driving on this route can be obtained with an acceptable computational burden. Then, facing the immeasurable acceleration of the preceding vehicle and slow time-varying road grade, an adaptive car-following speed controller is designed through the backstepping recursive technique to track the suggested speed accurately and safely. The control effects can be adjusted through vehicle-to-cloud (V2C) communication. The simulation results demonstrate the effectiveness and advantages of the proposed scheme in improving fuel economy.

Data-driven energy efficient speed planning for battery electric industrial vehicles: Forklift as a case study

Battery electric industrial vehicles (BEIVs) offer significant environmental protection and decarbonization potential for rapidly growing logistics and transportation sectors in China. However, the limited driving range remains a major obstacle to the widespread adoption of BEIVs. Eco-driving is an effective solution that can improve energy efficiency and extend the driving range of vehicles by controlling driving behavior and speed. Therefore, this paper introduces an energy efficient speed planning method for BEIVs, in which data-driven models are developed to estimate energy consumption. Real-world driving data collected from a BEIV are used to model the energy consumption by curve fitting, random forest, and LSTM. The speed planning process is considered a Markov Decision Process, which is solved using Dynamic Programming and Q-learning, respectively. The case study of a battery electric forklift shows that speed planning can reduce energy consumption by 13%–27% compared to time-optimal speed, while only increasing the driving time by 10%–20%.

Advisory versus automated dynamic eco-driving at signalized intersections: lessons learnt from empirical evidence and simulation experiments

Research in the field of dynamic eco-driving has been primarily coupled with connected and automated vehicles which are equipped with automation functions that can accurately execute energy-efficient speed advice. Advisory dynamic eco-driving that entails driver adaptation to energy-efficient speed advice has received lesser attention although mixed traffic is expected to prevail in the forthcoming decades. This study developed a decision tree model based on real-world data collected during the pilot deployment of an advisory speed advice service along an urban arterial corridor to emulate driver adaptation to speed advice. The decision tree model was integrated into the control logic of an enhanced velocity planning algorithm to replicate the behavior of manually driven connected vehicles along dynamic eco-driving service zones in a microscopic traffic simulation environment. The conducted simulation analysis encompassed scenarios with varying penetration rates of advisory dynamic eco-driving technology, automated dynamic eco-driving technology and manually driven unequipped vehicles. Evaluation of simulation scenarios was based on the estimation of several environmental, traffic efficiency and surrogate safety measures. Simulation results indicated that performance of advisory dynamic eco-driving depends on driver adaptation to speed advice and ranges between that of manually driven unequipped vehicles and its automated counterpart. Moreover, geometrical and operational characteristics of intersection approaches comprising dynamic eco-driving service zones can influence driver adaptation to speed advice. Environmental, safety and traffic efficiency benefits are maximized in the case of vehicle fleets fully equipped with automated dynamic eco-driving systems.

Energy-Aware Partitioned Scheduling of Imprecise Mixed-Criticality Systems

We consider partitioned scheduling of an Imprecise Mixed-Criticality (IMC) taskset on a uniform multiprocessor platform, with Earliest Deadline First-Virtual Deadline (EDF-VD) as the uniprocessor task scheduling algorithm, and address the optimization problem of finding a feasible task-to-processor assignment and low-criticality (LO) mode processor speed with the objective of minimizing the system’s average energy consumption in LO mode. We propose a task-to-processor assignment algorithm Criticality-Unaware Worst-Fit Decreasing (CU-WFD) algorithm, which allocates tasks with the Worst-Fit Decreasing (WFD) heuristic method based on utilization values at their respective criticality levels. We determine the energy-efficient speed for each processor based on EDF-VD scheduling, and present our algorithm Energy-Efficient Partitioned Scheduling for Imprecise Mixed-Criticality (EEPSIMC) with the CU-WFD heuristic algorithm to minimize system energy consumption. The experimental results show that our proposed algorithm has good performance in terms both schedulability ratio and normalized energy consumption compared to seven comparison baselines.

Research on Speed Control Methods and Energy-Saving for High-Voltage Transmission Line Inspection Robots along Cable Downhill

To ensure the safe operation of high-voltage transmission line inspection robots during downhill descents without power and extend their range after a single charge, this paper proposes an energy-saving speed control method for the inspection robot’s walking wheel motor on downhill slopes by integrating feedback braking and fuzzy PID control. By combining the parameter equation of the overhead catenary line and the structural characteristics of the overhead transmission line, this paper analyzes the relationship between the driving torque of the inspection robot’s wheels and the horizontal displacement along the transmission ground wire before and after descending. Based on this analysis, a speed control and energy recovery scheme is developed for the inspection robot, which combines front-wheel feedback braking with rear-wheel regenerative braking. The fuzzy PID method is utilized to adjust the PWM duty cycle to achieve energy-efficient speed control of the inspection robot’s rear walking wheels. Additionally, to improve the energy density and specific power of the robot’s energy storage unit, a composite power source consisting of lithium batteries and supercapacitors is employed to recover energy from the front walking wheels through feedback braking. The combined simulation results indicate that, compared to fuzzy control and PID control, fuzzy PID control better regulates the robot’s speed under varying slopes, wind resistance, and cable roughness. A downhill speed control system for the inspection of the robot’s walking wheel motor was designed, and its effectiveness was validated through simulated high-voltage line experiments. The fuzzy PID control was demonstrated to effectively maintain the rear walking wheel speed within the targeted range during downhill descents. When descending along a fixed 30° angle cable, the fuzzy PID control resulted in an increase of 5.28% and 14.26% in the state of charge (SOC) of the supercapacitor compared to PID control and fuzzy control, respectively. Moreover, when descending along fixed angle cables of 10°, 20°, and 30°, as well as a variable angle cable ranging from 30° to 0°, the SOC of the supercapacitor increased by 17.55%, 26.25%, 38.45%, and 31.29%, respectively. This demonstrates the effective absorption of regenerative braking energy during the robot’s downhill movement.

A Novel Real-Time Algorithm for Optimizing Train Speed Profiles Under Complex Constraints

Energy-efficient speed profile optimization of train operations is challenging due to complex constraints including passage points and speed limits. Moreover, it is critical to perform real-time re-optimization of speed profiles within a short control period. To speed up the speed profile re-optimization with complex constraints, we introduce various train characteristics into the shortest path problem with time windows (SPPTW) from the literature, and formulate energy-efficient optimization as a train-based SPPTW (TRAIN-SPPTW). This reduces the solution space and leads to reduced computation complexity. To further reduce computational time for solving the Train-SPPTW, we take advantage of the preprocessing + query structure used in existing pulse algorithms (PA) on one hand, and further overcome their limitations in computation efficiency on the other hand by proposing a tour-adaptive partial-bounding pulse algorithm (TPPA) to shorten the total time of the preprocessing and query stages. We use a simulation study on the tracks of the Dongguan-Huizhou intercity railway in China to demonstrate that the proposed TPPA reduces the computational time by at least 60 % compared to three existing algorithms without sacrificing the quality of the solution.

Onboard train speed optimization for energy saving using the prediction of block clearing times under real-time rescheduling

Energy-saving driving is crucial during railway operation, especially in case of disturbances that require timetable rescheduling. This paper presents a method for the dynamic onboard tuning of energy-efficient speed profiles after the real-time train rescheduling process under a fixed block signaling system for mixed traffic. Similar to the idea of connected driver advisory systems, trains constantly communicate with a central traffic management system. After the rescheduling process, this system provides recommended time corridors for the passing of block signals that depend on the predicted clearing times of the block sections. These recommendations are then used for individual energy optimization of single train runs by avoiding unnecessary braking in front of block signals and maximizing cruising distances. The method is tested and evaluated on a representative line segment of the ELVA, the Railway Signaling Lab at RWTH Aachen University under realistic conditions. Energy consumptions are compared for different prediction time horizons at which the recommendations are available to the train's onboard system. In the examined test case of two trains, the energy-consumption could be decreased by up to 53% compared to operation without any rescheduling system. Thus, the proposed method is able to reduce energy consumption significantly.

Design and Control of an Energy-Efficient Speed Regulating Method for Pump-Controlled Motor System under Negative Loads

Pump-controlled motor hydrostatic system (PCMH) is widely applied for rotary driving in heavy industry and construction machinery due to its high-power density and efficient speed regulation performance. However, the contradiction of the PCMH system between energy saving and speed control appears when it deals with negative loads. To address this contradiction, an energy-efficient speed regulating method based on electro-proportional counterbalance valves (EPCBVs) is designed, along with the corresponding controller. The working principle of the proposed scheme is that under a negative-load operation mode, determined by the supervisory controller according to system states and reference inputs, the speed of the hydraulic motor is controlled by a velocity controller through adjustment of the control signal of the EPCBV, and that the inlet pressure of the hydraulic motor is maintained at a defined low point by a pressure controller through pump displacement control. Comparative experiments between the EPCBV and T-CBV (a PCMH system based on a typical CBV) systems are conducted to verify the superiority of the proposed scheme in energy-efficient speed regulation under negative loads. The results show that, in most of the working conditions, the EPCBV system shows better adaption than the T-CBV system to varying negative loads and maintains higher stability than the T-CBV. Moreover, the speed accuracy of the EPCBV system can be maintained above 95%, which is greater than that of the T-CBV system, varying from 48% to 90%. Furthermore, the maximum power consumption is only about 4 Kw and is far less than that of the T-CBV system, which is about 13.79 Kw. The power-saving ratio changes from 20% to 82%, but it goes beyond 50% in most of the working conditions. The proposed method is easy to implement in practical application and is of great significance to the PCMH system for energy-efficient speed control under negative loads.

Hierarchical optimal control framework to automatic train regulation combined with energy-efficient speed trajectory calculation in metro lines

In high-density metro lines, frequent disturbances could lead to a domino effect of train delays if no adjustment strategy is imposed timely. In this paper, to enhance train timetable adherence and reduce energy consumption, we provide a hierarchical optimal control framework for the automatic train regulation problem with energy-efficient speed trajectory based on the model predictive control method. Specifically, by considering the dynamic passenger flows, the non-linear train regulation model is proposed at the higher level to enhance timetable adherence, while at the lower level the energy-efficient speed trajectories of the trains are generated on-line in a distributed manner. The interaction of real-time information exists between the higher level and the lower level, i.e., the upper model provides the train running time and the numbers of onboard passengers to the lower model, while the lower model feedbacks the updated train information (e.g., the actual arrival time) to the upper model, which provides the potential for the proposed framework to respond to disturbances at the operational stage. Moreover, a customized model predictive control method combined with Radau pseudospectral method (RPM) is designed to generate the energy-efficient train speed trajectory at the lower level in response to uncertain operational conditions. Numerical cases based on the Beijing Metro Yizhuang line demonstrate the effectiveness and robustness of our proposed hierarchical optimal control framework to deal with uncertain disturbances.

DVFS-based energy-aware scheduling of imprecise mixed-criticality real-time tasks

Energy-aware mixed-criticality (MC) real-time scheduling has attracted the attention of many researchers. However, they only focus on the conventional MC task model, in which all low-criticality (LO) tasks are discarded in the high-criticality (HI) mode. In this work, we focus on an imprecise mixed-criticality (IMC) task model that provides degraded service for LO tasks in HI mode. Firstly, we address the energy minimization problem of scheduling IMC task sets with dynamic voltage and frequency scaling (DVFS). Secondly, we present an energy-aware IMC scheduling algorithm (EA-IMC) to solve this problem while it schedules tasks with the energy-efficient speed of the LO mode SLO, and the maximum processor speed Smax in HI mode. Finally, the experiments are applied to evaluate the EA-IMC algorithm and the experimental results show that it can achieve on average 24.55% energy reduction compared with the existing algorithms.

Energy-Optimal Speed Control for Autonomous Electric Vehicles Up- and Downstream of a Signalized Intersection

Signalized intersections can increase the vehicle stops and consequently increase the energy consumption by forcing stop-and-go dynamics on vehicles. Eco-driving with the help of connectivity is a solution that could avoid multiple stops and improve energy efficiency. In this paper, an eco-driving framework is developed, which finds the energy-efficient speed profile both up- and downstream of a signalized intersection in free-flow situations (eco-FF). The proposed framework utilizes the signal phasing and timing (SPaT) data that are communicated to the vehicle. The energy consumption model used in this framework is a combination of vehicle dynamics and time-dependent auxiliary consumption, which implicitly incorporates the travel time into the function and is validated with real-world test data. It is shown that, by using the proposed eco-FF framework, the vehicle’s energy consumption is notably reduced.

Eco-Driving of General Mixed Platoons With CAVs and HDVs

Eco-driving has been widely investigated over the last decade, but most studies focused on an individual vehicle or a vehicle platoon consisting of pure connected and automated vehicles (CAVs). Recently, mixed vehicle platoons consisting of both CAVs and human-driven vehicles (HDVs) have attracted much interest, considering the fact that HDVs will mix with CAVs in the traffic system for a long period. This paper proposes an eco-driving strategy for mixed platoons, composing of both offline planning and online tracking. In offline planning, an energy-efficient speed reference and a gearshift reference are determined by using the characteristics of each vehicle and future traffic information through dynamic programming. Offline planning optimised the vehicle speed and gearshift to allow the vehicle powertrain working at a high efficiency region. In online tracking, two different types of model predictive controls (MPCs) are proposed to control the CAVs in real-time. The MPCs are designed to achieve precise speed reference tracking performance and guarantee platoon string stability, respectively. Meanwhile, HDVs within the mixed platoon can be located anywhere in the platoon except working as the first vehicle to improve flexibility. Therefore, the proposed eco-driving strategy is applicable to mixed platoons with more general structures in ordering. The key contribution of this study is that the proposed eco-driving strategy can optimise the total fuel consumption for general mixed platoons. Simulation results show that the proposed eco-driving strategy improves the fuel economy of a mixed platoon by up to 6.39% compared to the benchmark conventional-adaptive cruise control strategies.

Optimal Eco-Driving Control of Autonomous and Electric Trucks in Adaptation to Highway Topography: Energy Minimization and Battery Life Extension

This article develops a model to plan energy-efficient speed trajectories of electric trucks in real time by considering the information of topography and traffic ahead of the vehicle. In this real-time control model, a novel state-space model is first developed to capture vehicle speed, acceleration, and state of charge. An energy minimization problem is then formulated and solved by an alternating direction method of multipliers (ADMM) that exploits the structure of the problem. A model predictive control (MPC) framework is further employed to deal with topographic and traffic uncertainties in real time. An empirical study is finally conducted on the performance of the proposed eco-driving algorithm and its impact on battery degradation. The simulation results show that the energy consumption by using the developed method is reduced by up to 5.05%, and the battery life is extended by more than 100% compared to benchmarking solutions.

Energy-efficient speed profile: an optimal approach with fixed running time

Tracking the optimal speed profile in electric train operation has been proposed as an efficient and feasible solution for not only reducing energy consumption, but also no at costs to upgrading the existing railway systems. This paper focuses on finding the optimal speed profile based on Pontryagin’s maximum principle (PMP) while ensuring the fixed running time, and comparing energy saving levels in the cases of applying or not applying PMP. The way to determine the fixed running time also differs from works published is to calculate the total trip time equal to scheduled timetable exactly. Calculating accelerating time t a , coasting time t c , braking time t b via values of maximum speed v h , braking speed v b of optimal speed profile. The other hands, v h and v b are determined by solving nonlinear equations with constraint condition: the running time equal to the demand time. Simulation results with data collected from electrified trains of Cat Linh-Ha Dong metro line, Vietnam show that energy reduction for the entire route when PMP utilization is up to 8.7% and running time complied with scheduled timetables.

Energy efficient speed planning of electric vehicles for car-following scenario using model-based reinforcement learning

Eco-driving is a term used to refer to a strategy for operating vehicles so as to minimize energy consumption. Without any hardware changes, eco-driving is an effective approach to improving vehicle efficiency by optimizing driving behavior, particularly for autonomous vehicles. Several approaches have been proposed for eco-driving, such as dynamic programming, Pontryagin’s minimum principle, and model predictive control; however, it is difficult to control the speed of the vehicle optimally in various driving situations. This study aims to derive an eco-driving strategy for reducing the energy consumption of a vehicle in diverse driving situations, including road slopes and car-following scenarios. A reinforcement learning-based energy efficient speed planning strategy is proposed for autonomous electric vehicles, which learn an optimal control policy through a data-driven learning process. A model-based reinforcement learning algorithm is developed for the eco-driving strategy; based on domain knowledge of the vehicle powertrain, a battery energy consumption model and longitudinal dynamics model of the vehicle are approximated from the driving data and are used for reinforcement learning. The proposed algorithm is tested using a vehicle simulation, and is compared to a global optimal solution obtained using an exact dynamic programming method. The simulation results show that the reinforcement learning algorithm can adjust the speed of the vehicle by considering driving conditions such as the road slope and a safe distance from the leading vehicle while minimizing energy consumption. The reinforcement learning algorithm achieves a near-optimal performance of 93.8% relative to the dynamic programming result.

Energy-Efficient Speed Planner for Connected and Automated Electric Vehicles on Sloped Roads

This paper proposes an energy-efficient speed planning strategy for a connected and automated vehicle (CAV) considering the upcoming traffic and road gradient information, which can be provided by the vehicle-to-everything communication systems. Unlike human drivers, CAV that receives long and short sighted traffic and road geometry information can optimize their speed profile to increase energy efficiency, depending on the powertrain types. In particular, the developed speed planner reducing the battery output power through the energy-efficiency improvement systems in electrified vehicles. Consequently, the CAV that is aware of the existence of the upcoming road gradient increases the speed on the uphill, and decreases the speed on the downhill to minimize the battery output power, which is different from the natural behaviors of human-driven vehicles on sloped roads. To consider the constraints, the model predictive control-based speed planner is developed, and its effectiveness is verified under various driving conditions. Simulation results show that our approach significantly outperforms the alternative speed profiles in terms of battery energy-saving, achieving about 27.21% of the energy efficiency improvement.

Dynamic Programming-based Macroscopic Speed Planner for Electric Vehicle Platooning

Vehicle platooning has demonstrated significant energy saving potentials on highway via reduction of aerodynamic drag resistance by closely following a leading vehicle. However, taking full advantage of platooning opportunities for energy saving is a challenging task since accomplishable energy saving benefits depends on many factors such as the leading vehicle speed, relative position to the leading vehicle, platoonable miles with the leading vehicle, and others. This paper proposes a dynamic programming (DP)-based algorithm to optimize the speed profile for an electric vehicle to minimize the energy consumption when traveling on a given route with several platoonable vehicles sharing the same route. The proposed DP-based speed optimization framework was applied to a case study with five potential platooning vehicles over a 65-mile route with a time constraint. Through the case study, it was found that the DP-based optimization algorithm can determine the most energy-efficient speed profile for a given time deadline in a complex platooning environment based on the energy consumption from aerodynamic drag. Compared to the baseline control, the optimal speed profile from the DP optimization results can achieve significant energy saving benefits up to 18.12%.

Comfortable and energy-efficient speed control of autonomous vehicles on rough pavements using deep reinforcement learning

Rough pavements cause ride discomfort and energy inefficiency for road vehicles. Existing methods to address these problems are time-consuming and not adaptive to changing driving conditions on rough pavements. With the development of sensor and communication technologies, crowdsourced road and dynamic traffic information become available for enhancing driving performance, particularly addressing the discomfort and inefficiency issues by controlling driving speeds. This study proposes a speed control framework on rough pavements, envisioning the operation of autonomous vehicles based on the crowdsourced data. We suggest the concept of ‘maximum comfortable speed’ for representing the vertical ride comfort of oncoming roads. A deep reinforcement learning (DRL) algorithm is designed to learn comfortable and energy-efficient speed control strategies. The DRL-based speed control model is trained using real-world rough pavement data in Shanghai, China. The experimental results show that the vertical ride comfort, energy efficiency, and computation efficiency increase by 8.22%, 24.37%, and 94.38%, respectively, compared to an optimization-based speed control model. The results indicate that the proposed framework is effective for real-time speed controls of autonomous vehicles on rough pavements.

Energy-aware fixed priority scheduling with shared resources in standby-sparing systems

The problem of joint energy and reliability management for fixed priority periodic real-time tasks set with shared resources on a standby-sparing system is studied in this paper. A novel algorithm called FPMPSA is proposed. It considers frequency transition and processor state transition overheads imposed by dynamic voltage frequency scaling (DVFS) and dynamic power management (DPM). In addition, it applies the rate monotonic with dual priority scheduling policy (RM/DPP) to ensure that shared resources can be accessed in a mutual exclusive manner. Moreover, the mixed mapping method is applied to map the main task and backup task. Furthermore, the energy efficient speed of the main task SM is determined and the backup task executes with the maximum processor speed. For energy efficiency, the algorithm called DFPMPSA which is an extension of FPMPSA is proposed. It can use the dynamic slack time to reduce energy consumption. The experimental results show that DFPMPSA algorithm consumes average 8.15% less energy than that of FPMPSA.

Energy-efficient speed tuning for real-time applications

Energy-efficient speed tuning for real-time applications