Optimal Speed Trajectory Research Articles

Multi-objective online driving strategy optimization for energy storage tram considering battery life

Compared with the traditional overhead contact grid or third-rail power supply, energy storage trams equipped with lithium batteries have been developed rapidly because of their advantages of flexible railway laying and high regenerative braking energy utilization. However, trams may face expensive battery replacement costs due to battery degradation. Therefore, this paper proposes a multi-objective optimization method for the tram's driving strategy to reduce operational energy consumption and extend battery life. The method describes the optimization problem as second-order cone programming (SOCP). It uses the interior point algorithm for fast solving, thus obtaining the theoretical optimal speed trajectory in real-time. This feature enables the method to be applied online. To verify the effectiveness and robustness of the proposed method, we conducted simulations under actual operating conditions. The simulation results show that the proposed multi-objective optimization method can extend the battery life by 40 % compared to the single-objective optimization method for energy saving. As a result, the total cost of the whole life cycle is reduced by 6.54 %. Furthermore, this online optimization driving strategy method can effectively deal with unexpected conditions during the actual operation of the tram to ensure the secure operation, punctual arrival, and precise parking of trams. Finally, the effectiveness of the proposed method is further verified by a hardware-in-the-loop (HIL) test.

A reinforcement learning method for two‐layer shipboard real‐time energy management considering battery state estimation

AbstractIncreasing global environmental concerns encourage a continuous reduction in carbon emissions from the shipping industry. It has become an irreversible trend to replace traditional fossil fuels with advanced energy storage technology. However, an improper energy management leads to not only energy waste but also undesired costs and emissions. Accordingly, the authors develop a two‐layer shipboard energy management framework. In the initial stage, a shipboard navigation planning problem is formulated that considers battery state estimation and is subsequently solved using particle swarm optimisation to obtain an optimal speed trajectory. To track the scheduled speed, a reinforcement learning method based on a deep Q‐Network is proposed in the second stage to realise real‐time energy management of the diesel generator and energy storage system. This approach ensures that the state of charge remains within a safe range and that the performance is improved, avoiding excessive discharge from the energy storage systems and further enhancing the efficiency. The numerical results demonstrate the necessity and effectiveness of the proposed method.

Optimal hybrid strategy in adaptive cruise control system for enhanced autonomous vehicle stability and safety

The adaptive cruise control system relies heavily on the vehicle's longitudinal control algorithm. Traditional longitudinal control algorithms typically depend on precise vehicle dynamic modeling or complex controller parameter calibrations. In the research, an optimal hybrid strategy for the control of basic adaptive cruise control system is proposed. The proposed hybrid strategy is the combination of both the Model Predictive Control (MPC) and Coati Optimization Algorithm (COA) and hence it is named as MPCCOA strategy. The fundamental goal of the proposed work is that a vehicle should maintain its current velocity when it approaches another vehicle that is driving at a steady velocity. The inputs for the proposed strategy are vehicle speed and acceleration. The inter-vehicle distance error, velocity error and acceleration are regulated based on the upper and lower limit of acceleration command with the help of proposed strategy. For accomplishing the required steadiness with the limitations in the given input and actual velocity with constant previous vehicle velocity, the proposed controller is utilized. The proposed controller is used to control the traction force of the host vehicle such that the vehicle speed follows the optimal speed trajectory as good as possible while ensuring constraints like distance to a preceding vehicle or speed limits. The performance of the proposed is implemented in the MATLAB platform and compared with existing approaches. The proposed technique demonstrates superior performance metrics, achieving the collision rate, rate of reaching maximum deceleration and ratio of exceeding comfortable deceleration or jerk are 1.27 %, 3.131 % and 9.052 %, respectively.

Increasing realism in modelling energy losses in railway vehicles and their impact to energy-efficient train control

AbstractThe reduction of energy consumption is an increasingly important topic of the railway system. Energy-efficient train control (EETC) is one solution, which refers to mathematically computing when to accelerate, which cruising speed to hold, how long one should coast over a suitable space, and when to brake. Most approaches in literature and industry greatly simplify a lot of nonlinear effects, such that they ignore mostly the losses due to energy conversion in traction components and auxiliaries. To fill this research gap, a series of increasingly detailed nonlinear losses is described and modelled. We categorize an increasing detail in this representation as four levels. We study the impact of those levels of detail on the energy optimal speed trajectory. To do this, a standard approach based on dynamic programming is used, given constraints on total travel time. This evaluation of multiple test cases highlights the influence of the dynamic losses and the power consumption of auxiliary components on railway trajectories, also compared to multiple benchmarks. The results show how the losses can make up 50% of the total energy consumption for an exemplary trip. Ignoring them would though result in consistent but limited errors in the optimal trajectory. Overall, more complex trajectories can result in less energy consumption when including the complexity of nonlinear losses than when a simpler model is considered. Those effects are stronger when the trajectory includes many acceleration and braking phases.

Analysis of the Impact of Speed Trajectory Optimization on Energy Consumption during Highway Merging

Analysis of the Impact of Speed Trajectory Optimization on Energy Consumption during Highway Merging

Hierarchical optimization control strategy for intelligent fuel cell hybrid electric vehicles platoon in complex operation conditions

Encountering complex traffic conditions such as the dynamic interference of preceding and rear vehicles and gradients, a control strategy that simultaneously considers inter-vehicle cooperative control and energy economy is one of the key technologies, that improves traffic efficiency and exploits the energy-saving potential of platoon vehicles. In this paper, a hierarchical optimization control strategy is proposed for the intelligent fuel cell hybrid electric vehicles (FCHEV) platoon in a network-connected environment. The hierarchical control framework consists of upper speed control and lower power distribution. In the upper layer, the improved particle swarm optimization (PSO) algorithm is applied to calculate the global optimal speed trajectory, and then the model predictive control (MPC) is adopted for global speed trajectory tracking and self-adaptation, which can ensure the ego vehicle tracks the pre-calculated speed trajectory, and can re-plan the vehicle speed under the condition of safety priority when sudden disturbances occur in the foreground. The lower layer utilizes Q-learning (QL) to achieve power distribution between hybrid power sources, reducing the number of fuel cell starts and stops, slowing battery degradation and improving vehicle economy. Simulation results based on complex road conditions show that the proposed controller has good energy economy and tracking performance. Under the condition of the slope, the total platoon cost of the proposed strategy is reduced by 3.99% compared with the constant speed cruise strategy, and under the interference condition, the total platoon consumption cost of the proposed strategy decreased by 6.79% compared with adaptive cruise control (ACC).

Energy-Efficient Train Operation Optimization in Istanbul Metro Network Considering Variable Passenger Numbers

This manuscript presents a speed trajectory optimization to minimize driving energy consumption using four different nature-inspired algorithms. For this purpose, a model was developed that can simulate motion of a single train. To test the train motion model and speed trajectories, Istanbul M3 subway line was modeled with its actual parameters such as gradient, curve, and speed limit. Concerning computational time and energy usage, a straightforward speed profile and proposed speed profiles by the algorithms have been compared. Afterwards, variable passenger numbers at each station in a certain hour were taken as dynamic mass input. With the marine predator algorithm (MPA) that finds the best result among the four, speed profile optimization was performed again under the effect of unstable mass. Finally, a train was operated by a driver according to the optimal speed profile. As a result, energy saving is 18.75% in simulation environment and 21.27% in real-life test.

Enhancing Energy Efficiency in Connected Vehicles for Traffic Flow Optimization

In urban settings, the prevalence of traffic lights often leads to fluctuations in traffic patterns and increased energy utilization among vehicles. Recognizing this challenge, this research addresses the adverse effects of traffic lights on the energy efficiency of electric vehicles (EVs) through the introduction of a Multi-Intersections-Based Eco-Approach and Departure strategy (M-EAD). This innovative strategy is designed to enhance various aspects of urban mobility, including vehicle energy efficiency, traffic flow optimization, and battery longevity, all while ensuring a satisfactory driving experience. The M-EAD strategy unfolds in two distinct stages: First, it optimizes eco-friendly green signal windows at traffic lights, with a primary focus on minimizing travel delays by solving the shortest path problem. Subsequently, it employs a receding horizon framework and leverages an iterative dynamic programming algorithm to refine speed trajectories. The overarching objective is to curtail energy consumption and reduce battery wear by identifying the optimal speed trajectory for EVs in urban environments. Furthermore, the research substantiates the real-world efficacy of this approach through on-road vehicle tests, attesting to its viability and practicality in actual road scenarios. In the proposed case, the simulation results showcase notable achievements, with energy consumption reduced by 0.92% and battery wear minimized to a mere 0.0017%. This research, driven by the pressing issue of urban traffic energy efficiency, not only presents a solution in the form of the M-EAD strategy but also contributes to the fields of sustainable urban mobility and EV performance optimization. By tackling the challenges posed by traffic lights, this work offers valuable insights and practical implications for improving the sustainability and efficiency of urban transportation systems.

Reinforcement learning method for the multi-objective speed trajectory optimization of a freight train

With the rising urge to mitigate the green house effect, reducing the energy consumption of the freight train attracts much attention. Multiple constraints should be taken into account to solve the energy-efficient control problem, which can be reformulated as the multi-objective optimization. This paper proposes a Reinforcement Learning (RL) method for the multi-objective speed trajectory optimization with the goal of the energy-efficiency, punctuality and accurate parking simultaneously. Since the solution space for the optimization problem in this paper is large and discrete, a Gated Recurrent Unit (GRU)-based network is proposed to achieve the fast approximation of the optimal value function instead of the lookup Q-table. Meanwhile, a new architecture, including the embedding matrix, is used to model the control sequence that generates the speed trajectory. Besides, this paper constructs a Deep Q-Network (DQN) framework to train the GRU network without relying on the prior knowledge of the freight train model. Finally, the Intelligent Train Operation (ITO) algorithm is proposed and verified, using the data of Beijing–Guangzhou Railway Line and HXD1B electric locomotive. The case studies indicate that the reward function for the ITO algorithm converges rapidly and the energy consumption monotonically decreases with the trip time, which satisfies the multiple optimization objectives. In terms of saving the energy consumption, the ITO algorithm performs better than Fuzzy Predictive Control (FPC), Genetic Algorithm (GA) and the field test data. The computation time of different speed trajectories demonstrates that the ITO algorithm is applicable to generating the optimal speed trajectory off-line.

Energy-optimal control of intelligent track inspection trains: design and experiment

This study proposes and evaluates the effectiveness of a new energy-efficient control strategy for Intelligent Track Inspection Trains (ITITs). First, a sufficiently simple state-space model for a real ITIT is introduced. This model is employed to formulate and solve a constrained optimization problem to determine the vehicle's optimal speed profile using the pseudo-spectral method to achieve the highest energy savings. Then, we take advantage of the model predictive control (MPC) scheme to create a controller to follow the resulting optimal speed trajectory as closely as possible. The results obtained from co-simulation between a high-fidelity model of the ITIT built within AMESim and the MPC controller developed in the Matlab/Simulink environment show that the optimal speed trajectory is tracked very well when the MPC controller is applied to the vehicle's high-fidelity model. During the co-simulations, the energy consumption in terms of the battery's state of charge (SOC) changes for the MPC-based optimal speed trajectory following was around 6% less than that quantity for a conventional non-optimal cruise controller. Moreover, in the experiment with the real ITIT, the energy consumption in terms of the SOC changes for the non-optimal cruise controller was 5% more than that value for the MPC-based optimal speed trajectory following.

Predictive Cruise Control Under Cloud Control System for Urban Bus Considering Queue Dissipation Time

The driving conditions of urban consecutive signalized intersections are one of the main research scenarios for vehicle speed trajectory optimization, and typical for bus driving, where frequent acceleration and deceleration before and after the intersection can intensify the energy consumption of the bus. Prior research has predictive cruise controlled under Intelligent Transportation System, which is not feasible to directly communicate with controllers' units of Intelligent Connected Vehicles. Besides, the effect of queue dissipation is a topic that has received less attention in recent related work. Therefore, this paper proposes a vehicle-cloud hierarchical architecture based on Cloud Control System at first, under which a predictive cruise control for urban buses is deployed. Given the impact of intersection queue length and dissipation time on vehicle driving, a queue dissipation time estimation model based on shockwave theory is proposed to predict changes in intersection traffic state. The queue dissipation time equivalent to the extension of the red-light window is reflected in the constraints of the Receding Distance Horizon Dynamic Programming (RDHDP) algorithm for solving the optimal control problem. Eventually, comparison simulations, a segment of realistic trip between adjacent stops, are presented. The results show that the proposed method saves 44.94%-56.74% of energy consumption and at least 26.8s of waiting time compared to human drivers, and 22.72%-41.27% of energy consumption compared to vehicle with the Intelligent Vehicle Infrastructure Cooperative Systems.

Speed Trajectory Optimization Method for Electric Vehicles Based on Driving Style

In real driving conditions, speed trajectory optimization is an important part of the energy-saving driving assistance system of smart cars, and it can contribute to increase the cruising range. However, the existing speed trajectory optimization research for vehicles mainly focuses on energy consumption and does not consider whether the optimized trajectory can meet the driver’s driving style requirements. In response to this problem, this article establishes an electric vehicle energy consumption model and discusses the difference between the trajectory reflecting driving style (TRDS) and the optimal trajectory of energy consumption (OTOEC). Based on the driving speed trajectory and acceleration trajectory of the driver, this article obtains trajectory clusters reflecting the driving style of the driver by trajectory data mining and establishes the driving style characteristic space (DSCS) based on these clusters and proposes a speed trajectory optimization method based on driving style finally. Compared with the TRDS, the energy consumption of the trajectory obtained by this method can reduce significantly. The trajectory obtained by the method can reflect the driving style of the driver to a large extent compared with the OTOEC. The simulation results verify the effectiveness of the method.

Robotic-Arm-Based Force Control in Neurosurgical Practice

This research proposes an optimal robotic arm speed shape in neurological surgery to minimise a cost functional that uses an adaptive scheme to determine the brain tissue force. Until now, there have been no studies or theories on the shape of the robotic arm speed in such a context. The authors have applied a robotic arm with optimal speed control in neurological surgery. The results of this research are as follows: In this article, the authors propose a control scheme that minimises a cost functional which depends on the position error, trajectory speed and brain tissue force. This work allowed us to achieve an optimal speed shape or trajectory to reduce brain retraction damage during surgery. The authors have reached two main conclusions. The first is that optimal control techniques are very well suited for robotic control of neurological surgery. The second conclusion is that several studies on functional cost parameters are needed to achieve the best trajectory speed of the robotic arm. These studies could attempt to optimise the functional cost parameters and provide a mechanical characterisation of brain tissue based on real data.

Hierarchical eco‐driving control strategy for hybrid electric vehicle platoon at signalized intersections under partially connected and automated vehicle environment

AbstractIn recent years, eco‐driving for hybrid electric vehicles (HEVs) has been studied with the emerging connected and automated vehicle (CAV) technologies to improve the mobility, fuel economy and safety of HEVs. This paper develops a hierarchical eco‐driving control strategy for HEV platoons consisting of CAVs and human‐driven vehicles (HDVs) to improve fuel economy at signalized intersections. For each platoon, the speed trajectories of CAVs are optimized in the upper layer controller using model predictive control (MPC) to minimize the total fuel consumption of the whole platoon. For each HEV, the optimal power split between the engine and the battery is obtained in the lower layer controller using adaptive equivalent consumption minimization strategy (A‐ECMS). The time‐varying powertrain efficiencies of HEVs are explicitly considered in the speed trajectory optimization. At last, simulation studies are conducted using MATLAB and VISSIM to evaluate the performances of the strategy in mixed traffic scenarios and different CAV penetration rates. Simulation results indicate that compared with the single vehicle control strategy, the proposed strategy can improve the average fuel economy by up to 8.34% and considering the time‐varying powertrain efficiencies of HEVs in the optimization can further reduce the fuel consumption by up to 1.23%.

Collaborative optimization of train timetable and speed trajectory considering stochastic photovoltaic power: A two-step approach

Faced with the integrated system composed of the train power system, the photovoltaic (PV) power system, and the energy storage system (ESS), this research studies the energy-efficient operation and energy management strategy from the perspective of both train optimal control and timetable optimization, aiming at achieving a long-term energy consumption reduction. A two-step approach for collaboratively optimizing the train timetable, speed trajectory, and energy management strategy considering the stochastic characteristics of PV power generation is proposed to solve this large-scale complex problem. Before the two-step approach, a mixed-integer linear programming (MILP) model is established to optimize the energy consumption of the inter-station operation. On this basis, explicit energy consumption expressions for all inter-stations of the entire line are obtained by the proposed data fitting method. The historical PV power data is clustered to generate scenarios with different probabilities to characterize the stochastic PV power. The first step of this two-step approach is to minimize the total energy consumption expectations of all inter-stations determined by the obtained explicit energy consumption expressions to optimize the timetable while ensuring the total time and time window constraints are met. The second step is to minimize the weighted sum of energy consumption under all possible scenarios to obtain the optimal speed trajectory and energy management strategy based on the optimized timetable obtained in the first step. The validity of the model is verified by case studies using the real data of Qingdao Metro Line 11 under both scenarios with and without PV power. This study provides a novel method for energy-efficient operation and energy management of the integrated system and demonstrates the prospect of the proposed two-step stochastic optimization in reducing the net grid-supplied energy for the long-term operation of urban rail transportation systems.

Development of optimal speed trajectory control strategy for electric vehicles to suppress battery aging

In order to suppress the battery aging of electric vehicles (EVs), a multi-objective optimization function is established to describe the battery aging behavior based on a high-precision battery aging model, and the state–space equation is then constructed to reveal the intrinsic relationship between vehicle speed, acceleration, and battery state-of-charge (SOC). The constructed optimization model is solved by using a sequential quadratic programming (SQP) algorithm, and based on the model predictive control (MPC) theory, the efficient real-time control of vehicle speed is achieved. Simulation results show that the developed strategy extends the battery life by 10.33% compared to the baseline strategy when the traffic flow is not involved. In the case of involving the traffic flow, the optimization results of battery aging improves as the look-ahead time period increases, while the computational burden increases. The results show that the developed strategy reduces the battery aging of the target vehicle by 33.02% compared to the preceding vehicle while meeting the real-time requirement.

Energy-efficient tram speed trajectory optimization considering the influence of the traffic light

The tram typically adopts the form of semi-independent right of way, which is affected by the traffic light at junctions. The parking and wait time of the tram due to red lights will increase the energy consumption and reduce the passenger comfort. This paper proposes a speed trajectory optimization approach for the tram without parking when encountering a red light. This approach is realized by reasonably adjusting the running time and speed of the tram between two adjacent sections bounded by the traffic light at the junction, on the premise that the total travel time of the whole section remains unchanged. In the model, the constraints of tram movement, speed limits, and line conditions are considered. Moreover, considering the influence of the traffic light, the comfort and traffic light constraints are added to establish the energy-efficient tram speed trajectory optimization model. The nonlinear constraints in the optimization model are converted to linear constraints using the segmental approximation method and adding constraints. As a result, the optimization model is reformulated into a mixed-integer linear programming model, which could be solved by a commercial solver like CPLEX. Numerical experiments are conducted with a 3,660 m long section of the tram line with the traffic light at the junction. The experimental results show that the speed trajectory optimization approach considering the influence of the traffic light proposed in this paper can reduce the total energy consumption by 23.46% and improve passenger comfort.

Study on Speed Planning of Signalized Intersections with Autonomous Vehicles Considering Regenerative Braking

In order to reduce the energy consumption caused by the frequent braking of vehicles at signalized intersections, an optimized speed trajectory control method is proposed, based on braking energy recovery efficiency (BERE) in connection with an automated system for vehicle real-time interaction with roadside facilities and regional central control. Our objectives were as follows; firstly, to establish the simulation model of the hybrid energy regenerative braking system (HERBS) and to verify it by bench test. Secondly, to build up the genetic algorithm (GA) optimization model for the deceleration stopping of the HERBS. Then, to obtain signal light status and timing information to be the constraints; the BERE is to be the optimized objective, resulting in optimization for the speed trajectory under the deceleration stopping condition of a single signalized intersection. Finally, vehicle simulations in ADVISOR software are utilized to validate the optimization results. The results show that the BERE during deceleration stopping at a single signalized intersection after the speed trajectory optimization is 36.21% higher than that of inexperienced drivers, and 7.82% higher than that of experienced drivers.

Research on Speed Planning in the Constant Speed Interval and Quality Prediction on the Transition Surface

Aiming at the particularity on the transition surface, this paper carried out research on adaptive speed planning, local optimization of machining toolpaths to improve the surface quality of the transition surface, and visualization of motion parameters to predict the surface quality of the transition surface. Constant speed interval planning and trajectory optimization were carried out on the transition surface between the bottom of a Cola bottle and the curved surface with variable curvature, respectively, and the visualization maps before and after optimization were obtained. The comparison showed that the motion parameters of the optimized visualization map had better smoothness. Further, the effectiveness of the estimated surface quality was verified through the actual machining of the variable curvature transition surface and Wuzhi mountain parts. The consistency of the improvement of the processed surface quality and the quality of the visualization were verified.

Adaptive Speed Planning of Connected and Automated Vehicles Using Multi-Light Trained Deep Reinforcement Learning

Through shared real-time traffic information and perception of complex environments, connected and automated vehicles (CAVs) are endowed with global decision-making capabilities far beyond that of human drivers. Given more traffic lights information, this planning ability can be greatly strengthened. This study proposed an adaptive speed planning method of CAVs based on multi-light trained deep reinforcement learning (DRL), aiming to improve the fuel economy and comfort of CAVs. By setting a reasonable reward function, the training algorithm takes the key environmental information received by the vehicle as inputs, and finally outputs the acceleration that maximizes the cumulative reward. The results show that the trained DRL agent can adapt to variable scenarios with traffic lights, and quickly solve the approximate optimal speed trajectory. Multi-light DRL models save 6.79% fuel compared with single-light ones and have better performance on fuel economy and computational efficiency than a non-RL method using multi-light optimization.