Minimization Of Fuel Consumption Research Articles

Offline and Online Exergy-Based Strategies for Hybrid Electric Vehicles

Abstract Exergy-based control strategies for ground hybrid electric vehicles (HEVs) enable to pursue unconventional optimization goals that are inaccessible when standard energy-based modeling frameworks based on fuel consumption minimization are used. In this work, we formulate and solve offline and online exergy-based optimization strategies for military HEVs aimed at the minimization of genset exergy destruction and thermal emissions to increase vehicle efficiency and minimize the risk of thermal imaging detection, respectively. We refer to the offline version of these strategies as exergy minimization strategies (ExMSs). Adaptive ExMSs (A-ExMSs) are then formulated for online implementation. Moreover, charge increasing (CI) ExMSs and A-ExMSs are developed to charge the battery as much as possible during a driving mission that is followed by a silent watch phase. To assess the performance of the proposed strategies, the results obtained by the ExMSs and A-ExMSs are compared to the benchmark solutions obtained by Dynamic Programming.

Fuel Efficiency Optimization in Adaptive Cruise Control: A Comparative Study of Model Predictive Control-Based Approaches

This work investigates the fuel efficiency potential of Adaptive Cruise Control (ACC) systems, focusing on two optimization-based control approaches for internal combustion engine (ICE) vehicles. In particular, this study compares two model predictive control (MPC) designs. In the first approach, a strictly quadratic cost is adopted, and fuel consumption is indirectly minimized by adjusting the weights assigned to state tracking and control effort. In the second approach, a fuel consumption map is explicitly included in the MPC cost function, aiming to directly minimize it. Both approaches are compared to a globally optimal benchmark obtained with dynamic programming. Although these methods have been discussed in the literature, no systematic comparison of their relative performance has been conducted, which is the primary contribution of this article. The results demonstrate that, with proper tuning, the simpler quadratic approach can achieve comparable fuel savings to the approach with explicit fuel consumption minimization, with a maximum variation of 0.5%. These results imply that the first alternative is more suitable for online implementation, due to the more favorable characteristics of the associated optimization problem.

A practical vehicle routing problem in small and medium cities for fuel consumption minimization

Today, reducing fossil fuel consumption in the transport sector is one of the important requirements to preserve the ecological environment. In this paper, we investigated a logistics problem in small and medium cities. We proposes the time-dependent vehicle routing problem model in small and medium cities considering heterogeneous fleets, time windows, multi-trip, time dependency, and road network. We conducted a numerical analysis on fuel consumption minimizing path selection between two customer nodes and found that the path selection is independent of the payload in most cases. The model is formulated as a mathematical integer linear programming model, and small problems are solved using a commercial optimization solver. For large-scale instances, we proposed a heuristic algorithm based on the variable neighborhood search and evaluated its performance. The proposed algorithm can decrease fuel consumption by 25 % compared to manual routing and scheduling. Furthermore, for provide useful managerial insights, sensitivity analyses were performed considering different vehicle capacities.

Research on driving condition recognition in hybrid electric vehicle energy management strategy

Abstract The driving conditions encountered by vehicles during operation are complex and varied, and the ECMS (Equivalent fuel Consumption Minimization Strategy) based on minimum equivalent fuel consumption has poor adaptability to different driving conditions. To optimize the adaptability of the ECMS and improve the overall vehicle economy, this paper proposes an operating condition recognition model based on the Back Propagation neural network algorithm to optimize the ECMS energy management control strategy. Firstly, three types of driving conditions were identified: urban, suburban, and high-speed conditions. A complete vehicle model was constructed on the Matlab/Simulink platform. The efficacy of the strategy, before and after optimization, is verified under the WLTP (Worldwide Harmonized Light Vehicles Test Procedure) driving cycle. Simulation results show that the optimized ECMS can better adjust the power apportionment among the engine and motor, reducing the fuel consumption per hundred kilometers by 4.01% and thereby enhancing the overall vehicle economy.

Adaptive Equivalent Factor-Based Energy Management Strategy for Plug-In Hybrid Electric Buses Considering Passenger Load Variations

Energy management strategies (EMSs) are one of the key technologies for the development of plug-in hybrid electric buses (PHEBs). This paper addresses the issue of optimal energy distribution for PHEBs under significant variations in passenger load at different bus stations, which cannot be solved by a single equivalent factor equivalent fuel consumption minimization energy management strategy (ECMS). An adaptive equivalent factor equivalent fuel consumption minimization energy management strategy (A-ECMS) considering passenger load is proposed. First, the powertrain system of the PHEB is modeled, and the accuracy of the model is verified in a simulation environment. Then, the reference SOC descent trajectory of the battery is obtained using a dynamic programming (DP) algorithm, the recursive least squares (RLS) method is employed to identify the passenger load, and the influence of different loads on the state of charge (SOC) trajectory under a single equivalent factor is analyzed. Finally, a genetic algorithm (GA) is used to establish the correspondence between passenger load, bus station, and equivalent factor, enabling the actual SOC to follow the reference SOC descent trajectory, thereby achieving optimal energy distribution. Simulation results demonstrate that the A-ECMS reduces fuel consumption of the PHEB per 100 km by 2.59% and 10.10% compared to the ECMS and rule-based EMS, respectively, validating the effectiveness of the proposed strategy.

Adaptive ECMS With Gear Shift Control by Grey Wolf Optimization Algorithm and Neural Network for Plug-In Hybrid Electric Buses

The Plug-in hybrid electric bus (PHEB) is an important means of public transportation. For PHEB, this paper proposes an energy management strategy that considers both gear shift control and power splitting. For gear shift control, a large number of gear-switching data under different working conditions are calculated by using the dynamic programming algorithm. The neural network is trained by these data in the offline part so that it can provide the appropriate gear-switching signal in time in the online model. For power-split control, this paper mainly selects an improved equivalent fuel consumption minimization strategy (ECMS) and uses the grey wolf optimization algorithm to iteratively solve the optimal equivalent factor. The proposed control strategy has not only been verified under various operating conditions but also verified by relevant experiments on the HIL platform. The results show that the proposed strategy has better fuel economy than ECMS and the rule-based strategy under the provided mixed operating conditions. Compared with the dynamic programming algorithm, the fuel consumption is only increased by 3.23%, but the problem of the curse of dimensionality is avoided.

Safe Deep Reinforcement Learning-Based Constrained Optimal Control Scheme for HEV Energy Management

Considering physical constraints in online optimization and training safety is a challenge for implementation of deep reinforcement learning (DRL) algorithm. Especially for the non-linear system, the mapping relationship between output action of the agent and the control signals is difficult to obtain. This paper proposes a novel DRL framework for online optimization in energy management of a power-split hybrid electric vehicle (HEV) which combines a neural-network (NN)-based multi-constraints optimal strategy and a rule-based-restraints system (RBRS). The proposed method named Reward Directed Policy Optimization (RDPO) adopts exterior point method (EPM) and curriculum learning (CL) to direct the agent to recognize and avoid irrational control signals and optimize the fuel economy. The EMS considering fuel consumption minimization and irrational control signals avoidance is optimized by training the agent through WLTC. A competitive fuel economy, 4.495L/100km, is achieved with no irrational control signals. Based on the online adaptability evaluation conducted, the fuel consumption of the vehicle under NEDC and CTUDC has been reduced to 4.113L/100km and 3.221L/100km, respectively, with no irrational control signals. The superiority in optimization, calculation efficiency and safety is verified through the comparisons with various DRL agents.

Integrated optimization of speed schedule and energy management for a hybrid electric cruise ship considering environmental factors

Severe global energy crisis and greenhouse effect have promoted the rapid growth of hybrid electric propulsion systems (HEPSs) applied to cruise ships. However, the conventional methodologies, which optimize sailing speed and energy management separately, restrict the improvement of the ship energy efficiency. This study proposes an integrated optimization of speed schedule and energy management considering changes in wind, waves, and drifting ice during the voyage. First, a refined energy flow model of the HEPS against the resistance from wind, waves, and drifting ice in addition to the hydrostatic pressure is established. Second, the integrated optimization problem of speed schedule and energy management is formulated to pursue minimal fuel consumption. Third, the complex optimization problem is solved by a low computation load algorithm, in which the K-means algorithm and physical turning points divide the target route into several segments considering environmental characteristics, the particle swarm optimization algorithm optimizes the speed in each segment, and the equivalent fuel consumption minimization strategy optimizes the energy management during sailing. Finally, two case studies of a cruise ship sailing in the Drake Passage validate the effectiveness of the proposed method. The results show that the integrated optimization significantly reduces fuel consumption compared with the conventional separation optimization in terms of either speed schedule or energy management, as well as the conventional heuristic strategy. Furthermore, the integrated optimization demonstrates advantages in reducing greenhouse emissions caused by fuel and electricity consumption of the HEPS.

Energy Consumption Prediction and Control Algorithm for Hybrid Electric Vehicles Based on an Equivalent Minimum Fuel Consumption Model

The development of hybrid technology can effectively solve the problems of the high pollution and energy consumption levels of automobiles. Therefore, an energy consumption prediction and control algorithm for hybrid vehicles based on a minimum equivalent fuel consumption model is proposed. The model’s battery power consumption is equivalent to the fuel consumption, and the sum of the engine fuel consumption and the battery equivalent fuel consumption is established as the objective function. By utilizing these factors, an innovative minimum equivalent fuel consumption model was constructed that could be used to measure the energy efficiency of hybrid vehicles. The longitudinal force result of braking force distribution control was obtained, as well as the energy consumption prediction structure of a hybrid electric vehicle. The rolling resistance, air resistance, and climbing resistance of the hybrid electric vehicles were calculated, and the energy consumption control algorithm for hybrid electric vehicles was constructed according to the calculation results. The experimental results indicated that under this research algorithm, the driving energy consumption of hybrid electric vehicles was relatively low and the energy consumption and energy efficiency measurements effectively met the actual demand, and the energy consumption prediction and control results were good.

Optimized Energy Management Control of a Hybrid Electric Locomotive

Hybrid electric propulsion, using batteries for energy storage, is making significant inroads into railway transportation because of its potential for notable fuel savings and the related reductions in greenhouse gases emissions of hybrid railway traction over non-electrified railway lines. Due to the inherent complexity of hybridized powertrains, combining different power conversions and energy storage capabilities, the corresponding operation of their energy management needs to be precisely optimized in order to achieve the minimum possible fuel consumption. Having this in mind, this paper proposes a real-time energy management control strategy for a diesel–electric hybrid locomotive based on the optimization results obtained by means of a dynamic programming optimization algorithm aimed at fuel consumption minimization while honoring the battery state-of-charge constraints and powertrain physical constraints. The final optimization result, expressed in terms of the optimal battery state-of-charge reference (target), is used as an additional input into the state-of-charge controller within the real-time energy management system. The subsequent simulation analysis shows clear fuel economy improvement with 22.9% of fuel savings obtained for the locomotive featuring a hybrid powertrain equipped with batteries over the conventional one.

Conflict resolution in the case of convective weather cell circumvention

This research analyzes the area required for the conflict resolution between aircraft in two flows impacted by a convective weather cell (CWC). The CWC is introduced as a constrained area, forbidden to flight through, which affects the air traffic flows. Prior the conflict resolution, two flows and their intersection are relocated away from the CWC area (thus enabling circumvention of the CWC), which is followed by a tuning of the relocated flows intersection angle in order to create the minimal size of the conflict zone (CZ—a circular area centered at the intersection of two flows, which provides aircraft enough space to completely resolve the conflict within). Therefore, the essence of the proposed solution is in providing conflict free trajectories for the aircraft in intersecting flows that are affected by the CWC, with the goal of minimizing the CZ size, so the finite occupied airspace for the conflict resolution and the CWC circumvention could be reduced. Compared to the best solutions and current industry practice, this article is focused in reduction of the airspace required for aircraft to aircraft and aircraft to weather conflict resolution, and not to distance travelled, time savings, and fuel consumption minimization. The conducted analysis in the MicrosoftExcel2010 confirmed the relevance of the proposed model and demonstrated variations in efficiency of the utilized airspace. The proposed model's transdisciplinary nature makes it potentially applicable in other fields of study, such as the conflict resolution between unmanned aerial vehicles (UAVs) and fixed objects like buildings. Building on this model and taking in consideration large and complex data sets, such as weather related data and flight data (aircraft position, speed, and altitude), we believe it is possible to conduct more sophisticated analyses that would take advantage of Big Data.

Ride the Tide of Traffic Conditions: Opportunistic Driving Improves Energy Efficiency of Timely Truck Transportation

We study the problem of minimizing fuel consumption of a heavy-duty truck traveling across the national highway network subject to a hard deadline. We focus on a real-world setting that traversing a road segment is subject to variable speed ranges due to dynamic traffic conditions. The consideration of dynamic traffic conditions not only differentiates our work from existing ones but also allows us to leverage opportunistic driving to improve fuel efficiency. The idea is for the truck to strategically wait (e.g., at highway rest areas) for benign traffic conditions, so as to traverse subsequent road segments at favorable speeds for saving fuel. We observe that traffic conditions and thus speed ranges are mostly stationary within certain duration of the day, and we term them as phases. We formulate the fuel consumption minimization problem under phased speed ranges, considering path planning, speed planning, and opportunistic driving. We prove that the problem is NP-hard, and develop a dual-subgradient algorithm for large-/national-scale instances. We characterize conditions under which the algorithm generates an optimal solution. We carry out simulations based on real-world traces over the US highway system. The results show that our scheme saves up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$20\%$</tex-math> </inline-formula> fuel than a shortest-path based alternative, of which opportunistic driving contributes <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$13\%$</tex-math> </inline-formula> . Meanwhile, opportunistic driving also reduces driving time by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$6\%$</tex-math> </inline-formula> as compared to only optimizing path planning and speed planning. As such, it offers a desirable design option to simultaneously reduce fuel consumption and hours of driving. Last but not least, our results highlight a perhaps surprising observation that dynamic traffic conditions can be exploited to achieve fuel savings even larger than those under stationary traffic conditions.

Optimal Hybridization with Minimum Fuel Consumption of the Hybrid Fuel Cell Train

This paper describes a numerical study of the optimal distribution of energy between fuel cells and auxiliary energy storages in the hybrid train. Internal combustion engines (ICEs) are currently under pressure from environmental agencies due to their harmful gas emissions, and pure battery vehicles have a short range; a hybrid train powered by fuel cells, batteries, and supercapacitors can provide a viable propulsion solution. In this study, special energy management on the mountain railway with optimal power distribution and minimum hydrogen consumption is proposed. Considering the characteristics of the mountain railway, the vehicle uses recuperation of regenerative braking energy and thus charges additional power devices, and hybridization optimization gives favorable power to each power source device with a minimum consumption of hydrogen in the fuel cell. In this study, a simulation model was created in a Matlab/Simulink environment for the optimization of hybridized power systems on trains, and it can be easily modified for the hybridization of any type of train. Optimization was performed by using Sequential quadratic programming (SQP). The results show that this hybrid train topology has the ability to recover battery and supercapacitor state of charge (SOC) while meeting vehicle speed and propulsion power requirements. The effect of battery and supercapacitor parameters on power distribution and fuel consumption was also simulated.

Adaptive Equivalent Fuel Consumption Minimization Based Energy Management Strategy for Extended-Range Electric Vehicle

Unlike battery electric vehicles, extended-range electric vehicles have one more energy source, so a reasonable energy management strategy (EMS) is crucial to the fuel economy of the vehicles. In this paper, an adaptive equivalent fuel consumption minimization strategy (A-ECMS)-based energy management strategy is proposed for the extended-range electric vehicle. The equivalent fuel consumption minimization strategy (ECMS), which utilizes Pontryagin’s minimum principle (PMP), is introduced to design the EMS. Compared with other ECMS strategies, an adaptive equivalent factor algorithm, based on state of charge (SOC) feedback and a proportional–integral (PI) controller is designed to update the equivalent factor under different working conditions. Additionally, a start–stop penalty is added to the objective function to take the dynamic start–stop process of the range extender into account. As a result, under the WLTC driving cycle, the proposed strategy can achieve 6.78 L/100 km comprehensive fuel consumption, saving 6.2% and 3.4% fuel consumption compared with the conventional rule-based thermostat strategy and the power following strategy. Moreover, the proposed EMS achieves the lowest ampere-hour flux among the three EMSs, indicating its ability to improve battery life.

Optimal Energy Management of Series Hybrid Electric Vehicles With Engine Start–Stop System

This paper develops energy management (EM) control for series hybrid electric vehicles (HEVs) that include an engine start-stop system (SSS). The objective of the control is to optimally split the energy between the sources of the powertrain and achieve fuel consumption minimization. In contrast to existing works, a fuel penalty is used to characterize more realistically SSS engine restarts, to enable more realistic design and testing of control algorithms. The paper first derives two important analytic results: a) analytic EM optimal solutions of fundamental and commonly used series HEV frameworks, and b) proof of optimality of charge sustaining operation in series HEVs. It then proposes a novel heuristic control strategy, the hysteresis power threshold strategy (HPTS), by amalgamating simple and effective control rules extracted from the suite of derived analytic EM optimal solutions. The decision parameters of the control strategy are small in number and freely tunable. The overall control performance can be fully optimized for different HEV parameters and driving cycles by a systematic tuning process, while also targeting charge sustaining operation. The performance of HPTS is evaluated and benchmarked against existing methodologies, including dynamic programming (DP) and a recently proposed state-of-the-art heuristic strategy. The results show the effectiveness and robustness of the HPTS and also indicate its potential to be used as the benchmark strategy for high fidelity HEV models, where DP is no longer applicable due to computational complexity.

Flame, Combustion and Explosion Thermometry

Flame is a natural phenomenon and is a basic element of any combustion process. The majority of flames consist of a gas; there is, however, a small amount of ionisation occurring in the flame. Despite the increased focus on combustion-free energy production such as wind, air and water power, and the refocus on nuclear energy now considered to be carbon-free, nonetheless combustion will remain, for the next few decades, the major energy and heat production route worldwide. Apart from carbon dioxide, which is commonly considered to be the major pollutant, there are other gases like nitric oxide and nitrogen dioxide which, although found in significantly lower amounts in the exhaust gases from combustion units, still present a large environmental impact and are a concern. There are however well-established technologies for removing combustion products from the exhaust gas, and the combustion process can in general be made CO2 and environmentally neutral. Combustion optimisation is a route for further reduction of undesirable byproducts, fuel consumption minimisation and finally an overall energy and heat production enhancement. The key parameter in any combustion process is reliable flame and (post-) combustion gas temperature measurement and control. Various combustion environments such as waste incineration, internal combustion engines or solids explosions cause the appearance of various optical emission features in different spectral ranges not accessible to the human eye. A combination of modern and newly developed fast spectral optical techniques with extensive theoretical developments in spectral and heat radiative transfer modelling allows us to obtain detailed snapshots of what is happening in the combustion process. That also gives a possibility to establish a direct link to the industrial process control and pollutant emission reduction. In this article some examples of in situ flame and gas temperature measurements in various combustion environments and advanced spectral modelling are given and perspectives for further commercial instrumentation developments are discussed.

An Automatic Tuning MPC with Application to Ecological Cruise Control

Model predictive control (MPC) is a powerful tool for planning and controlling dynamical systems due to its capacity for handling constraints and taking advantage of preview information. Nevertheless, MPC performance is highly dependent on the choice of cost function tuning parameters. In this work, we demonstrate an approach for online automatic tuning of an MPC controller with an example application to an ecological cruise control system that saves fuel by using a preview of road grade. We solve the global fuel consumption minimization problem ofine using dynamic programming and find the corresponding MPC cost function by solving the inverse optimization problem. A neural network fitted to these offline results is used to generate the desired MPC cost function weight during online operation. The effectiveness of the proposed approach is verified in simulation for different road geometries.

Configuration Design and Optimal Energy Management for Coupled-Split Powertrain Tractor

High-power tractors are regarded as effective operation tools in agriculture, and plugin hybrid tractors have shown potential as agricultural machinery, due to their wide application in energy conservation. However, the allocation of the output power of the motors and engine is a challenging task, given that the energy management strategy (EMS) is nonlinearly constrained. On the other hand, the structure of the continuous variable transmission (CVT) system is complicated, and affects the price of tractors. In this paper, a variable configuration of a tractor that could have the same performance as a complex CVT system is proposed. To address the EMS issues that have shown poor performance in real time, where the programming runs online, firstly a demand power prediction algorithm is proposed in a rotary tillage operation mode. Secondly, an equivalent fuel consumption minimization strategy (ECMS) is used to optimize the power distribution between the engine and the motors. In addition, the equivalent factor is optimized with an offline genetic algorithm. Thirdly, the equivalent factor is converted into a lookup table, and is used for an online power distribution with different driving mileages and state-of-charge (SOC). The simulation results indicate that the equivalent fuel consumption is reduced by 8.4% and extends the operating mileage of pure electric power. Furthermore, the error between the actual and forecasted demand power is less than 1%. The online EMS could improve the mileage of the tractor working cycle with a more feasible fuel economy based on demand power predictions.

Fuzzy Adaptive Energy Management Strategy for a Hybrid Agricultural Tractor Equipped with HMCVT

In order to solve the problem of high fuel consumption and poor emission performance in high horsepower tractors, a parallel hybrid tractor system was designed using a dual power source of an engine and motor matched with a hydro-mechanical continuously variable transmission (HMCVT). An equivalent fuel consumption minimization strategy (ECMS) was used for power distribution of this hybrid system. To address the problem of poor adaptability of the equivalence factor to different working cycles in the conventional ECMS, a fuzzy adaptive equivalent fuel consumption minimization strategy (FA-ECMS) was proposed. A fuzzy PI controller based on battery SOC (State of Charge) feedback was designed to adjust the equivalence factor in real time, so as to achieve adaptive control of the equivalence factor. The physical model of the system was built by SimulationX, and the model of the control strategy was built using Matlab/Simulink. Two typical cycles of tractor plowing and road transportation were simulated. Under ECMS, the fuel consumption of the hybrid agricultural tractor was 14.3 L and 1.19 L in one plowing cycle and one transport cycle, respectively, with final battery SOC values of 60.75% and 60.32%, respectively. Under FA-ECMS, the hybrid farm tractor consumed 13.34 L and 1.13 L in one plowing cycle and one transport cycle, respectively, with final battery SOC values of 60.27% and 60.17%, respectively. The results showed that, with the introduction of a fuzzy PI controller to dynamically adjust the equivalence factor, the overall fuel consumption was reduced by 6.71% and 5.04%, respectively, and the battery power maintenance performance was improved. The designed control strategy could achieve a more reasonable power distribution between the engine and motor while maintaining the balance of the battery SOC.

Trajectory optimization of space vehicle in rendezvous proximity operation with evolutionary feasibility conserving techniques

In this paper, a direct approach is developed for discovering optimal transfer trajectories of close-range rendezvous of satellites considering disturbances in elliptical orbits. The control vector representing the inputs is parameterized via different interpolation methods, and an Estimation of Distribution Algorithm (EDA) that implements mixtures of probability models is presented. To satisfy the terminal conditions, which are represented as non-linear inequality constraints, several feasibility conserving mechanisms associated with learning and sampling methods of the EDAs are proposed, which guarantee the feasibility of the explored solutions. They include a particular implementation of a clustering algorithm, outlier detection, and several heuristic mapping methods. The combination of the proposed operators guides the optimization process in achieving the optimal solution by surfing the regions of the search domain associated with feasible solutions. Numerical simulations confirm that space transfer trajectories with minimum-fuel consumption for the chaser spacecraft can be obtained with terminal condition satisfaction in rendezvous proximity operation.