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• TIP-EMS enhances EV energy efficiency by integrated prediction and energy management. • Significant driving economy boost; 1.6%-4.2% better than state-of-the-art PEMS. • TIP-EMS shows robust performance with varying prediction weight adjustments. To enhance the energy efficiency of electrified vehicles (EVs), developing effective energy management strategies (EMS) for hybrid storage systems is essential. Predictive EMS (PEMS) that foresee future vehicle speeds have demonstrated substantial potential in boosting EMS performance. However, traditional PEMS models, employing a sequential approach of speed prediction followed by energy allocation, are hampered by cumulative errors. These errors from the initial speed predss this issue, this paper introduces a novel solution: the trainable integrated preiction negatively impact the efficiency of subsequent energy management. To addrediction and energy management strategy (TIP-EMS). Contrasting with conventional sequential PEMS, TIP-EMS features a dual-branch, integrated neural network, which is fully trainable. This network processes driving status inputs via attention layers, with one branch dedicated to energy management objectives using a reinforcement learning (RL) algorithm, and the other to vehicle speed prediction. Both branches are trained simultaneously, but post-training, only the RL branch is activated for energy management. Implemented with a soft actor-critic RL algorithm, TIP-EMS is applied to a fuel cell EV for optimized energy management. The validation involved training TIP-EMS using 27 driving profiles, which developed its prediction and energy management capabilities, followed by tests in untrained scenarios. The results show that TIP-EMS surpasses conventional sequential PEMS by up to 4.2% in scenarios where prediction accuracies are comparable, highlighting the efficacy of the trainable integrated mechanism. In addition, TIP-EMS exhibits superior energy conservation compared to non-predictive RL strategies. Lastly, TIP-EMS exhibits robustness to adjustments in the weight given to the prediction objective, further confirming its practical applicability. [ABSTRACT FROM AUTHOR]

Improving energy efficiency has important significance for hybrid electric UAVs. This paper aims at improving energy efficiency by maximizing utilization solar energy, reducing demand power and optimizing power allocation. The flight trajectory optimization and energy management are considered synthetically, and the coupled model is established. Adaptive sequential convex programming method (ASCP) is further proposed to solve this coupled model. The adaptive discretization method, fuzzy-based trust region update mechanism and two-stages solving strategy are proposed to improve its optimality and convergence. The optimized flight trajectory and global optimal battery S O C trajectory can be applied to trajectory tracking control and online energy management. Numerical simulation results show that ASCP can improve energy efficiency of hybrid electric UAVs. Compared with uniform discretized sequential convex programming (UDSCP) and Gauss pseudo-spectral method (GPM), it can improve solar energy by 9.3% and 24.1%. Energy management experiment results indicate that ASCP has excellent energy saving effect. Compared with nonlinear model predictive control (NMPC), fuzzy logic state machine (FLSM) and passive energy management strategy (PEMS), it can save hydrogen by 13.5%, 18.3%, and 22.2%, respectively. This work is conducive to promoting the application of renewable energy in UAVs. • Energy efficiency is improved by flight trajectory and energy management coupled optimization. • The flight trajectory optimization and energy management coupled model is established. • Adaptive sequential convex programming method is proposed to solve the coupled model. • Fuzzy logic control is proposed to adaptively adjust the sizes of trust region. [ABSTRACT FROM AUTHOR]

This paper focuses on energy management in an ultra-energy efficient vehicle powered by a hydrogen fuel cell with rated power of 1 kW. The vehicle is especially developed for the student competition Shell Eco-marathon in the Urban Concept category. In order to minimize the driving energy consumption a simulation model of the vehicle and the electric propulsion is developed. The model is based on vehicle dynamics and real motor efficiency as constant DC/DC, motor controllers and transmission efficiency were considered. Based on that model five propulsion schemes and driving strategies were evaluated. The fuel cell output parameters were experimentally determined. Then, the driving energy demand and hydrogen consumption was estimated for each of the propulsion schemes. Finally, an experimental study on fuel cell output power and hydrogen consumption was conducted for two propulsion schemes in case of hybrid and non-hybrid power source. In the hybrid propulsion scheme, supercapacitors were used as energy storage as they were charged from the fuel cell with constant current of 10 A. • Driving strategy to minimize energy consumption was determined at given restrictions. • Driving energy is related to acceleration phase when average speed is determined. • Fuel cell degradation requires modified energy management strategy. • Hydrogen consumption was evaluated for hybrid and non-hybrid propulsion. [ABSTRACT FROM AUTHOR]

Plug-in hybrid electric vehicles (PHEVs) have received a lot of interest because of their ability to cut fuel usage by using electricity from the grid as an alternative energy source. PHEVs differ from typical hybrid electric vehicles (HEVs) in terms of increased battery capacity, the availability of plug-in recharging, and improved battery state of charge (SOC) management systems. Efficient energy consumption and excellent performance are critical issues for hybrid powertrains. The estimation of SOC in PHEVs is a difficult NP-hard problem that is frequently tackled utilising metaheuristic techniques. In this paper, we offer an enhanced dingo optimisation with deep learning-based prediction (EDOA-DLP) model specifically tailored for PHEVs. The EDOA-DLP model includes an efficient SOC estimate algorithm for real-time energy management. The EDOA-DLP technique's performance has been thoroughly proven through considerable experimentation in a variety of circumstances. The results show that the proposed EDOA-DLP method beats other current methods in terms of predicting SOC accurately and reaching the targeted goals for PHEVs. Overall, this research presents a unique EDOA-DLP SOC prediction approach for PHEVs, with promising results in terms of energy management and performance optimisation. The experimental results highlight the EDOA-DLP model's improved performance. Overall, this research presents a unique EDOA-DLP SOC prediction approach for PHEVs, with promising results in terms of energy management and performance optimisation. The experimental results illustrate the EDOA-DLP model's improved performance when compared to competing methodologies, highlighting its potential to improve the efficiency and efficacy of PHEV systems. [ABSTRACT FROM AUTHOR]

—This paper proposes two real-time energy-oriented driving strategies to minimize the energy consumption for electric vehicles on highways with varying slopes. First, a novel strategy, called normalized-energy consumption minimization strategy (NCMS), adopts a designed kinetic energy conversion factor to convert the vehicle kinetic energy change into the equivalent battery energy consumption. By minimizing the total normalized energy consumption, the energy-orientated vehicle control sequence is calculated. In addition, a logic car-following algorithm is developed to enhance NCMS for avoiding collisions with the potential preceding vehicle on the journey. Second, an improved model predictive control (MPC) is developed with a hierarchical framework, which achieves a balance between optimization and computational efficiency. In the upper level, a global, coarse-grained, iterative dynamic programming is employed to penalize the MPC terminal state, while the lower level performs online rolling optimization of the vehicle within a moderate time step. Thirdly, the performance of the proposed driving strategies is verified through a traffic simulation to evaluate the energy efficiency improvement and processor computation time compared to dynamic programming and constant speed strategy. Finally, a vehicle-in-the-loop test is carried out to validate the feasibility of the proposed two novel driving strategies. • A normalized energy consumption model is presented, combining two energy usage. • A novel eco-driving strategy is developed based on the proposed energy consumption. • The algorithm suits both free traffic and car-following scenarios with safety. • The characteristics of the four eco-driving strategies are compared. • The proposed strategy is verified by both simulation and vehicle-in-the-loop tests. [ABSTRACT FROM AUTHOR]

This paper investigates an integrated energy management strategy (IMS) to improve energy efficiency of proton exchange membrane fuel cell (FC) system in intelligent fuel cell/battery hybrid vehicles (FCHVs). Unlike other similar works which solely control output current from the FC system, stack temperature regulation is added in this research due to its impact on FC output power performance. For liquid-cooled FC systems, joint management of FC current and stack temperature is more difficult than air-cooled FC systems due to complicated thermal management systems. To address this, firstly, a control-oriented integrated model is established to provide the foundation for controller design. For the purpose of decreasing the strong nonlinearity of integrated model, the heat generation term is parameterized with a polynomial function. Secondly, in order to determine reference trajectories for controller, a data set consisting of nearly optimal operation points (FC current and stack temperature) is constructed offline, which provides reference trajectories based on simple rules in real time. Finally, a contraction-based control (CBC) approach is utilized to regulate FC current and stack temperature jointly. The bounded convergence between system states and reference trajectories is guaranteed by contraction theory. The effectiveness of the proposed IMS is validated via MATLAB simulation. • An integrated management strategy is proposed to enhance FC system energy efficiency. • A data set is constructed to provide the nearly optimal operation points of FC system. • The online ascertainment of reference FC operation points is achieved with simple rules. • The bounded convergence to reference trajectories is ensured by a contraction-based control. [ABSTRACT FROM AUTHOR]

The proposed supervisory control system (SCS) uses a control map to maximize the powertrain efficiency of a hybrid electric vehicle (HEV) in real-time. The paper presents the methodology and structure of the control, including a novel, comprehensive and unified expression for the overall powertrain efficiency that considers the engine-generator set and the battery in depth as well as the power electronics. A control map is then produced with instructions for the optimal power share between the engine branch and battery branch of the vehicle such that the powertrain efficiency is maximized. This map is computed off-line and can thereafter be operated in real-time at very low computational cost. A charge sustaining factor is also developed and introduced to ensure the SCS operates the vehicle within desired SOC bounds. This SCS is then tested and benchmarked against two conventional control strategies in a high-fidelity vehicle model, representing a series HEV. Extensive simulation results are presented for repeated cycles of a diverse range of standard driving cycles, showing significant improvements in fuel economy (up to 20%) and less aggressive use of the battery. [ABSTRACT FROM AUTHOR]

• The OML-SOCE is aimed for estimating an accurate capacity of SOC of Li-ion batteries on HEVs. • To develop optimal machine learning based SOC estimation (OML-SOCE) model for HEVs in TEM. • SSAE are optimally adjusted by the use of SSA in such a way that the prediction performance can be considerably improved. Transactive energy is a next big thing in the energy sector which considerably transform the association between utility, consumer, and the environment. Transactive energy management (TEM) system comprises a collection of economical and controlling strategies which helps to dynamically balance the power infrastructure. Peer-to-Peer (P2P) trading becomes familiar, which mainly based on the process of power generation and consumption is completely decentralized. At the same time, Hybrid electric vehicles (HEV) have become an important technology to accomplish energy efficiency and environmental sustainability. Battery Management System (BMS) evaluates the power and State of Charge (SOC), confirms the well-being depending upon the measurement. Accurate SOC estimation is crucial to ensure the unfailing functioning of Li-ion battery that is mainly employed in HEVs. A reliable SOC prediction model is needed to assure the precise measurement of the residual driving range of the vehicle and appropriate battery balancing. In this view, this paper presents an optimal machine learning based SOC estimation (OML-SOCE) model for HEVs in TEM. The OML-SOCE is aimed for estimating an accurate capacity of SOC of Li-ion batteries on HEVs. The OML-SOCE technique involves a two stage process namely stacked sparse autoencoder (SSAE) based prediction and salp swarm algorithm (SSA). At the first stage, SSAE is used for the prediction of SOC. Sparse auto-encoder (AE) is an enhanced AE technique that improves any some sparsity restrictions in the hidden layer of standard AE. A stack of multiple sparse AE forms a deep network framework that is named as SSAE. Next, in the second stage, the parameters involved in the SSAE are optimally adjusted by the use of SSA in such a way that the prediction performance can be considerably improved. A wide range of experiments take place and the results are investigated under varying temperature levels. The experimental outcomes showcased the supremacy of the presented technique the recent techniques with respect to different measures. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, the energy management problem for a Fuel Cell Plug-in Hybrid Electric Vehicle is investigated using a real-time power splitting method that preserves the battery pack life. The reduction of high-power demand during the vehicle stop-and-go operations and the limitation of the batteries'' minimum energy to a prescribed level are the two aspects considered in this study. Using a longitudinal motion model of an experimental small utility truck, a multi-criteria optimization problem, including the hydrogen/electricity cost ratio and the anticipation of the high-power demand, is formulated and solved. The analysis of the effects of the fuel cell and the battery efficiency degradations demonstrated that there exists a minimum cost ratio that prevents the Energy Management System to adequately limit the batteries'' depletion with the contribution of the Fuel Cell power. The overall control system does not require a prior knowledge of the trip information. Therefore, the real-time implementation of this system is realistic. Compared with a rule-based Energy Management System, the proposed optimal and real-time approach provides up to 5% of hydrogen saving whilst maintaining the Battery Pack energy close to the minimum prescribed energy. The high-power demand reduction and the depletion limitation enable the Energy Management System to preserve the batteries'' life. [Copyright &y& Elsevier]

This paper studies the energy and traffic impact of a proposed Anticipative Cruise Controller in a PTV VISSIM microsimulation environment. We dissect our controller into two parts: 1. the unconnected mode, active when following a human-driven vehicle, and 2. the connected mode, active when following another automated vehicle equipped with connectivity. Probabilistic constraints balance safety considerations with inter-vehicle compactness, and vehicle constraints for acceleration capabilities are expressed through the use of powertrain maps. Emergent highway traffic scenarios are then modeled using time headway distributions from empirical traffic data. To study the impact of automation over a range of demands of free-flow to stop-and-go, we vary vehicle flux from low to high and vary automated vehicle penetration from low to high. When examining all-human driving scenarios, network capacity failed to meet demand in high-volume scenarios, such as rush-hour traffic. We further find that with connected automated vehicles introduced, network capacity was improved to support the high-volume scenarios. Finally, we examine energy efficiencies of the fleet for conventional, electric, and hybrid vehicles. We find that automated vehicles perform at a 10%–20% higher energy efficiency over human drivers when considering conventional powertrains, and find that automated vehicles perform at a 3%–9% higher energy efficiency over human drivers when considering electric and hybrid powertrains. Due to secondary effects of smoothing traffic flow and reducing unnecessary braking, energy benefits also apply to human-driven vehicles that interact with automated ones. Such simulated humans were found to drive up to 10% more energy-efficiently than they did in the baseline all-human scenario. • Considers conventional, electric, and hybrid vehicle types for energy effects. • Introduce probabilistic constraints for safety and traffic flow considerations. • Integrates empirical data and high fidelity models for increased realism in simulation. • Mixed fleets observe improved energy and flow effects. [ABSTRACT FROM AUTHOR]

With the advantages of no emission and low noise, battery rail vehicles (BRVs) are widely used in the construction of subway, high-speed railway and so on. However, the problems of low energy efficiency and peak power shock of traction motor have severely restricted the application and promotion of BRVs. To address these problems, an electric-hydrostatic hydraulic hybrid powertrain (EH3) is designed in this paper. Friction braking (FB) is replaced by hydraulic regenerative braking (HRB) and the mode of recovered hydraulic energy coupled at the inlet of pump (ECIP) to assist acceleration is proposed. The energy conservation characteristics of recovery and coupling of hydraulic energy in EH3 powertrain are verified by simulation and laboratory test bench. The energy recovery efficiency of HRB is up to 50% and the new coupling modes can greatly reduce the consumption of electric power. The control strategy is designed to coordinate different working modes which are established based on the high-pressure accumulator (HPA) and the driving state parameters of BRVs. The simulation result shows that the energy consumption of the battery can be reduced by 17.32%. The EH3 powertrain has broad application prospects in realizing energy conservation, reducing peak motor power shock and improving the braking performance of BRVs. • An EH3 powertrain and the laboratory test bench is presented. • The performance of HRB and three coupling modes are compared by simulation and test. • The control strategy is proposed to coordinate different working modes. [ABSTRACT FROM AUTHOR]

This paper proposes a new optimal design approach of a permanent magnet synchronous motor (PMSM) in hybrid electric vehicles (HEVs). It aims to solve the key research problem of how to find a viable and computationally efficient solution to achieve the maximum energy efficiency of the motor over the driving cycles. A one-dimensional analytical model is, firstly, built and validated to design the geometric parameters and calculate motor efficiency, maintaining high fidelity calculation with low computational cost. Then, by analyzing the motor energy distribution of the driving cycle, the energy efficiency is characterized by representative points, which can dramatically reduce the computation time during the optimal design. Leveraging by these points, the approximation model is presented to replace the PMSM optimization model to further reduce the computational cost. Finally, a combinatorial optimization algorithm is developed to return and characterize the PMSM optimal design in the studied scenario benefiting in the energy-loss reduction. The performance of the approach has been illustrated and verified with a HEV dynamics model. The results show that the optimal design approach can reduce the motor energy losses by 18.35% and improve the HEV fuel economy by 3.2% over the driving cycle compared with the initial design. • A one-dimensional analysis model of the motor is proposed and simplified with an approximate model. • An electric motor is optimized by a combinatorial algorithm to maximize the energy efficiency over the driving cycle. • The motor energy losses are decreased by 18.35% with the motor optimal design. [ABSTRACT FROM AUTHOR]

The energy efficiency of the battery charging system directly affects the distance which electric vehicles can get with per charge process. In addition, reducing current harmonics distortion (THD) increases the electrical quality and power conversion performance. This paper proposes intelligent control of high-efficiency two-stage battery charger topology for electric vehicles (EVs). In the first stage, for low harmonics of input current, high power factor and high efficiency, the average current mode controlled ac/dc boost power factor (pf) correction method constructed and analyzed with 98% efficiency and the power factor is achieved higher than 0.99. Additionally, in the second stage, for high energy efficiency, higher battery lifespan and allowing EVs to get more miles with per charge, a half-bridge LLC resonant converter controlled with a designed artificial neural network (ANN). Moreover, the LLC resonant converter controlled under dynamic load to analysis system from no-load to full-load. A 3.1 kW powered system has been designed for this study, the efficiency of the system has been calculated based on European efficiency standards and the peak efficiency of the whole system is 96.2%, the load voltage ripple is less than 0.5 V and also total harmonic distortion of the source current is less than 5%. • Design and Analysis of Two Stage Battery Charger Topology. • Design and Application of ANN Controller. • Analysis of AC-DC Rectifier with Boost PFC and LLC Resonant. • High Energy Efficiency. • Simplified System Control. [ABSTRACT FROM AUTHOR]