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This paper proposes a novel energy management strategy satisfying real-time and highly efficient energy management strategies for fuel cell hybrid electric vehicles (FCHEVs). The strategy is based on model predictive control (MPC), which integrates long short-term memory (LSTM) and dynamic programming (DP). A high-precision powertrain model of the investigated FCHEV is established for subsequent simulations. After training under several typical working conditions, LSTM is designed to forecast future power demands of the entire vehicle. Using the prediction results, the DP algorithm calculates the control scheme based on model predictive control. Considering the economy and durability of power sources, the results of four different control strategies are compared: thermostat, power following, traditional DP, and MPC. The MPC proposed in this paper reduces the total usage cost per 100 km on the test set by 9%, 33.5%, and −4.6%. • The economy and durability of vehicular fuel cell system are both considered. • The LSTM neural network is imported into MPC theory for vehicular power prediction. • The key parameters of MPC and LSTM are determined by referring to the greedy algorithm. • The computational load in MPC optimisation is reduced by simplifying DP algorithm. • The real-time performance of the supposed EMS on-board is validated. [ABSTRACT FROM AUTHOR]

Fuel cell hybrid electric vehicles (FCHEV) are one of the most promising new energy vehicles. The cost and lifetime of its powertrain have limited its commercial development. This paper proposed an energy management strategy based on nonlinear model predictive control (NMPC) technology to solve the economy and durability problem of FCHEVs. Based on Markov Monte Carlo(MCMC) method, a prediction model of multi-scale operating conditions is established, and dynamic programming(DP) is used to realize the optimal control in the predicted time domain. The "constant speed prediction" is innovatively adopted in the transition stage to improve the prediction accuracy and enable the model to be realized online. The ways to reduce calculating amount of NMPC are also discussed in this paper. This simplification leads to suboptimal fuel economy and durability of control system but can have obvious reduction in calculating time. The simulation results show that, compared with the thermostat strategy and the power following strategy, the degradation cost decrease of 11.1% and 23.9% and the total operation cost of NMPC decrease of 11.0% and 23.5% respectively. The NMPC strategy has better economy and durability than the rule-based energy management strategy, is close to the global optimal result obtained by dynamic programming and can meet the requirements of real-time control. • The economy and durability of vehicular fuel cell system are both considered. • Markov Chain Monte Carlo is imported into MPC theory for vehicular power prediction. • Adaptability to changing conditions is strengthened via "constant speed prediction". • The computational load in MPC optimization is reduced by simplifying DP algorithm. [ABSTRACT FROM AUTHOR]

To achieve the 8000-h proton exchange membrane fuel cell stack (PEM FCS) life target set by the U.S DoE and promote fuel cell hybrid electric vehicles (FCHEVs) massive introduction in the automotive market, using multi-fuel cell stack (MFCS) systems instead of single-fuel cell stack systems seems to be an interesting solution that deserves to be explored. MFCS systems' concept combines several small FCSs modules instead of using a single high-powered FCS module. The modularity in such systems can be exploited through energy management to improve their durability and extend their good energy-efficiency power range. However, FCSs' multiplicity makes it challenging to implement effective energy management strategies (EMSs). This paper proposes a remaining useful life (RUL) prognostic-based EMS to extend MFCS systems' lifetime while keeping their hydrogen consumption reasonable. For this purpose, a prognostic algorithm is developed to predict PEM FCSs' RUL in real-world automotive application scenarios. Then a rule-based EMS allocates the demand between stacks using prognostic results. The proposed strategy's performance is evaluated on a hybrid MFCS/battery system using Matlab/Simulink's environment. Simulation results show that implementing the proposed strategy instead of conventional EMSs can extend MFCS systems' lifetime by at least a factor of 2.35 while keeping their hydrogen consumption reasonable. © 2001 Elsevier Science. All rights reserved. • A post-prognostic decision-making strategy is proposed for MFCS systems. • RUL predictions are performed in real-world automotive scenarios. • The MFCS system's lifetime is significantly extended with the proposed EMS. • RUL is a suitable parameter for adaptive energy management decision-making. [ABSTRACT FROM AUTHOR]

This paper introduces a smart cyber physical multi-source energy system for electric vehicle applications. This system is realized in order to increase the autonomy of the vehicle as well as a good self-dispatch energy system. In this paper, three energy sources have modeled and integrated in the energy conversion chain of the vehicle. Moreover, the design of cyber energy dispatching system is proposed based on the genetic algorithm NSGA-II. Finally, the simulation and experimental results are demonstrated. [ABSTRACT FROM AUTHOR]

This paper presents a novel off-grid hybrid renewable energy system integrated with hydrogen production and retired electric vehicle (EV) batteries for combined power and heat supply to a small rural community. The system consists of photovoltaic (PV) panels, evacuated tube solar collectors, a proton exchange membrane fuel cell (PEMFC), an alkaline electrolyzer, compressed hydrogen storage tanks, and reused lithium-ion batteries from EVs. A new battery model is developed in the Transient System Simulation Program (TRNSYS) to capture the capacity degradation of retired batteries. A two-stage energy management strategy is proposed to regulate power flow and maximize solar energy utilization. The system is optimized for minimum power supply disruption, hot water supply disruption, energy waste, and annualized costs over a 20-year lifetime using a multi-objective NSGA-II algorithm coupling MATLAB and TRNSYS. Optimization results reveal tradeoffs between the competing objectives. Dynamic simulations show the water tank temperature fluctuates between 20 and 100 °C with mean values dropping slightly in later years due to battery degradation. Energy waste from components rises in later years for the same reason. The replacement interval for a retired EV battery module is around 2.5 years. The integrated system proves feasible for off-grid combined power and heat supply using solar energy and retired EV batteries as storage, though battery degradation impacts long-term performance. • Integrates solar, hydrogen, retired EV battery for off-grid power and heat supply with optimized storage and backup. • Utilizes multi-objective optimization to balance system reliability, waste minimization, and costs through Pareto solutions. • Implements long-term performance over twenty years to provide insights into component lifetimes and replacement needs. [ABSTRACT FROM AUTHOR]

For saving fuel and extending the fuel cells (FC)/battery lifetime, this paper proposes a real-time cost-optimal predictive energy management strategy of FC hybrid electric vehicles based on continuation/general minimal residuals (C/GMRES) algorithm. To ensure the preferable continuation for algorithm application, the continuity method is proposed for accurate model approximations. The external penalty method is employed to transform the inequality constraints as an equivalent index cost. Then, the continuous and unconstrained model predictive control problem is reformulated, and the C/GMRES algorithm is proposed for real-time optimization. Given the output, the expected FC control command can be decided by the designed postprocessing rules. The performance of the proposed strategy is validated under simulations and hardware-in-the-loop (HIL) tests. The results manifest that the proposed strategy can effectively save the total cost for the predictive horizon of 5s–60s even when the neural network-based predictive velocity is adopted. Besides, compared with the interior point method, the proposed C/GMRES algorithm achieves similar solving effects while exhibiting more than 100 times computing efficiency. In addition, the execution time of the proposed strategy in each step is less than 1.2 ms under HIL tests, indicating its real-time applicability. • State of health for fuel cells and battery are modeled and optimized. • Continuity method of health-aware system model is proposed. • Real-time optimization algorithm is proposed for this highly nonlinear problem. • Results about horizon length and predictive errors are gained and discussed. • HIL test verifies the control effects and real-time applicability. [ABSTRACT FROM AUTHOR]

The accelerated adoption of Electric Vehicles (EVs) necessitates innovative and effective pricing strategies for charging infrastructure. This study leverages the MATSim (Multi-Agent Transport Simulation) framework to meticulously evaluate the performance of two distinctive EV charging pricing models: time-based charging and a hybrid model integrating both usage-based and time-based components. Driven by the pivotal question of how EV charging should be optimally priced—whether contingent on energy consumption or charging duration—the research endeavors to conduct two simulations. These simulations aim to provide a comprehensive comparative analysis, evaluating metrics such as total power derived from grids, utility revenue, charging station queues and served vehicles. In the time-based charging scenario, EVs incur charges based on their plugged-in duration, reflecting a pricing approach that correlates directly with the time a vehicle remains connected to the charging station. In contrast, in the hybrid model, EVs undergo initial billing based on usage until a predetermined battery charge point is reached, such as achieving a full battery. Subsequently, time-based pricing takes effect until the user disconnects the vehicle. The findings indicate that, in the combined approach, utility owners have the potential to generate more revenue. Conversely, the time-based approach demonstrates a capacity to serve a higher number of electric EVs, with comparable queue lengths observed in both approaches. Importantly, using the results of this paper, policymakers can suggest pricing schemes that maximize benefits for utility owners, reduce queues at charging stations, and ensure the security of power grids. [ABSTRACT FROM AUTHOR]

The hydrogen combustion engine (H 2 ICE) is known to be able to burn H 2 , producing no CO 2 emissions and extremely low engine-out NO x eo emissions. In this work, the potential to reduce the NO x eo emissions through the implementation of electric hybridization of an H 2 ICE-equipped passenger car (H 2 -HEV), combined with a dedicated energy management system (EMS) is discussed. Achieving a low H 2 consumption and low NO x eo emissions are conflicting objectives, the trade-off of which depends on the EMS and can be represented as a Pareto front. The dynamic programming algorithm is used to calculate the Pareto-optimal EMS calibrations for various driving missions. Through the utilization of a dedicated energy management calibration, H 2 -HEVs exhibit the potential to decrease the NO x eo emissions by more than 90%, while, decreasing the H 2 consumption by over 16%, compared to a comparable non-hybridized H 2 -vehicle. The present paper represents the initial potential analysis, suggesting that H 2 -HEVs are a viable option towards a CO 2 -free mobility with extremely low NO x eo emissions. • H 2 -HEVs with a dedicated energy management can achieve extremely low NO x eo • Mixed H 2 -HEVs have a superior H 2 -NO x eo trade-off than series and parallel H 2 -HEVs • Mixed H 2 -HEVs lower the NO x eo emissions by 90% and decrease the H 2 consumption by 16% • H 2 -HEV's optimal EMS calibrations for the entire H 2 - NO x eo Pareto front [ABSTRACT FROM AUTHOR]

During the transient mode switching process of the hybrid electric vehicle (HEV) from motor driving mode to hybrid driving mode, dynamic coordinated control of different components is essential to improve the vehicle comfort and dynamic performance. The key to highly quality mode switching control includes fast and stable speed and/or displacement tracking of the engine and motor. The transient mode switching stages of the HEV is divided in this paper. On this basis, by combing the nonlinear sliding mode control and the finite-time stability theory, the global fast integral terminal sliding mode controller (GFITSMC) is designed for the transition stages involving clutch slip. The GFITSMC consists of the global fast integral terminal sliding mode surface (GFITSMS) and the non-smooth reaching law (NSRL). In order to improve the controller convergence and anti-disturbance performance, the proposed controller is synthesized from the perspective of finite-time stability. It is proved that, with proper NSRL and GFITSMS parameters, the speed and displacement tracking error of the motor and engine can reach the sliding mode surface and further converge to zero in a finite time. Simulation and hardware-in-the-loop (HIL) tests are performed to validate the effectiveness of the proposed control method. Research results demonstrate that the proposed strategy not only achieves faster transient mode switching by improving the state trajectory tracking performance, but also reduces the longitudinal jerk caused by the transient mode switching significantly. • The global fast integration terminal sliding mode strategy for the coordinated control of HEV is developed. • The control scheme of the coordinated control for the HEV mode switching is proposed. • The dynamic coordinated controller is synthesized and stabilized in terms of the finite-time stability theory. • The non-smooth reaching law is designed to improve the controller convergence. • Hardware in the loop test is conducted to verify the superiority of the proposed controller. [ABSTRACT FROM AUTHOR]

[Display omitted] • Different efficient energy conversions of ammonia are comprehensively reviewed. • Existing work on theoretical and experimental aspects of ammonia hybrid systems for hybrid vehicles is summarized. • The possibility of ammonia hybrid systems for automotive use is recognized. • The future research direction of ammonia new energy hybrid vehicles is pointed out. Ammonia is recognized as a promising and environmentally friendly energy source. This paper provides a comprehensive overview of emerging ammonia hybrid power systems and a detailed examination of the potential applications of this technology in automotive scenarios. Firstly, it summarizes three important ammonia energy conversion technologies in hybrids: ammonia internal combustion engine (A-ICE), direct ammonia fuel cell (DAFC), and ammonia decomposition, analyzing the technical characteristics and future development trends of each. Subsequently, this paper further discusses the architectural features of the new energy hybrid powertrain, highlighting the potential for a highly efficient hybrid powertrain based on the combination of the aforementioned powerplants. Currently, modeling optimization plays a crucial role in advancing research on the architecture of ammonia hybrid powertrains. The paper presents a comprehensive review of current research on energy management strategies (EMS) for hybrid vehicles. It discusses the key elements of model building, optimization objectives, and method selection in detail. Furthermore, the direction of these key elements in ammonia hybrid systems is also indicated. [ABSTRACT FROM AUTHOR]

Hybrid electric vehicles (HEVs) feature multiple working modes. Thoughtful selection of these modes can optimally balance driving performance, power demands, and energy consumption, thereby enhancing the overall efficiency of the vehicle. This paper presents a soft actor-critic (SAC) approach trained on a multi-modal driving cycle (MDC) for selecting operational modes of electro-hydraulic hybrid electric vehicle (EHHEV). Firstly, characteristic parameters are extracted and clustered for five typical driving cycles through principal component analysis and K-means clustering, creating a multi-modal driving cycle. Secondly, based on the operational characteristics of EHHEV, state variables, action variables, reward functions, learning rates, and other parameters are set for the SAC algorithm, and the EMS framework is built based on the electro-hydraulic hybrid electric power system. Subsequently, the SAC algorithm is trained using the MDC to construct the SAC-MDC EMS. Results demonstrate that compared to EV, RB EMS, and SAC EMS, IREC achieves maximum improvements of 22.38 %, 5.55 % and 0.80 %, respectively. The dynamic performance and the motor load optimization capability are also enhanced. To further validate the practicality and reliability of the SAC-MDC EMS, this paper validates it using actual driving data, revealing that it still exhibits outstanding performance. • A multi-modal driving cycle containing information about multiple driving situations is created. • The SAC algorithm is proposed for switching working modes in electro-hydraulic hybrid electric vehicles. • The output actions of the SAC algorithm are transformed into rule-based control to enhance algorithm interpretability. • The multi-modal driving cycle is applied to the training of the SAC algorithm to improve its adaptability. [ABSTRACT FROM AUTHOR]

The hybridization of energy storage systems consisting of fuel cells, batteries, and ultracapacitors has tremendous potential in fuel cell hybrid electric vehicles. However, the utilization of ultracapacitors in existing energy management strategies is insufficient, preventing complete exploitation of the energy storage system's benefits. To strengthen the utilization of ultracapacitors, this paper proposes an energy management strategy for fuel cell vehicles with three energy sources, considering the ideal state of charge (SOC) of ultracapacitors. The proposed method disassembles the motor power demand, with the ultracapacitor primarily supplying the acceleration portion. It calculates the ideal SOC of the ultracapacitor, and controlling the actual SOC of the ultracapacitor to match the ideal SOC. Furthermore, a fuzzy logic controller is designed to modify the fuel cell's output power, reducing power fluctuation. Additionally, according to the variation of the SOC of the battery, the fuel cell output power is modified to enhance the overall performance of the composite energy storage system. Simulation and hardware-in-loop results demonstrate that, compared to the adaptive filtering strategy with fuel cell power constraints and dynamic programming strategy, the strategy proposed in this paper reduces operational costs by 14.56 % and 4.99 %, respectively. • An energy management strategy considering ideal SOC in ultracapacitor is proposed. • The ideal SOC of UC is defined, with adjusting based on the average vehicle speed. • A fuzzy logic controller is designed to adjust the limits of the FC output power. • The output power of FC is adjusted according to the changes in the SOC of BAT. • This strategy offers better overall costs compared with the AFS strategy. [ABSTRACT FROM AUTHOR]

• This paper presents efficient energy management for loads with and without EVs. • This is based on optimal scheduling of solar-powered battery energy storage system. • A case study of a practical system is investigated. • The model considers seasonal variation effects of power generation and EV charging. • The results of proposed model are compared with other models. The increasing adoption of electric vehicles (EVs) and variable energy usage patterns substantially strain the electrical grid; indeed, optimal energy management, monitoring, and utilization are required for the reliable operation of the grid. This paper introduces a novel model design of a solar-powered battery energy storage system (SPBESS) as a viable substitute for conventional demand-side management (DSM) and time of use (ToU) pricing schemes, intending to optimize energy management and utilization with IoT monitoring. In addition, the IoT-based prototype has been developed to monitor and control the proposed system. To validate the proposed SPBESS model, the study examines two distinct cases: one encompassing domestic loads and the other integrating EV loads alongside household demand. Using ToU pricing, it determines the optimal charging and discharging strategies for the SPBESS, evaluates their implications for the system configuration and grid ToU pricing, and quantifies the annual reductions in power purchases, electricity bills, energy costs, and carbon emissions. Moreover, the study comprehensively analyzes the optimal power exchanges between the photovoltaic system, battery energy storage system, and the grid, precisely considering the specific load requirements and grid ToU pricing. The conducted research findings manifest considerable decreases in energy costs, with the domestic load condition witnessing a reduction from $0.312 per kilowatt-hour to $0.245 per kilowatt-hour and the scenario involving EV and residential loads experiencing a decline from $0.27 per kilowatt-hour to $0.197 per kilowatt-hour. Furthermore, the annualized cost savings for cases 1 and 2 amount to $1,336 and $4,544, respectively, yielding substantial reductions in polluting gas emissions. Besides, the IoT-based constructed prototype model design for real-time power parameters monitoring and control shows the importance of IoT-based monitoring for remote load control, allowing users to maximize energy efficiency, resource utilization, and system visualization. [ABSTRACT FROM AUTHOR]

Neural Networks (NNs) can be used for energy management of hybrid vehicles, but they are hard to tune in inference to adapt to different driving conditions. To make the NN-based energy management strategy more flexible, this paper proposes a controllable NN for optimal energy management of fuel cell hybrid electric vehicles. Inspired by the equivalent factor in the Equivalent Consumption Minimization Strategy (ECMS), we introduce an adjustable target variable for the final state as an input to the NN-based strategy. During training, classification and regression networks with single-step and multi-step inputs are considered. An efficient shooting method and an adaptive method are then introduced to realize the precise control of the final state and online parameter adaptation. Simulations of the proposed method and the benchmarking method are carried out in different battery discharge modes. Results demonstrate that the proposed shooting neural classifier can achieve 99.7% fuel optimality of dynamic programming in a similar computational time to the shooting ECMS, and the proposed adaptive neural classifier can adapt to different driving conditions and has better fuel economy than the adaptive ECMS. • A controllable neural model for energy management of fuel cell vehicles is proposed. • Neural regressors and classifiers are trained under single-step and multi-step inputs. • A shooting method and an adaptive method for neural models are proposed. • The proposed method is comprehensively analyzed and compared with benchmarks. [ABSTRACT FROM AUTHOR]

Fuel Cell Hybrid Electric Vehicles (FCHEV) use batteries and Photo Voltaic (PV) arrays to overcome Fuel Cell (FC) limitations like slow reaction times and low power densities. In a traditional power conversion system, three converters link FC, PV array, and battery to the output load. Despite using the FC, PV array, and battery, it has three inductors and several switches. Each switch also endures high voltage stress, possibly lowering power density and efficiency. This paper proposes a single-inductor multi-input converter (SI-MIC) that achieves 550 V as output where the input is variable. The fuzzy logic controller enables the integration of multiple power sources by selectively turning the ON and OFF switches in various operating modes. Fewer active switches during the powering process make it possible to employ semiconductors with a lower voltage. Thus, the converter generates high power with low conduction and switching losses. The state space model verifies the validity and stability of the converter under various operating modes. • Battery and PV array are integrated with the fuel cell using the single inductor multi-input converter (SI-MIC). • Fuzzy logic controller is used for switching the semiconductor devices for operating in various modes. • Battery can be charged and discharged in standby and normal operating modes. • Even if the input voltage fluctuates, the output voltage is always maintained at 550 V. [ABSTRACT FROM AUTHOR]

The paper emphasizes the significance of sustainable energy solutions centered around electric vehicles (EVs). This involves Electric Intelligent Parking Lots (IPLs) that are interconnected with Renewable Energy Sources (RES) and Hydrogen Storage Systems (HSS) to achieve both technical and environmental goals. The study introduces a probabilistic approach to managing HSS-based energy in IPLs, employing the Probabilistic Two-Point Estimate Method (TPEM) to consider variables like sun radiation, temperature, wind speed, and IPL load. The Honey Badger Algorithm (HBA) is employed for optimization, striving to minimize operational costs related to energy distribution while maximizing profits by exploring revenue opportunities within the IPL system. This strategy enhances energy management and intelligent EV charging in IPLs, improving efficiency and economic viability. The approach is tested in a standard IPL environment integrated with RES and load demand, showcasing its successful technical and financial outcomes. Results reveal a 7.8 % profit increase from renewable resources and an additional 4.89 % when uncertainties are factored in. Ultimately, the proposed energy management framework utilizing HSS and RES integration proves effective in achieving objectives, with the potential for greater profitability through renewable resource incorporation and uncertainty consideration. • Optimal energy management framework proposed for electric vehicle integration in intelligent parking lots. • Simulation of efficient charging and discharging strategy in an IPL for electric vehicles. • Impact of renewable sources integration (wind and solar) on IPL efficiency and revenue generation. • Uncertainty analysis of renewable sources and load demands in IPL using two-point estimation method. • Smart integration of renewable sources, hydrogen storage, and electric vehicle charging for optimal operation of IPL. [ABSTRACT FROM AUTHOR]

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). • A hierarchical strategy of IFCHEV platoon in complex conditions is proposed. • Speed planning is conducted using offline optimization with real-time adjustment. • Online adjustment adopts fixed headway to ensure platoon stability and safety. • Complex working conditions are considered to guarantee strategy adaptability. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries (LIBs) have emerged as an indispensable component in the development of green transportation such as electric vehicles (EVs) and large-scale applications of renewable energy such as smart grid energy storage systems. The detection, judgment, and prediction of various battery states such as State of Charge (SOC) and State of Health (SOH) in the battery management system (BMS) play a critical role in guaranteeing the LIBs work under a safe and reliable situation. After decades of intensive investigation, accompanied by the fast development of big-data techniques (BDT) and artificial intelligence (AI) algorithms, the framework of BMS is moving from the traditional onboard system towards the functional integrated scheme. This paper starts with a comprehensive overview of the underlying degradation mechanism of the battery and algorithm distinction and judgment of the battery states in BMS. Subsequently, the paper has systematically reviewed and discussed the most commonly used approaches and state-of-the-art algorithms for battery state estimation in BMS from the perspective of three different BMS configurations: onboard-BMS, cloud-BMS, and functional integrated-BMS. This review expects to stimulate more new insights and encourage more efforts to develop advanced BMS for intelligent and innovative battery control. • Various battery degradation phenomena and their model development are explained. • Three distinct BMS structures—onboard-BMS, cloud-BMS, and Fi-BMS—are explained. • The latest advancements in battery state estimation algorithms are reviewed. • Emerging technical innovation prospects are highlighted in four areas. [ABSTRACT FROM AUTHOR]

• An adaptive MAE evaluation for the lithium battery in hybrid UAV applications was proposed. • An OSMO was developed to estimate the real-time lithium battery OCV. • Adaptive MAE evaluation for the lithium battery can be achieved with the real-time estimated OCV. • The MAE evaluation method was adaptive to dynamic ambient temperature and uncertain aging levels. Accurate evaluation of the maximum available energy (MAE) of the lithium battery is crucial for energy management and optimization in hydrogen-electric hybrid unmanned aerial vehicle (UAV) applications. However, uncertainties in ambient temperature and cell aging level would affect the MAE of the lithium battery, posing challenges for its accurate state evaluation. This paper presents an adaptive MAE evaluation method based on an optimized sliding mode observer (OSMO) for the lithium battery. The study begins by conducting comprehensive long-term experimental tests to analyze the dependencies of the lithium battery's MAE on different ambient temperatures and cell aging levels. Subsequently, an OSMO with a Kalman filter is developed to estimate the variations of the battery's open circuit voltage (OCV) on the basis of a dynamic lithium battery model. Furthermore, the real-time OCV is successfully used to achieve accurate adaptive MAE evaluation for the lithium battery. The originality of this paper is that the model parameters and proportional coefficients can be updated adaptively based on the OSMO with the Kalman filter. In this case, the OCV can converge to their actual value such that accurate MAE evaluation can be guaranteed, even with uncalibrated model parameters under dynamic ambient temperature and uncertain aging levels. Finally, experimental and simulation results verify that the proposed method exhibits a convergence speed four times higher than the traditional method. The mean absolute percentage error (MAPE) of MAE evaluation is less than 1.5 % under dynamic ambient temperature and uncertain aging levels. [ABSTRACT FROM AUTHOR]

• To solve the problem of regenerative braking energy recovery in traction power supply systems, a techno-economic capacity configuration strategy considering the decoupling control strategy for the interphase- bridging inverter-based three-port topology for railway energy router (IBI-RER) is proposed in this paper. • In the technical model, a multi-layer decoupling control strategy considering capacity constraints is proposed to address the complex coupling relationship between the active and reactive power injected into the traction bus by IBI-RER. • In the economic model, a multi-threshold energy management strategy is suggested to solve the issue of the current single-threshold energy management strategy being unable to reduce energy costs and power demand costs simultaneously. • Additionally, the economic model considers the constraint of payback period, making the project more attractive in practical engineering situations. The interphase-bridging inverter-based three-port railway energy router (IBI-RER) is a promising technology compared to conventional schemes due to its simple architecture and reduced equipment power rating. However, the three-port structure is strongly coupled, resulting in complex interactions among α/β-phase AC-side active and reactive power and DC-side power, which have a significant impact on the system capacity. Therefore, this paper proposes a techno-economic capacity configuration strategy considering the power decoupling control strategy for the IBI-RER. The capacity configuration strategy considers several objectives, including regenerative braking energy (RBE) utilization, power quality, comprehensive benefit, and payback period. To address the strong coupling of active and reactive power, a multi-layer optimal control strategy considering capacity constraints is proposed to ensure that the capacity configuration objectives can be achieved. Furthermore, a multi-threshold energy allocation strategy is designed to target the reduction of the power demand charge and the energy charge, based on the objective of maximizing the comprehensive benefit. The simulations and experiments based on the measured data of a traction substation verify the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

• The source-load uncertainties within the multi-energy microgrid are comprehensively considered, and a data-driven box-based uncertainty set is formulated. In the uncertainty analysis of EVs, an unsupervised clustering method is introduced in the construction of the data-driven uncertainty set containing EVs. • A three-stage min–max robust optimization model for MEM is developed to handle multiple uncertainties. Meanwhile, this paper adopts Stackelberg game theory to portray the interaction between multi-energy microgrid system operators and EV users. • Finally, this paper employs the column and constraint generation (C&CG) algorithm in solving the robust optimization model and constructs a numerical simulation experiment based on a real integrated energy park model. To improve the flexibility and robustness of energy systems, we propose a three-stage robust optimization (RO) model for a multi-energy microgrid (MEM). First, the source-load uncertainties within the multi-energy microgrid are comprehensively considered, and a data-driven box-based uncertainty set is formulated. In the uncertainty analysis of electric vehicles, an unsupervised clustering method is introduced in the construction of the data-driven uncertainty set containing EVs. Secondly, a three-stage min–max robust optimization model for MEM is developed to handle multiple uncertainties. Meanwhile, this paper adopts Stackelberg game theory to portray the interaction between multi-energy microgrid system operators and EV users. Finally, this paper employs the column and constraint generation (C&CG) algorithm in solving the robust optimization model and constructs a numerical simulation experiment based on a real integrated energy park model. The simulation results show that the uncertainty set constructed based on historical data can eliminate some low probability cases in describing EV uncertainty. Compared with deterministic optimization, the robust optimization approach can effectively resist multiple uncertainties without significantly increasing the operating cost of the MEM. Meanwhile, the C&CG algorithm can ensure fast convergence of the process when the model solves complex multilevel models. [ABSTRACT FROM AUTHOR]

• Waste Collection Vehicle Routing Problem with Time Windows is studied. • Compressed natural gas plug-in hybrid electric vehicles are investigated. • A new Hybrid Threshold Acceptance algorithm is presented. • Extensive computational experiments are carried out. Commercial waste collection is an essential service requiring efficient and reliable provision for customers. At the operational level, one of the most challenging problems is to design a set of refuse vehicle routes to collect waste from a set of bins. To be used multiple times, these vehicles must be emptied regularly throughout the day. This paper investigates a waste collection problem with a homogeneous fleet of plug-in hybrid electric refuse vehicles powered by two different power sources, i.e., electricity and compressed natural gas (CNG). In addition, realistic fuel consumption functions are used to estimate total energy requirements for each type of fuel, including refueling and recharging, and the detailed energy consumption along the path between two nodes of interest. We propose a Hybrid Threshold Acceptance (HTA) algorithm for this problem and denote it as the Hybrid Waste Collection Problem (HWCP). Extensive computational experiments confirm that the proposed HTA algorithm provides good results against current state-of-the-art algorithms designed for the electric vehicle routing problem. Out detailed computational results demonstrate the performance of our method considering either full or partial recharging, as well as the effect of different battery/tank capacities. Compared to the standard CNG or electric vehicles, we also show the benefits of using a fleet of hybrid electric refuse vehicles in terms of operational costs and total distance traveled. [ABSTRACT FROM AUTHOR]

This paper proposes a dual-objective eco-routing strategy aimed at optimizing driving routes by minimizing both energy consumption and travel time. First, the vehicle and powertrain dynamics are modelled. The energy consumption of three powertrain types, including hybrid electric vehicle (HEV), battery electric vehicle (BEV) and conventional vehicle with internal combustion engine (CV), are calculated based on a white-box model. Second, this paper models the traffic network by considering the intensity of each road. The energy consumption associated with different intensity levels is also modelled using a typical driving cycle. The dual-objective routing problem, considering both energy consumption and travel time, is solved by an Iterative Dijkstra's algorithm. Finally, several simulation results demonstrate that the proposed dual-objective eco-routing strategy achieves a favorable balance between energy consumption and travel time. Specifically, for HEV, the dual-objective eco-routing strategy resulted in a 5.3% reduction in travel time with a marginal increase of only 0.4% in fuel consumption compared to the single-objective eco-route. In addition, the eco-routing strategy exhibits distinct patterns across different vehicle powertrains. HEV and BEV tend to utilize heavy traffic conditions more frequently compared to the CV. • Build the energy consumption models of hybrid electric vehicle (HEV), battery electric vehicle (BEV) and conventional vehicle with internal combustion engine (CV). • Energy consumption associated with different intensity levels is modelled. • Formulate the dual-objective routing problem, considering both energy consumption and travel time. • Eco-routing strategy exhibits distinct patterns across different vehicle powertrains and traffic conditions. [ABSTRACT FROM AUTHOR]

Decarbonization will allow the automotive sector to achieve net-zero greenhouse gases emissions. Electric and hydrogen vehicles will contribute to that final target. Fuel Cell Hybrid Electric Vehicles FCHEVs aim at solving some of the issues of the otherwise standalone options: higher range in comparison with Battery Electric Vehicles, and allowing the fuel cell to be downsized, operated at greater efficiency, and less exposed to degradation. In this paper, a FCHEV Digital Twin DT is proposed to characterize the vehicle behavior during the WLTP driving cycle with accurate modeling of the auxiliary systems. Two control strategies, Range Extender and optimized Fuzzy logic control, are compared to show the impact on the vehicle and components performance and highlight the impact, energy-wise, of such systems in the Balance of Plant. Increased range by nearly 30 km is demonstrated while simultaneously limiting load stresses on the battery pack. • Detailed modeling of Fuel Cell Hybrid Electric Vehicle (powertrain and auxiliaries). • Vehicle auxiliaries account for almost 35–45% of the total energy consumption. • Ambient temperature strongly impacts on vehicle range (up to 13% of reduction). • HVAC is the most impacting auxiliary subsystem from energy standpoint. • Optimized fuzzy logic control increases range up to 4.5% respect to Range Extender. [ABSTRACT FROM AUTHOR]

As the energy transformation in the transportation sector is taking place driven by the development of fuel cell technologies, fuel cell hybrid electric vehicles become promising solutions owing to their long driving duration and zero emissions. However, the unsatisfied lifespan of fuel cells is an inevitable obstacle for their massive commercialization. This paper aims to propose an online adaptive prognostics-based health management strategy for fuel cell hybrid electric vehicles, which can improve the durability of the fuel cell thanks to online health monitoring. Here, particle filtering method is adapted for online fuel cell prognostics and the uncertainty of the predicted results is calculated based on the distribution of particles. A health management strategy is developed based on prognostics and a decision-making process is designed by considering the prognostics uncertainty through a decision fusion method. The obtained results show that the developed strategy has effectively improved the durability of the on-board fuel cell by up to 95.4%. Moreover, a sensitivity analysis of the prognostics occurrence frequency and probability calculation has also been conducted in this paper. • A health management strategy is proposed combining with on-line prognostic results. • RUL probability is assessed using particle filtering based on particle distribution. • Decision fusion is used to determine FLC parameters with prognostics uncertainty. • Long-term simulation is conducted using data from real-world driving experiments. [ABSTRACT FROM AUTHOR]

Due to air quality concerns and stricter carbon targets, surface transport electrification is quickly spreading, posing questions on the impact it will have on national and local electrical networks. This paper proposes an agent-based model to assess the per-minute weekday and weekend impact of the uptake of Electric Vehicles (EVs) over the next decade on local electrical and charging infrastructures, aimed at local decision-makers and stakeholders for transport electrification forecasting purposes. This study compares two scenarios, the first assessing the case where no restrictions are imposed on the daily charging events, and the second scenario where the peak electrical demand period between 5pm and 8.30pm is constrained for charging. Swindon Borough is selected as case study since it has one of UK's highest EV adoption rates and has ambitious aims for decarbonisation. The results show that, over time, scenario two consistently lessened the constraints imposed on the grid by lowering the weekday and weekend peak loads up to 7% and 20%, respectively, and through lowering the usage rate of the charging infrastructure by around 12%. This scenario postponed the 5pm to 8.30pm EV charging demand to later hours, resulting in delayed load waves in residential areas that, over time, took values of higher proportion of the daily peak, forecasted to match it by 2036. However, controlling the EV demand through this strategy became less effective over time, and so, constraining charging between 7am and 2.30pm is suggested for further control. To conclude, this scenario can be portrayed in reality by adding flexibility to the grid, through the use of time-of-use tariffs (TOUTs), hence, if well implemented, postponing the upgrade of the grid and the charging infrastructure, presenting savings to the network operator, charging network stakeholders and EV users. The paper thus highlights the advantages of using a model of a heterogeneous population with fine spatial and temporal detail to provide decision-support to key stakeholders in planning energy transitions. [ABSTRACT FROM AUTHOR]

Urbanization and population growth has led to an increase in the per unit energy consumption. As majority of the energy is derived from fossil fuel based sources, they have created adverse effects on the environment. Efforts to safeguard the environment have led to the development of hybrid electric vehicles. Proliferation of EV in the markets depends on factors like price, battery technology, economy and development of fast charging stations. Fast charging stations are an important factor in EV market penetration. This paper focusses in designing fast charging solar hybrid EV charging station. To decrease dependence on the grid power and to increase profitability, the charging station includes a 100 kW solar power plant with storage. The paper presents a comprehensive model of the hybrid solar charging station. To determine the load on the station, a stochastic model is used to predict the arrival time, the SOC and the charging demand. Stochastic firefly algorithm (SFA) is used for MPPT control to obtain maximum power from the solar power plant and to ensure fast charging of the station batteries. The station is designed to supply power from the batteries during off-peak hours and from the grid during high peak hours. The paper also presents a multi-objective planning using SFA to minimize the investment cost and to increase the profit of the charging station. Results show that using SFA enables fast charging of the batteries and also increases the profit of the charging station. Higher profit will encourage utilities to invest in charging station and hence this will help in higher penetration of EVs in the market. [ABSTRACT FROM AUTHOR]

In this paper, two distributed finite-time neurodynamic algorithms are proposed to collaboratively manage the charging scheme of electric vehicles (EVs) in the microgrid scenario. First, the upper level model is constructed to optimize the disorderly charging problem of EV users under private charging posts , and explore the optimal charging scheme under charging constraints and time-varying conditions to ensure the benefits of users. The lower layer model explores the optimal public charging scheme under the system operation constraint and the supply–demand balance constraint with the objective of minimizing the overall microgrid operation cost. The optimal solution of the upper model, i.e., the load of EV users under the private charging post, is considered as a parameter of the lower model. In this context, two finite-time neurodynamics with fast convergence rate, executed in a distributed manner, are proposed to track the optimal solution of the problem in real time. Furthermore, the stability and convergence in finite time of the two proposed algorithms are proved using Lyapunov theorem and finite time theorem. Numerical case studies of small-scale and large-scale power systems demonstrate the effectiveness, robustness, and real-time performance of the two proposed algorithms. [ABSTRACT FROM AUTHOR]

The increase in temperature, regulations of CO2 emissions, and the cost of conventional fuels forced the transportation industry to move towards the electrical energy sector. The increasing nature of electrification of transportation will increase the stress on the existing power system because of the charging of battery-based electric vehicles (EV). This paper briefly reviews the effect of electrification of transportation on the existing power system. The Lion's share of the EVs is battery-based, and these EVs extensively depend on Lithium. The Lithium is a non-renewable energy source just like fossil fuels, and reserves may last one day. The availability of Lithium reserves across the world and alternate for the grid-dependent EVs are illustrated in this paper. This paper also briefly discusses the fuel cell-based EVs, solar PV based EVs along with battery-based EVs and compare the battery with fuel cell. There is a significant need for research and development on grid-independent hybrid electric vehicles (GIHEV). From the last few years, the extensive part of the hybrid electric vehicles is making use of multi-input DC-DC converters to interface multiple sources to enhance the performance and reliability of the vehicle. Among different types of multi-input DC-DC converters, non-isolated multi-input DC-DC converters are best suited for low and medium power electric vehicle applications. This paper displays the synthesizing process of multi-input converters. Some of the existing efficient non-isolated multi-input high step-up DC-DC converter topologies are immensely discussed and compared. This review is intended to serve as a suitable guideline and reference for future work in the field of non-isolated multi-input high step-up DC-DC converter topologies for GIHEVs. • Effect of electrification of transportation on the existing power system. • Identified Lithium reserves in the world. • Fuel cell-powered electric vehicles and comparison of the battery with fuel cell. • The significance of grid-independent hybrid electric vehicles. • Non-isolated multi-input high step-up DC-DC converter topologies and comparison. [ABSTRACT FROM AUTHOR]

Anti-jerk controllers, commonly implemented in production vehicles, reduce the longitudinal acceleration oscillations transmitted to the passengers, which are caused by the torsional dynamics of the drivetrain during torque transients. Hence, these controllers enhance comfort, drivability, and drivetrain component durability. Although anti-jerk controllers are commonly implemented in conventional production internal-combustion-engine-driven vehicles, the topic of anti-jerk control has recently been the subject of increased academic and industrial interest, because of the trend towards powertrain electrification, and the distinctive features of electric powertrains, such as the high torque generation bandwidth and absence of clutch dampers. This paper reviews the state-of-the-art of automotive anti-jerk control, with particular attention to control structures that are practically implementable on real vehicles. The survey starts with an overview of the causes of the longitudinal vehicle acceleration oscillations that follow abrupt changes in the powertrain torque delivery. The main body of the text reviews examples of anti-jerk controllers, and categorizes them according to the adopted error variable. The ancillary functions of typical anti-jerk controllers, e.g., their activation and deactivation conditions, are explained. The paper concludes with the most recent development trends, and ideas for future work, including possible applications of model predictive control as well as integration of anti-jerk controllers with autonomous driving systems and other vehicle control functions. [ABSTRACT FROM AUTHOR]

The electric vehicle to grid (V2G) interaction technology can improve the utilization of renewable energy and stabilize its grid connection. At the same time, renewable energy can be used for a microgrid nearby, or incorporated into a large grid, to effectively address the volatility of renewable energy sources. Motivated by the increasing number of electric vehicles (EVs) and the randomness of renewable energy output, this paper proposes an effective strategy to improve the security and economy of the microgrid system. The uncertainty of wind power and EV's state of charge (SOC) is modeled as uncertainty prediction sets. And considering the worst-case scenario, this proposed strategy can increase the absorption ratio of renewable energy while orderly guiding the charging and discharging of EVs in peak-load reduction and valley filling and thus, lower operating costs under various practical constraints. To solve the problem of over-conservatism of the robust optimization, this paper introduces a dispatch interval coefficient to adjust the degree of conservatism, while improving the economy of microgrids system. The robustness and feasibility of the proposed dispatch strategy are demonstrated by numerical case studies. • Electric vehicle is introduced as both load and source (V2G mode) to help the consumption of wind power. • Operation stability and economic performance of microgrid is improved with the adjustable robust optimization (RO). • The robustness and economy of the system has been quantified. • Sensitivity analysis of uncertain variables to system total cost is carried out. [ABSTRACT FROM AUTHOR]

• The paper deals with a real multi-compartment container loading problem. • The problem arises in the beverage distribution under several practical constraints. • A hybrid approach is proposed based on constructive heuristics and MILPP models. • Computational tests are performed with a variety of instances based on real data. • The approach obtains feasible solutions for all instances under all constraints. In this paper, we address a real problem of packing boxes into a multi-compartment container, in which the boxes must be delivered to customers in a predefined route. This problem arises, for example, in the distribution of beverages (packed in "boxes") by trucks whose container has multiple compartments. The objective is to find a feasible packing plan that minimizes the handling of boxes inside the container along the whole truck route. The following practical constraints must be met: orientation of the boxes, cargo stability, load bearing strength of the boxes and load balancing. To the best of our knowledge, this is the first study focusing on a vehicle packing problem with multi-compartment containers in the context of beverage distribution. To solve this problem, we present a hybrid approach which consists of a heuristic method based on the generation of horizontal layers and on the solution of mixed integer linear programming models. Computational tests were performed with this approach and a large variety of instances based on real data from a soft drink company. The results show that the approach is able to find feasible solutions for all instances considered under all constraints, contrary to what was observed in practice with the manual procedures available in the company. In practice, if a feasible solution is not obtained, it is necessary to change the predefined route plan and/or to consider an additional new delivery route and/or to use a solution that violates some of the constraints involved. In this sense, the proposed solution approach has good potential for being embedded into existing expert and intelligent systems for supporting the decision making process. [ABSTRACT FROM AUTHOR]

• A hierarchical convex optimization framework for HEV-PCC is proposed. • A TSI-MPC approach is designed to address the speed planning problem. • An energy management strategy that combines regression model with PMP is designed. Predictive Cruise Control (PCC) achieves automated driving with improved vehicle economy by incorporating road gradient information from high-precision maps. However, existing research on PCC has primarily centered around conventional gasoline vehicles. Hybrid Electric Vehicle (HEV) offer superior fuel efficiency compared to their traditional counterparts. To further enhance vehicle economy by integrating the PCC system with HEVs, this paper proposes a hierarchical control architecture for HEV-PCC based on convex optimization. Firstly, considering spatial slope information from the road ahead, this paper introduces a Time-Space Integration Model Predictive Control (TSI-MPC) based on the principles of kinetic energy. Secondly, the paper combines a current regression model with the Pontryagin Minimum Principle (PMP) to present a Regression Analysis - Pontryagin Minimum Principle (RA-PMP) approach for the direct analytical solution of the energy management problem. Finally, the strategy is validated through simulation analysis and Hardware-in-Loop (HIL) testing on various real road scenarios. The result indicate that, in comparison to the Equivalent Consumption Minimization Strategy (ECMS) with constant speed cruising (CC), this method achieves an average fuel economy improvement of 9.45%. [ABSTRACT FROM AUTHOR]

The fuel cell hybrid electric vehicle (FCHEV) has earned great interest among the automotive industry in recent years. However, the power allocation strategy between proton exchange membrane fuel cell (PEMFC) and lithium-ion battery remains a technological challenge. To conquer this problem, a multi-objective predictive energy management strategy (EMS) based on model predictive control (MPC) is proposed in this paper, combined with velocity forecast and driving pattern recognition. The comparative study is conducted to reveal the interaction between each optimization objectives. Simulation results illustrate that the proposed EMS could maintain SOC around reference, reduce fuel consumption by 6.67%, and avoid PEMFC degradation which caused by frequent start-off and rapid load change. • Velocity forecast further improves fuel economy. • Three optimization objectives are considered. • The hydrogen consumption is promoted by over 3% compared with traditional non-forecast EMS. • The effectiveness of proposed method is validated by simulations. [ABSTRACT FROM AUTHOR]

Learning-based intelligent energy management systems for plug-in hybrid electric vehicles (PHEVs) are crucial for achieving efficient energy utilization. However, their application faces system reliability challenges in the real world, which prevents widespread acceptance by original equipment manufacturers (OEMs). This paper begins by establishing a PHEV model based on physical and data-driven models, focusing on the high-fidelity training environment. It then proposes a real-vehicle application-oriented control framework, combining horizon-extended reinforcement learning (RL)-based energy management with the equivalent consumption minimization strategy (ECMS) to enhance practical applicability, and improves the flawed method of equivalent factor evaluation based on instantaneous driving cycle and powertrain states found in existing research. Finally, comprehensive simulation and hardware-in-the-loop validation are carried out which demonstrates the advantages of the proposed control framework in fuel economy over adaptive-ECMS and rule-based strategies. Compared to conventional RL architectures that directly control powertrain components, the proposed control method not only achieves similar optimality but also significantly enhances the disturbance resistance of the energy management system, providing an effective control framework for RL-based energy management strategies aimed at real-vehicle applications by OEMs. • Enhancing RL applicability through data-driven modeling and reliable control framework. • Data-driven model error calibration toolchain for high-fidelity environment modeling. • Learning-based energy management combined with real-time optimization method. • Corrects flawed short-term evaluation of equivalent factors using horizon-extended RL. • The proposed framework enhances disturbance resistance without damaging optimality. [ABSTRACT FROM AUTHOR]

Power quality challenges are a major problem these days because of the significant increase in load demand and the existence of various reasons for disruptions in the distribution system. A voltage sag or a decrease in the magnitude of the line voltage can be brought on by rapid fluctuations in load or distribution system issues. Enhancing the grid current profile, reducing grid voltage sag, and improving other performances can all be achieved through the combination of electric vehicles with distribution generation systems. In this paper, the integration of Distributed Generation with Electric Vehicles in the distribution system has been done to enhance system performances such as true power loss index, imaginary power loss index, voltage deviation index, short circuit current index, cost function, and environmental impact reduction index for 16 bus distribution system in constant impedance, constant current, and constant power load models, or ZIP load models through proper location, sizing, coordinated controlling, and types of Distributed Generation and Electric Vehicle pairs for both charging and discharging modes by adopting Hybrid Genetic Algorithm - Monte Carlo Simulation optimization methodologies. This study's primary goal is to determine the optimal distribution generation and electric vehicle pairing to improve the aforementioned parameters, which will assist researchers in their future planning. Researchers, industry professionals, scientists, and individuals working with Distributed Generation and Electric vehicles in the smart grid will find this statement to be of great use. [ABSTRACT FROM AUTHOR]

In this article, a novel optimization algorithm named hybrid Osprey Optimization and Particle Swarm Optimization (OOPSO) is proposed to solve the Probabilistic Optimal Power Flow (POPF) problem in power systems with high penetration of renewable energy resources (RERs), such as photovoltaic, wind, and wave energy and integration of electric vehicles (EVs). The algorithm's results are compared with established optimization algorithms like Osprey Optimization Algorithm (OOA), Particle Swarm Optimization (PSO), and Heap Optimization. Applied to the 30 and 118-bus IEEE test systems, the OOPSO algorithm efficiently deals with the uncertainties produced by RERs generation. This research study includes EV charging profiles for weekdays and weekends. It considers home and work-level charging scenarios. The RERs and EVs penetration ensure system reliability while minimizing generation costs and satisfying power flow constraints. Compared to existing algorithms, the OOPSO algorithm achieves superior performance, faster convergence, and higher solution accuracy. The simulation results show the effectiveness of OOPSO. It appears to be a robust tool for POPF, integrating RERs in power systems with EVs. This work highlights the potential of OOPSO as a feasible approach for power system optimization in the context of RERs and EV integration, paving the way for further research into unconventional grid optimization techniques. • This paper presents impact of EVs and wave energy systems on OPF of power networks. • The optimization problem is solved by the proposed hybrid Osprey-PSO Algorithm. • Measured data are implemented in the study. • The effectiveness of the proposed method is compared with other methods. [ABSTRACT FROM AUTHOR]

The precise prediction of energy usage in Battery Electric Vehicles (BEVs) effectively mitigates drivers' concerns over "mileage anxiety". However, the conventional approach to predicting energy consumption, which relies solely on historical data and a single model, exhibits significant limitations in terms of accuracy and applicability. These limitations are particularly evident in scenarios lacking traffic information, where uncertainty about velocity and driving patterns can result in suboptimal predictions. As a result, a hybrid method based on driving style and route information recognition is proposed in this paper to accurately predict future energy consumption. This method relies on multi-source information and achieves its objective through a driving cycle prediction and residual fitting model. Simulation results indicate that the framework exhibits acceptable predictive performance in urban, motorway, and suburban settings, with Terminal Relative Errors (TRE) of 5.40%, 5.60%, and 4.26%, respectively. • The proposed energy consumption method eases "mileage anxiety" concerns effectively. • Route information and driving style recognition enhance adaptability in the prediction model. • Integrating proposed energy model with residual fitter enhances calculation accuracy. [ABSTRACT FROM AUTHOR]

Deep reinforcement learning stands as a powerful force in the realm of intelligent control for hybrid power systems, yet some imperfections persist in the positive progression of learning-based strategies, necessitating the proposal of essential solutions to address these flaws. Firstly, a public and reliable benchmark model for hybrid powertrains and the optimization results of energy management strategies are essential. Hence, two Python-based standard deep reinforcement learning agents and four Simulink-based hybrid powertrains are employed, forming a co-simulation training approach as the reliable solution. Secondly, a detailed analysis from the perspectives of range, magnitude, and importance reveals that the optimization terms in traditional reward functions can mislead the agent during the training process and require cumbersome weight tuning. Accordingly, this paper proposes a novel training idea that combines the rule-based engine start-stop with an unweighted reward tailored for optimizing engine efficiency and facilitating training progress. Finally, a hardware-in-the-loop test is performed, treating the P2 hybrid electric vehicle as the target. The results show that two deep reinforcement learning-based energy management strategies achieved fuel economies of 6.537 L/100 km and 6.330 L/100 km, respectively, and more efficient and reasonable control sequences ensure the working state of the engine as well as the state of charge of batteries. • Relying on the Python/Simulink-based co-simulation training (standard RL agents and public hybrid powertrains) achieves more reliable results as benchmarks. • A novel training idea is proposed, an unweighted reward function is designed, and the imperfections of the traditional reward are also analyzed in detail. • The hardware-in-the-loop test is conducted, and standard models, algorithms, and results are made public. Without modifying any parameters, these results can be reproduced and used for verification. [ABSTRACT FROM AUTHOR]

For series hybrid electric vehicles, due to the limitation of engine operating characteristics, speed regulation is usually performed using discrete operating points. This leads to the problem of mixed integer programming when solving energy management problem. In this paper, an analytical method with strong real-time performance and small computational load is proposed. First, an intelligent cycle recognition system is designed. It can cluster driving cycles offline based on the probability distribution of the required power and efficiently recognize unknown driving cycles online. Then the analytical expression of power distribution is derived, and the selection scheme of engine operating point is given. Finally, the required power information obtained from the clustering is substituted into the analytical expression. The specific values of the corresponding analytical solutions for each type are computed offline. When facing unknown conditions, this method can match the power distribution scheme quickly once the driving cycle is recognized. The results of the simulation and hardware-in-loop experiments demonstrate that this method can effectively control the SOC trajectory and allocate power more optimally. Compared to the benchmark method, the proposed strategy improves the fuel economy by 5.52 % and 7.49 % in two cases, and the computational efficiency is significantly enhanced. • An intelligent driving cycle recognition system using deep clustering is designed. • An improved fully analytical ECMS is proposed. • Coordinative optimization for engine operation and power flow of SHEV. • The control performance is validated both in simulation and hardware-in-loop test. [ABSTRACT FROM AUTHOR]

In this paper, a novel artificial intelligence (AI)-based energy management strategy across three levels is designed for isolated microgrids. During the initial phase, AI is not employed. A precise and rapid-response microcontroller, namely the FPGA, is utilized. The performance of the FPGA is experimentally investigated under various operation conditions. In the second phase, AI plays a crucial role in enhancing both the reliability and economic effectiveness of the system. The multi-objective snake optimization (SO) algorithm is employed to attain high technical and economic performance within predefined constraints. Three objective functions are involved in this phase, focusing on the minimization of operational costs, loss of power supply probability (LPSP), and electrical energy losses in the dummy load. In the third level, a novel control strategy-based coordinated model predictive control is presented as part of the proposed AI-embedded energy management strategy. A hybrid optimization algorithm combining the multilayer feed-forward neural networks (MFFNN) and SO algorithms is proposed to predict the output power of backup sources. The microgrid under consideration is supported by a hybrid backup system comprising battery energy storage systems (BESS), electric vehicle (EV) batteries, and fuel cells (FCs). The effectiveness of this hybrid algorithm is evaluated through comparison with many other algorithms, aiming to assess its performance in accurately predicting the output power of backup sources. The results show the feasibility of using AI to achieve efficient operation and energy management in microgrids. The proposed MFFNN-SO algorithm achieves the best performance, as the normalized root mean square error (NRMSE) for FCs, BESS, and EV is about 0.1296 %, 0.0094 %, and 0.1304 %, respectively. The SO algorithm achieves a notable 6.3361% reduction in operating costs, resulting in a final operational cost of 166.4811 $. [ABSTRACT FROM AUTHOR]

Battery cooling is crucial for electric vehicles' thermal safety, energy consumption, and battery life in hot climatic conditions. For electric vehicles with battery/supercapacitor hybrid energy storage system, battery cooling is deeply coupled with load power split from the electrical-thermal-aging perspective, leading to challenging thermal and energy management issues. This paper proposes a hierarchical multi-horizon model predictive control (MH-MPC) method to optimize battery cooling and energy management simultaneously. First, the electrical-thermal-aging coupling relationship between battery cooling and energy management is systematically analyzed. Then, by decoupling a centralized MH-MPC, an upper-level MH-MPC is designed to optimize the battery capacity loss cost and battery cooling cost by generating optimal compressor power, then a lower-level MH-MPC tends to minimize the battery capacity loss cost by allocating the total load power demand. The prediction horizon and sampling time are determined. Numerical results show that, compared with the centralized method, the proposed hierarchical method provides a lower battery capacity loss for long-term driving with only about 20% computation burden. Compared with standalone energy management without battery cooling, the total cost can be reduced by 12%–16% under long-term driving. Compared with optimizing energy management with Bangbang cooling, the battery degradation and total costs can be reduced by 15%–52% under short-term driving without deteriorating long-term performance. • First integrated battery thermal and energy management work for EVs with HESS. • Multi-horizon MPC for simultaneous battery cooling and power allocation optimization. • Hierarchical optimization for problem decoupling and better computation burden. • Excellent battery degradation and total costs for both short and long-term driving. [ABSTRACT FROM AUTHOR]

• Transforming to ASEV would help airports to reduce carbon emission. • An improved rollout algorithm is proposed to control the ASEVs with the consideration of dynamic programming. • The rollout approach is designed based on two heuristic algorithms. • The control strategy of ASEVs would consider not only satisfying the business tasks but also consuming the PV generation. • The simulation results shows that the rollout approach could guide the ASEV control and reduce the energy cost comparing with the benchmark algorithms. Traditional diesel-based airport service vehicles are characterized by a heavy-duty, high-usage-frequency nature and a high carbon intensity per vehicle per hour. Transforming these vehicles into electric vehicles would reduce CO2 emissions and potentially save energy costs in the context of rising fuel prices, if a proper energy management of airport service electric vehicles (ASEVs) is performed. To perform such an energy management, this paper proposes a new customized rollout approach, as an optimal control method for a new ASEV dynamics model, which models the ASEV states, their transitions over time, and how control decisions affect them. The rollout approach yields an optimal control strategy for the ASEVs to transport luggage and to charge batteries, with the objective to minimize the operation cost, which incentivizes the charging of the ASEVs to match renewable generation. Case studies demonstrate that the rollout approach effectively overcomes the "curse of dimensionality" challenge. The rollout algorithm results in a total cost approximately 10 % less than that of the underlying "greedy charging" heuristic, which charges a battery whenever its state of charge is not the maximum. The rollout algorithm is proven to be adaptive towards flight schedule changes at short notice. [ABSTRACT FROM AUTHOR]

Feature extraction techniques are widely used in fields such as machine learning, pattern recognition, and image processing. The quality of feature extraction is crucial to the generalization ability of a model. This paper proposes a feature extraction technique based on the Shapley value method and an improved Minimum Redundancy Maximum Relevance (mRMR) analysis method. The improved mRMR algorithm enhances the recognition ability for fitting effects of multi-variable feature subsets by traversing all possible combinations of input variable subsets. That is, the result of feature subset selection with fewer input variables does not affect the feature subset selection process when there are more input variables, thus avoiding the limitations of the original algorithm. The research results indicate that using the Shapley value method in conjunction with the improved mRMR algorithm proposed in this study can select the optimal feature subset containing fewer feature variables and achieve a lower MSE value. This contributes to achieving lower computational complexity and higher data fitting accuracy. This technique is applied to the feature extraction scenarios of power battery output power signals and instantaneous fuel consumption signals in hybrid electric vehicles, and constructs the optimal feature subsets for the aforementioned signals. • Exhibits superior global search capabilities, achieving improved data fitting results. • Improves the construction efficiency of the optimal feature subset. • Helps to improve the data fitting accuracy of the plant model. [ABSTRACT FROM AUTHOR]

• Dual heat source high efficiency Organic Rankine cycle system. • Energy management strategies for complex power source vehicle systems. • Strategy parameter optimization and tuning for road condition adaptation. • Results of hybrid and integrated system selection for different road conditions. Developing hybrid electric vehicle architectures and Organic Rankine Cycle-based waste heat recovery for heavy commercial vehicles can improve engine efficiency and reduce energy consumption. Previous studies have focused on a single type of hybrid integrated with a simple Organic Rankine Cycle system, with engine efficiency to be further improved. In order to investigate the applicability of different hybrid configurations in different road conditions, this paper establishes series, parallel and 48 V mild hybrid simulation models based on SIMULINK software, as well as a model integrated with an efficient Organic Rankine cycle system, and operates them on highway and suburban road conditions respectively. The optimal selection of hybrid and integrated configurations under different road conditions is derived: the parallel hybrid and integrated system is selected for highway conditions. For suburban road conditions, hybrid only is selected in series, and 48 V mild hybrid configuration is selected after integrating the Organic Rankine Cycle system. The modelling of the waste heat recovery system and the derived hybrid selection basis provide a reference for energy saving and emission reduction of heavy-duty vehicles. [ABSTRACT FROM AUTHOR]

• A scalable energy management strategy for multi-stack fuel cell systems is developed. • The method serves as a benchmark for energy management in fuel cell systems. • The method is applicable to various heavy-duty vehicle applications. • The strategy considers the health condition of fuel cell stacks during operation. • Designed filters enhance the time efficiency of the methodology. This paper puts forward an offline optimal energy management strategy (EMS) for a multi-stack fuel cell (FC) hybrid electric vehicle (HEV) aimed at reducing both fuel consumption and degradation of FC stacks. This method is anticipated to serve as a benchmark for evaluating the performance of online EMSs in future studies. To achieve this goal, a constrained exploration method (CEM) is proposed for power allocation among different energy/power sources. Exploration methods often suffer from time-consuming processes and the curse of dimensionality, particularly when the number of control and state variables increases. To address these issues, the proposed CEM incorporates various filters for the effective calculation of battery power, battery state of charge (SOC) range, and SOC interpolation. The proposed method also utilizes a health-conscious multi-objective cost function intended for minimizing operational costs, including hydrogen consumption and degradation. CEM is introduced as a novel scalable, health-conscious and time-efficient EMS benchmark for hybrid multi-stack FC systems, efficiently decreasing operational expenses and notably shortening simulation duration. The effectiveness of the proposed method is validated through tests conducted on three heavy-duty vehicles as case studies. The first case involves an aircraft, the NASA X-57 Maxwell, fitted with two 50 kW stacks and a battery pack. The validation of the CEM is established by comparing it to level-set dynamic programming (DP) and sequential quadratic programming (SQP) in terms of accuracy and required calculation time. In the second case study, a tramway containing two 125 kW FC stacks is investigated. The validity of the CEM is approved by level-set DP and SQP algorithms. In the third case study, a truck is considered, equipped with four 75 kW FC stacks and a battery system. Level-set DP fails to do the power distribution in this case owing to the numerous state and control variables, whereas the proposed CEM successfully distributes the power among the power sources. Also, results from a comparison of the CEM and SQP methods demonstrate the superiority of CEM. This case study highlights the potential for scalability of the proposed methodology compared to the conventional DP approach found in the literature. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) combined with bidirectional home chargers (vehicle-to-home (V2H) units) and electricity interchange between residences are promising options for the self-consumption of rooftop photovoltaics (PVs) in residences. Furthermore, considering that EVs have large storage capacities, one EV + V2H might support neighborhood-scale electricity storage by electricity interchange between residences, resulting in more cost-efficient self-consumption systems. Therefore, this paper investigates how permitting electricity interchange between residences affects the economically optimal design of distributed energy systems, such as PVs, EVs and V2H units. For this purpose, a mixed-integer linear programming model is developed to optimize the configurations and operation strategies of distributed energy resources (DERs) considering electricity interchange in neighborhoods. V2H units, which supply electricity from EVs to residences, are considered DER options independent of EVs. Furthermore, the optimization results are compared under two scenarios wherein electricity interchange is and is not permitted between two residences. The results indicate that permitting electricity interchange improves the self-consumption rate of PV power while reducing investment in storage facilities. The one influencing factor is sharing one EV + V2H between two residences and effectively utilizing the available storage capacity. • Comprehensive optimization of energy system configurations in residences. • Installation of bidirectional chargers for electric vehicles is also optimized. • Impact of permitting electricity interchange between residences is investigated. • Sharing electric vehicle batteries is valuable for photovoltaic self-consumption. • Electric vehicles can replace the role of the neighborhood's electricity storage. [ABSTRACT FROM AUTHOR]

• Modeling the environmental aspects of virtual energy hubs with carbon offsetting. • Hedging against uncertainties using a stochastic scenario-oriented analysis. • Risk aversion operation under operational risks driven from chance constraints. • Optimization of hybrid vehicles under both electric and fossil fuel driving modes. Virtual energy hubs (VRHBs) combine dispersed energy resources to enhance the efficient use of various energy sources, grid flexibility, and renewable energy adoption. This paper investigates the adoption of VRHBs with power-to-gas (P2G) facilities and hybrid electric-compressed natural gas vehicles (HNGEVs) in the presence of renewable resources to effectively tackle carbon emissions and increase the flexibility of VRHBs. The proposed P2G facility is constructed from carbon capture to collect carbon pollution from fossil fuel resources and HNGEVs to recycle into synthesis gas in a methanation plant. Furthermore, due to severe uncertainties due to renewable resources and energy consumption intensity, the proposed framework utilizes stochastic-driven chance-constrained optimization, which enables decision-makers in VRHBs to account for the probabilistic nature of uncertainties. Chance-constrained optimization explicitly considers uncertainty through probabilistic constraints, providing a more robust and reliable solution compared to deterministic methods. By modeling uncertainties using chance-constrained programming, VRHBs can manage the economic risks under unexpected worst realizations in uncertainties. Based on the results, the P2G facility leads to a significant reduction in the daily emissions by 63.06 % and 42.77 % observed under no-risk and full-risk conditions, respectively. In addition, the suggested chance-constrained method resulted in a major decrease of 69 % in overall operational costs. [ABSTRACT FROM AUTHOR]