MPC-informed ECMS based real-time power management strategy for hybrid electric ship

A long-term goal of future naval shipboard power systems is the ability to manage energy flow with sufficient flexibility to accommodate future platform requirements such as, better survivability, continuity, and support of pulsed and other demanding loads. To attain this vision of shipboard energy management, shipboard power and energy management systems must coordinate operation of all major components in real-time. The primary components of a shipboard power system are the generators, energy storage modules, and increasingly power electronics that interface those sources and main load centers to the system. Flexible management of energy flow throughout shipboard distribution systems can be realized by automated coordination of multiple power electronic converters along with storage and generation systems. Use of power converters in power distribution systems has continuously increased due to continued development of the power electronics building blocks (PEBB) concept which reduces cost and increasing reliability of converters. Recent developments in SiC power devices are yielding PEBBs with far greater switching frequencies than Si based devices resulting in an order of magnitude reduction of the time scales as compared to converter systems utilizing conventional IGBT based PEBBs. In addition there have also been advancements in highly modularized converter systems with hundreds of PEBBs such as the Modular Multilevel Converter. Both of those trends have resulted in the continued evolution of the Universal Controller Architecture which attempts to standardize control interfaces for modular power electronic systems. Further development of interface definitions and increasing communication and computational capabilities of new FPGA based controllers provides opportunities beyond simply supporting SiC PEBBs. Fast control coordination across the system using an appropriate communication architecture provides a degree of energy management not previously realizable in shipboard power systems. The paper will present recent research results in networked control architectures for power electronic converter coordination and control. It will demonstrate that current FPGA and gigabit speed serial communication technologies allow for a very high degree of energy flow control.

Performance evaluation of isolated three-phase voltage source inverter with LC filter adopting different MPC methods under various types of load

Energy Management Strategies (EMSs) are crucial for enhancing fuel economy and reducing emissions in light commercial vehicles (LCVs). This paper presented three EMS approaches for LCVs with hybrid powertrains: Rule-Based Control (RBC) and two optimization-based strategies, the Equivalent Consumption Minimization Strategy (ECMS) and Model Predictive Control (MPC). To enhance robustness under varying operating conditions, optimization algorithms were designed and tuned using the WLTC City driving cycle, and adaptive components were included. For a fair assessment of overall efficiency, all strategies were compared under identical constraints on hydrogen and electrical energy consumption. The results showed that, under these constraints, MPC achieved the longest driving distance, highlighting its superior energy utilization capability. In a broader comparative analysis, both the ECMS and MPC outperformed the benchmark RBC, with MPC demonstrating the most consistent performance, enhanced stability, and strong adaptability in dynamic scenarios. The findings indicate that MPC offers notable advantages for LCV energy management, combining efficiency, robustness, and interpretability, positioning it as a promising candidate for practical implementation in future hybrid powertrain systems.

In order to achieve nearly optimal fuel economy for hybrid electric vehicles (HEVs) using an equivalent consumption minimum strategy (ECMS), it is necessary to dynamically tune the equivalent factor (EF). Unlike widely studied ECMSs that adapt EF to driving conditions, the proposed self-adaptive equivalent consumption minimization strategy (SECMS) applies a new idea of determining the EF from the historical driving conditions. To this end, a dynamic EF self-determining algorithm is designed according to the assumption that the electrical energy used at the current moment comes from past recuperation and charging, and an adaptive charge-sustaining algorithm is developed to balance the electrical self-sustainability and optimization performance. To show its effectiveness, the SECMS is implemented to a single-shaft parallel HEV over four standard driving cycles and benchmarked against two conventional ECMS-type control strategies: standard ECMS and adaptive ECMS (AECMS). The results show that the SECMS can achieve significant improvements regarding both the fuel economy and battery charge-sustainability compared to ECMS and AECMS. The proposed SECMS could facilitate a new development of energy management strategies for HEVs based on historical driving information.

An equivalent consumption minimization strategy (ECMS) is one of the most powerful and practical ways to improve the fuel efficiency of hybrid electric vehicles (HEVs). In an ECMS, it is important to determine the optimal equivalent factor to reach a global optimal solution. The optimal equivalent factor is determined by driving conditions. Previous studies have used an adaptive ECMS (A-ECMS) to determine the appropriate equivalent factor according to changing driving conditions. An A-ECMS adjusts the equivalent factor by controlling the battery’s state of charge (SOC) to follow a reference SOC trajectory. It is therefore critical to identify a reference SOC trajectory that reflects real-world driving conditions. These conditions, which are composed of the HEV’s nonlinear dynamics and complex constraints, can be formulated into a nonlinear optimal control problem (NOCP). Here, we propose applying nonlinear programming (NLP) to an A-ECMS. The NLP-based ECMS algorithm can be divided into two parts: the use of an NLP to solve an NOCP to obtain the reference SOC trajectory and the application of an NLP solution (the result of the first part) to an A-ECMS. Simulation results demonstrate that the proposed NLP-based ECMS closely resembles a global optimal solution for dynamic programming in a relatively brief calculation time.

The increasing stringent emissions regulation over the years have shifted the focus of automotive industry towards more efficient fuel economy solutions. One such solution is Hybrid electric architecture, which is able to improve the fuel economy and consequently cutting down emissions. A well known control strategy to solve optimization problem for energy management of Hybrid electric vehicles is ECMS (Equivalent Consumption Minimization Strategy). Finding the best control parameters (equivalence factors) of this strategy may become quite involved. This paper proposes a method for the selection of the optimal equivalence factors, for charging and discharging, by applying genetic algorithm in the case of a P0 mild hybrid electric vehicle. This method is a systematic and deterministic way to guarantee an optimal solution with respect to the trial and error method. The proposed ECMS is compared to a technique available in literature, known as the shooting method, which relies only on one equivalence factor for discharging. It is demonstrated that the performance in terms of pollutant emissions are comparable. However, ECMS with GA always guarantees an optimal solution even in the case of heavy accessory load, when shooting method is not valid anymore, as it does not guarantee a charge sustaining condition.

The development of a three-phase electronic power converter is presented, the converter uses IGBTs as electronic power devices, it can act bi-functionally as a threephase full-wave rectifier or as a three-phase three-arm inverter, using the same three-phase power bridge For the conversion, the control design is carried out using the FCS-MPC technique (Finite Commutations Set -Model Predictive Control). The quality of the electrical energy conversion is evaluated, evaluating the average and effective parameters of the voltages and currents, with various types of loads. As well as distortion with the THD coefficient, frequency and phase. The converter seeks to be very flexible and somewhat universal for the conversion of AC-DC and DC-AC energy, for diverse applications in the field of distributed power generation systems, particularly with the presence of renewable energy sources. Modeling and testing have been done with MATLAB, SIMULINK. The loads have also been modeled for typical applications with real characteristics, so in the case of rectifier the test load has been considered a lithium ion battery, then with a 5HP, 240 v and 1750 rpm DC motor, to later test with a general load of RL characteristics. For the inverter, it is tested with a balanced three-phase RL load and then with a three-phase squirrel cage-type asynchronous motor. The results are presented by means of graphs and tables for a better understanding of said results obtained in the simulations. The functionality and operation of the developed converter is explained by means of block diagrams that incorporate the units that make up the converter including the control units, the command unit, the operation selection switch unit, as well as the power unit. The results obtained show good functional characteristics of the converter in its two modes of operation, reflected in the parameters measured in the simulation tests.

Efficient power management in Hybrid Energy Storage Systems (HESS) for off-grid PV inverters remains a challenge, particularly in terms of energy efficiency and battery degradation mitigation. In this context, this paper proposes a Model Predictive Control (MPC) based power management designed to mitigate Li-ion battery degradation while improving the overall system efficiency. To validate this proposal, extensive experimental results are presented. The proposed formulation aims to minimize battery stress by smoothing the current profile and reducing cycling effects. The performance of the MPC power management is verified through a comparative experimental analysis of four distinct strategies: rule-based, frequency-filtering, a benchmark MPC, and the proposed MPC. Evaluation is based on four key metrics: RMS current, total power losses, Equivalent Full Cycles (EFC), and State of Health variation (ΔSOH). Experimental results demonstrate that the proposed method outperforms the compared strategies across all evaluated criteria, significantly reducing battery stress in terms of cycling and current distribution. Furthermore, long-term simulations corroborate these findings, showing a substantial reduction in battery degradation compared to other established methods. These results confirm that the proposed method, provides a robust solution for mitigating battery degradation in hybrid inverter applications.

To address the limitations of traditional energy management strategies in fuel cell hybrid power systems—specifically their difficulty in simultaneously accommodating dynamic driving condition adaptability, hydrogen fuel economy, and energy storage system stability—this study proposes a power distribution optimization strategy based on Deep Deterministic Policy Gradient (DDPG). The strategy targets a hybrid powertrain architecture dominated by a fuel cell (FC) and assisted by a lithium battery and a supercapacitor. By constructing a multi-dimensional state space that integrates vehicle speed, acceleration, the state of charge (SOC) of the energy storage system, and load power demand, a multi-objective reward function encompassing hydrogen consumption, SOC deviation, system efficiency, and power fluctuation is designed to achieve dynamic power allocation in a continuous action space. Simulation studies are conducted under three typical driving cycles—WLTP, CLTC-P, and UDDS—with comparative evaluations against the conventional Equivalent Consumption Minimization Strategy (ECMS) and Deep Q-Network (DQN)-based strategies. The results demonstrate that the DDPG-based strategy reduces hydrogen consumption to 607.1 g/100 km, 580.2 g/100 km, and 560.0 g/100 km under the three driving cycles, respectively, achieving a maximum reduction of 28% compared with ECMS. The average system efficiency increases to 64–66%, representing an improvement of 38.9%, while the operating proportion of the fuel cell within the high-efficiency region (40–80% load) increases by 15%. In addition, the strategy exploits the high-frequency response capability of the supercapacitor to smooth instantaneous power fluctuations, effectively reducing the inefficient start–stop events of the fuel cell. Although the SOC fluctuation range of the lithium battery increases by 32.5% compared with ECMS, a dynamic balance between energy efficiency and battery lifespan can be achieved through optimized weighting of the SOC deviation penalty term in the reward function. Overall, this study provides a solution with both theoretical significance and engineering feasibility for global energy optimization of fuel cell–energy storage systems under complex driving conditions.

Real-time optimization strategy by using sequence quadratic programming with multivariate nonlinear regression for a fuel cell electric vehicle

Equivalent Consumption Minimization Strategy Based on Dynamic Programming for Plug-in Hybrid Electric Vehicle

In order to improve fuel economy further, an equivalent consumption minimization strategy (ECMS) is designed for the parallel hybrid electric vehicle (PHEV). Firstly, based on the relations among the battery state of charge (SOC) and the equivalent factor and fuel consumption, the equivalent fuel consumption minimization objective function is constructed. Secondly, a variable equivalent factor over real-time driving cycle is obtained by introducing the Pontryagin's Minimum Principle (PMP) and the penalty function of SOC. Finally, the simulation results show that compared with ECMS based on a PI controller, the ICE efficiency increases by 2% and the fuel consumption to travel 100 km decreases by 4 % with ECMS based on a variable equivalent factor.

In the years to come, Connected and Automated Vehicles (CAVs) are expected to substantially improve the road safety and environmental impact of the road transport sector. The information from the sensors installed on the vehicle has to be properly integrated with data shared by the road infrastructure (smart road) to realize vehicle control, which preserves traffic safety and fuel/energy efficiency. In this context, the present work proposes a real-time implementation of a control strategy able to handle simultaneously motion and hybrid powertrain controls. This strategy features a cascade of two modules, which were implemented through the model-based design approach in MATLAB/Simulink. The first module is a Model Predictive Control (MPC) suitable for any Hybrid Electric Vehicle (HEV) architecture, acting as a high-level controller featuring an intermediate layer between the vehicle powertrain and the smart road. The MPC handles both the lateral and longitudinal vehicle dynamics, acting on the wheel torque and steering angle at the wheels. It is based on a simplified, but complete ego-vehicle model, embedding multiple functionalities such as an adaptive cruise control, lane keeping system, and emergency electronic brake. The second module is a low-level Energy Management Strategy (EMS) of the powertrain realized by a novel and computationally light approach, which is based on the alternative vehicle driving by either a thermal engine or electric unit, named the Efficient Thermal Electric Skipping Strategy (ETESS). The MPC provides the ETESS with a torque request handled by the EMS module, aiming at minimizing the fuel consumption. The MPC and ETESS ran on the same Microcontroller Unit (MCU), and the methodology was verified and validated by processor-in-the-loop tests on the ST Microelectronics board NUCLEO-H743ZI2, simulating on a PC-host the smart road environment and a car-following scenario. From these tests, the ETESS resulted in being 15-times faster than than the well-assessed Equivalent Consumption Minimization Strategy (ECMS). Furthermore, the execution time of both the ETESS and MPC was lower than the typical CAN cycle time for the torque request and steering angle (10 ms). Thus, the obtained result can pave the way to the implementation of additional real-time control strategies, including decision-making and motion-planning modules (such as path-planning algorithms and eco-driving strategies).

<div class="section abstract"><div class="htmlview paragraph">Plug-In Hybrid Vehicles (PHEV) have been of significant importance recently to comply with future CO<sub>2</sub> and pollutant emissions limit. However, performance of these vehicles is closely related to the energy management strategy (EMS) used to ensure minimum fuel consumption and maximize electric driving range. While conventional EMS concepts are developed to operate in wide range of scenarios, this approach could potentially compromise the fuel consumption benefit due to the omission of route and traffic information. With the advancements in the availability of real-time traffic, navigation and driving route information, the EMS can be further optimized to extract the complete potential of a PHEV. In this context, this paper presents application of predictive energy management (PEM) functionalities combined with information such as live traffic data to reduce the fuel consumption for a P1/P3 configuration PHEV vehicle. The proposed PEM uses on-board navigation and E-horizon data based on Advanced Driver Assistance Systems Interface Specifications (ADASIS). A multi-layer optimization approach is implemented across different prediction horizons. In the long horizon, Dynamic Programming (DP) calculates the optimal battery SoC (State of Charge) trajectory for the entire driving route. The search domain and discretization step of DP are optimized to ensure real-time capability. Based on this target SoC, Pontryagin’s Minimum Principle (PMP) is used in the short to medium horizon to calculate an Equivalence Factor (EF) that defines the optimal distribution between fuel and electrical energy. For the low-level EMS, Equivalent Consumption Minimum strategy (ECMS) is used that computes the torque split, gear ratio and engine on/off decision based on the EF from PMP. The fuel consumption savings for the developed PEM functions are investigated in comparison to conventional rule-based (RB) EMS for different real-world use-cases.</div></div>

The optimal location of renewable distributed generations (DGs) into a radial distribution system (RDS) has attracted major concerns from power system researchers in the present years. The main target of DG integration is to improve the overall system performance by minimizing power losses and improving the voltage profile. Hence, this paper proposed a hybrid approach between an analytical and metaheuristic optimization technique for the optimal allocation of DG in RDS, considering different types of load. A simple analytical technique was developed in order to determine the sizes of different and multiple DGs, and a new efficient metaheuristic technique known as the Salp Swarm Algorithm (SSA) was suggested in order to choose the best buses in the system, proportionate to the sizes determined by the analytical technique, in order to obtain the minimum losses and the best voltage profile. To verify the power of the proposed hybrid technique on the incorporation of the DGs in RDS, it was applied to different types of static loads; constant power (CP), constant impedance (CZ), and constant current (CI). The performance of the proposed algorithm was validated using two standards RDSs—IEEE 33-bus and IEEE 69-bus systems—and was compared with other optimization techniques.