Search Results (13)

As public concern over environmental pollution and the urgent need for sustainable development grow, the popularity of new-energy vehicles has increased. Hybrid electric vehicles (HEVs) represent a significant segment of this movement, undergoing robust development and playing an important role in the global transition towards sustainable mobility. Among the various factors affecting the fuel economy of HEVs, energy management strategies (EMSs) are particularly critical. With continuous advancements in vehicle communication technology, vehicles are now equipped to gather real-time traffic information. In response to this evolution, this paper proposes an optimization method for the adaptive equivalent consumption minimization strategy (A-ECMS) equivalent factor that incorporates traffic information and efficient optimization algorithms. Building on this foundation, the proposed method integrates the charge depleting–charge sustaining (CD-CS) strategy to create a combined EMS that leverages traffic information. This approach employs the CD-CS strategy to facilitate vehicle operation in the absence of comprehensive global traffic information. However, when adequate global information is available, it utilizes both the CD-CS strategy and the A-ECMS for vehicle control. Simulation results indicate that this combined strategy demonstrates effective performance, achieving fuel consumption reductions of 5.85% compared with the CD-CS strategy under the China heavy-duty truck cycle, 4.69% under the real vehicle data cycle, and 3.99% under the custom driving cycle. Full article

The equivalent factor (EF) of adaptive equivalent consumption minimization strategy (A-ECMS) has a direct impact on the performance of hybrid electric trucks (HETs). Although EF on the state of charge (SoC) can effectively achieve fuel economy and SoC maintenance, battery life issues still need to be considered. Battery replacement costs are extremely high, directly affecting the operational costs of HETs. Thus, A-ECMS with enhanced battery life (A-ECMS-EBL) is proposed. Firstly, the near-optimal boundary of EF is determined to ensure the fuel economy of A-ECMS-EBL by analyzing the working mechanism of the HET powertrain. Secondly, a new EF calculation method is developed to enhance battery life. This method utilizes accelerator pedal opening (APO) feedback to optimize the power distribution between the engine and battery under high load conditions, thereby reducing the ratio of battery output power and number of battery cycle (NBC). Finally, the simulation results show that under typical cycle conditions, the equivalent fuel consumption (EFC) of A-ECMS-EBL increased by only 2.3% compared to the dynamic programming (DP), decreased by 1.1% compared to the A-ECMS, and the NBC significantly decreased by 6.12%. The results indicate that A-ECMS-EBL has significant advantages in improving fuel economy and enhancing battery life. Full article

Fuel cell hybrid systems, due to their combination of the high energy density of fuel cells and the rapid response capability of power batteries, have become an important category of new energy vehicles. This paper discusses energy management strategies in hydrogen fuel cell vehicles. Firstly, a detailed comparative analysis of existing PID control strategies and Adaptive Equivalent Consumption Minimization Strategies (A-ECMSs) is conducted. It was found that although A-ECMS can balance the energy utilization of the fuel cell and power battery well, the power fluctuations of the fuel cell are significant, leading to increased hydrogen consumption. Therefore, this paper proposes an improved Adaptive Low-Pass Filter Equivalent Consumption Minimization Strategy (A-LPF-ECMS). By introducing low-pass filtering technology, transient changes in fuel cell power are smoothed, effectively reducing fuel consumption. Simulation results show that under the 6*FTP75 cycle, the energy loss of A-LPF-ECMS is reduced by 10.89% (compared to the PID strategy) and the equivalent hydrogen consumption is reduced by 7.1%; under the 5*WLTC cycle, energy loss is reduced by 5.58% and equivalent hydrogen consumption is reduced by 3.18%. The research results indicate that A-LPF-ECMS performs excellently in suppressing fuel cell power fluctuations under idling conditions, significantly enhancing the operational efficiency of the fuel cell and showing high application value. Full article

The energy management strategy plays an essential role in improving the fuel economy and extending the energy source lifetime for fuel cell hybrid electric vehicles (FCHEVs). However, the traditional energy management strategy ignores the lifetime of the energy sources for good fuel economy. In this work, an adaptive equivalent consumption minimization strategy considering performance degradation (DA-ECMS) is proposed by incorporating fuel cell and battery performance degradation models and establishing an optimal covariate predictor based on a long short-term memory (LSTM) neural network. The comparative simulations show that, compared with the adaptive equivalent consumption minimization strategy (A-ECMS), the DA-ECMS reduces the fuel cell stack voltage degradation by 17.1%, 23.2%, and 16.6% for the Worldwide Harmonized Light Vehicle Test Procedure (WLTP), the China Light-Duty Vehicle Test Cycle (CLTC), and the New European Driving Cycle (NEDC), respectively, and the corresponding battery capacity degradation is reduced by 5.1%, 11.1%, and 11.2%. The average relative error between the hardware-in-the-loop (HIL) test and simulation results of the DA-ECMS is 5%. In conclusion, the proposed DA-ECMS can effectively extend the lifetime of the fuel cell and battery compared to the A-ECMS. Full article

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

This paper analyzes a hybrid power system containing a fuel cell (FC) and proposes an improved scheme involving the replacement of a single energy storage system with a hybrid energy storage system. In order to achieve a reasonable power distribution between fuel cells and energy storage units and stable operation of the power grid, an efficient energy management system (EMS) based on the equivalent consumption minimization strategy (ECMS) is proposed. To enhance the dynamic response capability of hybrid energy storage systems, a low-pass filter with a variable time constant based on the ultracapacitor SOC feedback is proposed. This paper describes the design of a fuzzy logic controller that adaptively improves the equivalent consumption minimization control strategy, adjusts the equivalent factor in real time, optimizes the operating points of the fuel cell system, and improves system efficiency. The simulation model of the fuel cell hybrid system was established using MATLAB/Simulink. The proposed adaptive equivalent consumption minimization strategy (A-ECMS) was simulated and compared to the state-based and fuzzy logic energy management systems under the simulation of the real operating conditions of the ship. The results show that the proposed strategy can maintain a fuel cell system efficiency above 60% under most operating conditions and can significantly suppress the fluctuation of a fuel cell’s output power. The proposed strategy outperforms the state-based and fuzzy logic-based EMS in terms of stabilizing the hybrid power system and reducing hydrogen consumption. The effectiveness of the proposed strategy has been verified. Full article

The mechanical coupling of multiple powertrain components makes the energy management of 4-wheel-drive (4WD) plug-in fuel cell electric vehicles (PFCEVs) relatively complex. Optimizing energy management strategies (EMSs) for this complex system is essential, aiming at improving the vehicle economy and the adaptability of operating conditions. Accordingly, a novel adaptive equivalent consumption minimization strategy (A-ECMS) based on the dragonfly algorithm (DA) is proposed to achieve coordinated control of the powertrain components, front and rear motors, as well as the fuel cell system and the battery. To begin with, the equivalent consumption minimization strategy (ECMS) with extraordinary instantaneous optimization ability is used to distribute the vehicle demand power into the front and rear motor power, considering the different motor characteristics. Subsequently, under the proposed novel hierarchical energy management framework, the well-designed A-ECMS based on DA empowers PFCEVs with significant energy-saving advantages and adaptability to operating conditions, which are achieved by precise power distribution considering the operating characteristics of the fuel cell system and battery. These provide state-of-the-art energy-saving abilities for the multi-degree-of-freedom systems of PFCEVs. Lastly, a series of detailed evaluations are performed through simulations to validate the improved performance of A-ECMS. The corresponding results highlight the optimal control performance in the energy-saving performance of A-ECMS. Full article

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. Full article

Range-extended Electric Vehicles (REVs) have become popular due to their lack of emissions while driving in urban areas, and the elimination of range anxiety when traveling long distances with a combustion engine as the power source. The fuel consumption performance of REVs depends greatly on the energy management strategy (EMS). This article proposes a practical energy management solution for REVs based on an Adaptive Equivalent Fuel Consumption Minimization Strategy (A-ECMS), wherein the equivalent factor is dynamically optimized by the battery’s State of Charge (SoC) and traffic information provided by Intelligent Transportation Systems (ITS). Furthermore, a penalty function is incorporated with the A-ECMS strategy to achieve the quasi-optimal start–stop control of the range extender. The penalty function is designed based on more precise vehicle velocity forecasting through a nonlinear autoregressive network with exogeneous input (NARX). A model of the studied REV is established in the AVL Cruise environment and the proposed energy management strategy is set up in Matlab/Simulink. Lastly, the performance of the proposed strategy is evaluated over multiple Worldwide Light-duty Test Cycles (WLTC) and real-world driving cycles through model simulation. The simulation conditions are preset such that the range extender must be switched on to finish the planned route. Compared with the basic Charge-Depleting and Charge-Sustaining (CD-CS) strategy, the proposed A-ECMS strategy achieves a fuel-consumption benefit of up to 9%. With the implementation of range extender start–stop optimization, which is based on velocity forecasting, the fuel saving rate can be further improved by 6.7% to 18.2% compared to the base A-ECMS. The proposed strategy is energy efficient, with a simple structure, and it is intended to be implemented on the studied vehicle, which will be available on the market at the end of October 2022. Full article

The Equivalent Consumption Minimization Strategy (ECMS) is a well-known control strategy for the definition of optimal power-split in hybrid-electric vehicles, because of its effectiveness and reduced calibration effort. In this kind of Energy Management Systems (EMS), the correct identification of an equivalence factor (K), which translates electric power in equivalent fuel consumption, is of paramount importance. To guarantee charge sustaining operation, the K factor must be adjusted to different mission profiles. Adaptive ECMS (A-ECMS) techniques have thus been introduced, which automatically determine the optimal equivalence factor based on the vehicle mission. The aim of this research activity is to assess the potential in terms of fuel consumption and charge sustainability of different A-ECMS techniques on a gasoline hybrid-electric passenger car. First, the 0D vehicle and powertrain model was developed in the commercial CAE software GT-SUITE. An ECMS-based EMS was used to control the baseline powertrain and three alternative versions of an auto-adaptive algorithm were implemented on top of that. The first A-ECMS under study was based on feedback from the battery State of Charge, while the second and third on a Driving Pattern Recognition/Prediction algorithm. Fuel consumption was assessed using the New European Driving Cycle (NEDC), the Worldwide Harmonized Light Vehicles Test Cycle (WLTC) and Real Driving Emissions (RDE) driving cycles by means of numerical simulation. A potential improvement of up to 4% Fuel Economy was ultimately achieved on an RDE driving cycle with respect to the baseline ECMS. Full article

Advanced energy management strategies (EMS) are used to control the power flow through a vehicle’s powertrain. However, the cost of high-power computational hardware and lack of a priori knowledge of future road conditions poses difficult challenges for engineers attempting to implement globally optimal frameworks. One solution is to use advanced driver assistance systems (ADAS) and connectivity to obtain a prediction of future road conditions. This paper presents a look-ahead predictive EMS which combines approximate dynamic programming (ADP) methods and an adaptive equivalent consumption minimization strategy (A-ECMS) to obtain a near-optimal solution for a future prediction horizon. ECMS is highly sensitive to the equivalence factor (EF), making it necessary to adapt during a trip to account for disturbances. A novel adaptation method is presented in this work which uses an artificial neural network to learn the nonlinear relationship between a speed and the state of charge (SOC) trajectory prediction obtained from ADP to estimate the corresponding EF. A traffic uncertainty analysis demonstrates an approximately 10% fuel economy (FE) improvement over traditional A-ECMS. Using a data-driven adaptation method for A-ECMS informed by a dynamic programming (DP) based prediction results in an EMS capable of online implementation. Full article

This work investigates an innovative low-voltage (<60 V) hybrid device that enables engine boosting and downsizing in addition to mild hybrid functionalities such as regenerative braking, start-stop, and torque assist. A planetary gear set and a brake permit the power split supercharger (PSS) to share a 9 kW motor between supercharging the engine and direct torque supply to the crankshaft. In contrast, most e-boosting schemes use two separate motors for these two functionalities. This single motor structure restricts the PSS operation to only one of the supercharging or parallel hybrid modes; therefore, an optimized decision making strategy is necessary to select both the device mode and its power split ratio. An adaptive equivalent consumption minimization strategy (A-ECMS), which uses the battery state of charge (SoC) history to adjust the equivalence factor, is developed for energy management of the PSS. The A-ECMS effectiveness is compared against a dynamic programming (DP) solution with full drive cycle preview through hardware-in-the-loop experiments on an engine dynamometer testbed. The experiments show that the PSS with A-ECMS reduces vehicle fuel consumption by 18.4% over standard FTP75 cycle, compared to a baseline turbocharged engine, while global optimal DP solution decreases the fuel consumption by 22.8% compared to the baseline. Full article

A hybrid electric vehicle (HEV) is a product that can greatly alleviate problems related to the energy crisis and environmental pollution. However, replacing such a battery will increase the cost of usage before the end of the life of a HEV. Thus, research on the multi-objective energy management control problem, which aims to not only minimize the gasoline consumption and consumed electricity but also prolong battery life, is necessary and challenging for HEV. This paper presents an adaptive equivalent consumption minimization strategy based on a recurrent neural network (RNN-A-ECMS) to solve the multi-objective optimal control problem for a plug-in HEV (PHEV). The two objectives of energy consumption and battery loss are balanced in the cost function by a weighting factor that changes in real time with the operating mode and current state of the vehicle. The near-global optimality of the energy management control is guaranteed by the equivalent factor (EF) in the designed A-ECMS. As the determined EF is dependent on the optimal co-state of the Pontryagin’s minimum principle (PMP), which results in the online ECMS being regarded as a realization of PMP-based global optimization during the whole driving cycle. The time-varying weight factor and the co-state of the PMP are map tables on the state of charge (SOC) of the battery and power demand, which are established offline by the particle swarm optimization (PSO) algorithm and real historical traffic data. In addition to the mappings of the weight factor and the major component of the EF linked to the optimal co-state of the PMP, the real-time performance of the energy management control is also guaranteed by the tuning component of the EF of A-ECMS resulting from the Proportional plus Integral (PI) control on the deviation between the battery SOC and the optimal trajectory of the SOC obtained by the Recurrent Neural Network (RNN). The RNN is trained offline by the SOC trajectory optimized by dynamic programming (DP) utilizing the historical traffic data. Finally, the effectiveness and the adaptability of the proposed RNN-A-ECMS are demonstrated on the test platform of plug-in hybrid electric vehicles based on GT-SUITE (a professional integrated simulation platform for engine/vehicle systems developed by Gamma Technologies of US company) compared with the existing strategy. Full article