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To increase the efficiency of electrified vehicles, many energy management strategies (driving strategies) have been proposed. These include both offline optimization techniques to identify a system's theoretical optimum and online optimization techniques created for onboard use in the vehicle. In the field of online optimization, predictive approaches can achieve additional savings. However, predictions are challenging, and robust usability in all driving situations of the vehicle is not guaranteed. In this study, a new approach for a predictive energy management strategy is presented. It is demonstrated how this so-called predictive Online Equivalent Consumption Minimization Strategy (ECMS) can achieve additional fuel savings compared to a non-predictive Online ECMS by predicting recuperation events using map data. As long as the route is known, map data are available, and the current position of the global navigation satellite system (GNSS) is given, the predictive Online ECMS can be applied. If these requirements are not met, the non-predictive basic implementation can still be used to ensure robust functionality. The methodology is investigated using a backward simulation model of a D-segment vehicle powered by a 48 V hybrid electric system in a P2 topology. A dataset including real driving cycles including map data from Open Street Map (OSM) is used. However, the investigations are limited to the consideration of traffic signal (TS) positions on the upcoming route. Simulation results focus on the interaction between the energy management strategy (EMS) and usable battery energy. More than 1 % average saving potentials compared to a non-predictive implementation are shown. The highest saving potentials are found with a usable battery energy of 100 Wh. [ABSTRACT FROM AUTHOR]

This paper presents nonlinear model predictive control based adaptive equivalent consumption minimization strategy for fuel cell hybrid electric bus. The proposed strategy considers the average travel speed profile of road segments in route of fuel cell hybrid electric bus. The proposed nonlinear model predictive control based adaptive equivalent consumption minimization strategy determines the optimal control input by considering the battery current-rate and the fuel cell balance-of-plant. The nonlinear model predictive control based adaptive equivalent consumption minimization strategy consists of three main stages: the data pre-processing stage, the nonlinear model predictive control stage, and the adaptive equivalent consumption minimization strategy stage. In the data preprocessing stage, reference power trajectory is generated while considering the response time of the fuel cell hybrid electric bus. In the nonlinear model predictive control stage, the reference state of charge trajectory is generated by considering the battery current-rate and the fuel cell balance-of-plant. In the adaptive equivalent consumption minimization strategy stage, the optimal control input is determined to minimize instantaneous equivalent fuel consumption by considering the reference state of charge trajectory. The proposed energy management system is compared with dynamic programming using actual bus route based real-driving scenarios. The comparison results demonstrate that the proposed energy management system can generate control inputs that are similar to the global optimal solution calculated by dynamic programming with a reasonable computation time.

An adaptive energy management strategy (EMS) is proposed to improve the economy and reliability of the fuel cell vehicle. Firstly, a variable horizon speed prediction method based on the principal component analysis and the K-means clustering is constructed. Then, an adaptive equivalent consumption minimization strategy (AECMS) with power slope constraints was designed to minimize the hydrogen consumption while ensuring reliability. Finally, a proportional-integral controller is used to track the air flow and pressure of the fuel cell engine (FCE) under energy distribution. Simulation results under West Virginia University Suburban (WVUSUB) show that the proposed strategy can improve the speed prediction accuracy by 2.80% and 25.57%, and reduce the hydrogen consumption by 2.79% and 2.66%, respectively, compared with the fixed 12 s and 15 s horizon. Moreover, the control error of oxygen excess ratio and the cathode pressure under energy distribution are 0.0102 (0.51%) and 189.4 Pa (0.0935%), respectively, indicating better reliability than the strategy without constraint. • An AECMS is proposed to improve the economy and reliability of fuel cell vehicles. • A variable horizon speed prediction is designed to update the EF in real-time. • Power slope constraint is introduced into EMS to ensure the reliable operation of FCE. • OER and cathode pressure co-control methods are used to respond to the FCE air supply. [ABSTRACT FROM AUTHOR]

With the world's energy reserves under strain and the requirements of national carbon emission regulations, the fuel efficiency and environmental friendliness of automobiles are becoming increasingly important. Due to the combination of long cruising range and energy efficiency, hybrid electric vehicles (HEVs) have been adopted as a reliable option for improving fuel economy and reducing emissions. In order to fully exploit the advantages of hybrid electric vehicles, energy management and torque distribution have become the focus of control strategies for HEVs, while also ensuring battery life and meeting requirements for fuel consumption, emissions and driving performance. Therefore, a great deal of research has been carried out on energy management strategies and many approaches have been offered in the literature. This review provides a comprehensive assessment of the literature, highlighting its contributions and making a complete reference for scholars interested in hybrid vehicle development, control, and optimization. [ABSTRACT FROM AUTHOR]

Traditional mine dump truck using diesel-electric drive technology have high energy consumption and serious pollution. To improve fuel economy of a hybrid power dump truck, firstly, it analyzes system composition, working mode and transportation condition characteristics of the hybrid power electric-wheel mining dump truck. Secondly, according to the mine driving conditions of the electric-wheel dump truck, an energy management strategy based on the driving pattern recognition is proposed according to the driver's intention and the vehicle state. Finally,to minimize the instantaneous equivalent fuel consumption of the whole hybrid power system, the equivalent consumption minimization strategy is used to optimize the instantaneous power distribution between the engine and battery. In order to verify the proposed energy management strategy, on site mining road test is conducted. Test results show that the fuel consumption of the hybrid power electric-wheel dump truck is 45.52 L/trip, which is 10.2% lower than that of the electric-wheel dump truck with full diesel engine mode.

To increase the efficiency of electrified vehicles, many energy management strategies (driving strategies) have been proposed. These include both offline optimization techniques to identify a system’s theoretical optimum and online optimization techniques created for onboard use in the vehicle. In the field of online optimization, predictive approaches can achieve additional savings. However, predictions are challenging, and robust usability in all driving situations of the vehicle is not guaranteed. In this study, a new approach for a predictive energy management strategy is presented. It is demonstrated how this so-called predictive Online Equivalent Consumption Minimization Strategy (ECMS) can achieve additional fuel savings compared to a non-predictive Online ECMS by predicting recuperation events using map data. As long as the route is known, map data are available, and the current position of the global navigation satellite system (GNSS) is given, the predictive Online ECMS can be applied. If these requirements are not met, the non-predictive basic implementation can still be used to ensure robust functionality. The methodology is investigated using a backward simulation model of a D-segment vehicle powered by a 48 V hybrid electric system in a P2 topology. A dataset including real driving cycles including map data from Open Street Map (OSM) is used. However, the investigations are limited to the consideration of traffic signal (TS) positions on the upcoming route. Simulation results focus on the interaction between the energy management strategy (EMS) and usable battery energy. More than 1 % average saving potentials compared to a non-predictive implementation are shown. The highest saving potentials are found with a usable battery energy of 100 Wh.

An energy management strategy is a key technology used to exploit the energy-saving potential of a plug-in hybrid electric vehicle. This paper proposes the environmental perceiver-based equivalent consumption minimization strategy (EP-ECMS) for parallel plug-in hybrid vehicles. In this method, the traffic characteristic information obtained from the intelligent traffic system is used to guide the adjustment of the equivalence factor, improving the environmental adaptiveness of the equivalent consumption minimization strategy (ECMS). Two main works have been completed. First, a high-accuracy environmental perceiver was developed based on a graph convolutional network (GCN) and attention mechanism to complete the traffic state recognition of all graph regions based on historical information. Moreover, it provides the grade of the corresponding region where the vehicle is located (for the ECMS). Secondly, in the offline process, the search for the optimal equivalent factor is completed by using the Harris hawk optimization algorithm based on the representative working conditions under various grades. Based on the identified traffic grades in the online process, the optimized equivalence factor tables are checked for energy management control. The simulation results show that the improved EP-ECMS can achieve 7.25% energy consumption optimization compared with the traditional ECMS. [ABSTRACT FROM AUTHOR]

Energy management strategies are vitally important to give full play to the energy-saving of the four-wheel drive electric vehicle (4WD EV). The cooperative output of multi-power components is involved in the process of driving and braking energy recovery of 4WD EV. This paper proposes a novel energy management strategy of dual equivalent consumption minimization strategy (D-ECMS) to improve the economy of the vehicle. According to the different driving and braking states of the vehicle, D-ECMS can realize the proportional control of the energy cooperative output among the multi-power components. Under the premise of satisfying the dynamic performance of the vehicle, the operating points of the power components are distributed more in the high-efficiency range, and the economy and driving range of the vehicle are optimized. In order to achieve the effectiveness of D-ECMS, MATLAB/Simulink is used to realize the simulation of the vehicle. Compared with the rule-based strategy, the economy of D-ECMS increased by 4.35%. [ABSTRACT FROM AUTHOR]

Hybrid electric vehicles (HEVs) are considered the most practical option for reducing emissions and fuel consumption of conventionally powered vehicles. Energy management strategies (EMSs) are the core technology of HEVs because of decreasing the cost of the system and limiting its negative effects. Equivalent consumption minimization strategy (ECMS) can achieve instantaneous optimal control and has attracted attention in recent years. In this study, an adaptive equivalent consumption minimization strategy (A-ECMS) based on driving cycle recognition is constructed for a parallel HEV. First, select the standard driving cycle and analyze its characteristic parameters. And then training learning vector quantization (LVQ) neural network-based driving cycle recognizer to achieve an average of 98% accuracy. At last, the optimal equivalent factor (EF) is selected for ECMS by recognizing the current driving cycle. It is jointly simulated and analyzed by AVL CRUISE and MATLAB/Simulink software under NEDC and CHTC-LT driving cycle. The results show that compared with the logic-based EMS, in the NEDC driving cycle the 100 km fuel consumption of A-ECMS decreases by 3.8%, and the battery state of charge (SOC) increases by 1.1%. In the CHTC-LT driving cycle, fuel economy improves by 3.6%, proving the superiority of the A-ECMS.

In the field of new energy vehicles, 4WD PHEVs show strong energy-saving potential. A single energy management strategy, nevertheless, has difficulty achieving the energy-saving potential due to the complex, nonlinear energy system of the 4WD PHEV. To cope with it, a hierarchical energy management strategy (H-EMS) for 4WD PHEVs is proposed in this paper to achieve energy management optimization. Firstly, the future speed information is predicted by the speed prediction method, and the upper energy management strategy adopts the model predictive control (MPC) based on the future speed information to carry out the power source distribution between the engine and the battery. Secondly, the lower energy management strategy performs the power component distribution of the front motor and the rear motor based on an equivalent consumption minimization strategy (ECMS). Finally, the simulation based on MATLAB/Simulink is performed, validating that the proposed method has more energy-saving capabilities, and the economy is improved by 11.87% compared with the rule-based (RB) energy management strategies. [ABSTRACT FROM AUTHOR]

Energy management strategies are vitally important to give full play to energy-saving four-wheel-drive plug-in hybrid electric vehicles (4WD PHEV). This paper proposes a novel dual-adaptive equivalent consumption minimization strategy (DA-ECMS) for the complex multi-energy system in the 4WD PHEV. In this strategy, management of the multi-energy system is optimized by introducing the categories of future driving conditions to adjust the equivalent factors and improving the adaptability and economy of driving conditions. Firstly, a self-organizing neural network (SOM) and grey wolf optimizer (GWO) are adopted to classify the driving condition categories and optimize the multi-dimensional equivalent factors offline. Secondly, SOM is adopted to identify driving condition categories and the multi-dimensional equivalent factors are matched. Finally, the DA-ECMS completes the multi-energy optimization management of the front axle multi-energy sources and the electric driving system and releases the energy-saving potential of the 4WD PHEV. Simulation results show that, compared with the rule-based strategy, the economy in the DA-ECMS is improved by 13.31%. [ABSTRACT FROM AUTHOR]

Many researchers spent much effort on the online power management strategies for plug-in hybrid vehicles (PHEVs) and hybrid electric vehicles (HEVs). Nowadays, artificial neural networks (ANNs), one of the machine learning techniques, have also been applied to this problem due to their good performance in learning non-linear and complicated multi-inputs multi-outputs (MIMO) dynamic systems. In this paper, an ANN is applied to the online power management for a plug-in hybrid electric vehicle (PHEV) by predicting the torque split between an internal combustion engine (ICE) and an electric motor (e-Motor) to optimize the greenhouse gas ( G H G ) emissions by using dynamic programming (DP) results as training data. Dynamic programming can achieve a global minimum solution while it is computationally intensive and requires prior knowledge of the entire drive cycle. As such, this method cannot be implemented in real-time. The DP-based ANN controller can get the benefit of using an ANN to fit the DP solution so that it can be implemented in real-time for an arbitrary drive cycle. We studied the hyper-parameters' effects on the ANN model and different structures of ANN models are compared. The minimum training mean square error (MSE) models in each comparison set are selected for comparison with DP and equivalent consumption minimization strategy (ECMS). The total G H G emissions and state of charge ( S O C ) are the metrics used for the analysis and comparison. All the selected ANNs provide results that are comparable to the optimal DP solution, which indicates that ANNs are almost as good as the DP solution. It is found that the multiple hidden-layer ANN shows more efficiency in the training process than the single hidden-layer ANN. By comparing the results with ECMS, the ANN shows great potential in real-time application with the smallest deviation from the results of DP. In addition, our approach does not require any additional trip information, and its output (torque split) is more directly implementable on real vehicles. [ABSTRACT FROM AUTHOR]

To exploit the energy-saving potential and optimize the battery state of charge (SOC) maintaining capability of energy management strategies for fuel cell hybrid vehicles in specific driving scenarios, this study proposes a scenario-oriented adaptive equivalent consumption minimization strategy (SA-ECMS) based on a Nanjing-oriented driving cycle (NODC) and future speeds predicted via a hybrid neural network model. The proposed strategy determines the initial value of the equivalent factor (EF) and the proportional coefficient of the adaptive increment based on the NODC. Then, it periodically adjusts the EF via local optimization process according to the predicted speed to enhance scenario-specific adaptability and energy efficiency performance. Simulation results show that the hybrid neural network model achieves an average calculation time of 0.0033 s with a root-mean-square error of 0.85 m/s for 10 s prediction horizon, outperforming existing speed prediction models. Compared with the existing SOC feedback-based ECMS, the proposed SA-ECMS effectively suppresses the battery SOC within a narrower fluctuation range of −0.12% to 0.33%, achieves a deviation of only 0.0026 from the SOC reference value, and reduces the equivalent hydrogen-fuel consumption by 2.49% to 7.06 g/km. • A scenario-oriented driving cycle construction method is proposed. • A novel hybrid neural network model with higher prediction accuracy is proposed. • The scenario-oriented adaptability of the proposed strategy is enhanced. • Both battery SOC maintaining capability and fuel economy are improved. [ABSTRACT FROM AUTHOR]

—The plug-in hybrid electric bus (PHEB) is one of the vehicles that can address global environmental and energy problems. Due to the complex characteristics of the urban cycle condition, a fixed parameter energy management strategy may not be suitable for fuel economy. Thus, an adaptive real-time control strategy seems to be of great significance for PHEBs. The paper designs an advanced equivalent consumption minimization strategy (ECMS) with segments divided from the driving cycle and optimized equivalent factor (EF). The classification of different segments is based on the time that the vehicle stops. The EF is optimized under two different driving conditions by using the grey wolf optimization (GWO) algorithm. A new model, as the difference between the actual state of charge (SOC) and the reference SOC, is presented and minimized by optimizing the EF. Segmented EF optimization adds a correction factor, which can adjust EF according to the kinematic characteristics of each segment. The results show that the fuel consumption decreased by 18.72 % compared with the conventional ECMS. • An advanced equivalent consumption minimization strategy with segmented equivalent factor (EF) optimization is proposed. • The rational reference state of charge (SOC) trajectory is established using current operating conditions. • The EF is optimized by the grey wolf optimization (GWO) algorithm as to track the reference SOC. • A correction coefficient is created to maximize the superior EF for various road segments within the same cycle. [ABSTRACT FROM AUTHOR]

With the world’s energy reserves under strain and the requirements of national carbon emission regulations, the fuel efficiency and environmental friendliness of automobiles are becoming increasingly important. Due to the combination of long cruising range and energy efficiency, hybrid electric vehicles (HEVs) have been adopted as a reliable option for improving fuel economy and reducing emissions. In order to fully exploit the advantages of hybrid electric vehicles, energy management and torque distribution have become the focus of control strategies for HEVs, while also ensuring battery life and meeting requirements for fuel consumption, emissions and driving performance. Therefore, a great deal of research has been carried out on energy management strategies and many approaches have been offered in the literature. This review provides a comprehensive assessment of the literature, highlighting its contributions and making a complete reference for scholars interested in hybrid vehicle development, control, and optimization.

Range-extended electric vehicles combine the benefits of electric propulsion with the convenience and range flexibility of conventional internal combustion engine vehicles. In the energy management system, an equivalent consumption minimization strategy (ECMS) ensures balanced utilization of various energy sources to meet driving demand in real time, thereby enhancing energy efficiency. This paper proposes a driving pattern personalized reconstruction (DPPR) method to maximizes the accuracy of velocity prediction and to serve a predictive ECMS (P-ECMS) with high adaptability to uncertain scenarios. In this context, open-source driving cycles are segmented, and features of each driving pattern are extracted for cluster analysis. Utilizing the results from clustering, new driving cycles are reconstructed for the purpose of training adaptive neuro-fuzzy inference system (ANFIS) models. Personalized velocity prediction is achieved through bias fusion in the multi-ANFIS model, adapting to various driving conditions. The performance of the proposed P-ECMS is validated across various driving conditions and compared with conventional ECMS (C-ECMS), adaptive ECMS (A-ECMS), dynamic programming (DP), and fuzzy logic (FL) strategy. The results indicate that using the proposed P-ECMS is strongly robust in terms of battery pack SoC stabilization under various driving conditions, whose SoC difference achieves 97.02% of using the DP strategy. Compared to using other strategies, using P-ECMS achieves better SoC's stability and lower fuel consumption than using the FL strategy (11.1%), using A-ECMS (1.7%), and using C-ECMS (26.6%). • A method of driving pattern personalized reconstruction is proposed. • Adaptability of the equivalent factor for uncertain scenarios is improved. • A multi-ANFIS model is designed to capture the diversity of driving conditions. • A co-simulation platform is established for algorithm validation. • Better charge-sustaining performance and fuel economy are achieved. [ABSTRACT FROM AUTHOR]

In order to improve the motion range of mobile welding robot, energy management strategy of fuel cell hybrid power system were studied, which involves energy economy optimization and comprehensive performance improvement. The energy management strategy adopts hierarchical optimization control. Inner layer realizes power distribution and energy optimization, using the maximum power point tracking control (MPPT) based on hybrid algorithm and an improved equivalent consumption minimization strategy (ECMS) to optimize the output power distribution. MPPT allows fuel cell to output maximum power stably and maximize the fuel cell utilization. The improved ECMS optimizes the equivalence factor by fuzzy logic control to increase the calculation accuracy. This strategy improves the economy of system energy consumption. The outer layer uses state machine control to switch the control strategy according to the operating state of the system. When the SOC of the battery is lower than the set threshold, MPPT control is used. And when the SOC is higher than the set threshold, ECMS is used. Experimental results show that the rationality of PEMFC output power and battery charge-discharge has been improved, and the economy of hybrid power system has also been improved. [ABSTRACT FROM AUTHOR]

Hybrid electric vehicles, operated by engines and motors, require an energy management strategy to achieve competitive fuel economy performance. The equivalent consumption minimization strategy is a well-known algorithm that can be employed for the energy management of hybrid electric vehicles, based on the concept of the equivalent cost of fossil fuels and electric battery energy. However, in the equivalent consumption minimization strategy approach, a parameter called the equivalent factor should be determined to obtain the optimal control policy. In this study, reinforcement learning based approaches are proposed to determine the equivalent factor. First, we show that the equivalent factor can be indirectly extracted from the reinforcement learning results, using the control action from reinforcement learning for the specific driving cycle. In addition, a novel approach that combines reinforcement learning and the equivalent consumption minimization strategy is proposed, where the equivalent factor is determined based on the interaction between the reinforcement learning agent and driving environment, while the control input is decided by the equivalent consumption minimization strategy based on the determined equivalent factor. A model-based reinforcement learning method is used, and the proposed method is validated for vehicle simulation using a parallel hybrid electric vehicle. The simulation results show that the proposed method can achieve a near-optimal solution, which is close to the global solution obtained with the dynamic programming approach (96.7% compared to dynamic programming result in average), and improved performance of 4.3% in average compared with the existing adaptive equivalent consumption minimization strategy.

This article presents an economic nonlinear hybrid model predictive control strategy for optimal energy management of parallel hybrid electric vehicles. Hybrid electric vehicles are controlled for operation in various driveline modes and the associated optimal control problem involves both continuous and discrete control variables. To solve the resultant mixed-integer nonlinear optimal control problem, we propose a hierarchical supervisory control architecture that consists of demand prediction, driveline mode determination, and real-time optimization. These three modules are designed independently and connected in series to perform computer-aided control. The demand prediction module uses a times series model to forecast the mechanical traction power requests of the driver over a prediction horizon based on vehicle speed, road grade, acceleration pedal scale, brake pedal scale, and past and current power demands. For a given forecasted power demand profile, the mode determination module decides a sequence of driveline modes that are presumed to be operated over the prediction horizon. The model-based real-time optimization corresponding to nonlinear model predictive control computes the optimal motor power over a prediction horizon, and the receding horizon scheme as feedback control is applied to repeat the processes of the three control modules. A dedicated case study with real driving data obtained from Hyundai IONIQ PHEV 2018 is presented to demonstrate the effectiveness in fuel economy and emission reduction offered by the proposed optimal energy management strategy. The proposed hierarchical real-time predictive optimization-based strategy is competitive with any exiting power management strategies such as dynamic programming and equivalent consumption minimization strategy in fuel economy and emission reduction while showing better charge-sustaining capability. This trade-off between fuel economy and charge-sustainability can be further improved by tuning the hyper-parameters in the proposed optimal control problem.

In the field of new energy vehicles, 4WD PHEVs show strong energy-saving potential. A single energy management strategy, nevertheless, has difficulty achieving the energy-saving potential due to the complex, nonlinear energy system of the 4WD PHEV. To cope with it, a hierarchical energy management strategy (H-EMS) for 4WD PHEVs is proposed in this paper to achieve energy management optimization. Firstly, the future speed information is predicted by the speed prediction method, and the upper energy management strategy adopts the model predictive control (MPC) based on the future speed information to carry out the power source distribution between the engine and the battery. Secondly, the lower energy management strategy performs the power component distribution of the front motor and the rear motor based on an equivalent consumption minimization strategy (ECMS). Finally, the simulation based on MATLAB/Simulink is performed, validating that the proposed method has more energy-saving capabilities, and the economy is improved by 11.87% compared with the rule-based (RB) energy management strategies.

This paper presents an adaptive equivalent consumption minimization strategy (ECMS) and a linear quadratic tracking (LQT) method for optimal power-split control of a combustion engine and an electric machine in a hybrid electric vehicle (HEV). The objective is to deliver demanded torque and minimize fuel consumption and usage of service brakes, subject to constraints on actuator limits and battery state of charge (SOC). First, we derive a function for calculating maximum deliverable torque that is as close as possible to demanded torque and then reduce the number of control inputs to one while minimizing the usage of service brakes in the case of negative demanded torque. Next, we propose modeling SOC constraints by tangent or logarithm functions that provide an interior point to both the ECMS and the LQT. Then, we show that the resulting objective functions are convex, and we propose sub-optimal analytic solutions for the optimization problem by applying their second order approximation about a given reference. We also consider robustness of the controllers to measurement noise using a simple model of noise. Simulation results of the two controllers are compared, and their effectiveness is discussed. [ABSTRACT FROM AUTHOR]

Analytical optimal solution to the energy management of hybrid electric vehicles is of interest from theoretical and practical perspectives. Particularly, effort has been made to derive analytical solution to the energy management problem for series hybrid electric vehicles using Pontryagin's minimum principle (PMP). However, admissibility of the system input was not fully explored in determining the optimal input candidates. In this paper, the analytical solution for the same problem is found by partitioning the positive power demand set into four subsets, where the solution is derived for each case separately according to the corresponding admissible input set. The analytical solution is verified through comparison with numerical solution for a series hybrid electric wheel loader, and two different drive cycles are considered for this purpose. From the proposed solution, effective equivalence factor bounds are found and used to construct an adaptive equivalent consumption minimization strategy. The proposed strategy and the analytical solution are implemented together for the same vehicle to demonstrate their effectiveness in dealing with real-world applications. Simulations are performed for 12 drive cycles, and the results are compared to the ones achieved by PMP-based optimal control where the optimization is done numerically. Simulation results suggest that the proposed methodology is relatively fast and has satisfactory performance in presence of drive cycle uncertainty. It is observed that the proposed method fulfills charge sustenance, and the achieved fuel consumption figures are very close to the optimal benchmarks found by the non-causal method. [ABSTRACT FROM AUTHOR]

Hybrid electric vehicles (HEVs) have proved a feasible option to reduce fuel consumption and emissions. Furthermore, energy management strategies (EMSs) play a pivotal role in the performance of HEVs. This paper presents a novel real-time EMS, namely fuzzy adaptive-equivalent consumption minimization strategy (Fuzzy A-ECMS), for a parallel HEV. The proposed EMS is formulated by combining the ECMS, which is derived from Pontryagin's minimum principle (PMP), with a fuzzy logic controller adjusting the equivalent factor (EF) based on the deviation between reference state of charge (SOC) and actual SOC for a better SOC trajectory. Improved fuel economy and SOC charge sustainability are the main control objectives. To test and verify the performance of the studied controller, comparative simulations of the Fuzzy A-ECMS and rule-based EMS, conventional SOC-based A-ECMS together with standard ECMS under different standard driving cycles and a real driving cycle are conducted via MATLAB/Simulink and AVL CRUISE. The simulation results show the feasibility and effectiveness of Fuzzy A-ECMS, yielding 0.46% to 5.91% reduction of fuel consumption and more stable SOC charge sustainability compared with the other three EMSs.

The 48-V diesel mild-hybrid electric vehicle equipped with an electrically heated catalyst is a cost-effective solution to reduce tailpipe emissions, while its supervisory control design is more challenging, due to cross-coupled energy and thermal subsystems. This article presents a distributed control design for the energy and thermal management system, aiming to optimize fuel economy and reduce catalyst warm-up time, by cooperation between electrical heating and engine load upshift. A control-oriented model of HEV and catalyst is developed, which captures the dynamics of the battery state of charge and catalyst temperature. The energy and thermal management problem is cast as a hybrid optimal control problem, to minimize entire trip fuel consumption under different switching (warm-up) times. An off-line solution method is designed to exploit the tradeoff between fuel and warm-up time based upon hybrid minimum principle and a nested shooting algorithm. For online implementation, a distributed supervisory control is proposed, which covers both cold-start and normal operations, and is compatible with the equivalent consumption minimization strategy. Both off-line and online methods are validated in simulation, using two driving cycles and five warm-up speeds. [ABSTRACT FROM AUTHOR]

The energy management strategy (EMS) or power management strategy (PMS) unit is the core of power sharing control in the hybridization of automotive drivetrains in hybrid electric vehicles (HEVs). Once a new topology and its corresponding EMS are virtually designed, they require undertaking different stages of experimental verifications toward guaranteeing their real-world applicability. The present paper focuses on a new and less-extensively studied topology of such vehicles, HEVs equipped with an electrical variable transmission (EVT) and assessed the controllability validation through hardware-in-the-loop (HiL) implementations versus model-in-the-loop (MiL) simulations. To this end, first, the corresponding modeling of the vehicle components in the presence of optimized control strategies were performed to obtain the MiL simulation results. Subsequently, an innovative versatile HiL test bench including real prototyped components of the topology was introduced and the corresponding experimental implementations were performed. The results obtained from the MiL and HiL examinations were analyzed and statistically compared for a full input driving cycle. The verification results indicate robust and accurate actuation of the components using the applied EMSs under real-time test conditions. [ABSTRACT FROM AUTHOR]

The design of energy management strategy (EMS) that can be applied in real-time with nearly optimal fuel economy for connected plug-in hybrid electric vehicles (C-PHEVs) is an urgent need at present, especially in the context where the benefits of rapid development of intelligent transportation system are increasingly evident. Equivalent consumption minimization strategy (ECMS) is able to achieve the desired performance, on the premise that the equivalent factor (EF) is adjusted reasonably. To achieve this goal, a variable optimization domain-based cooperative EMS for C-PHEVs is proposed in this paper. First, an offline sub-cycle division method is designed, which is effectively utilized to construct a fitting function to output reference EF based on the latest driving information. Second, the theoretical relationship between EF and operating efficiency of the driving components is explored, and the variable optimization domain of EF is designed to avoid inefficient operation of engine. Third, the EF optimization is then simplified into a quadratic problem within the designed domain, which is rapidly solved online with the golden section search algorithm. Finally, the comparison work is conducted under two real-world driving cycles. The results exhibit that the proposed strategy improves the fuel economy of C-PHEV by 6.440 % and 7.936 % over that using the conventional adaptive ECMS (CA-ECMS), respectively. The average engine fuel consumption rates of the proposed strategy are significantly reduced compared with that of CA-ECMS. Moreover, the effectiveness of the proposed strategy is verified on the experiment platform, demonstrating its application prospects. • A sub-cycle division method is proposed to construct fitting function for online optimization. • The theoretical relationship between EF and engine efficiency is explored. • A variable optimization domain-based cooperative EMS is designed for C-PHEV. • The application performance of the proposed strategy is verified on the hybrid test bench. [ABSTRACT FROM AUTHOR]

Hybrid electric vehicle (HEV) technology is an effective way to resolve the problems of energy consumption and air pollution. Energy management strategies are critical to the performance of HEVs. In this paper, a novel energy management strategy of equivalent consumption minimization strategy (ECMS)-type is proposed for parallel HEVs based on energy prediction (ECMS-EP). The energy prediction is estimated based on the predicted velocity that is calculated by a chaining-neural-network method over different temporal horizons. A novel adaptive rule has been developed by eliminating the need to reset the initial equivalent factor (EF) based on the energy prediction to adjust the EF of ECMS-EP in real time. The control objective is to improve the fuel economy and sustain the state of charge (SoC). Then, via MATLAB/Simulink, simulations are conducted in three different prediction horizon lengths to verify the performance of the proposed ECMS-EP with adaptive rules. The simulation results show that the proposed ECMS-EP is able to achieve more stable SoC trajectories and better fuel economy with a fuel consumption reduction of 2.7%-7% compared with the traditional adaptive-ECMS.

Improving fuel economy and lowering emissions are key societal goals. Standard driving cycles, pre-designed by the US Environmental Protection Agency (EPA), have long been used to estimate vehicle fuel economy in laboratory-controlled conditions. They have also been used to test and tune different energy management strategies for hybrid electric vehicles (HEVs). This paper aims to estimate fuel consumption for a conventional vehicle and a HEV using personalized driving cycles extracted from real-world data to study the effects of different driving styles and vehicle types on fuel consumption when compared to the estimates based on standard driving cycles. To do this, we extracted driving cycles for conventional vehicles and HEVs from a large-scale U.S. survey that contains real-world GPS-based driving records. Next, the driving cycles were assigned to one of three categories: volatile, normal, or calm. Then, the driving cycles were used along with a driver-vehicle simulation that captures driver decisions (vehicle speed during a trip), powertrain, and vehicle dynamics to estimate fuel consumption for conventional vehicles and HEVs with power-split powertrain. To further optimize fuel consumption for HEVs, the Equivalent Consumption Minimization Strategy (ECMS) is applied. The results show that depending on the driving style and the driving scenario, conventional vehicle fuel consumption can vary widely compared with standard EPA driving cycles. Specifically, conventional vehicle fuel consumption was 13% lower in calm urban driving, but almost 34% higher for volatile highway driving compared with standard EPA driving cycles. Interestingly, when a driving cycle is predicted based on the application of case-based reasoning and used to tune the power distribution in a hybrid electric vehicle, its fuel consumption can be reduced by up to 12% in urban driving. Implications and limitations of the findings are discussed. [ABSTRACT FROM AUTHOR]

Improving fuel economy and lowering emissions are key societal goals. Standard driving cycles, pre-designed by the US Environmental Protection Agency (EPA), have long been used to estimate vehicle fuel economy in laboratory-controlled conditions. They have also been used to test and tune different energy management strategies for hybrid electric vehicles (HEVs). This paper aims to estimate fuel consumption for a conventional vehicle and a HEV using personalized driving cycles extracted from real-world data to study the effects of different driving styles and vehicle types on fuel consumption when compared to the estimates based on standard driving cycles. To do this, we extracted driving cycles for conventional vehicles and HEVs from a large-scale U.S. survey that contains real-world GPS-based driving records. Next, the driving cycles were assigned to one of three categories: volatile, normal, or calm. Then, the driving cycles were used along with a driver-vehicle simulation that captures driver decisions (vehicle speed during a trip), powertrain, and vehicle dynamics to estimate fuel consumption for conventional vehicles and HEVs with power-split powertrain. To further optimize fuel consumption for HEVs, the Equivalent Consumption Minimization Strategy (ECMS) is applied. The results show that depending on the driving style and the driving scenario, conventional vehicle fuel consumption can vary widely compared with standard EPA driving cycles. Specifically, conventional vehicle fuel consumption was 13% lower in calm urban driving, but almost 34% higher for volatile highway driving compared with standard EPA driving cycles. Interestingly, when a driving cycle is predicted based on the application of case-based reasoning and used to tune the power distribution in a hybrid electric vehicle, its fuel consumption can be reduced by up to 12% in urban driving. Implications and limitations of the findings are discussed. [ABSTRACT FROM AUTHOR]

This paper aims to develop an Advanced Equivalent Consumption Minimization Strategy (AECMS) for efficient fuel cell hybrid electric vehicles (FCHEV). The Equivalent Consumption Minimization Strategy (ECMS) is one of the most feasible solutions for hybrid electric vehicles as it has less complexity and low computational cost in making the optimal control decision. However, typical ECMS designs with fixed gain use could not enhance an optimal energy control when dealing with multi decisive factors for highly non-linear and uncertain systems like FCHEVs. To address this control challenge, the AECMS is developed as the combination of the ECMS and an adaptive equivalence factor to optimally split the energy flow between the fuel cell and battery in the FCHEV. First, a cost function is formulated based on two key objectives which are power consumptions of the fuel cell and battery, the equivalence factor and a set of soft and hard constraints, considering the number of fuel cell start/stop, fuel cell optimal operating range, state of charge (SoC) range, temperature ranges and power limits. Second, the adaptive equivalence factor is newly designed as the combination of states and dynamics of the fuel cell and battery and corresponding weighting factors. Third, fuzzy inferences are derived to adjust online the weighting factors in an adaptive manner according to the working conditions of the energy sources, subsequently improving the overall vehicle energy efficiency and maintaining the battery SoC while prolonging their lifetime. Numerical simulations using a FCHEV model have been carried out under UDDS and HWFET drive cycles to illustrate the superior performance of the AECMS against the traditional ECMS. • An Equivalent Consumption Minimization Strategy for Fuel Cell Hybrid electric vehicle. • Adaptive equivalence factor for Advanced Equivalent Consumption Minimization Strategy. • The adaptive equivalence factor has weighted sub-factors to improve performance. • Constituent weights of sub-factors are generated during runtime using fuzzy logic. • A comparative study has confirmed the superiority of the proposed strategy. [ABSTRACT FROM AUTHOR]

Driving intention and speed prediction are essential factors in the energy management of plug-in hybrid electric vehicles (PHEVs). This paper proposes an improved energy management strategy for the subject vehicle by speed prediction fused with driving intention and LIDAR data in a vehicle-following scenario. A driving intention recognition model is developed based on the gated recurrent unit (GRU), which takes the vehicle speed, throttle opening, and brake pedal force of the subject vehicle as input. Then integrating the LIDAR point cloud data and driving intention result of the subject vehicle to achieve more accurate speed prediction, where joint probabilistic data association and interacting multiple models methods are used to process LIDAR data. The more accurate speed prediction is then applied to design a prediction-informed adaptive equivalent consumption minimization strategy (PIA-ECMS) for real-time energy management optimization. Experimental results demonstrate the recognition accuracy of up to 88%, indicating that the driver's driving intention can be identified effectively. The speed prediction has an error margin of no more than 5.9 km/h. Compared with existing adaptive ECMS without speed prediction, the proposed PIA-ECMS can enhance fuel economy by 1.3–2.7% while achieving better SOC charge sustainability. • Integration of driving intention recognition and LIDAR into energy management. • Speed prediction is fused by driving intention recognition and LIDAR. • A new prediction-informed adaptive ECMS to achieve better fuel economy. [ABSTRACT FROM AUTHOR]

The energy management strategy (EMS) or power management strategy (PMS) unit is the core of power sharing control in the hybridization of automotive drivetrains in hybrid electric vehicles (HEVs). Once a new topology and its corresponding EMS are virtually designed, they require undertaking different stages of experimental verifications toward guaranteeing their real-world applicability. The present paper focuses on a new and less-extensively studied topology of such vehicles, HEVs equipped with an electrical variable transmission (EVT) and assessed the controllability validation through hardware-in-the-loop (HiL) implementations versus model-in-the-loop (MiL) simulations. To this end, first, the corresponding modeling of the vehicle components in the presence of optimized control strategies were performed to obtain the MiL simulation results. Subsequently, an innovative versatile HiL test bench including real prototyped components of the topology was introduced and the corresponding experimental implementations were performed. The results obtained from the MiL and HiL examinations were analyzed and statistically compared for a full input driving cycle. The verification results indicate robust and accurate actuation of the components using the applied EMSs under real-time test conditions.

The strategy for energy management (EM) of a hybrid electric vehicle (HEV) has a considerable impact on the vehicle fuel economy. One well-known EM strategy is the equivalent consumption minimization strategy (ECMS) that is a form of Pontryagin’s minimum principle (PMP). PMP proves under certain conditions that ECMS yields the maximum fuel economy. However, even if the required conditions are met, the optimal value of the costate still has to be estimated. Many approaches have been suggested for estimating the optimal value of the costate, or the equivalent factor for using battery power in the ECMS cost function. Instead of direct estimation of ECMS optimal equivalent factor, this brief derives estimations for the upper and lower bounds of the optimal equivalent factor. The derived bounds are functions of the HEV configuration and independent of the drivecycle, verified by simulation results. The knowledge about these bounds can be employed in designing new types of adaptive ECMSs (A-ECMSs). To demonstrate the application of the bounds, this brief introduces a new A-ECMS. Finally, the simulation results are presented comparing the fuel economy of the introduced A-ECMS with the fuel economies of an existing A-ECMS and global optimal controller. [ABSTRACT FROM AUTHOR]

For both globally suboptimal solution and implementable strategy, a real-time energy management strategy, based on equivalent consumption minimization strategy (ECMS), is proposed for commuter hybrid electric vehicles (HEVs) running on fixed routes. The determination of the adaptive equivalence factor is a focus. By the statistical characteristics deriving from historical driving data, the infinite-horizon stochastic dynamic programming (SDP) optimization with a discount factor is first formulated for finding proper equivalence factor according to uncertain driving cycles on a fixed route. And then, a mapping of equivalent factor on the system state is established off-line by stochastic optimal solution deriving from SDP policy iteration algorithm. In the power splits online, the equivalence factor of the implemented adaptive ECMS is obtained from the mapping according to the real time driving condition to achieve the near global optimal control objective that fuel consumption is minimized and the battery state of charge (SOC) is maintained within the boundaries over the whole driving route. Based on the HEV test platform established by specialized GT-Suite, simulation results and comparisons in some real driving cycles are presented to verify the effectiveness of the proposed strategy and to evaluate the advantages over other strategies.

This paper presents an optimized equivalent consumption minimization strategy (ECMS) for four-wheel-drive (4WD) hybrid electric vehicles (HEVs) incorporating vehicle connectivity. In order to be applicable to the 4WD architecture, the ECMS is designed based on a rule-based strategy and used under the condition that a certain propulsion mode is activated. Assuming that a group of 4WD HEVs are connected and position information can be shared with each other, we formulate a decentralized model predictive control (MPC) framework that compromises fuel efficiency, mobility, and inter-vehicle distance to optimize the velocity profile of each individual vehicle. Based on the optimized velocity profile, an optimization problem considering both fuel economy and battery state of charge (SOC) sustainability is formulated to optimize the equivalent factors (EFs) of the ECMS for HEVs over an appropriate time window. MATLAB User Datagram Protocol (UDP) is used in the codes run on multiple computers to simulate the wireless communication among vehicles, which share position information via UDP-based communication, and dSPACE is used as a software-in-the-loop platform for the simulation of the optimized ECMS. Simulation results validate the control effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Plug-in hybrid electric vehicles (PHEVs) provide the flexibility to be used as pure electric vehicles in daily commuting, as well as common hybrid vehicles (HEVs) on longer trips. To appropriately distribute power between the engine and the energy buffer (normally battery) without sacrificing fuel economy, optimal control strategies of varies kinds have been proposed in recent years. Some strategies, however, cannot be applied in real time environment for the tightly independent on the future driving information. Some others could identify the different road condition but the identification accuracy is questionable, making their real-world effectiveness could suffer significantly. Moreover, the proposed energy management method seldom consider the effect from the driver behavior, which is an essential factor that can determine the vehicle energy consumption. For such reasons, this paper proposes a novel energy management strategy, the instantaneous optimization method--Equivalent Consumption Minimization Strategy (ECMS) is chosen as the foundation solution to the PHEV energy management problem. Then the paper studies the effect from different driver behavior and uses fuzzy logic method to identify different drive behavior. Combined with the pre-knowledge provide by Intelligent Transport System (ITS), the equivalent factor, which is the core of ECMS, is adjusted, making the real instantaneous application of ECMS. Then the simulation is performed to prove its viability and effectiveness. [ABSTRACT FROM AUTHOR]

Conclusive evidence has justified great importance of energy management strategies in the performance and economy of plug-in hybrid electric vehicles (PHEVs). This article pays attention to improve adaptive equivalent consumption minimization strategy (A-ECMS) for parallel PHEV based on driving behavior recognition and real time traffic information prediction. Three main efforts have been made to distinguish our work from exiting research. Firstly, a hierarchical driving behavior model is constructed, providing in-depth knowledge about behavior generation, transmission, and consequence. Secondly, an online driving behavior classification method is designed. The proposed method is the coefficient result of offline driving behavior study based on self-report driving behavior questionnaire (DBQ) and online driving behavior discrimination by BP neural network. Thirdly, an improved adaptive equivalent consumption minimization strategy (IA-ECMS) is formulated based on identified driving behavior and predicted real time traffic information. The IA-ECMS can realize equivalent factor tuning instantaneously and reasonably. The simulation results indicate the proposed energy management strategy holds potential in fuel economy improvement than A-ECMS. [ABSTRACT FROM AUTHOR]

Previously, an equivalent consumption minimization strategy (ECMS) was developed that provides near-optimal performance of hybrid vehicles based on an adaptation of equivalence factor from state of charge feedback. However, under real-world driving conditions with uncertainties, such as hilly roads, ECMS requires a predictive scheme utilizing future driving information in order to prevent a loss of optimality. In this paper, we synthesize predictive ECMS in a feedforward way to adjust the equivalence factor based on its theoretical connection with future driving statistics, in a systematic manner. First, a useful noncausal adaptation strategy is extracted from dynamic programming results. Then, the inverse problem is formulated and solved to derive an explicit representation of the constant optimal equivalence factor with justified assumptions. Finally, a causal, predictive adaptation strategy using this closed-form solution is synthesized to mimic the noncausal one, and its effectiveness is evaluated for fuel cell hybrid electric vehicles. Results show that if the predicted statistical information reflects well the future driving conditions, the proposed strategy accurately estimates the constant optimal equivalence factor, including the jump behavior, thereby yielding less than 1.5% loss of fuel optimality. Moreover, this approach is extendible to other configurations. [ABSTRACT FROM PUBLISHER]

The performance of energy management in hybrid electric vehicles is highly dependent on the forecasted velocity. To this end, a new velocity-prediction approach utilizing the concept of chaining neural network (CNN) is introduced. This velocity forecasting approach is subsequently used as the basis for an equivalent consumption minimization strategy (ECMS). The CNN is used to predict the velocity over different temporal horizons, exploiting the information provided through vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication channels. In addition, a new adaptation law for the so-called equivalent factor (EF) in ECMS is devised to investigate the effects of future velocity on fuel economy and to impose charge sustainability. Compared with traditional adaptation law, this paper considers the impact of predicted velocity on EF. The control objective is to improve the fuel economy relative to the ECMS without considering predicted velocity. Finally, simulations are conducted in three cases over different prediction horizons to demonstrate the performance of the proposed velocity-prediction method and ECMS with adaptation law. Simulation results confirm that ECMS with EF adjusted by the proposed adaptation law produces between 0.2% and 5% improvements in fuel economy relative to ECMS with traditional adaptation law. In addition, better charge sustainability is achieved as well. [ABSTRACT FROM PUBLISHER]

The adoption of Plug-in Hybrid Electric Vehicles (PHEVs) is widely seen as an interim solution for the decarbonization of the transport sector. Within a PHEV, determining the required energy storage capacity of the battery remains one of the primary concerns for vehicle manufacturers and system integrators. This fact is particularly pertinent since the battery constitutes the largest contributor to vehicle mass. Furthermore, the financial cost associated with the procurement, design and integration of battery systems is often cited as one of the main barriers to vehicle commercialization. The ability to integrate the optimization of the energy management control system with the sizing of key PHEV powertrain components presents a significant area of research. Contained within this paper is an optimization study in which a charge blended strategy is used to facilitate the downsizing of the electrical machine, the internal combustion engine and the high voltage battery. An improved Equivalent Consumption Method has been used to manage the optimal power split within the powertrain as the PHEV traverses a range of different drivecycles. For a target CO2 value and drivecycle, results show that this approach can yield significant downsizing opportunities, with cost reductions on the order of 2%–9% being realizable.

The energy management strategy (EMS) of a hybrid electric vehicle is a supervisory control meant to steer power flows among the different energy sources of the powertrain. The EMS is designed for the purpose of reaching the maximum potential of a given powertrain architecture. Fuel consumption is not the only criterion to be considered for evaluating the global performance of an EMS, since the EMS also impacts other features such as drivability, through engine starts/stops and gear shifts. This paper proposes a general approach to include drivability constraints in the EMS of powertrains provided with discrete automated transmissions. As a first step, determinist dynamic programming is used to compute the optimal solution of the resulting optimization problem. This optimal solution is then used to assess the performance of the proposed online strategy, which is based on the equivalent consumption minimization strategy. Finally, simulation results are completed by a discussion of experimental results, as the proposed online strategy has been implemented in a prototype vehicle designed at Renault and tested on a dynamometric bench. [ABSTRACT FROM AUTHOR]

Stochastic dynamic programming (SDP) is applied to the optimal control of a hybrid electric vehicle in a concerted attempt to deploy and evaluate such a controller in the real world. Practical considerations for robust implementation of the SDP algorithm are addressed, such as the choice of discount factor used and how charge sustaining characteristics of the SDP controller can be examined and adjusted. A novel cost function is used incorporating the square of battery charge (C-rate) as an indicator of electrical powertrain stress, with the aim of lessening the affliction of real-world concerns such as battery health and motor temperature, while allowing short spells of operation toward the system peak power limits where advantageous. This paper presents the simulation and chassis dynamometer results over the LA92 drive cycle, as well as the results of testing on open roads. The hybrid system is operated at several levels of aggressivity, allowing the tradeoff between fuel savings and electrical powertrain stress to be evaluated. In dynamometer testing, this approach yielded a 13% reduction in electrical powertrain stress without sacrificing any fuel savings, compared with a controller that does not consider aggressivity in its optimization. [ABSTRACT FROM AUTHOR]

This paper presents a new power distribution strategy (PDS) for a series electric hybrid vehicle (SHEV) in the sense of minimizing fuel usage. The target vehicle has three energy sources, i.e., an engine and generator set (EGS), a battery, and an ultracapacitor, and two traction motors for the front and rear wheels. A two-step PDS based on the real-time optimization technique is proposed to distribute the demand power among three energy sources. Several existing PDSs, such as the thermostat strategy and the power-follower strategy, are modified and applied to be compared with the proposed strategy. An optimal torque distribution strategy between front and rear traction motors is also proposed to minimize the electric energy consumption and to further improve fuel economy. Simulation results demonstrate the feasibility and effectiveness of the proposed strategies. [ABSTRACT FROM AUTHOR]

The adoption of Plug-in Hybrid Electric Vehicles (PHEVs) is widely seen as an interim solution for the decarbonization of the transport sector. Within a PHEV, determining the required energy storage capacity of the battery remains one of the primary concerns for vehicle manufacturers and system integrators. This fact is particularly pertinent since the battery constitutes the largest contributor to vehicle mass. Furthermore, the financial cost associated with the procurement, design and integration of battery systems is often cited as one of the main barriers to vehicle commercialization. The ability to integrate the optimization of the energy management control system with the sizing of key PHEV powertrain components presents a significant area of research. Contained within this paper is an optimization study in which a charge blended strategy is used to facilitate the downsizing of the electrical machine, the internal combustion engine and the high voltage battery. An improved Equivalent Consumption Method has been used to manage the optimal power split within the powertrain as the PHEV traverses a range of different drivecycles. For a target CO2 value and drivecycle, results show that this approach can yield significant downsizing opportunities, with cost reductions on the order of 2%-9% being realizable. [ABSTRACT FROM AUTHOR]

As the dependence on fossil fuel increases in the transportation sector, more attention has been paid to the energy management control of proton exchange membrane fuel cell (PEMFC) plug-in hybrid electric vehicles (PHEVs). In this paper, the energy management control problem for a series plug-in PEMFC/Li-ion battery hybrid midsize sedan is formulated and investigated using a two-stage controller (TSC). The control objective is to minimize hydrogen consumption and simultaneously protect PEMFC health. The proposed TSC consists of two controllers designed in two stages with different control functions. During the first design stage, a predictive controller is developed using the telemetry equivalent consumption minimization strategy (T-ECMS) approach to predict the global battery state-of-charge (SOC) optimality trend and local control reference, without regard for the PEMFC health constraints. During the second stage of design, a tracking controller is designed to track the local control reference with respect to the PEMFC health constraints and other physical limitations at the current control step, which ensures that the system follows the optimal battery SOC reference over a long time horizon. Finally, the effectiveness of the proposed TSC is compared with the T-ECMS and an electric vehicle controller (EVC) under the Matlab/Simulink software environment. The results demonstrate that the TSC achieves a reasonable tradeoff between hydrogen fuel consumption and PEMFC health protection. [ABSTRACT FROM PUBLISHER]

This paper presents a novel approach to the solution of the energy management problem of a microturbine-powered plug-in hybrid electric vehicle (PHEV). A series hybrid midsize sedan, utilizing a microturbine and a chargeable Li-ion battery stack as its primary energy source and energy storage system, respectively, is modeled in this paper. The equivalent consumption minimization strategy (ECMS) is utilized to minimize the driving cost based on Pontryagin's minimum principle. To identify the equivalent factor (EF), a new concept called the energy ratio is defined, which is demonstrated to be closely related to the EF over all possible trips. By detecting the vehicle position with a telemetry system and measuring the battery state of charge (SOC), the EF is updated in real time and is used as an input for the computation of the ECMS. Simulation results demonstrate that the proposed ECMS exhibits driving cost and diesel consumption equivalent to that determined from numerical dynamic programming. Significantly, the proposed approach reduces the driving cost from 7.7% to 21.6%, compared with a baseline control over both urban and highway cycles. In addition, through numerical simulations, the computational cost of the proposed strategy is demonstrated to be acceptable for industrial applications. Furthermore, because this strategy uses the feedback of the battery SOC, the control performance is insensitive to the control parameter errors. [ABSTRACT FROM PUBLISHER]

The main aim in the control of a hybrid electric vehicle (HEV) is to decrease the fuel consumption and emissions without significant loss of driving performance. The performance of the vehicle in terms of fuel economy and emissions is very much dependent on the vehicle's supervisory control strategy. In this paper, the equivalent consumption minimization strategy (ECMS) is developed with a novel approach for the charge sustaining of the batteries to provide an overall improved optimization performance for series hybrid electric vehicles (SHEVs), considering the efficiencies of the internal combustion engine (ICE), generator, and battery. Another novelty is the development of a combined map, which simultaneously facilitates the optimization of the fuel consumption and multiple emission components, unlike most past studies that have concentrated on one component at a time. After the derivation of the cost map, the algorithm is divided into two main parts. The first part optimizes the engine–generator set (GENSET), and the second part determines how much power is needed from the GENSET according to the ECMS. The algorithm is implemented using generic emission and fuel consumption maps of an actual mid-sized series hybrid bus to reduce the desired emissions. The hybrid electric vehicle in consideration is converted from a conventional bus that is driven by an ICE. The performance of the novel ECMS strategy is compared with the conventional vehicle, as well as the SHEV version that is driven by an on–off strategy. In addition to reduced fuel consumption, the results of this paper demonstrate a significant reduction of 14.58% in CO2 production with ECMS, whereas the on–off control strategy achieves only 6.47% reduction over the conventional vehicle. [ABSTRACT FROM PUBLISHER]